THIS POST IS CONTINUED FROM PART 7, BELOW--
Even if a machine can accurately mimic human behaviour, it is not actually thinking. It is simply following a set of rules and cannot be said to possess a consciousness. Strong AI usually refers to the question if machines are able to have a consciousness, intentionality, self-awareness, understanding or other vague philosophical concepts often ascribed as human traits.
Machines do not have the following abilities:--
1. The ability to learn by example.
2. The ability to improve the efficiency of performing a certain task by analysing your own behaviour ( swadyaya ) and modifying it appropriately.
3. The ability to generalise solutions and so that they can be applied to varying situations, while realising that using a generalised solution in a specific situation rarely is as efficient as using a solution optimised for that specific situation.
4. The ability to communicate the solution to a problem to another intelligent being in such a way that the other intelligent being can accurately solve the problem.
5. The ability to learn the solution of a problem by communicating with another intelligent being.
6. The ability to work together with another intelligent being in order to accomplish a task that was previously impossible (or simply difficult) to accomplish alone.
Artificial intelligence is a tool humanity is wielding with increasing recklessness. We don’t have the code of ethics, laws, government accountability, corporate transparency and capability of monitoring the space to be able to achieve AI regulation as of now
AI is just a tool which helps to free up our time spent in ordinary routine work so that we can focus our time and energy in achieving bigger creative tasks. These machines would save us from workaday drudgery
If AI is when a computer can carry out a set of tasks based on instruction, ML is a machine's ability to ingest, parse, and learn from that data itself in order to become more accurate or precise about accomplishing that task
Unable to process high dimensional data: Machine Learning can process only small dimensions of data that contain a small set of variables. If you want to analyze data containing hundreds of variables, then Machine Learning cannot be used.
AI systems thrive when the machine learning algorithms used to train them are given massive amounts of data to ingest, classify and analyze. The more precisely that data can be classified according to specific characteristics, or features, the better the AI will perform.
Here are some limitations of Machine Learning:
Feature engineering is manual: Consider a use case where you have 100 predictor variables and you need to narrow down only the significant ones. To do this you have to manually study the relationship between each of the variables and figure out which ones are important in predicting the output. This task is extremely tedious and time-consuming for a developer.
Not ideal for performing object detection and image processing: Since object detection requires high-dimensional data, Machine Learning cannot be used to process image data sets, it is only ideal for data sets with a restricted number of features.
The biggest limitation of artificial intelligence is it’s only as smart as the data sets served AI’s main limitation is that it learns from given data. There is no other way that knowledge can be integrated, unlike human learning.
This means that any inaccuracies in the data will be reflected in the results. a true AI system objectively studies all available data and cannot lie. There is no Improvement with experience, never mind the hype: Unlike humans, artificial intelligence cannot be improved with experience. ...
There can never be any original creativity , unless the creators are making an ass out of you ... AI can never ever match human reasoning…
Now we know AI contributes to the forgery of documents, pictures, audio recordings, videos, and online identities which can and will occur with unprecedented ease. We are unleashing an open-source toolkit of cybersecurity weapons that will complicate our online interactions.
A world where the militarization of AI will mean automated warfare that could increasingly be triggered by covert means of cybersecurity threats.
AI-based forgery can be used to create false information and commit identity theft. With the help of AI-based forgery, influential people can be misrepresented, leading to a drastic backlash against them. Hence, several internet users might find it difficult to trust the internet as a reliable source of critical information.
Using AI-based forgery, photorealistic videos of any person can be generated by collecting large volumes of data such as video footage and audio samples. In a similar manner, AI-based forgery is being used to generate fake porn videos of several celebrities.
AI-based forgery can also be used to generate synthetic images. For instance, a Twitter bot called ‘Smile Vector’ puts fake smiles on pictures of numerous celebrities. For this purpose, the bot browses through the internet for pictures of celebrities and morphs their expressions with the help of a neural network. Such tools can be used to manipulate various details of different pictures and post them online with malicious intent.
AI-based forgery can be used to develop synthetic audio. For this purpose, AI systems can collect various speech fragments in a database and reconstruct the audio to generate words and sentences.
Alternatively, AI systems can also be trained using waveforms of acquired audio samples. With this approach, AI systems can utilize a model to predict sounds based on sounds that were generated before. Using these techniques, AI systems can produce realistic audio recordings of people saying controversial things.
Synthetic audio can be used to fool voice-based security systems and generate spoof phone calls. In case of phone calls, people would be unable to understand whether the voice on the other end belongs to AI or a real human. Hence, AI-based audio forgery can be used to make fake phone calls for illicit purposes.
Handwriting mimicking can be used with malicious intentions. AI-powered handwriting mimicking can be used to forge signatures on legal documents. Such AI-based forgery can lead to legitimacy issues with documents and identity theft.
Phishing is a major cybersecurity threat for several organizations as well as casual internet users. Phishing involves sending fake emails to people for manipulating them into sending confidential data or downloading infected software. However, one of the significant challenges in phishing is generating convincing emails.
For this purpose, AI systems can be trained with several legitimate emails. By analyzing these emails, AI systems can generate convincing emails that can be used for phishing. Also, AI can generate phishing emails automatically, making cyber attacks more easily executable. Hence, AI-based forgery can give rise to automated cyber attacks.
Bots can post convincing tweets, pictures, and videos with the help of AI-based forgery. These bots will be able to spread more manipulative propaganda, which can be increasingly complicated for moderators to detect.
Currently, there are no regulations to control the use of AI-powered forgery. Effective regulations can prove to be a deterrent to use of AI for malicious activities. In addition to regulations, law enforcement agencies and internet platforms can implement the following methods to detect and curb AI-based forgery:
AI is poised to make high-fidelity forgery inexpensive and automated, leading to potentially disastrous consequences for democracy, security, and society.
A website, an e-mail address, and even the origin of a phone call can be easily faked or “spoofed”.
Machine Learning based algorithms - One uses historical data of the markets and feed this to the machine learning algorithm that they have designed. The data is divided into training data and testing data.
The machine learning algorithm learns the patterns and features from the training data and trains itself to take decisions like identifying, classifying or predicting new data or outcomes. The algorithm continues to learn from the positive/negative outcomes, to improve on accuracy & performance.
Training data, is labeled data used to teach AI models (or) machine learning algorithms. Whereas, the Test dataset is the sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Training data, is the information used to train an algorithm. ... Testing data, on the other hand, includes only input data, not the corresponding expected output
Any machine learning model that we choose needs data to train its algorithm on. Without training data, all the algorithm understands is how to approach the given problem, and without proper calibration, so to speak, the results won’t be accurate enough. Before training, the model is just a theorist, without the fine-tuning to its settings necessary to start working as a usable tool.
While using datasets to teach the model, training data needs to be of a large size and high quality. All of AI’s learning happens only through this data. So it makes sense to have as big a dataset as is required to include variety, subtlety, and nuance that makes the model viable for practical use. Simple models designed to solve straight-forward problems might not require a humongous dataset, but most deep learning algorithms have their architecture coded to facilitate a deep simulation of real-world features.
The other major factor to consider while building or using training data is the quality of labeling or annotation. If you’re trying to teach a bot to speak the human language or write in it, it’s not just enough to have millions of lines of dialogue or script. What really makes the difference is readability, accurate meaning, effective use of language, recall, etc.
Similarly, if you are building a system to identify emotion from facial images, the training data needs to have high accuracy in labeling corners of eyes and eyebrows, edges of the mouth, the tip of the nose and textures for facial muscles. High-quality training data also makes it faster to train your model accurately. Required volumes can be significantly reduced, saving time, effort (more on this shortly) and money.
Datasets are also used to test the results of training. Model predictions are compared to testing data values to determine the accuracy achieved until then. Datasets are quite central to building AI – your model is only as good as the quality of your training data.
Training data can be defined as the initial set of data used to help AI & ML models understand how to apply technologies such as neural networks to learn and produce accurate results.
A training set that accounts for all variations of the variables in the real world would result in developing more accurate models. Just like in the case of a company collecting survey data to know about their consumer, larger the sample size for the survey is, more accurate the conclusion will be.
If the training set isn’t large enough, the resultant system won’t be able to capture all variations of the input variables resulting in inaccurate conclusions.
Training a system on a poor dataset or a dataset that contains wrong data, the system will end up learning wrong lessons, and generate wrong results. And eventually, not work the way it is expected to. On the contrary, a basic algorithm using a high-quality dataset will be able to produce accurate results and function as expected.
The process of building a training dataset can be classified into 3 simple steps: data collection, data preprocessing, and data conversion.
The amount of training data one needs depends on several factors — the task you are trying to perform, the performance you want to achieve, the input features you have, the noise in the training data, the noise in your extracted features, the complexity of your model and so on.
Essentially, training data is the textbook that will teach your AI to do its assigned task, and will be used over and over again to fine-tune its predictions and improve its success rate. Your AI will use training data in several different ways, all with the aim of improving the accuracy of its predictions. It does this through the variables contained in the data.
By identifying these and evaluating their impact on the algorithm, data scientists can strengthen the AI through copious adjustments. The best data will be extremely rich in detail, capable of improving your AI after hundreds of training cycles by hinting at a wide range of variables that affect your algorithm.
The majority of training data will contain pairs of input information and corresponding labeled answers, which are sometimes called the target. In some fields, it will also have highly relevant tags, which will help your AI to make more accurate predictions. However, since variables and relevant details are so important in the training process, datasets for different machine learning tasks will often look very different from each other
Training data is the part of your data which you use to help your machine learning model make predictions. Your model will be run on this set of data exhaustively, churning out results which your data scientists can use to develop your algorithm. It’s the largest part of your overall dataset, comprising around 70-80% of your total data used in the project.
Validation data is a second set of data, also containing input and target information, which the machine learning model has never seen before. By running the model on validation data, it’s possible to see whether it can correctly identify relevant new examples. This is where it’s possible to discover new values that are impacting the process.
Another common problem often identified during validation is overfitting, where the AI has been wrongly trained to identify examples that are too specific to the training data. As you can imagine, after validation, data scientists will often go back to the training data and run through it again, tweaking values and hyperparameters to make the model more accurate.
Testing data comes into play after a lot of improvement and validation. While validation data has tags and target information left on as training wheels, testing data provides no help to the model at all. Asking the model to make predictions based on this data is meant to test whether it will work in the real world, where it won’t have helpful tags scattered around. The final test is the moment of truth for the model, to see if all the hard work has paid off.
Quite simply, without training data there is no AI. The cleanliness, relevance and quality of your data has a direct impact on whether your AI will achieve its goals. It’s best to think of training data in parallel with a human example of learning. Give a student an outdated textbook with half the pages missing and they won’t come close to passing their course.
Similarly, without quality data, your AI will learn to do its job haphazardly, if at all. In the same way that you want to throw the weight of world-renowned professors behind your star pupil, your AI deserves the best data you can find, bursting with detailed tags and relevant annotations. Only then will your AI project catapult your business into the next stage of development.
A weapon system operating partially or wholly without human intervention is considered autonomous… AI allows machines such as drones to make decisions and operate themselves on the behalf of their human controllers.. This is one way the kosher deep state can get rid of their enemies.
Blockchain in its ten years of existence has done only one thing— allowed Jews to grab land in Israel and Georgia
Already, the decision to use an offensive weapon frequently is made by a machine, with humans left to decide only whether or not to pull the trigger.
