THIS POST IS CONTINUED FROM PART 2 , BELOW--
WHEN HINDUS TALK ABOUT ENDLESS REBIRTHS TILL FINAL MOKSHA , THE WHITE MAN RIDICULES..
THE MOST DIFFICULT HUMAN SKILLS TO REPLICATE BY AI ARE THE UNCONSCIOUS ONES, THE PRODUCT OF MILLENNIA OF EVOLUTION WHERE THE SOUL PICKS UP EXPERIENCE FROM BACTERIA TO PLANT TO ANIMAL TO CONSCIOUS HUMAN TO MOKSHA HUMAN...
IN AI THIS IS KNOWN AS MORAVEC'S PARADOX.
MORAVEC'S PARADOX IS THE OBSERVATION BY ARTIFICIAL INTELLIGENCE AND ROBOTICS RESEARCHERS THAT, CONTRARY TO TRADITIONAL ASSUMPTIONS, HIGH-LEVEL REASONING REQUIRES VERY LITTLE COMPUTATION, BUT LOW-LEVEL SENSORIMOTOR SKILLS REQUIRE ENORMOUS COMPUTATIONAL RESOURCES.
MORAVEC WROTE " IT IS COMPARATIVELY EASY TO MAKE COMPUTERS EXHIBIT ADULT LEVEL PERFORMANCE ON INTELLIGENCE TESTS OR PLAYING CHECKERS, AND DIFFICULT OR IMPOSSIBLE TO GIVE THEM THE SKILLS OF A ONE-YEAR-OLD WHEN IT COMES TO PERCEPTION AND MOBILITY"
It seems almost paradoxical to suggest that a technology ruled by logic — such as AI — could fall prey to paradoxes.
"Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it," he wrote in his 1988 book "Mind Children.""The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge."
Moravec’s paradox proposes that this distinction has its roots in evolution. As a species, we have spent millions of years in selection, mutation, and retention of specific skills that has allowed us to survive and succeed in this world. Some examples of such skills include learning a language, sensory-motor skills like riding a bicycle, and drawing basic art.
It is comparatively easy to make computers exhibit adult level performance, and difficult or impossible to give them the skills of a one-year-old.
Artificial intelligence can complete tricky logical problems and advanced mathematics. But the ‘simple’ skills and abilities we learn as babies and toddlers — perception, speech, movement, etc. — require far more computation for an AI to replicate.
Minsky emphasized that the most difficult human skills to reverse engineer are those that are unconscious. "In general, we're least aware of what our minds do best", he wrote, and added "we're more aware of simple processes that don't work well than of complex ones that work flawlessly"
A compact way to express this argument would be:
We should expect the difficulty of reverse-engineering any human skill to be roughly proportional to the amount of time that skill has been evolving in animals.
The oldest human skills are largely unconscious and so appear to us to be effortless.
Therefore, we should expect skills that appear effortless to be difficult to reverse-engineer, but skills that require effort may not necessarily be difficult to engineer at all.
It is very difficult to reverse engineer certain human skills that are unconscious. It is easier to reverse engineer motor processes (think factory automation), cognitive skills (think big data analytics), or routinised computations (think predictive/ prescriptive algorithms).
In general, we’re less aware of what our minds do best…. We’re more aware of simple processes that don’t work well than of complex ones that work flawlessly
In other words, for AI the complex is easy, and the easy is complex.
AI ‘learns’ through us telling it how to do things. We’ve consciously learned how to do mathematics, win games and follow logic. We know the steps (computations) needed to complete these tasks. And so, we can teach them to AI.
But how do you tell anything how to see, hear, or move? We don’t consciously know all the computations needed to complete these tasks. These skills are not broken down into logical steps to feed into an AI. As such, teaching them to an AI is extremely difficult.
There are two key implications of this:--
High level reasoning is a very new phenomenon, and so humans haven’t had much time to perfect it. As a result, it still feels “hard” for us to conduct.
“Simple” skills, which took hundreds of millions of years to develop ( soul evolution as per Sanatana Dharma ) , have had plenty of time to be refined, making them seem comparatively easy and natural to us.
As AI hasn’t had the benefit of a hundred million years of evolution, developing sensory motor skills is quite a tall order. Complex calculations, problem solving, and analysis however, are a computer’s strong suit, and were commensurately developed in a fraction of the time by humans.
The explanation of these contradictions, as well as that of Moravec’s paradox, is related to the different functions [and different thinking strategies] of the left and right hemispheres of humans.”
Thus, while the formal logical thinking of the left hemisphere organizes the information in “a strictly ordered monosemantic context and without ambiguities.. Such a thinking strategy makes it possible to construct a pragmatically convenient but simplified reality model”.
In contrast, the function of the right hemisphere is to “simultaneously capture an infinite number of real connections and shape an integral but ambiguous polysemantic context.” This hemisphere plays a key role in creativity … but also “it is especially related to the limbic system, which controls bodily functions.”
While creativity is one of the last skills that appeared in biological evolution (and the area of the brain responsible for it has been the last to mature), “it is very difficult – and, even now even impossible – find an algorithm capable of processing and computerizing creativity.”