In the case of defensive weapons, machines often make autonomous decisions (to use the defensive systems) without any human involvement at all.
THIS IS WHY WE SHOT DOWN OUR OWN ARMY HELICOPTER ON BALAKOT RAID NIGHT
Today modern weaponry relying on machine and deep learning can achieve a worrisome autonomy, although the military officially claims that no contemporary armaments are fully autonomous. However, it does admit that a growing proportion of arsenals meet the technological criteria for becoming fully autonomous.
In other words, it’s not a question of if weapons will be able to act without human supervision, it’s a matter of when, and whether we allow them to choose targets to attack and carry those attacks out.
Machine learning models are trained using large datasets pertaining to the subject being learned about. As an example, if an automotive company wanted to teach their automated car how to identify a stop sign, then that company may feed thousands of pictures of stop signs through a machine learning algorithm.
A malicious attack such as adversarial machine learning could be employed against that machine learning algorithm, exploiting the algorithms input data (in this case images of stop signs) to misinterpret that data, causing the overall system to then misidentify stop signs when deployed in either practice or production.
Machine learning tools learn by finding relationships within training data and applying that knowledge to real-life situations. In a perfect world, this approach lets the system accurately interpret new information, but bad actors can manipulate the process to sway its decisions in their favor.
Among the most common techniques for corrupting AI are so-called poisoning attacks, in which adversaries feed the tool rigged training data to alter its decisions. Bad actors can also use inference attacks to figure out what data was used to train existing tools—and thus how they can be manipulated.
By targeting the machine learning process, cybercriminals will be able to train devices or systems to not apply patches or updates to a particular device, to ignore specific types of applications or behaviors, or to not log specific traffic to evade detection. This will have an important evolutionary impact on the future of machine learning and AI technology.
Machine learning algorithms excel at finding complex patterns within big data, so researchers often use them to make predictions.
But machine learning methods are narrower and more specialized than humans. Machine learning is a statistical modeling technique, like data mining and business analytics, which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships.
The ‘soul’ of any AI or machine learning system remains the human mind that designed or manages it. AI needs explicit and appropriate goals: algorithms do as they’re told. And if they can identify patterns too subtle for human detection, generate accurate insights and allow better, more informed decisions, they don’t explain why they offer particular recommendations.
Nor does prediction equal advice - this assumes a certain “socio-ethical value” dimension. So for the foreseeable future, AI may be smart, but still need humans to set the right goals and engage in creative interpretation. Human intelligence alone, and by association, artificial intelligence, do not equal wisdom.
The main difference between AI and ML is that AI includes all systems, methods, and robots performing intelligent actions. ML only focuses on the AI methods and applications that learn and change their decision patterns based on data.
One of the soft skills that humans have that AI does not possess is critical thinking. While technology has advanced to an extent that it can perform tasks with great speed and precision, it is still by far incapable of employing critical thinking. AI is often taught to perform tasks in routine, but it cannot make decisions when faced by eventualities that go beyond what it has learned. For example, a human can improvise or follow gut instinct, but a machine cannot.
Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.
TWO PASSENGER LINERS CRASHED — BOEING HAS PUT THE BLAME ON AI AUTOMATION.. TWO THIRD WORLD NATIONS WERE USED AS GUINEA PIGS..
WHY HAVE WE ALLOWED THIS?
Artificial general intelligence (AGI) is the intelligence of a machine that can understand or learn any intellectual task that a human being can. AGI is not possible because it is not possible to model the human brain
No matter how smart a machine becomes, it can never replicate a human. Machines are rational but, very inhuman as they don’t possess emotions and moral values. They don’t know what is ethical and what’s legal and because of this, don’t have their own judgment making skills. They do what they are told to do and therefore the judgment of right or wrong is nil for them. If they encounter a situation that is unfamiliar to them then they perform incorrectly or else break down in such situations.
Humans are sensitive and intellectuals and they are very creative too. They can generate ideas, can think out of the box. They see, hear, think and feel which machine can’t. Their thoughts are guided by the feelings which completely lacks in machines. No matter how much a machine outgrows, it can’t inherent intuitive abilities of the human brain and can’t replicate it.
Mathematician Alan Turing proposed what is now called “the Turing test” to define artificial intelligence. Loosely speaking, place a computer in one secluded room and a human in another secluded room. If an observer queried these two entities and could not identify which entity was the computer, then the computer had achieved intelligence on a par with a human.
With the Turing test, you've ended up having loads of teams coming up with slimy canned rules to trick you
Turing opted to replace the question of “Can machines think?” or in this case “Is AI possible?” with a test. A test no machine since has ever passed. The machine’s goal in this test is to fool a human to think that the machine is human.
The purpose of the Turing test is not specifically to determine whether a computer is able to fool an interrogator into believing that it is a human, but rather whether a computer could imitate a human.
We all know artificial intelligence systems such as Alexa can tell jokes but can they understand them
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Linguists and computer scientists agree that humor is one aspect that makes humans special.
“Artificial intelligence will never get jokes like humans do. In themselves, they have no need for humor. They miss completely context.
Subjectivity and creativity an genius emanates from the subconscious brain lobe
Creative language — and humor in particular — is one of the hardest areas for computational intelligence to grasp,
A computer doesn’t have these real-world experiences to draw on. It only knows what you tell it and what it draws from
AI “is not about replacing humans, but interacting with them
COMPUTER CAN ONLY TELL JOKES IN ITS DATABASE.. IT CAN NEVER CREATE AN ORIGINAL JOKE ON THE SPOT, IN CONTEXT OF WHAT HAS HAPPENED .
Sometimes even we humans don't know why a joke is funny,"
ARTIFICIAL INTELLEGNCE IS JUST THE LEFT BRAIN LOVE—ALBEIT FASTER AND TIREESS.
HUMOR COMES FROM THE RIGHT BRAIN LOBE ( SUBCONSCIOUS ) FOR A RIGHT HANDER..
FOR 30 YEARS AS SHIP CAPTAIN I DID “HUMOR MANAGEMENT”..
NOBODY ON THIS PLANET HAD DARED TO DO THAT, AS IT REQUIRED EXTREME INTELLIGENCE ( SORRY MENSA FOLKS ) AND THE ABILITY TO BE IN THE MOMENT
John McCarthy is one of the "founding fathers" of artificial intelligence, together with Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. Simon. McCarthy coined the term "artificial intelligence" in 1955, and organized the famous Dartmouth conference in Summer 1956
AI can help chemists crack the molecular structure of crystals much faster than traditional modelling methods, a machine learning programme called ShiftML to predict how the atoms in molecules shift when exposed to a magnetic field.
Nuclear magnetic resonance (NMR) is commonly used to work out the structure of compounds. Groups of atoms oscillate at a specific frequencies, providing a tell-tale sign of the number and location of electrons each contains. But the technique is not good enough to reveal the full chemical structure of molecules, especially complex ones that can contain thousands of different atoms.
Another technique known as Density functional theory (DFT) is needed. It uses complex quantum chemistry calculations to map the density of electrons in a given area, and requires heavy computation. ShiftML, however, can do the job at a much quicker rate
Scientists have developed a machine-learning approach that can be combined with experiments to determine, in record time, the location of atoms in powdered solids. Their method can be applied to complex molecules containing thousands of atoms and could be of particular interest to the pharmaceutical industry.
Many drugs today are produced as powdered solids. But to fully understand how the active ingredients will behave once inside the body, scientists need to know their exact atomic-level structure. For instance, the way molecules are arranged inside a crystal has a direct impact on a compound’s properties, such as its solubility.
Researchers are therefore working hard to develop technologies that can easily identify the exact crystal structures of microcrystalline powders.
Scientists has now written a machine-learning program that can predict, in record time, how atoms will respond to an applied magnetic field. This can be combined with nuclear magnetic resonance (NMR) spectroscopy to determine the exact location of atoms in complex organic compounds. This can be of huge benefit to pharmaceutical companies, which must carefully monitor their molecules’ structures to meet requirements for patient safety.
NMR spectroscopy is a well-known and highly efficient method for probing the magnetic fields between atoms and determining how neighboring atoms interact with each other. However, full crystal structure determination by NMR spectroscopy requires extremely complicated, time-consuming calculations involving quantum chemistry – nearly impossible for molecules with very intricate structures.
To predict the NMR signature of a crystal with nearly 1,600 atoms, the technique – ShiftML – requires about six minutes; the same feat would have taken 16 years with conventional techniques
Optimization is a core part of machine learning. Optimization is how learning algorithms minimize their loss function. The loss function represents the difference between predicted and actual values, so machine learning use optimization to minimize this function leading to better ability to make predictions on new data.
You can think of optimization as trying to find the lowest point on a “surface” defined by the loss function. Often these surfaces have hills and valleys with more than one low point, thus finding the lowest point is challenging.
An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till an optimum or a satisfactory solution is found. ... There are two distinct types of optimization algorithms widely used today. (a) Deterministic Algorithms. They use specific rules for moving one solution to other.
Artificial intelligence (AI) is today support for different powerful data science and optimization techniques. ... On the other hand, complex optimization problems that cannot be tackled via traditional mathematical programming techniques are commonly solved with AI-based optimization approaches such as the metaheuristics.
The difference is very slim between machine learning (ML) and optimization theory. In ML the idea is to learn a function that minimizes an error or one that maximizes reward over punishment. ... The goal for ML is similarly to optimize the performance of a model given an objective and the training data.
Optimization problems goal is to find the maximum or minimum value of the objective function subject to the constraints. The goal in optimization is to find the best decision variable values that satisfy all constraints. Hence, the blanks are filled with objective function and constraints.
The purpose of optimization is to achieve the “best” design relative to a set of prioritized criteria or constraints. These include maximizing factors such as productivity, strength, reliability, longevity, efficiency, and utilization. ... This decision-making process is known as optimization.
AI techniques is an organized way and method to use the knowledge derived such that it can be easily modified to correct errors, or useful in several circumstances. AI techniques are models made from advanced forms of a statistical and mathematical model
A branch of mathematics which encompasses many diverse areas of minimization and optimization. Optimization theory is the more modern term for operations research. ...
In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories depending on whether the variables are continuous or discrete
In computing, optimization is the process of modifying a system to make some features of it work more efficiently or use fewer resources.
Machine intelligence depends on algorithms, processing power and memory. Processing power and memory have been growing at an exponential rate. As for algorithms, until now we have been good at supplying machines with necessary algorithms to use their processing power and memory effectively.
AGI will simply be different not superior to human intelligence.
This is true and human intelligence is also different than animal intelligence. Some animals are capable of amazing mental feats like squirrels remembering where they hid hundreds of nuts for months.
A new program-writing AI, SketchAdapt, trained on tens of thousands of program examples, learns how to compose short, high-level programs, while letting the second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.
SketchAdapt is capable of writing concise programs. The researchers don’t exaggerate the role of AI, making it clear that the program is intended to complement programmers and not replace them. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details.
Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps. SketchAdapt outperformed their reimplemented versions of RobustFill and .Coder at their respective specialized tasks.
SketchAdapt outperformed the RobustFill-like program at string transformations; for example, writing a program to abbreviate Social Security numbers as three digits, and first names by their first letter.
SketchAdapt also did better than the DeepCoder-like program at writing programs to transform a list of numbers. Trained only on examples of three-line list-processing programs, SketchAdapt was better able to transfer its knowledge to a new scenario and write correct four-line programs.
In yet another task, SketchAdapt outperformed both programs at converting math problems from English to code, and calculating the answer.
Key to its success is the ability to switch from neural pattern-matching to a rules-based symbolic search SketchAdapt learns how much pattern recognition is needed to write familiar parts of the program, and how much symbolic reasoning is needed to fill in details which may involve new or complicated concepts.”
SketchAdapt is limited to writing very short programs. Anything more requires too much computation. Nonetheless, it’s intended more to complement programmers rather than replace them
SketchAdapt has been used to create programs that can manipulate strings, process lists, and convert math problems into code. SketchAdapt won’t replace programmers but should be able to assist them with writing mundane code. Indeed, rather than replacing programmers, this technology should free them up to work on more complicated projects where man-power is currently lacking.
AI cannot create safe and moral self-driving cars. Automotive companies are all in the chase of self-driven vehicles. A BI Intelligence report states that there will be 10 million self-driving cars on the road by 2020. But, they all come with human overseers. Without this supervision, safety on the streets would not be possible
AI helps us in reducing the errors and the chance of reaching accuracy with a greater degree of precision. It is applied in various studies such as exploration of space.
Intelligent robots are fed with data and are sent to explore space. Since they are more resistant and have a greater ability to endure the space and hostile atmosphere due to their metal bodies. They are built and acclimatized in such a way that they cannot be altered or get damaged or malfunction in a hostile environment.
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Repetitive tasks are monotonous in nature can be carried out with the help of machine intelligence. Machines think faster than humans . Machine intelligence can be employed to carry out dangerous tasks. Their parameters, unlike humans, can be adjusted. Their speed and time are calculation based parameters only.
Artificial intelligence and the science of robotics can be put to use in mining and other fuel exploration processes. Not only that, these complex machines can be used for exploring the ocean floor and hence overcome the human limitations.
Due to the programming of the robots, they can perform more laborious and hard work with greater responsibility. Moreover, they do not wear out easily. Hard cased robots can be used to defuse bombs
The complete absence of the emotional side, makes the robots think logically and take the right objective program decisions.
Unlike humans, machines do not require frequent breaks and refreshments. They are programmed for long hours and can continuously perform without getting bored or distracted or tired.
The US Air Operations Division (AOD) uses AI for the rule-based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post-processing of the simulator data into symbolic summaries..
Most of the AI artworks that have emerged over the past few years have used a class of algorithms called generative adversarial networks (GANs).
Christie’s recently sold its first piece of auctioned AI art—a blurred face titled “Portrait of Edmond Belamy”—for $432,500. Garbage sold for a high price.
JEWS ARE GOOD IN CONVERTING SHIT TO GOLD..
Professors at Rutgers University have built a machine that autonomously produces “art” using an algorithm called AICAN. The machine incorporates existing styles to generate images on its own. ., AICAN’s work is not art because it has no feelings that it has lived through to communicate. ..
If artificial intelligence is used to create images, can the final product really be thought of as art- which is meant to be poetry on canvas?
The algorithm might create appealing images, but it lives in an isolated creative space that lacks social context. Human artists, on the other hand, are inspired by people, places, and politics. They create art to tell stories and make sense of the world
Ai has no feelings at all. It has no mind. It produces a counterfeit of art.
Art demands that a person evoke in his or her mind a previous experience, and then transmit that feeling so that others experience the same feeling. In fact, AICAN cannot feel at all.
Magic tricks and circuses also give people pleasure, and those are not art, .
AI cannot produce art. Instead, silicon chips with human-inscribed algorithms pass through a sequence of events. There is a connection, a succession, a certain order; there is activity, there is change. But there is no agency, there is no cause, there is no mind, there is no feeling, there is no art. .
Can AI ever write poetry ?
Balls !
AI has no sense of belonging or togetherness or a human touch.
Humans and animals alike have a feature called a neural network. The nerves responsible for all the feelings, thoughts etc cannot be made for an AI. Human neural network has a certain trait called imagination. That is something an AI won’t be able to do.
The irony is that AI solutionism is possible due to human imagination and overestimation. The best AI can do is make business processes streamlined, help in business strategies and military fields but AI cannot solve problems that cannot be solved by humans.
AI is eventually a set section of computer codes. It will perform tasks it has been created for. Those tasks, it might perform better than any human can but doing something that humans can’t is another story altogether.
AI could help lighten the workload of classroom teachers, in areas like analysing students’ past performance and optimising lessons accordingly; marking their tests and quizzes, and correcting their homework and assignments. Robot teachers could also deliver preprogrammed lessons and provide answers to frequently asked questions.
However, AI is unlikely to replace teachers in certain subjects
AI cannot provide the emotional support of a human teacher. Students learn when they interact with teachers and their peers. A good teacher can be a great influence, motivating and inspiring students. I doubt whether AI would be able to do the same.
Also, AI can present knowledge that human beings feed into them, so they still depend on humans to add new data, as knowledge changes from year to year, and decade to decade. Robots have to depend on human programmers
AI can complement human teachers, but they won’t be able to replace them completely. Rather, robots would need to work together with human teachers, in order to maximise the effectiveness of the learning process.
While it cannot see how humans do, AI can be used as machine vision to ‘see’ the world, analyse visual data, and make informative decisions about what has been seen. There are plenty of incredible ways machine vision is used today and it includes self-driving cars, facial recognition, payments and more. In manufacturing, machine vision is widely used to help in predictive maintenance and product quality control.
AI can also detect gun shots by analysing the sound and alerting relevant agencies. This is by far one of the most incredible things that AI can do when it merely hears and is able to understand sounds.
AI researchers are in the process of manufacturing AI tools that will be able to detect types of illnesses by merely smelling a person’s breath. It can detect chemicals called aldehydes that are associated with human illnesses and stress, including cancer, diabetes, brain injuries, and detecting the "woody, musky odor" emitted from Parkinson's disease even before any other symptoms are identified.
Also, AI bots are able to identify gas leaks or other caustic chemicals. IBM is even using AI to produce new perfumes.
Using AI, there is now a robot that can identify the ripeness of fruit and even place them in a basket.
There are AI tools out there that can track a person’s emotions as they watch videos. Artificial Emotional Intelligence works by gathering data from an individual’s facial expressions, body language, and more. It then analyses it against an emotions database to predict what emotion is being expressed. It then determines an action based on this information.
Researchers have found a black-box algorithm which helped in predicting patient’s. This was done by using ECG results to sort historical patient data into groups based on who would die within the span of a year.
The inherent power of AI lies in its ability to deliver the “science” of work, the computational ability to rapidly analyse millions of calculations a second and present these in an easy to consume fashion to humans. This ability to process vast data sets augments human workers with superpowers they have never possessed. Through collaboration with intelligent machines human ability is amplified through augmented intelligence which opens up vast new pathways of value creation.
Apart from communication, machine to man communication is tricky. We can industrial “pick-and-place” robots in industrial contexts; can we have “give-and-take” robots in customer service settings? Imagine a food serving robot in a fine dining restaurant … how do we train the robot to read the moods and suggest the right cuisine?
Most of the robots exhibit puppy-like behaviour, a far cry from naturally intelligent human beings. Humans need friendliness, understanding, and empathy in their social interactions, which are very complex to programme.
When presented with a danger, different humans respond differently based on their psychologies and personalities, most often, shaped from a series of prior experiences and perceived self-efficacies. Robots still find it difficult to sense, characterise, and respond to such interactions. Today’s social robots are designed for short interactions with humans, not learning social and moral norms leading to sustained long term relationships.
Reasoning is ability to interpret something in a logical way in order to form a conclusion or judgment. For instance, it is easy for a robot to pick up a screwdriver from a bin, but quite something else to be able to pick it up in the right orientation and be able to use it appropriately. It needs to be programmed to realise when the tool is held in the wrong orientation and be able to self-correct it to the right orientation for optimal use.
Even when we can train the robot with a variety of sensors to develop logical reasoning through detailed pattern-evaluations and algorithms, it would be difficult to train it to make judgments. For instance, to make up what is good or evil.
AI is in fact a piece of software that we people design. AI is not some kind of magic over which we have no control. We people are the ones that make AI happen. Putting it simply, AI is a term that refers to computational techniques and artefacts that are able to perceive and react to their context, and are able to make inferences about how that context evolves.
In the last few years, the algorithms, or ‘recipes’, that are behind these calculations have become increasingly more effective and efficient at analysing data about us and about our environment, and with it their impact on society is also becoming much larger.
AI and autonomous systems have made human lives easier, but there is no denying that if algorithms go wrong, the result can be disastrous.
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Project Maven is a controversial artificial intelligence program that uses machine learning to sort through millions of hours of drone footage to help systems distinguish people from their surroundings. The project would help reduce the burden on human analysts and improve the intelligence that's captured in cameras.
Optimization models use machine learning, to simulate the electric system and the severity of various problems. In a region with 1,000 electric power assets, such as generators and transformers, an outage of just three assets can produce nearly a billion scenarios of potential failure.
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Graph convolutional neural network can recommend optimal controls that would prevent transmission lines from overloading if there were a problem with any of the lines. Machine learning can quickly find a solution, with far fewer errors than more conventional ones.
Machines are much better and more cost-efficient than humans when it comes to handling huge amounts of data and performing routine tasks. This is exactly what the cyber security industry needs at the moment, especially with the large number of new threats appearing every day. Human classification, especially in bulk, will be error-prone due to boredom and distractions. .
AI uses machine learning to detect similarities and differences within a data set and report any anomalies.
Machine learning can help in anti-malware, performing dynamic risk analysis and detecting anomaly. AI techniques can be made to learn to remove the noise or unwanted data, and facilitate security experts to understand cyber environment for detection of any anomalous activity. AI can also benefit cyber security with automated techniques to generate cyber courses of action (COAs) whenever cyber threats are detected.
If it weren’t for artificial intelligence and machine learning, the cybersecurity landscape would be very different than it is right now.
As cyber threats evolve, and the attacks become more complex and widespread, conventional defense tools are often not enough to detect and stop them on time. Therefore, security solutions that are powered by machine learning are the next big thing in cybersecurity.
Due to their ability to learn and adapt over time, such tools can promptly eliminate well-known threats, as well as respond to new emerging risks before they do any harm, by recalling and processing data from prior attacks.
AI has the ability to perform specific tasks on its own, this way saving time and reducing the risk of human error. Unlike people, AI systems don’t make mistakes as they handle threats according to a standardized playbook, this way responding to each threat in the most effective way.
With the AI systems on their side, security experts can spend less time performing routine tasks and focus on building a stronger defense that would allow stopping sophisticated cyber-attacks before they even occur. Therefore, implementing machine learning and AI systems is crucial to stay one step ahead of cybercriminals. And yet, no technology is a silver bullet, and AI is just a tool, which can only do what criminals or security experts command it to do.
AI and ML enable predictive analytics to draw statistical inferences to mitigate threats with fewer resources. Applications for automated network security include self-encrypting and self-healing drives to protect data and applications.
Application of AI based technologies into the existing systems will bring in much enhanced systems that help in better decision making. Some of the key areas where in the functionalities of AI makes a difference are:
Data Mining
Pattern Recognition
Fraud Detection
Analytics
Fuzzy Logic
Development of expert Systems
Machine learning based antivirus systems and tools can help in quickly and accurately identifying malware like Polymorphic virus based on its continuous learning capabilities. Such systems can detect suspicious files based on the behavioural or structural analysis and it helps in detecting threats at an early stage. It can easily determine the likelihood of a malicious virus attack by analysing and breaking down the DNA of each file.