ANCIENT 12 STRAND DNA MAHARISHIS COULD ARRIVE AT RESULTS FASTER AND MORE ACCURATE THAN MODERN SUPER COMPUTERS ARMED WITH ARTIFICIAL INTELLIGENCE ..
TODAY WE ARE 2 STRAND DNA ( 97% JUNK ) DEGRADED MACHINES.
WHY SHOULD THE CAPTAIN OF A SHIP , THE GOVERNOR OF A STATE OR THE PRESIDENT OF INDIA, BE AFFORDED DISCRETIONARY POWERS WITH VETO POWERS, WHICH IS "SUBJECTIVE" ?
THE REASON IS THE HUMAN BRAIN WORKS DIFFERENT FROM THAT OF A OBJECTIVE COMPUTER
NO COMPUTER CAN TELL THE MORAL OF A STORY EVER .. BECAUSE IT DOES NOT HAVE A SUBJECTIVE CONSCIOUS BRAIN .
ARTIFICIAL INTELLIGENCE IS OBJECTIVE..
ON CHEMICAL TANKERS WE HAVE TEN HOUR VETTING INSPECTIONS BY OIL MAJORS LIKE SHELL/ MOBIL ETC ..
THE WHOLE INQUIRY IS OBJECTIVE.. HUNDREDS OF QUESTIONS..
THE LAST QUESTION IS SUBJECTIVE, AND THIS IS AIMED AT THE HEART OF THE INSPECTOR ( NOT HIS LEFT BRAIN LOBE ) " WOULD YOU SAIL ON THIS SHIP FOR A VOYAGE UNDER THE PRESENT CREW AND CAPTAIN WITHOUT RESERVATION"
THE INSPECTOR COULD HAVE GIVEN 100% MARKS ON THE TEN HOUR OBJECTIVE INQUIRY.. BUT IF HE WRITES THE WORD "NO" FOR THE LAST SUBJECTIVE QUESTION, THE SHIP HAS FAILED..
WE CANNOT HAVE EVERYTHING OBJECTIVE-- WE ARE HUMANS NOT MONKEYS OR COMPUTERS ..
SUBJECTIVE MUST HOLD THE "VETO POWER"..
VETO POWER CAN NEVER BE GIVEN TO THE OBJECTIVE..
OBJECTIVE IS FOR MEDIOCRE BRAINS, WHO NEEDS CHECKLISTS TO DRESS UP.. OR HE MIGHT LAND UP LIKE PHANTOM WITH UNDIES OUTSiDE PANTS..
A SUBJECTIVE MORALITY IS ONE ROOTED IN HUMAN FEELINGS AND CONSCIENCE .
NATURAL JUSTICE IS INHERENT. THESE ARE THE THINGS THAT ARE MOST IMPORTANT TO US, INDEED THE ONLY THINGS IMPORTANT TO US!
RELIGION IS OBJECTIVE .
SPIRITUALITY IS SUBJECTIVE.
HINDUISM TELLS ALL TO USE THEIR CONSCIENCE. OBJECTIVE MORALITY BREEDS FALSE EXCUSES
TANGIBLE LAW IS THE OBJECTIVE FORM OF MORALITY. OBJECTIVE IS INDEPENDENT OF PEOPLEs OPINIONS.
OBJECTIVE MORALITY IGNORED CONTEXT .
ATHEIST COMMUNISTS AND SINGLE MESSIAH / HOLY BOOK RELIGIONS ARE OBJECTIVE WITH MORALITY. THEY HAVE FAILED .
RELIGION IS DOING WHAT YOU ARE TOLD REGARDLESS OF WHAT IS RIGHT.
RULES DON’T MAKE US MORAL.
SANATANA DHARMA IS SUBJECTIVE.
SUBJECTIVE IS STRICTLY WITHIN HUMANS BEINGS –IT DERIVES FROM OUR INTANGIBLE CONSCIENCE ALONE.
SUBJECTIVE IS STRICTLY WITHIN HUMANS BEINGS –IT DERIVES FROM OUR INTANGIBLE CONSCIENCE ALONE.
IN SUBJECTIVE MORALITY PERCEPTION WITHIN PERIMETER OF CONTEXT IS PARAMOUNT.
SUBJECTIVE IS DEPENDENT ON PEOPLEs OPINIONS.
SANATANA DHARMA IS BASED ON CONSCIOUS HUMAN CONSCIENCE. NO MAN CAN MANIPULATE OR SILENCE HIS CONSCIENCE. WE ARE NOT THE SOURCE OF OUR OWN CONSCIENCE.
BHAGAWAD GITA IS OUR GUIDE NOT ASHTAVAKRA GITA COOKED UP BY JEW ROTHSCHILD WITH DOs AND DONTs
ENEMIES OF HINDUISM USES FAKE GURUS LIKE TRIPLE SRI TO CONVERT SANATANA DHARMA TO AN OBJECTIVE RELIGION. SORRY, IT WONT WORK
SPIRITUALLY SOAKED HINDUS ARE SUBJECTIVE WITH MORALITY. ONLY THIS WORKS
MORALITY IS DOING WHAT IS RIGHT, REGARDLESS OF WHAT YOUR ARE TOLD.