More than 95 % of all malicious executables we encounter are polymorphic
Polymorphic refers to a malware’s ability to continually change and adapt its features to avoid detection. Polymorphic malware pairs a mutation engine with self-propagating code to continually change its “appearance,” and it uses encryption (or other methods) to hide its code.
A polymorphic virus is a complicated computer virus that affects data types and functions. It is a self-encrypted virus designed to avoid detection by a scanner. Upon infection, the polymorphic virus duplicates itself by creating usable, albeit slightly modified, copies of itself
A polymorphic virus is a harmful, destructive or intrusive type malware that can change, making it difficult to detect with anti-malware programs. A metamorphic virus is a virus that is rewritten with every iteration so that every succeeding version of the code is different from the proceeding one.
Metamorphic malware can evade more conventional detection methods than polymorphic malware because of its greater complexity and transformative capabilities
To a large extent, cybersecurity relies on file signatures to detect malware, and rules-based systems for detecting network abnormalities. Protection often stems from an actual virus outbreak – as security experts isolate the malicious files and identify unique signatures that help other systems become alert and immune.
The same is true for the rules-based system: Rules are set based on experience of potential malicious activity, or systems are locked down to restrict any access to stay on the safe side. The only problem with these approaches is their reactive nature. Hackers always find innovative ways to bypass the known rules. Before a security expert discovers the breach, it’s often too late.
Hackers have started using AI to accelerate polymorphic malware, causing code to constantly change and make it undetectable. Advanced tactics allow hackers to work around security to bypass facial security and spam filters, promote fake voice commands, and bypass anomaly detection engines.
The good news is this intelligence be used to protect the infrastructure as well. What makes AI cybersecurity unique is its adaptability. Intelligent cybersecurity doesn’t need to follow specific rules. Rather, it can watch patterns and learn. Even better, AI can be directly integrated into everyday protection tools – such as spam filters, network intrusion and fraud detection, multi-factor authentication, and incident response.
Polymorphic malware exists in many forms — Digital Guardian identifies some of these types of malware as viruses, bots, trojans, worms, and keyloggers. Regardless of the type, what makes this malware so effective is its complexity and speed. Polymorphic malware uses polymorphic code to changes rapidly — as frequently as every 15-20 seconds!
Because many anti-malware vendors use traditional signature-based detection methods to detect and block malicious code, it means that by the time they identify the new signature, the malware has already evolved into something new. As a result, most security solutions simply can’t keep up with or aren’t able to detect these threats.
Polymorphic malware takes advantage of employee ignorance and unrecognized zero day vulnerabilities to wreak havoc. If an employee clicks on an attachment in a phishing email or provides information via a phishing website, they open your entire network, company, and sensitive data to attack.
And when the threat is continually evolving, it’s harder to identify let alone eliminate.
One of the things that differentiates metamorphic from polymorphic malware is that it completely re-writes its code so that each newly propagated version of itself no longer matches its previous
iteration. This differs from polymorphic malware, which alters part of its code but retains one part of its code that remains the same (making it a bit easier to identify than metamorphic malware). To put it simply, polymorphic malware is a leopard that changes its spots; metamorphic malware is a tiger that becomes a lion.
The fight against mutating malware is a race between hackers and cybersecurity experts.Each side is doing their best to stay ahead of the other — the first by trying to come up with new and more inventive (and invasive) threats; the other by trying to create new detection capabilities and response methods. It’s a good ol’ fashion arms race in the cyber world. The good news, however, is that these threats are not unstoppable.
Criminals ( sponsored by deep state ) now poison machine learning-based defence systems by throwing garbage data at them to subvert the machine learning.
Cyber attackers are turning to machine learning to create smarter attacks and defenders will require similar technology to detect them, But AI-enabled attacks are not the only big threat on the horizon, – the proliferation of internet-connected devices that make up the internet of things (IoT) is cause for concern.
IoT is the network of interconnected things/devices which are embedded with sensors, software, network connectivity and necessary electronics that enables them to collect and exchange data making them responsive.
An IoT ecosystem consists of web-enabled smart devices that use embedded processors, sensors and communication hardware to collect, send and act on data they acquire from their environments. ... Sometimes, these devices communicate with other related devices and act on the information they get from one another.
Three years ago a major cyber attack took place. Cyber-criminals attacked IoT devices, while several attacks affected many websites. Amazon, Twitter, Reddit, Spotify and PayPal, were all affected by this attack.
This means two things. Firstly, that the world is vulnerable to cyber-attacks at the moment, and secondly that IoT has entered for good in our lives, and plays a much bigger role than we may have imagined.
Of course, shipping forms a part of this web as well. In fact, IoT sensors are integrated into almost everything in the shipping era, which means they collect everything.
Just two years ago, in June 2017, A.P. Moller - Maersk fell victim to a major cyber-attack caused by the NotPetya malware, which also affected many organisations globally. As a result, Maersk’s operations in transport and logistics businesses were disrupted, leading to unwarranted impact.
The attack reportedly created huge problems to the world’s biggest carrier of seaborne freight which transports about 15 per cent of global trade by containers. In particular, Maersk’s container ships stood still at sea and its 76 port terminals around the world ground to a halt.
The recovery was fast, but within a brief period the organisation suffered financial losses up to USD300m covering, among other things, loss of revenue, IT restoration costs and extraordinary costs related to operations.
It may sound naive, but changing the default password on electronic devices is crucial. If the password remains the same, then the device that is connected to the internet may be infected.
The fact is that cyber-attackers know many of the default passwords that are attributed to IoT devices and they use them to affect them. Leaving the default password unchanged is common. As a recent ESET research has shown, at least 15% of home routers are unsecured, which is translated to around 105 million routers.
In the Maersk case, all began when an employee in Ukraine responded to an email which featuring the NotPetya Malware. The system affected and therefore operations practically had to be on hold until system’s restoration.
Artificial Intelligence does not depend on learning or feedback, rather it has directly programmed control systems. The AI systems come up with the solutions to the problems on their own by calculations.
ML is the use of statistics to give computers the ability to learn from data. This differentiation is key because fast advances in ML have led to the sudden interest in AI worldwide. The initial set of improvements have been in the underlying algorithms and data architectures, but the current key improvements are just two: data and computation.
Most ML methods are suited to tackling two key problems: prediction type problems and classification type problems.
Prediction-type problems include 'Can I predict when this equipment will fail?’ (If so, I can deploy maintenance before failure to ensure the plant doesn’t grind to a halt, while saving on unnecessary maintenance).
Classification-type problems include 'Is this customer different from another, based on the data I have from them?’ (If so, I can further study the differences and maybe deploy a new marketing program to retain them).
Machine learning is a continuation of the concepts around predictive analytics, with one key difference: The AI system is able to make assumptions, test and learn autonomously. AI is a combination of technologies, and machine learning is one of the most prominent techniques utilized for hyper-personalized marketing.
Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms. This means that data science helps AIs figure out solutions to problems by linking similar data for future use. ... But, machine learning is the branch of AI that works best with data science.
Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade
Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient's health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
Like statistical models, the goal of ML is to understand the structure of the data – fit theoretical distributions to the data that are well understood. So, with statistical models there is a theory behind the model that is mathematically proven, but this requires that data meets certain strong assumptions too.
The challenge for banks and financial institutions then is to quickly identify and separate fraudulent transactions from those that are legitimate, without impacting on customer experience.
Traditionally, banks and financial institutions have approached fraud detection with manual procedures, or rule-based solutions, which have been limited in their success. A rule-based approach means that a complex set of criteria for flagging suspicious transactions has to be established and reviewed manually.
While this can be effective in discovering anomalies which conform to known patterns, it is not capable of detecting fraud which follows new, or unknown patterns. This gives criminals the incentive to develop ever more sophisticated techniques to circumnavigate the rules, and they themselves are leveraging new technologies to achieve this. The solution that is helping banks and financial institutions get one step ahead, is machine learning.
The high volume of transactional and customer data readily available in the financial services sector makes it ideal for the application of complex machine learning algorithms. Banks and financial institutions are able to automate the analysis of their customers’ behavioral patterns for any signs of abnormality, giving them the ability to identify and flag fraudulent activity in real-time.
The increased accuracy of machine learning provides financial firms with a significant reduction in the number of false positives, where transactions are incorrectly flagged as fraudulent and declined, and false negatives, where genuine incidences of fraud are missed. Overall, firms are able to mitigate financial losses, as well as protect their reputations, maintain public confidence, and improve customer experience.
To more aggressively stop criminal transactions, banks are shifting from a passive methodology of “observe, react, and report” to a proactive approach of blocking fraud before it occurs. However, this strategy risks producing higher rates of false positives, and the system might mistakenly freeze a customer’s legitimate transaction. Every time a legal transaction is blocked, the bank induces a negative impact on customer satisfaction—and that increases the probability of customer churn.
The challenge is to maintain a balance of meticulous and rapid detection without interrupting legitimate customer transactions. Banks face enormous pressures to satisfy the customer experience with fraud detection. The new goal is precise, real-time detection.
fraudulent transactions do not occur randomly. So, a bank could find one transaction to be fraudulent, but miss three more that were in sequential order. Therefore, sample detection could miss some fraudulent transactions. In addition, sampling does not fully account for multiple occurrences of fraud within the current populations of transactions—hindering probability models or estimates. Banks could also omit several much smaller, less frequent fraudulent transactions adding up to be extremely large fraud losses in aggregate.
The business of fraud detection is not easy. From the evolving forms of fraud to the growing complexity of illegal transactions, banks are caught in the land of “no easy answers.” There is no right or wrong answer to how to throttle fraud detection. Every bank has different risk tolerances and variables beyond that one customer transaction. However, leveraging cloud capabilities increases the capacity to handle higher volumes of transactions, and ever-changing fraud tactics
For digital payments fraud prevention strategies to be successful, the processes must integrate with transaction processing systems. This integration enables real-time interdiction and drives actions automatically. Automated systems can provide a comprehensive view of customer behavior by leveraging analytic calculations and algorithms to detect and flag suspicious payments activity. A core benefit of these new technologies is their delivery of low false positives. False positives impact revenue negatively
To keep up with this growing threat, payment providers must evolve from the traditional, siloed method of fraud detection to a proactive, analytic approach. The traditional models were trained on historical data, frozen, then weighted or adjusted in batches. This led to almost no co-operative learning and decision-making, as well as harmful business outcomes, such as customer abandonment, payment denial, fraud, missed cross-sell, and bad customer experience.
Modern systems of engagement are incorporating a new generation of application architecture that eliminates the wall between transaction processing and analytics. Many companies are now building transactional analytics systems for fraud to complement their existing architecture
Hybrid Transaction/Analytical Processing (HTAP) is best enabled by in-memory computing technology to allow analytical processing on the same in-memory data store used to perform transaction processing.
Hybrid transaction/analytical processing (HTAP) is an emerging application architecture that "breaks the wall" between transaction processing and analytics. It enables more informed and "in business real time" decision making
By removing the latency with moving data from operational databases to data warehouses and data marts for analytical processing, this architecture enables real-time analytics and situation awareness on live transaction data. The ability to run analytics on live data and provide immediate feedback to the system is key to fraud deterrence.