SUBJECTIVE MORALITY HAS NO SCOPE FOR EXCUSES. LOVE , COMPASSION AND FAIRNESS MAKE US MORAL.
MANAGERS CAN ONLY DO OBJECTIVE EVALUATIONS
LEADERS CAN DO SUBJECTIVE EVALUATIONS
THE PERFORMANCE OF A TEAM MEMBER CAN BE EVALUATED ONLY SUBJECTIVELY..
A TEACHER EVALUATES OBJECTIVELY
A MENTOR EVALUATES SUBJECTIVELY
WHEN I RECOMMEND PROMOTION IT IS NEVER ON OBJECTIVE PAST/ CURRENT PERFORMANCE. IT IS BASED ON SUBJECTIVE EVALUATION OF FUTURE POTENTIAL
I HAVE NEVER EVALUATED A OFFICER ON THE ANSWERS HE GAVE ME, RATHER I EVALUATED HIM BASED ON THE QUESTIONS HE ASKED ME – AFTER I ASKED HIM TO READ AN DIGEST A FEW PAGES .
IN OUR HUMAN WORLD, THERE ARE THINGS THAT WE CAN MEASURE OR TEST AND, THEREFORE, VERIFY OR FALSIFY. CONSEQUENTLY, THERE IS NO DIFFICULTY IN DISCOVERING OR DESCRIBING THE FACTS. THESE WE CALL OBJECTIVE JUDGMENTS.
TO SUSTAIN DHARMA YOU CANNOT APPLY NUMBER CRUNCHING OR OBJECTIVE JUDGMENT--YOU HAVE TO APPLY SUBJECTIVE JUDGMENTS.
THIS IS WHY A GOVERNOR OR PRESIDENT IS AN EXPERIENCED AND LEVEL HEADED MAN .
THIS IS WHY THE PRESIDENT IS THE SUPREME COMMANDER OF OUR ARMED FORCES..
THE NEW WORLD ORDER OF KOSHER BIG BROTHER WANTS ONLY OBJECTIVE JUDGMENTS
LEADERSHIP IS SUBJECTIVE: WE NEED YOUR EXPERIENCE, EXPERTISE, AND JUDGMENT; WE NEED YOUR RELATIONSHIPS, INITIATIVE, AND INNOVATION; WE NEED YOUR THOUGHTS, OPINIONS, AND INSTINCTS. WE NEED CORE VALUES. IF WE DIDN’T, YOU WOULD BE REPLACED BY A CALCULATOR.
AI IS BEING USED TO FOOL THE PEOPLE OF THE WORLD BY BIG BROTHER..
GEORGE ORWELL NEVER THOUGHT OF ARTIFICIAL INTELLIGENCE BEING USED BY BIG BROTHER TO PUT FOG ON HIS MALICIOUS DEEDS..
AFTER ALL AN ALGORITHM CANNOT BE TAKEN TO COURT OF INCARCERATED OR HUNG
http://ajitvadakayil.blogspot.com/2019/03/crash-of-boeing-737-max-flight-root.html
POISON INJECTED AI IS THE REASON WHY BEGGAR NATIONS ( WITHOUT INDIA/ CHINA ) SIT AT G6 SUMMITS SIPPING PREMIUM WINE.
BIG BROTHER CAN CRASH THE ECONOMY ( LOWER GDP/ LOWER GROWTH RATE/ RAISE INFLATION ) OF ANY NATION WHOSE RULER DOES NOT ALLOW JEWS TO STEAL ( ZIMBABWE/ VENEZUELA )..
BIG BROTHER CAN CAUSE WORLD RECESSION AT WILL, TO STEAL.
AT SEA I NEVER USED AUTOMATION UNLESS IT WAS CALIBRATED.. I KEPT RECORDS WITH SIGNATURES.
ANYBODY LYING IN THIS CALIBRATION EXERCISE I BLED THEM PHYSICALLY AS THIS COULD MEAN LOSS OF LIVES ..
IF THE SHIP WAS UNMANNED AUTOMATION I LAID OUT STRICT PROCEDURES.
MACHINE-LEARNING CODE, PICKS UP ALL OF ITS PREJUDICES FROM ITS HUMAN CREATORS. IN ISRAEL PALESTINIANS ARE AT THE RECEIVING END
Machine learning models can only regurgitate what they’ve learned. Poor model performance is often caused by various kinds of actual bias in the data or algorithm, sometimes deliberate. Machine learning algorithms do precisely what they are taught to do and are only as good as their mathematical construction and the data they are trained on.
Algorithms that are biased will end up doing things that reflect that bias. Sample bias is a problem with training data. It occurs when the data used to train your model does not accurately represent the environment that the model will operate in.
There is virtually no situation where an algorithm can be trained on the entire universe of data it could interact with.
But there’s a science to choosing a subset of that universe that is both large enough and representative enough to mitigate sample bias. This science is well understood by social scientists, but not all data scientists are trained in sampling techniques.
Sometimes the sampling is deliberately biased like what happens in Israel against Palestinians..
It includes monitoring the media, social media and the population as a whole — and now it turns out also the biometric signature of West Bank Palestinians. This monitoring op is now competing with the Chinese regime, that intensively uses facial recognition and monitors its civilians' activity on social networks.
AI cannot make moral decisions.
AI, does not have the human faculty of understanding, which makes it incapable of writing software. Software writing is a process requiring deep comprehension of the real world and the ability to transform those intricacies into rules. Bug detection is the key to delivering useful software. While
AI can detect patterns it cannot exercise free will. AI can make choices based on the rules of the program. These rules are deterministic, i.e. the resulting behavior is determined by initial inputs. With free will, every decision made is backed by infinite ways of doing it with countless outcomes. In computing, there are only two states – do or do not.
For AI to have free will, infinite states would have to be present, something that has not been achieved to date. AI cannot question their existence as humans do, nor can AI explain their decisions as humans do. These questions tied to philosophy and free will are not in AI’s zone of reach.
AI cannot find bugs.
AI cannot write software, in spite of the advancements nor can it detect malware
AI cannot do creative writing. While AI has generated content, it cannot create without guidelines. Natural language generation (NLG) is a software process that automatically creates content from data. It is being used by businesses for making data reports, messaging communication, and portfolios.
NLG creates thousands of more documents than humans. However, all these documents are data-driven and devoid of spontaneous creativity humans are capable of. Writers create stories with nuanced emotions that machines do not have. Fear, joy, love, and anger are some of the emotions that make compelling storytelling.
AI cannot bring inventions. AI can follow rules; it cannot create from scratch like humans. . AI uses past observations to learn a general model or a pattern, that can be used to make predictions about future similar occurrences. AI cannot think out of the box like humans.
While AI can recognize objects in images, translate languages, speak, navigate maps, predict crop yields, use visual data analysis to clarify disease diagnoses, verify user identity, prepare documents, make lending decisions in financial management and scores of related tasks, it cannot do everything.
AI works best only with human collaboration, as seen from the above examples. We must be realistic about the scope of AI, while we can tickle ourselves and get hajaaar excited about its prospects.
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.
Human resource constraints will be the ultimate limitation for successful AI development.
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.
Artificial intelligence cannot be improved with experience, they can perform the same function again if no different command is given to them.
AI can’t cope up with the dynamic environment and so they are unable to alter their responses to changing environments.
Machines can’t be creative. They can only do what they are being taught or commanded. Though they help in designing and creating, they can’t match the power of a human brain.
Humans are sensitive and they are very creative . 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.
Whilst AI can process huge data sets and suggest best possible scenarios, what the technology can’t do is contextualise these findings with data it doesn’t have. For example, some law firms are using AI to identify relevant documents in legal cases but a human judge is still needed to adjudicate a decision.
Electronic calculators are superhuman at arithmetic. Calculators didn’t take over the world; therefore, there is no reason to worry about superhuman AI..
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Machine learning is a term describing the feature, function or characteristic of computer systems or machines trying to simulate human-thinking behavior or human intelligence.. It is the science that deals with machine performance tasks that require intelligence based on humans. ..
Machine learning, is where machines can learn by experience and acquire skills without human involvement.. Similarly to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome.
AI algorithms need assistance to unlock the valuable insights lurking in the data your systems generate. You can help by developing a comprehensive data strategy that focuses not only on the technology required to pool data from disparate systems but also on data availability and acquisition, data labeling, and data governance.
Although newer techniques promise to reduce the amount of data required for training AI algorithms, data-hungry supervised learning remains the most prevalent technique today.
And even techniques that aim to minimize the amount of data required still need some data. So a key part of this is fully knowing your own data points and how to leverage them.
Machine Learning can be done in the following ways:--
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Ensemble Learning
In Supervised ML , the outputs are labeled, and the inputs are mapped to corresponding outputs
In Unsupervised ML , the inputs are unlabeled, and the algorithms have to find patterns.
Reinforcement ML is similar to supervised ML, but in this case, instead of a labeled output, there are rewards and the algorithm’s goal is to maximize rewards
An ensemble contains a number of hypothesis or learners which are usually generated from training data with the help of a base learning algorithm..
The idea is to generate a large number of scenarios and train the machine learning model to tell the answer . Train the model ahead of time and then get the answer right away
The researchers train the machine by feeding it a set of data that includes the solutions, as if the machine were studying previous “exams” before trying new ones. This is called supervised learning.
In supervised learning, the training data you feed to the algorithm includes a label.
Imagine you are meant to build a program that recognizes objects. To train the model, you will use a classifier. A classifier uses the features of an object to try identifying the class it belongs to.
In the example, the classifier will be trained to detect if the image is a:--
Bicycle
Boat
Car
Plane
The four objects above are the class the classifier has to recognize. To construct a classifier, you need to have some data as input and assigns a label to it. The algorithm will take these data, find a pattern and then classify it in the corresponding class.