The amount of data that needs to be processed or learned from can be massive. The data could consist of: billions of historical payment data points; analysis of activity correlated to hundreds of millions of devices; behavior and device mismatch across many locations; user actions, preferences, and interactions; geo-policies, dependencies, and myriad sets of third-party information; social and third-party consumer information; and e-commerce transactions.
It is important to note the efficiency and efficacy of the systems that prevent payment fraud depend on their power to harness data, analyze, learn it, and act upon it – with a high accuracy rate and at near-instant speed.
Probability is about a finite set of possible outcomes, given a probability. Likelihood is about an infinite set of possible probabilities, given an outcome
An unconditional probability is the independent chance that a single outcome results from a sample of possible outcomes. The term refers to the likelihood that an event will take place independent of whether any other events take place or any other conditions are present.
Probability is a measure of uncertainty. Probability applies to machine learning because in the real world, we need to make decisions with incomplete information. Hence, we need a mechanism to quantify uncertainty – which Probability provides us.
Using probability, we can model elements of uncertainty such as risk in financial transactions and many other business processes. In contrast, in traditional programming, we work with deterministic problems i.e. the solution is not affected by uncertainty.
Probability forms the basis of sampling. In machine learning, uncertainty can arise in many ways – for example - noise in data. Probability provides a set of tools to model uncertainty. Noise could arise due to variability in the observations, as a measurement error or from other sources. Noise effects both inputs and outputs.
Apart from noise in the sample data, we should also cater for the effects of bias. Even when the observations are uniformly sampled i.e. no bias is assumed in the sampling – other limitations can introduce bias.
Typically, we are given a dataset i.e. we do not have control on the creation and sampling process of the dataset. To cater for this lack of control over sampling, we split the data into train and test sets or we use resampling techniques. Hence, probability (through sampling) is involved when we have incomplete coverage of the problem domain.
Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.
Simply put, BIG DATA is a large volume of data collected from various sources, which contains a greater variety and increasing volume of data from millions of users.
Computers can be used to store millions of records and data, but the power to analyze this data is provided by the Big Data. Big data helps the organizations in analyzing their existing data and in drawing meaningful insights from the same.
Big Data is the raw input that needs to be cleaned, structured and integrated before it becomes useful, while artificial intelligence is the output, the intelligence that results from the processed data. That makes the two inherently different.
There are several AI technologies that are used with Big Data and below-listed are a few of them:--
Anomaly Detection
For any dataset, if an anomaly is not detected then Big Data analytics can be used. Here fault detection, sensor network, eco-system distribution system health can be detected with big data technologies.
Bayes Theorem
Bayes theorem is used to identify the probability of an event based on the pre-known conditions. Even the future of any event can also be predicted on the basis of the previous event. For Big Data analysis this theorem is of best use and can provide a likelihood of any customer interest in the product by using the past or historical data pattern.
Pattern Recognition
Pattern recognition is a technique of machine learning and is used to identify the patterns in a certain amount of data. With the help of training data, the patterns can be identified and are known as supervised learning.
Graph Theory
Graph theory is based on graph study that uses various vertices and edges. Through node relationships, the data pattern and relationship can be identified. This pattern can be useful and help the big data analysts in pattern identification. This study can be important and useful for any business.
AI’s ability to work so well with data analytics is the primary reason why AI and Big Data are now seemingly inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics. Problems arise, however, when the data being used is not good data.
Data and AI are merging into a synergistic relationship, where AI is useless without data and data is insurmountable without AI.
Data is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function. Unfortunately, organizations struggle to integrate data from multiple sources to create a single source of truth on their customers. AI will not solve these data issues – it will only make them more pronounced
AI enables us to make sense of massive data sets, as well as unstructured data that doesn’t fit neatly into database rows and columns. AI is helping organizations create new insights from data that was formerly locked away in emails, presentations, videos, and images
Big Data hoovers up massive amounts of data and the wheat has to be separated from the chafe first before anything can be done with it. Data used in AI and ML is already “cleaned,” with extraneous, duplicate and unnecessary data already removed.
After that, AI can thrive. Big Data can provide the data needed to train the learning algorithms. There are two types of data learning: the initial training, which is a sort of priming the pump, and routinely gathered data. AI apps never stop learning once the initial training is done. They continue to take in new data and adjust their actions along the way as the data changes. So data is needed initially and continuously.
The two styles of computing both use pattern recognition, but differently. Big Data analytics finds patterns through sequential analysis, sometimes of cold data, or data that is not freshly gathered. Hadoop, the basic framework for Big Data analysis, is a batch process originally designed to run at night during low server utilization.
Machine learning learns from collected data and keeps collecting. Your self-driving car never stops gathering data, and it keeps learning and honing its processes. Data is always coming in fresh and always acted upon.
AI doesn’t deduce conclusions like humans do. It learns through trial and error, and that requires massive amounts of data to teach the AI.
The more data and more variety, the better the accuracy of the Machine learning models trained on this data. Although more data is good, it is not useful if it does not contain variety.
There are the three ‘Vs’ of big data, namely:--
Volume: In simple language, defined as the amount of data available.
Variety: Variety in big data refers to all the structured and unstructured data that has the possibility of being generated either by humans or by machines. Variety is essential because it allows ML models trained on the data to be able to handle a wider range of predictions.
Velocity: The rate at which data is received from other users and acted upon.
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Because with AI and big data:--
Analysts will be able to perform more thorough analysis.
Portfolio managers will make better informed decisions.
No asset is more prized in today's digital economy than data. It has become widespread to the point of cliche to refer to data as "the new oil."
Indeed data is "the world's most valuable resource and those who rule data will rule the world
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Data storage requirements for Big Data are substantial. One approach is to capture and process the localized data and then forward the storage to a more extensive storage system that is maintained in the cloud.
Another approach is to use a “virtualized” data system that creates a virtual layer of the data. This virtual layer knows where the data is stored on the network. When calculations are being made using the AI algorithm in a virtual system, only the data needed for that specific calculation is accessed. The original data storage remains intact and in place, without the need for copying data files
This approach utilizes a network-wide data management protocol. It reduces the need for data storage memory as well as improves the computational processing speeds.
In the modern era, enterprise data comes in many forms and is stored in many locations. There is both structured and unstructured data, including rows and columns of data in a traditional database, and data in formats like logs, email, and social media content. Big Data in its many forms is stored in databases, log files, CRM, SaaS, and other apps.
So how do you get an overview of your far-flung data and manage it in all of its disparate forms? You use data virtualization, an umbrella term to describe any approach to master data management that allows for retrieval and manipulation of data without knowing where it is stored or how it is formatted.
Data virtualization integrates data from disparate sources without copying or moving the data, thus giving users a single virtual layer that spans multiple applications, formats, and physical locations. This means faster, easier access to data.
It is the ultimate in modern data integration because it breaks down silos and formats, performing data replication and federation in a real-time format, allowing for greater speed and agility and response time. It helps with data mining, it enables effective data analytics, and is critical for predictive analytics tools. Effective use of machine learning and artificial intelligence is unlikely without data virtualization.
It should be noted that data virtualization is not a data store replicator. Data virtualization does not normally persist or replicate data from source systems. It only stores metadata for the virtual views and integration logic. Caching can be used to improve performance but, by and large, data virtualization is intended to be very lightweight and agile.
Data virtualization is any approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located, and can provide a single customer view (or single view of any other entity) of the overall data.
Unlike the traditional extract, transform, load ("ETL") process, the data remains in place, and real-time access is given to the source system for the data. This reduces the risk of data errors, of the workload moving data around that may never be used, and it does not attempt to impose a single data model on the data (an example of heterogeneous data is a federated database system).
The technology also supports the writing of transaction data updates back to the source systems. To resolve differences in source and consumer formats and semantics, various abstraction and transformation techniques are used. This concept and software is a subset of data integration and is commonly used within business intelligence, service-oriented architecture data services, cloud computing, enterprise search, and master data management.
Some ways that AI is applied to Big Data Analytics include:--
Detecting Anomalies: AI can analyze Big Data to detect anomalies (unusual occurrences) in the data set. This can be applied to networks of sensors and parameters that have a predefined appropriate range. Any node of the network that is outside of the range is identified as a potential problem that needs attention.
Probabilities of Future Outcomes: AI can analyze Big Data using Bayes theorem. The likelihood of an event occurring can be determined using known conditions that have a certain probability of influencing the future outcome.
Recognizing Patterns: AI can analyze Big Data to look for patterns that might otherwise remain undetected by human supervision.
Data Bars and Graphs: AI can analyze Big Data to look for patterns in bars and graphs that are made from the underlying data set.
Another key driver of this trend is that Big Data is increasing through the explosion of connected devices being deployed with the expansion of the Internet of Things (IoT).
The entire universe of AI can be split into these two groups. A computer system that achieves AI through a rule-based technique is called rule-based system. A computer system that achieves AI through a machine learning technique is called a learning system.
It's easy to confuse the two as they can look very similar. Both involve machines completing tasks, seemingly on their own. The difference is that AI can determine the action to take itself; it can learn and adapt. Meanwhile, rule-based systems do exactly as instructed by a human.
A rule-based system is like a human being born with fixed knowledge. The knowledge of that human being doesn’t change over time. This implies that, when this human being encounters a problem for which no rules have been designed, then this human gets stuck and so won’t be able to solve the problem. In a sense, the human being doesn’t even understand the problem.
In computer science, a rule-based system is a set of "if-then" statements that uses a set of assertions, to which rules on how to act upon those assertions are created. ... Rule-based systems are also used in AI (artificial intelligence)programming and systems.
Rule-based logic is at the heart of most automated processes. The term refers to the way in which automation software — like ThinkAutomation — works.
Rule-based systems rely on explicitly stated and static models of a domain. Learning systems create their own models.
A rule-based system is a system that applies human-made rules to store, sort and manipulate data. In doing so, it mimics human intelligence.
To work, rule-based systems require a set of facts or source of data, and a set of rules for manipulating that data. These rules are sometimes referred to as ‘If statements’ as they tend to follow the line of ‘IF X happens THEN do Y’.
Automation software like ThinkAutomation is a good example. It automates processes by breaking them down into steps. First comes the data or new business event. Then comes the analysis: the part where the system processes the data against its rules. Then comes the automated action.
So, what is a rule-based system? It’s a logical program that uses pre-defined rules to make deductions and choices to perform automated actions.
Rule-based systems cause other problems. For example, it’s tough (to nearly impossible) to add rules to an already large knowledge base without introducing contradicting rules. The maintenance of these systems then often becomes too time-consuming and expensive. As such, rule-based systems aren’t very useful for solving problems in complex domains or across multiple different but simple domains.
Rule-based systems, unsurprisingly, work based on rules. These rules outline triggers and the actions that should follow (or are triggered). For example, a trigger might be an email containing the word “invoice”. An action might then be to forward the email to the finance team.
These rules most often take the form of if statements. ‘IF’ outlines the trigger, ‘THEN’ specifies the action to complete. So, if you want to create a rule-based system capable of handling 100 different actions, you’d have to write 100 different rules. If you want to then update the system and add actions, then you would need to write new rules.
In short, you use rules to tell a machine what to do, and the machine will do exactly as you tell it. From there, rule-based systems will execute the actions until you tell it to stop. But remember: if you tell it to do something incorrectly, it will do it incorrectly.