Algorithms are like an engine: they run, but someone still needs to turn the ignition. The marketer is still very much needed in order to plan, design and run the marketing campaign. They are the ones feeding the AI system with all the new information required for them to learn in the first place.
This form of ‘supervised learning’ does not mimic the way a human learns naturally and this is one of the biggest obstacles when it comes to creating a more human-like AI.
Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection).
Over 82% of the time spent in AI projects are spent dealing with and wrangling data. Even more importantly, and perhaps surprisingly, is how human-intensive much of this data preparation work is.
In order for supervised forms of machine learning to work, especially the multi-layered deep learning neural network approaches, they must be fed large volumes of examples of correct data that is appropriately annotated, or “labeled”, with the desired output result.
For example, if you’re trying to get your machine learning algorithm to correctly identify cats inside of images, you need to feed that algorithm thousands of images of cats, appropriately labeled as cats, with the images not having any extraneous or incorrect data that will throw the algorithm off as you build the model
There are many steps required to get data into the right “shape” so that it works for machine learning projects:
In supervised learning, often used when labeled data are available and the preferred output variables are known, training data are used to help a system learn the relationship of given inputs to a given output— for example, to recognize objects in an image or to transcribe human speech.
Most current AI models are trained through “supervised learning.” This means that humans must label and categorize the underlying data, which can be a sizable and error-prone chore.
For example, companies developing self-driving-car technologies are hiring hundreds of people to manually annotate hours of video feeds from prototype vehicles to help train these systems.
Supervised learning uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case the cost function is related to eliminating incorrect deductions.
A commonly used cost is the mean-squared error, which tries to minimize the average squared error between the network's output and the desired output.
Tasks suited for supervised learning are pattern recognition (also known as classification) and regression (also known as function approximation).
Supervised learning is also applicable to sequential data (e.g., for hand writing, speech and gesture recognition). This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.
Supervised Learning is like teacher-student learning. The relation between the input and the output variable is known. The machine learning algorithms will predict the outcome on the input data which will be compared with the expected outcome.
The error will be corrected and this step will be performed iteratively till an acceptable level of performance is achieved.
In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. The correct solution thus produced can be used for mapping new examples. Credit card fraud detection is one of the examples of Supervised Learning algorithm.
Supervised machine learning excels at examining events, factors, and trends from the past. Historical data trains supervised machine learning models to find patterns not discernable with rules or predictive analytics.
In Supervised ML , the algorithm helps to understand how the system has learned in the past and also at the present and also understand how accurate are the outputs for future analysis. They help in considering a dataset or say a training dataset, and then with the use of this algorithm, we can produce a function that can make predictions for the resulting outputs.
Later the outputs will be checked for errors for more accurate results comparing it with the already calculated output initially.
Supervised learning: requires a data set and classification of the dataset. The training process attempts to match patterns in the data to the classification. Can be applied to forecast data.
Supervised machine learning refers to providing input and output to an algorithm. The algorithm then learns the relation between the two and is able to make predictions on the training data. The humans supervising machine learning can correct and adjust until the algorithm reaches acceptable performance when predicting outcomes.
Pattern recognition involves classification and cluster of patterns. In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning.
Supervised learning allows you to collect data or produce a data output from the previous experience. .
Two most common supervised tasks are classification and regression. The Classification process models a function through which the data is predicted in discrete class labels. On the other hand, regression is the process of creating a model which predict continuous quantity. The classification algorithms involve decision tree, logistic regression, etc
A classification problem requires that examples be classified into one of two or more classes. A classification can have real-valued or discrete input variables.A regression problem requires the prediction of a quantity. A regression can have real valued or discrete input variables.
In Supervised MLAlgorithms, input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
In this, a model is prepared through a training process. Also, this required to make predictions. And is corrected when those predictions are wrong. The training process continues until the model achieves the desired level.
Example problems are classification and regression.
Example algorithms include logistic regression and back propagation Neural Network.
In Supervised learning, you train the machine using data which is well "labeled." It means some data is already tagged with the correct answer. It can be compared to learning which takes place in the presence of a supervisor or a teacher.
A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data. Successfully building, scaling, and deploying accurate supervised machine learning Data science model takes time and technical expertise from a team of highly skilled data scientists. Moreover, Data scientist must rebuild models to make sure the insights given remains true until its data changes.
Supervised learning allows you to collect data or produce a data output from the previous experience.
Helps you to optimize performance criteria using experience..Supervised machine learning helps you to solve various types of real-world computation problems.
Supervised machine learning is the simplest way to train an ML algorithm as it produces the simplest algorithms. Supervised ML learns from a small dataset, known as the training dataset. This knowledge is then applied to a bigger dataset, known as the problem dataset, resulting in a solution. The data fed to these machine learning algorithms is labeled and classified to make it understandable, thus requiring a lot of human effort to label the data.
Most current AI models are trained through "supervised learning." It means that humans must label and categorize the underlying data, which can be a sizable and error-prone chore. For example, companies developing self-driving-car technologies are hiring hundreds of people to manually annotate hours of video feeds from prototype vehicles to help train these systems.
Labeling is an indispensable stage of data preprocessing in supervised learning. Historical data with predefined target attributes (values) is used for this model training style.
Annotation is nothing but labeling or marking of data which could be in various forms like images, videos, audios, text etc. Various entities such as tree, dog, etc are usually labeled or tagged in order to teach (train) other ML systems about those objects.
Sometimes mistakes in annotations can happen due to a language barrier or a work division. Asking workers to pass a qualification test is another strategy to increase annotation accuracy.
Annotation is often the most arduous part of the artificial intelligence (AI) model training process. That’s particularly true in computer vision — traditional labeling tools require human annotators to outline each object in a given image.
Again, Supervised learning is a technique in which we teach or train the machine using data which is well labeled. To understand Supervised Learning let’s consider an analogy. As kids we all needed guidance to solve math problems. Our teachers helped us understand what. addition is and how it is done.
Similarly, you can think of supervised learning as a type of Machine Learning that involves a guide. The labeled data set is the teacher that will train you to understand patterns in the data. The labeled data set is nothing but the training data set.
Supervised Learning can be used to solve two types of Machine Learning problems:--
Regression
Classification
Regression algorithm builds a model on the features of training data and using the model to predict value for new data
Classification problems can be solved using the following Classification Algorithms:0-
Logistic Regression
Decision Tree
Random Forest
Naive Bayes Classifier
Support Vector Machine
K Nearest Neighbour
Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.
This is called supervised learning because the algorithm needs to be taught with a specific dataset to help it form the inferred function. The data set is clearly labelled to help the algorithm ‘understand’ the data better. The algorithm can compare its output with the labelled output to modify its model to be more accurate.
In Supervised Learning an algorithm takes a labelled data set (data that’s been organized and described), deduces key features characterizing each label, and learns to recognize them in new unseen data. One example of supervised machine learning: having been shown multiple labelled images of cats, an algorithm will learn how to recognize a cat and identify one in other previously unseen pictures
Supervised learning is a machine learning task of learning a function that maps an input to an output based on example input-output pairs. A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. In supervised learning, we have labelled training data.
In Supervised Learning inputs and outputs are identified, and algorithms are trained using labeled examples.
Approximately 71 percent of Machine Learning is supervised learning, while unsupervised learning ranges from 10 – 20 percent. Other methods that are used less often include semi-supervised and reinforcement learning.
The supervised learning algorithm receives a set of inputs along with the corresponding output to find errors. Based on these inputs, it would modify the model accordingly. This is a form of pattern recognition since supervised learning uses methods like classification, regression, prediction, and gradient boosting. Supervised learning then uses these patterns to predict the values of the label on other unlabeled data.
Supervised learning is typically used in applications with which historical data predicts future events, such as fraudulent credit card transactions.
In supervised learning, the machine observes a set of cases (think of “cases” as scenarios like “The weather is cold and rainy”) and their outcomes (for example, “ Krishnan will go to the beach”) and learns rules with the goal of being able to predict the outcomes of unobserved cases (if, in the past, Krishnan usually has gone to the beach when it was cold and rainy, in the future the machine will predict that Krishnan will very likely go to the beach whenever the weather is cold and rainy).
In Supervised Learning, as the name rightly suggests, it involves making the algorithm learn the data while providing the correct answers or the labels to the data. This essentially means that the classes or the values to be predicted are known and well defined for the algorithm from the very beginning.
Supervised learning trains an algorithm based on example sets of input/output pairs. The goal is to develop new inferences based on patterns inferred from the sample results. Sample data must be available and labeled. For example, designing a spam detection model by learning from samples labeled spam/nonspam is a good application of supervised learning.
Supervised Learning is like teacher-student learning. The relation between the input and the output variable is known. The machine learning algorithms will predict the outcome on the input data which will be compared with the expected outcome.
Unsupervised learning, involves feeding a computer raw data and allowing it to sift out patterns without telling it any “answers.”
Unsupervised learning is a set of techniques used without labeled training data—for example, to detect clusters or patterns
In Unsupervised learning you do not need to supervise the model. Instead, you need to allow the model to work on its own to discover information. It mainly deals with the unlabelled data.
Unsupervised learning algorithms allows you to perform more complex processing tasks compared to supervised learning. Although, unsupervised learning can be more unpredictable compared with other natural learning methods.
In Supervised learning, Algorithms are trained using labelled data while in Unsupervised learning Algorithms are used against data which is not labelled.
Anomaly detection can discover important data points in your dataset which is useful for finding fraudulent transactions.
Unsupervised learning does not rely on trained data sets to predict the outcomes but it uses direct techniques such as clustering and association in order to predict outcomes. Trained data sets mean the input for which the output is known.
Unsupervised learning is used against data that has no historical labels. The system is not told the "right answer." The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within.
This algorithm helps to check if the system can actually draw data and inferences from no resulted outputs and no information for the training. Now the system from the hidden structure and from all the relevant and several unused data draws a pattern to actually give details of the hidden structure. Here they give an output but it is not necessary to check whether the given output is accurate or not.