In contrast to rule-based systems, learning systems have a very ambitious goal. The vision of AI research, which turns out to be more a hope than a concrete vision, is to implement general AI through the learning capability of these systems. Hence, the hope is that a learning system is in principle unlimited in its ability to simulate intelligence. It’s said to have adaptive intelligence.
The ability to learn causes adaptive intelligence, and adaptive intelligence means that existing knowledge can be changed or discarded, and new knowledge can be acquired. Hence, these systems build the rules on the fly. That is what makes learning systems so different from rule-based testing. A neural network is an instance of a learning system.
Machine learning is the study of algorithms that perform specific tasks without the need for explicit instructions. The system relies on common patterns and interference. Good machine learning systems should be able to prepare and interpret different types of data, have intelligent algorithms, follow iterative and automated processes and be scalable.
The key differences between AI and machine learning
PROFORMA: S No. / MACHINE LEARNING / ARTIFICIAL INTELLIGENCE
1/ Set of statistical data and techniques that a machine uses to improve itself and learn about its environment with experience. / Any technique that enhances a computer’s capacity to mimic human intelligence using set of rules, decision trees and machine learning logic.
2/ Focuses on more accuracy by improving algorithms over time. / Focuses more on increasing the success percentage rather than 100 percent accuracy.
3/ The machine takes facts and data, analyses the data and learns from the data. This cycle goes on. / It is a computer program that uses its own intelligence to think and perform like a human would do.
4/The ultimate aim is to maximize the performance of the machine for a particular task based on data. / The main aim is to simulate human-like intelligence using neural networks to solve complex problems.
5/The system learns from data. It does not make its own decisions. / The system makes decisions.
6/Since the entire derivation is from data, there is no behavioral learning. For example, the user may have clicked on a few links by mistake, but the data will still be interpreted as the user’s choice or interest. / AI leads to the development of a system that mimics how a human would behave under certain circumstances. For example, a driverless car would try to think and drive like a human.
7/ML is a center of information and knowledge based on facts and data collected over time. / AI is intelligence based on language processing, perception, motion and manipulation, and social intelligence.
8/ML is one of the many goals of AI. /AI has multiple goals like reasoning, computer vision, robotics, machine learning, etc…
9/ Some examples include –
Detection of spam emails
The personalized online shopping experience
Stock market analysis
/ Some examples include –
Personal assistants like Alexa, Siri, and Cortana
Driverless cars
Fraud detection and prevention in online transactions
Language, pattern and image recognition
From the differences, we find that machine learning has evolved from AI just like many other goals of AI. Consider AI as the huge cap which contains machine learning and many other associated goals.
A rule-based system is a system that applies human-made rules to store, sort and manipulate data. In doing so, it mimics human intelligence. To work, rule-based systems require a set of facts or source of data, and a set of rules for manipulating that data.
Artificial intelligence (AI) has been described as a set of “prediction machines.” In general, the technology is great at generating automated predictions. But if you want to use artificial intelligence in a regulated industry, you better be able to explain how the machine predicted a fraud or criminal suspect, a bad credit risk, or a good candidate for drug trials.
The strength of rule engines is their interpretability; a human with reasonable levels of expertise can look at the rules, see if they make sense, and modify them relatively easily. (This is handy in a court-room situation.) They are well-suited to small to medium-complexity decisions; above a few hundred rules, they can develop interactions that are difficult for humans to understand, and maintaining them is challenging.
Historically, rules relied more on logic than large amounts of data. Rather than learning from data, they learn from human experts. The process of extracting domain expertise from experts has been called “knowledge engineering.” Constructing a rule set for a simple knowledge domain is easy, and many non-technical experts can do it.
Rule based AI overcomes the challenge of building models when there aren’t large sets of structured or unstructured data sets to test and refine the software. This is easier in fields such as reading medical images, where there might be tens of millions of examples of relevant MRI’s or CAT scans.
For many other fields and any new knowledge domain, there are just not enough large data sets available to train or maintain the software’s accuracy.
Rule based approaches solve that problem.
Rule-based chatbots are also referred to as decision-tree bots. As the name suggests, they are powered through a series of defined rules. These rules are the basis for the types of problems the chatbot is familiar with and can deliver solutions for.
Like a flowchart, conversations are mapped out to anticipate what a customer might ask, and how the chatbot should respond.
Rule-based chatbots can be built with very simple or complicated rules. They can’t, however, answer any questions outside of the defined rules. These chatbots do not learn through interactions and only perform and work with the scenarios they are trained for.
In comparison, AI chatbots that use machine learning are built to understand the context and intent of a question before formulating a response.
These chatbots generate their own answers to more complicated questions using natural-language responses. The more these bots are used and trained, the more they learn and the better they operate with the user.
Advantages of a rule-based chatbot
While rule-based bots have a less flexible conversational flow, these guard rails are also an advantage. You can better guarantee the experience they will deliver, whereas chatbots that rely on machine learning are a bit less predictable.
Some other advantages of a rule-based chatbot are that they:--
are generally faster to train (less expensive)
integrate easily with legacy systems
streamline the handover to a human agent
are highly accountable and secure
can include interactive elements and media
are not restricted to text interactions
Advantages of AI chatbots--
AI Bots can be seen as a more sophisticated cousin of chatbots. They work well for companies that will have a lot of data. Although they take longer to train initially, AI chatbots save a lot of time in the long run.
AI chatbots:--
learn from information gathered
continuously improve as more data comes in
understand patterns of behavior
have a broader range of decision-making skills
can be programmed to understand many languages
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.
A knowledge-based system (KBS) is a form of artificial intelligence (AI) that aims to capture the knowledge of human experts to support decision-making. ... Some systems encode expert knowledge as rules and are therefore referred to as rule-based systems. Another approach, case-based reasoning, substitutes cases for rules.
Neural networks are extremely susceptible to something called adversarial examples. Adversarial examples are inputs to a neural network that result in an incorrect output from the network.
Adversarial examples are inputs to a neural network that result in an incorrect output from the network.
Adversarial example are not model or architecture specific. Adversarial examples generated for one neural network architecture will transfer very well to another architecture. In other words, if you wanted to trick a model you could create your own model and adversarial examples based off of it. Then these same adversarial examples will most probably trick the other model as well.
It is possible to create adversarial examples for a completely black box model where we have no prior knowledge of the internal mechanics.
As we move toward a future that incorporates more and more neural networks and deep learning algorithms in our daily lives we have to be careful to remember that these models can be fooled very easily.
Despite the fact that neural networks are to some extent biologically inspired and have near human capabilities in a wide variety of tasks, adversarial examples teach us that their method of operation is nothing like how real biological creatures work. As we’ve seen neural networks can fail quite easily and catastrophically, in ways that are completely alien to us humans.
We do not completely understand neural networks and to use our human intuition to describe neural networks would be unwise..
The thing is a neural network does not “think” in the sense that humans “think.” They are fundamentally just a series of matrix multiplications with some added non-linearities. And as adversarial examples show us, the outputs of these models are incredibly fragile. We must be careful not to attribute human qualities to neural networks despite the fact that they have human capabilities. That is, we must not anthropomorphize machine learning models.
Adversarial examples are typically created by adding a small amount of carefully calculated noise to a natural image.
Adversarial examples don’t depend much on the specific deep neural network used for the task — an adversarial example trained for one network seems to confuse another one as well. In other words, multiple classifiers assign the same (wrong) class to an adversarial example.
This “transferability” enables attackers to fool systems in what are known as “black-box attacks” where they don’t have access to the model’s architecture, parameters or even the training data used to train the network.
One of the easiest and most brute-force way to defend against these attacks is to pretend to be the attacker, generate a number of adversarial examples against your own network, and then explicitly train the model to not be fooled by them.
This improves the generalization of the model but hasn’t been able to provide a meaningful level of robustness — in fact, it just ends up being a game of whack-a-mole where attackers and defenders are just trying to one-up each other.
Adversarial examples are imperceptible to human but can easily fool deep neural networks in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying deep neural networks in safety-critical environments. Therefore, attacks and defenses on adversarial examples draw great attention
Adversarial perturbations are imperceptible to human but can easily fool DNNs ( deep neural networks ) in the testing/deploying stage. The vulnerability to adversarial examples becomes one of the major risks for applying DNNs in safety-critical environments.
Creating adversarial samples is remarkably simple and is closely related to the way neural networks are trained i.e. gradient descent. The principle of gradient descent is simple. To find the minimum of an unknown function, first start at a random point then take a small step in the direction of the gradient of that function.
Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model
.A neural network is, in essence, an attempt to simulate the brain. Neural network theory revolves around the idea that certain key properties of biological neurons can be extracted and applied to simulations, thus creating a simulated (and very much simplified) brain ..
Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input..
A neural network is a machine learning algorithm based on the model of a human neuron. The human brain consists of millions of neurons. The key difference is that neural networks are a stepping stone in the search for artificial intelligence. Artificial intelligence is a vast field that has the goal of creating intelligent machines, something that has been achieved many times depending on how you define intelligence
The mathematical model that is used in order to predict as well as classify the results from a given data set is referred to as the neural networks. They are even called as the artificial neural networks.
The neural network classifies these inputs by the process which is called as the learning. Like I said, the first artificial neural network was invented in 1958 by psychologist Frank Rosenblatt. Called Perceptron, it was intended to model how the human brain processed visual data and learned to recognize objects.
Neural networks follow a dynamic computational structure, and do not abide by a simple process to derive a desired output. ... Similarly, in neural networks, different inputs are processed and modified by a weight, or a sort of equation that changes the original value.
Convolution neural networks thus allow neural networks to be feasible for large data sets, or for complex images, since it reduces the computational power needed for the analysis. There are many applications for machine learning methods such as neural nets. Most of these applications focus on classification of images
Neural networks can be used for either regression or classification. Under regression model a single value is outputted which may be mapped to a set of real numbers meaning that only one output neuron is required An Artificial Neural Network is an information processing model that is inspired by the way biological nervous systems, such as the brain, process information.
Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time series predictions, anomaly detection in data, and natural language understanding A neural network is an artifical network or mathematical model for information processing based on how neurons and synapses work in the human brain.
Adversarial AI is the malicious development and use of advanced digital technology and systems that have intellectual processes typically associated with human behaviour
An adversarial example is an instance with small, intentional feature perturbations that cause a machine learning model to make a false prediction. Adversarial examples are inputs to deep learning models that another network has designed to induce a mistake
Adversarial machine learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. This technique can be applied for a variety of reasons, the most common being to attack or cause a malfunction in standard machine learning models.
Machine learning techniques were originally designed for stationary and benign environments in which the training and test data are assumed to be generated from the same statistical distribution.
However, when those models are implemented in the real world, the presence of intelligent and adaptive adversaries may violate that statistical assumption to some degree, depending on the adversary. This technique shows how a malicious adversary can surreptitiously manipulate the input data so as to exploit specific vulnerabilities of learning algorithms and compromise the security of the machine learning system
Examples include attacks in spam filtering, where spam messages are obfuscated through the misspelling of “bad” words or the insertion of “good” words; attacks in computer security, such as obfuscating malware code within network packets or to mislead signature detection; attacks in biometric recognition where fake biometric traits may be exploited to impersonate a legitimate user;or to compromise users' template galleries that adapt to updated traits over time.
For as smart as artificial intelligence systems seem to get, they’re still easily confused by hackers who launch so-called adversarial attacks — cyberattacks that trick algorithms into misinterpreting their training data, sometimes to disastrous ends. AI “vaccine” that trains algorithms on weak adversaries so they’re better prepared for the real thing — not entirely unlike how vaccines expose our immune systems to inert viruses so they can fight off infections in the future.
AI systems have an additional vulnerability: inputs can be manipulated in small ways that can completely change decisions. A credit score, for example, might rise significantly if one of the data points used to calculate it were altered only slightly.
That’s because computer systems classify each bit of input data in a binary manner, placing it on one side or the other of an imaginary line called a classifier. Perturb the input—say, altering the ratio of debt to total credit—ever so slightly, but just enough to cross that line, and that changes the score calculated by the AI system.
The stakes for making such systems resistant to manipulation are obviously high in many domains, but perhaps especially so in the field of medical imaging.
The core benefit of the new generation of neural networks is a capacity to deal with the visual world. AI-based image analysis software can drive vehicles, analyze medical imaging, recognize faces, carry out safety inspections, empower robots, categorize image databases, create spaces in augmented reality, analyze and interpret video footage for events and language, and even assist in surgical procedures.
To accomplish this, an image-based machine learning system needs two resources. The first is a training set of still images or video, which allows it to gain a suitably accurate understanding of objects and events that it might later need to recognize. The second is actionable imagery, such as CCTV footage.
All known machine learning algorithms are vulnerable to adversarial examples — inputs that an attacker has intentionally designed to cause the model to make a mistake. While previous research on adversarial examples has mostly focused on investigating mistakes caused by small modifications in order to develop improved models, real-world adversarial agents are often not subject to the “small modification” constraint.
Furthermore, machine learning algorithms can often make confident errors when faced with an adversary, which makes the development of classifiers that don’t make any confident mistakes, even in the presence of an adversary which can submit arbitrary inputs to try to fool the system, an important open problem.
Adversarial AI causes machine learning models to misinterpret inputs into the system and behave in a way that’s favorable to the attacker.
To produce the unexpected behavior, attackers create “adversarial examples” that often resemble normal inputs, but instead are meticulously optimized to break the model’s performance.
Attackers typically create these adversarial examples by developing models that repeatedly make minute changes to the model inputs.
Eventually these changes stack up, causing the model to become unstable and make inaccurate predictions on what appear to be normal inputs.
What makes adversarial AI such a potent threat? In large part, it’s because if an adversary can determine a particular behavior in a model that’s unknown to developers, they can exploit that behavior. There’s also the risk of “poisoning attacks,” where the machine learning model itself is manipulated.
The reliability of a machine learning model is assessed based on how erroneous it is. Lesser the number of errors, better the prediction. In theory, ML models should be able to predict, classify and recommend right every single time. However, when deployed in the real world, the model has a very good chance of running into information that has never appeared during training. To prepare the model for such untimely adversities, adversarial techniques have been developed.
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake.
For instance, attackers could target autonomous vehicles by using stickers or paint to create an adversarial stop sign that the vehicle would interpret as a ‘yield’ or other sign. A confused car on a busy day is a potential catastrophe packed in a 2000 pound metal box.
Nevertheless, extreme cases need not only be scenarios of life and death but also can be a flawed facial recognition system installed at a money lending machine or a smart door system that doesn’t recognise its owner.
The whole idea of introducing adversarial examples to a model during training is to ensure AI safety. In short, avoid misclassification.
In an attempt to fortify a model’s defense strategy to avoid misclassification, researchers at Open AI introduce a new metric, UAR (Unforeseen Attack Robustness), which evaluates the robustness of a single model against an unanticipated attack, and highlights the need to measure performance across a more diverse range of unforeseen attacks.
Simply put, “adversarial examples” are small image modifications designed to fool an image classifier.
New techniques for probing and manipulating machine-learning systems—known in the field as “adversarial machine learning” methods—could cause big problems for anyone looking to harness the power of AI in business.
Adversarial machine learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. This technique can be applied for a variety of reasons, the most common being to attack or cause a malfunction in standard machine learning models.
Adversarial AI is the malicious development and use of advanced digital technology and systems that have intellectual processes typically associated with human behaviour.
Machine learning techniques were originally designed for stationary and benign environments in which the training and test data are assumed to be generated from the same statistical distribution.
However, when those models are implemented in the real world, the presence of intelligent and adaptive adversaries may violate that statistical assumption to some degree, depending on the adversary. This technique shows how a malicious adversary can surreptitiously manipulate the input data so as to exploit specific vulnerabilities of learning algorithms and compromise the security of the machine learning system
An adversarial example is an instance with small, intentional feature perturbations that cause a machine learning model to make a false prediction.
Adversarial AI is the malicious development and use of advanced digital technology and systems that have intellectual processes typically associated with human behaviour. These include the ability to learn from past experiences, and to reason or discover meaning from complex data.
“Poisoning attacks: machine learning algorithms are often re-trained on data collected during operation to adapt to changes in the underlying data distribution. For instance, intrusion detection systems (IDSs) are often re-trained on a set of samples collected during network operation.
Within this scenario, an attacker may poison the training data by injecting carefully designed samples to eventually compromise the whole learning process. Poisoning may thus be regarded as an adversarial contamination of the training data.”
“Adversarial attacks have proven capable of tricking a machine learning model into incorrectly labelling a traffic stop sign as a speed sign, which could have disastrous effects in the real world,”
Protecting deep learning algorithms against adversarial perturbation will be key to deploying AI in more sensitive settings.
Adversarial machine learning involves experimentally feeding input into an algorithm to reveal the information it has been trained on, or distorting input in a way that causes the system to misbehave. By inputting lots of images into a computer vision algorithm, for example, it is possible to reverse-engineer its functioning and ensure certain kinds of outputs, including incorrect ones.
Unsurprisingly, adversarial machine learning is also of huge interest to the defense community. With a growing number of military systems—including sensing and weapons systems—harnessing machine learning, there is huge potential for these techniques to be used both defensively and offensively.
This year, the Pentagon’s research arm, DARPA, launched a major project called Guaranteeing AI Robustness against Deception (GARD), aimed at studying adversarial machine learning.
AI models need specific cyber security defense and protection technologies to combat adversarial machine learning, preserve privacy in machine learning, secure federated learning, etc.
Machine learning algorithms work with numbers, so objects like images, documents, or emails are converted into numerical form through a step called feature engineering, which, in traditional machine learning methods, requires a significant amount of human effort..
Neural networks can learn their weights and biases using the gradient descent algorithm.
Machine learning (ML), the de facto approach to achieve artificial intelligence, provides a convenient way for AI practitioners to rapidly implant intelligence to machines, with the help of labeled data, and without needing to make clear the logics and theory behind data. All in a sudden, the convenient approach was acquired by professionals in nearly every fields.
People continually collect data from their users, train machine learning models using the collected data and pack the trained models to their products to provide better service to their users. The intelligent service, in turn, attracts more users and usages, and simultaneously provides more data to refine the machine learning models, resulting in a virtuous circle that absorbing users and practitioners.
Deep neural networks (DNN), an algorithm class for machine learning with breakthrough in accuracy, gained a great success in fields like image processing, natural language processing etc, however has security risks. For a lot of problems, solutions employing DNN outperform human beings , making DNNs so popular.
A key factor of such an achievement is that DNN features a cascade of many layers, which makes the neurons inside the network very hard to be understood even by their designers. Though the indigestibility does not affect its wide application, it indeed increased the difficulty for researchers to analyze the vulnerabilities of DNN models and fortify the security. What’s worse, adversaries may make use of those vulnerabilities to attack DNN models.
A huge risk results from crafted malicious inputs. An assumption of DNN is that the test data fall to the same distribution of the training data. Therefore, it is not surprising that the output of a model for data from a deviated distribution is prone to be unpredictable, especially when the model is not specially treated for security protections. This observation was be exploited by adversaries to craft artificially generated inputs that mislead DNN models to targeted outputs, in which case the inputs were called adversarial examples
Adversarial examples were firstly noticed , when they found that the mappings of DNNs are so discontinuous. They designed an optimization approach to search perturbations such that a natural input adding a small perturbation together can lead the model to output differently from when the input is the natural input only. The perturbation can be small enough, so human beings can barely notice the existence of the perturbation.
Research around adversarial examples developed from different directions, including defenses against adversarial examples or attacks with the examples. Some works focus on the defense mechanism to avoid the generation of adversarial examples, while some others aim at designing algorithms to generate examples satisfying all kinds of requirements.
Researchers defend adversarial examples mainly by masking the gradient, through which adversaries’ optimizers are expected to fail to move toward malicious. However, this mechanism was proven to be null and void, as a bunch of works got around this kind of protection and successfully generated effective examples, mainly by training substitutional models to remove the mask.
There are still technical barriers between a generated adversarial example and a successful exploit to a system. Even for commercially deployed models, it is not difficult for an attacker to generate effective adversarial examples with the help of gradient descending optimizers.
However, there are only several works where real world systems were cracked because of adversarial examples, mainly because that only little research concerned how an example can be input to the target model, which usually resides inside the target system without direct interface to attackers. To have a successful and practical attack, attackers must mount the worked out perturbation to construct an example, which is usually difficult for different scenarios.
The largest challenge for practical adversarial example attackers lie in that the input interface of the target model does not expose to adversaries. The adversarial examples calculated by adversaries require pixel modifications to input images. However, in a lot of practical cases, it is impossible for the attacker to find an interface to inject the perturbed image to the model inside the target system, so the adversarial examples though can be generated while cannot be directly used for attacks.
For example, one can calculate perturbation of only some pixels for a face image, but an attacker does not know how to make up herself to feed such an pixel level modified image to the target face authentication system. It is obviously less practical to find an interface to directly inject the image to the model behind the face authentication system.
In white box attacks the attacker has access to the model’s parameters, while in black box attacks, the attacker has no access to these parameters, i.e., it uses a different model or no model at all to generate adversarial images with the hope that these will transfer to the target model.
White-box attack methods assume the adversary has direct access to a model, i.e. the adversary has local access to the code, the model’s architecture, and the trained model’s parameters. In some cases, the adversary may also have access to the data set that was used to train the model. White-box attacks are commonly used in academia to demonstrate attack-crafting methodologies.
Black box attacks assume no direct access to the target model (in many cases, access is limited to performing queries, via a simple interface on the Internet, to a service powered by a machine learning model), and no knowledge of its internals, architecture, or the data used to train the model. Black box attacks work by performing iterative queries against the target model and observing its outputs, in order to build a copy of the target model. White box attack techniques are then performed on that copy.
Techniques that fall between white box and black box attacks also exist. For instance, a standard pre-trained model similar to the target can be downloaded from the Internet, or a model similar to the target can be built and trained by an attacker. Attacks developed against an approximated model often work well against the target model, even if the approximated model is architecturally different to the target model, and even if both models were trained with different data (assuming the complexity of both models is similar).
The aim of non-targeted attacks is to enforce the model to misclassify the adversarial image, while in the targeted attacks the attacker pretends to get the image classified as a specific target class, which is different from the true class.
Most successful attacks are gradient-based methods. Namely the attackers modify the image in the direction of the gradient of the loss function with respect to the input image. There are two major approaches to perform such attacks: one-shot attacks, in which the attacker takes a single step in the direction of the gradient, and iterative attacks where instead of a single step, several steps are taken. Three of the most common attacks are briefly described next. The first two are examples of one-shot attacks, and the last one is an iterative attack.
Attacks against machine learning models can be divided into four main categories based on the motive of the attacker.