Unsupervised learning works well on transactional data. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Or it can find the main attributes that separate customer segments from each other.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. These algorithms are also used to segment text topics, recommend items and identify data outliers.
Unsupervised learning is associated with unclassified data set. It analyzes data without human intervention. The training process allows the algorithm to recognize patterns and structure in the data that is usually not obvious.
Here, are prime reasons for using Unsupervised Learning:--
Unsupervised machine learning finds all kind of unknown patterns in data.
Unsupervised methods help you to find features which can be useful for categorization.
It is taken place in real time, so all the input data to be analyzed and labeled in the presence of learners.
It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention.
Disadvantages of Unsupervised Learning--
You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known
Less accuracy of the results is because the input data is not known and not labeled by people in advance. This means that the machine requires to do this itself.
The spectral classes do not always correspond to informational classes.
The user needs to spend time interpreting and label the classes which follow that classification.
Spectral properties of classes can also change over time so you can't have the same class information while moving from one image to another.
The biggest drawback of Unsupervised learning is that you cannot get precise information regarding data sorting.
The future of AI-based fraud prevention relies on the combination of supervised and unsupervised machine learning.
Combining both unsupervised and supervised machine learning defines the future of AI-based fraud prevention and is the foundation of the top nine ways AI prevents fraud..
Combining supervised and unsupervised machine learning as part of a broader Artificial Intelligence (AI) fraud detection strategy enables digital businesses to quickly and accurately detect automated and increasingly complex fraud attempts.
AI is a necessary foundation of online fraud detection, and for platforms built on these technologies to succeed, they must do three things extremely well. First, supervised machine learning algorithms need to be fine-tuned with decades worth of transaction data to minimize false positives and provide extremely fast responses to inquiries.
Second, unsupervised machine learning is needed to find emerging anomalies that may signal entirely new, more sophisticated forms of online fraud. Finally, for an online fraud platform to scale, it needs to have a large-scale, universal data network of transactions to fine-tune and scale supervised machine learning algorithms that improve the accuracy of fraud prevention scores in the process.
Unsupervised Learning algorithms are much harder because the data to be fed is unclustered instead of datasets. Here the goal is to have the machine learn on its own without any supervision. The correct solution of any problem is not provided. The algorithm itself finds the patterns in the data. .
Unsupervised classification seeks pattern recognition in unlabeled data. This classification finds the hidden structures present in such data using clustering or segmentation strategies.
Clustering is considered unsupervised learning, because there's no labeled target variable in clustering. Clustering algorithms try to, well, cluster data points into similar groups (or… clusters) based on different characteristics of the data
Clustering is an unsupervised machine learning task that automatically divides the data into clusters, or groups of similar items. ... The definition of similarity might vary across applications, but the basic idea is always the same—group the data so that the related elements are placed together
The difference is that classification is based off a previously defined set of classes whereas clustering decides the clusters based on the entire data. . Supervised clustering still clusters based on the entire data and thus would be clustering rather than classification.
Four common unsupervised tasks included clustering, visualization, dimensionality reduction , and association rule learning.
In unsupervised learning, there is no training data set and outcomes are unknown. ... Incredible as it seems, unsupervised machine learning is the ability to solve complex problems using just the input data, and the binary on/off logic mechanisms that all computer systems are built on. No reference data at all.
The goal of unsupervised learning is to create general systems that can be trained with little data. It models the underlying structure or distribution in the data in order to learn more about the data
The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data..
Both Classification and Clustering is used for the categorisation of objects into one or more classes based on the features. They appear to be a similar process as the basic difference is minute. In the case of Classification, there are predefined labels assigned to each input instances according to their properties whereas in clustering those labels are missing.
Classification is used for supervised learning whereas clustering is used for unsupervised learning.
The process of classifying the input instances based on their corresponding class labels is known as classification whereas grouping the instances based on their similarity without the help of class labels is known as clustering.
As Classification have labels so there is need of training and testing dataset for verifying the model created but there is no need for training and testing dataset in clustering.
Classification is more complex as compared to clustering as there are many levels in classification phase whereas only grouping is done in clustering.
Classification examples are Logistic regression, Naive Bayes classifier, Support vector machines etc. Whereas clustering examples are k-means clustering algorithm, Fuzzy c-means clustering algorithm, Gaussian (EM) clustering algorithm etc.
Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels. It is also known as self-organization and allows modeling probability densities of given inputs.
The Hebbian Learning Rule is a learning rule that specifies how much the weight of the connection between two units should be increased or decreased in proportion to the product of their activation. ..
The Hebbian Rule works well as long as all the input patterns are orthogonal or uncorrelated.Two of the main methods used in unsupervised learning are principal component and cluster analysis.
Hebbian network is a single layer neural network which consists of one input layer with many input units and one output layer with one output unit. This architecture is usually used for pattern classification.
Two of the main methods used in unsupervised learning are principal component and cluster analysis. Cluster analysis is used in unsupervised learning to group, or segment, datasets with shared attributes in order to extrapolate algorithmic relationships.