Confidentiality attacks expose the data that was used to train the model. Confidentiality attacks can be used to determine whether a particular input was used during the training of the model. An adversary obtains publicly available information about a politician (such as name, social security number, address, name of medical provider, facilities visited, etc.), and through an inference attack against a medical online intelligent system, is able to ascertain that the politician has been hiding a long-term medical disorder. The adversary blackmails the politician. This is a confidentiality attack.
Integrity attacks cause a model to behave incorrectly due to tampering with the training data. These attacks include model skewing (subtly retraining an online model to re-categorize input data), and supply chain attacks (tampering with training data while a model is being trained off-line).
Adversaries employ integrity attacks when they want certain inputs to be miscategorised by the poisoned model. Integrity attacks can be used, for instance, to avoid spam or malware classification, to bypass network anomaly detection, to discredit the model / SIS owner, or to cause a model to incorrectly promote a product in an online recommendation system.
An adversary employs a Sybil attack to poison a web browser’s auto-complete function so that it suggests the word “fraud” at the end of an auto-completed sentence with a target company name in it. The targeted company doesn’t notice the attack for some time, but eventually discovers the problem and corrects it. However, the damage is already done, and they suffer long-term negative impact on their brand image. This is an integrity attack (and is possible today).
Availability attacks refer to situations where the availability of a machine learning model to output a correct verdict is compromised. Availability attacks work by subtly modifying an input such that, to a human, the input seems unchanged, but to the model, it looks completely different (and thus the model outputs an incorrect verdict).
Availability attacks can be used to ‘disguise’ an input in order to evade proper classification, and can be used to, for instance, defeat parental control software, evade content classification systems, or provide a way of bypassing visual authentication systems (such a facial or fingerprint recognition). From the attacker’s goal point of view, availability attacks are similar to integrity ones, but the techniques are different: poisoning the model vs. crafting the inputs.
An adversary introduces perturbations into an environment, causing self-driving vehicles to misclassify objects around them. This is achieved by, for example, applying stickers or paint to road signs, or projecting images using light or laser pointers. This attack may cause vehicles to ignore road signs, and potentially crash into other vehicles or objects, or cause traffic jams by fooling vehicles into incorrectly determining the colour of traffic lights. This is an availability attack.
An attacker forges a ‘leaked’ phone call depicting plausible scandalous interaction involving high-ranking politicians and business people. The forged audio contains embedded hidden voice commands. The message is broadcast during the evening news on national and international TV channels. The attacker gains the ability to issue voice commands to home assistants or other voice recognition control systems (such as Siri) on a potentially massive scale. This is an availability attack.
Fake news detection is a relatively difficult problem to solve with automation, and hence, fake news detection solutions are still in their infancy. As these techniques improve and people start to rely on verdicts from trusted fake news detection services, tricking such services infrequently, and at strategic moments would be an ideal way to inject false narratives into political or social discourse.
In such a scenario, an attacker would create a fictional news article based on current events, and adversarially alter it to evade known respected fake news detection systems. The article would then find its way into social media, where it would likely spread virally before it can be manually fact-checked. This is an availability attack.
By use of an adversarial attack against a reinforcement learning model, autonomous military drones are coerced into attacking a series of unintended targets, causing destruction of property, loss of life, and the escalation of a military conflict. This is an availability attack.
By use of a strategically timed policy attack, an attacker fools an autonomous delivery drone to alter course and fly into traffic, fly through the window of a building, or land (such that the attacker can steal its cargo, and perhaps the drone itself). This is an availability attack.
Over an extended period of time, an attacker publishes and promotes a series of adversarially created social media messages designed to trick sentiment analysis classifiers used by automated trading algorithms. One or more high-profile trading algorithms trade incorrectly over the course of the attack, leading to losses for the parties involved, and a possible downturn in the market. This is an availability attack.
Replication attacks allow an adversary to copy or reverse-engineer a model. One common motivation for replication attacks is to create copy (or substitute) models that can then be used to craft attacks against the original system, or to steal intellectual property.
An adversary employs a replication attack to reverse-engineer a commercial machine-learning based system. Using this stolen intellectual property, they set up a competing company, thus preventing the original company from earning all the revenue they expected to. This is a replication attack.
Classifiers are a type of machine learning model designed to predict the label of an input (for instance, when a classifier receives an image of a dog, it will output a value indicative of having detected a dog in that image).
Classifiers are some of the most common machine learning systems in use today, and are used for a variety of purposes, including web content categorization, malware detection, credit risk analysis, sentiment analysis, object recognition (for instance, in self-driving vehicles), and satellite image analysis. The widespread nature of classifiers has given rise to a fair amount of research on the susceptibility of these systems to attack, and possible mitigations against those attacks.
Classifier models often partition data by learning decision boundaries between data points during the training process.
Adversarial samples can be created by examining these decision boundaries and learning how to modify an input sample such that data points in the input cross these decision boundaries. In white box attacks, this is done by iteratively applying small changes to a test input, and observing the output of the target model until a desired output is reached.
Reinforcement learning is the process of training an agent to perform actions in an environment. Reinforcement learning models are commonly used by recommendation systems, self-driving vehicles, robotics, and games. Reinforcement learning models receive the current environment’s state (e.g. a screenshot of the game) as an input, and output an action (e.g. move joystick left).
Two distinct types of attacks can be performed against reinforcement learning models.
A strategically timed attack modifies a single or small number of input states at a key moment, causing the agent to malfunction. For instance, in the game of pong, if a strategic attack is performed as the ball approaches the agent’s paddle, the agent will move its paddle in the wrong direction and miss the ball.
An enchanting attack modifies a number of input states in an attempt to “lure” the agent away from a goal. For instance, an enchanting attack against an agent playing Super Mario could lure the agent into running on the spot, or moving backwards instead of forwards.
Transferability attacks are used to create a copy of a machine learning model (a substitute model), thus allowing an attacker to “steal” the victim’s intellectual property, or craft attacks against the substitute model that work against the original model. Transferability attacks are straightforward to carry out, assuming the attacker has unlimited ability to query a target model.
In order to perform a transferability attack, a set of inputs are crafted, and fed into a target model. The model’s outputs are then recorded, and that combination of inputs and outputs are used to train a new model. It is worth noting that this attack will work, within reason, even if the substitute model is not of absolutely identical architecture to the target model.
It is possible to create a ‘self-learning’ attack to efficiently map the decision boundaries of a target model with relatively few queries. This works by using a machine learning model to craft samples that are fed as input to the target model. The target model’s outputs are then used to guide the training of the sample crafting model. As the process continues, the sample crafting model learns to generate samples that more accurately map the target model’s decision boundaries.
Inference attacks are designed to determine the data used during the training of a model. Some machine learning models are trained against confidential data such as medical records, purchasing history, or computer usage history. An adversary’s motive for performing an inference attack might be out of curiosity – to simply study the types of samples that were used to train a model – or malicious intent – to gather confidential data, for instance, for blackmail purposes.
A black box inference attack follows a two-stage process. The first stage is similar to the transferability attacks described earlier. The target model is iteratively queried with crafted input data, and all outputs are recorded. This recorded input/output data is then used to train a set of binary classifier ‘shadow’ models – one for each possible output class the target model can produce.
For instance, an inference attack against an image classifier than can identify ten different types of images (cat, dog, bird, car, etc.) would create ten shadow models – one for cat, one for dog, one for bird, and so on. All inputs that resulted in the target model outputting “cat” would be used to train the “cat” shadow model, and all inputs that resulted in the target model outputting “dog” would be used to train the “dog” shadow model, etc.
The second stage uses the shadow models trained in the first step to create the final inference model. Each separate shadow model is fed a set of inputs consisting of a 50-50 mixture of samples that are known to trigger positive and negative outputs. The outputs produced by each shadow model are recorded. For instance, for the “cat” shadow model, half of the samples in this set would be inputs that the original target model classified as “cat”, and the other half would be a selection of inputs that the original target model did not classify as “cat”.
All inputs and outputs from this process, across all shadow models, are then used to train a binary classifier that can identify whether a sample it is shown was “in” the original training set or “out” of it.
So, for instance, the data we recorded while feeding the “cat” shadow model different inputs, would consist of inputs known to produce a “cat” verdict with the label “in”, and inputs known not to produce a “cat” verdict with the label “out”. A similar process is repeated for the “dog” shadow model, and so on. All of these inputs and outputs are used to train a single classifier that can determine whether an input was part of the original training set (“in”) or not (“out”).
This black box inference technique works very well against models generated by online machine-learning-as-a-service offerings, such as those available from Google and Amazon. Machine learning experts are in low supply and high demand.
Many companies are unable to attract machine learning experts to their organizations, and many are unwilling to fund in-house teams with these skills. Such companies will turn to machine-learning-as-a-service’s simple turnkey solutions for their needs, likely without the knowledge that these systems are vulnerable to such attacks.
Anomaly detection algorithms are employed in areas such as credit card fraud prevention, network intrusion detection, spam filtering, medical diagnostics, and fault detection. Anomaly detection algorithms flag anomalies when they encounter data points occurring far enough away from the ‘centers of mass’ of clusters of points seen so far. These systems are retrained with newly collected data on a periodic basis. As time goes by, it can become too expensive to train models against all historical data, so a sliding window (based on sample count or date) may be used to select new training data.
Poisoning attacks work by feeding data points into these systems that slowly shift the ‘center of mass’ over time. This process is often referred to as a boiling frog strategy. Poisoned data points introduced by the attacker become part of periodic retraining data, and eventually lead to false positives and false negatives, both of which render the system unusable.
Federated learning is a machine learning setting where the goal is to train a high-quality centralized model based on models locally trained in a potentially large number of clients, thus, avoiding the need to transmit client data to the central location. A common application of federated learning is text prediction in mobile phones.
Each phone contains a local machine learning model that learns from its user (for instance, which recommended word they clicked on). The phone transmits its learning (the phone’s model’s weights) to an aggregator system, and periodically receives a new model trained on the learning from all of the other phones participating.
Attacks against federated learning can be viewed as poisoning or supply chain (integrity) attacks. A number of Sybils, designed to poison the main model, are inserted into a federated learning network. These Sybils collude to transmit incorrectly trained model weights back to the aggregator which, in turn, pushes poisoned models back to the rest of the participants.
For a federated text prediction system, a number of Sybils could be used to perform an attack that causes all participants’ phones to suggest incorrect words in certain situations. The ultimate solution to preventing attacks in federated learning environments is to find a concrete method of establishing and maintaining trust amongst the participants of the network, which is clearly very challenging.
The understanding of flaws and vulnerabilities inherent in the design and implementation of systems built on machine learning and the means to validate those systems and to mitigate attacks against them are still in their infancy, complicated – in comparison with traditional systems – by the lack of explainability to the user, heavy dependence on training data, and oftentimes frequent model updating.
This field is attracting the attention of researchers, and is likely to grow in the coming years. As understanding in this area improves, so too will the availability and ease-of-use of tools and services designed for attacking these systems.
As artificial-intelligence-powered systems become more prevalent, it is natural to assume that adversaries will learn how to attack them. Indeed, some machine-learning-based systems in the real world have been under attack for years already.
As we witness today in conventional cyber security, complex attack methodologies and tools initially developed by highly resourced threat actors, such as nation states, eventually fall into the hands of criminal organizations and then common cyber criminals. This same trend can be expected for attacks developed against machine learning models.
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