Cluster analysis is a branch of machine learning that groups the data that has not been labelled, classified or categorized. Instead of responding to feedback, cluster analysis identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach helps detect anomalous data points that do not fit into either group.
Apriori algorithm is nothing but an algorithm that is used to find out patterns or co-occurrences between items in a data set. Apriori algorithm is called apriori because it uses prior knowledge of frequent item set properties
Apriori algorithm is a classical algorithm in data mining. It is used for mining frequent itemsets and relevant association rules. It is devised to operate on a database containing a lot of transactions, for instance, items brought by customers in a store.
Given a threshold , the Apriori algorithm identifies the item sets which are subsets of at least transactions in the database. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data.
Apriori is designed to operate on database containing transactions (for example, collections of items bought by customers, or details of a website frequentation)
The algorithm gets terminated when the frequent itemsets cannot be extended further. The advantage is that multiple scans are generated for candidate sets. The disadvantage is that the execution time is more as wasted in producing candidates everytime, it also needs more search space and computational cost is too high.
The primary limitation of this alogirithm is the efficiency, as mentioned above. Apriori algorithm may become really slow especially when there are more candidates to analyze. 1) When the size of the database is very large, the Apriori algorithm will fail. because large database will not fit with memory(RAM)
Unsupervised learning means there is no output variable to guide the learning process (no this or that, no right or wrong) and data is explored by algorithms to find patterns. We only observe the features but have no established measurements of the outcomes since we want to find them out.
As opposed to supervised learning where your existing data is already labeled and you know which behaviour you want to determine in the new data you obtain, unsupervised learning techniques don’t use labelled data and the algorithms are left to themselves to discover structures in the data.
Within the universe of clustering techniques, K-means is probably one of the mostly known and frequently used
The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. Initially k number of so called centroids are chosen. A centroid is a data point (imaginary or real) at the center of a cluster.
K-Means performs division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.
The term ‘K’ is a number. You need to tell the system how many clusters you need to create. For example, K = 2 refers to two clusters. There is a way of finding out what is the best or optimum value of K for a given data.
K-Means clustering is used in a variety of examples or business cases in real life, like:--
Academic performance
Diagnostic systems
Search engines
Wireless sensor networks
In Unsupervised Machine Learning the data given to algorithms is neither labeled nor classified. This means that the ML algorithm is asked to solve the problem with minimal manual training. These algorithms are given the dataset and left to their own devices, which enables them to create a hidden structure. Hidden structures are essentially patterns of meaning within unlabeled datasets, which the ML algorithm creates for itself to solve the problem statement.
Unsupervised learning algorithms can perform more complex processing tasks than supervised learning systems.
Unlike supervised learning, unsupervised learning works with data sets without historical data. An unsupervised learning algorithm explores collected data to find a structure.
This works best for transactional data; for instance, it helps identify customer segments and clusters with specific attributes, often used in content personalization. techniques where unsupervised learning is used also include self-organizing maps, nearest-neighbor mapping, singular value decomposition, and k-means clustering.
In other words: online recommendations, identification of data outliers, and segment text topics are examples of unsupervised learning.
Think of unsupervised learning as a smart kid that learns without any guidance. In this type of Machine Learning, the model is not fed with labeled data, as in the model has no clue that ‘this image is Tom and this is Jerry’, it figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.
For example, it identifies prominent features of Tom such as pointy ears, bigger size, etc, to understand that this image is of type 1. Similarly, it finds such features in Jerry and knows that this image is of type 2. Therefore, it classifies the images into two different classes without knowing who Tom is or Jerry is.
Unsupervised machine learning categorizes entries within datasets by examining similarities or anomalies and then grouping different entries accordingly.
Unsupervised Learning can be used to solve Clustering and association problems. One of the famous clustering algorithms is the K-means Clustering algorithm.
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem.
The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed apriori. The main idea is to define K centers, one for each cluster.
With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.
Artificial intelligence and machine learning platforms can be designed to combine supervised and unsupervised machine learning. As a result, it can be possible to deliver a weighted score for any activity associated with digital businesses in less than a second.
AI-based fraud prevention is increasingly becoming more dependent on the marriage of supervised and unsupervised machine learning. According to Forbes, artificial intelligence should be “Explainable” and “Understandable.”
Let’s first take the so-called Explainable AI. It’s to do with the fields of data science and AI engineering or the creation and coding of AI algorithms. The goal is to give birth to new algorithms to shed light on intermediate outcomes or their solutions.
As for Understandable AI, the latter brings together the technical expertise of engineers and the design usability knowledge of user interface (UI)/user experience (UX) experts. Besides, it also connects the people-focused design of product developers.
Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. ... The technical challenge of explaining AI decisions is sometimes known as the interpretability problem.
They try have an efficient trade-off between accuracy and explainability along with a great human-computer interface which can help translate the model to understandable representation for the end users.
There need to be three steps which should be fulfilled by the system :--
1) Explained the intent behind how the system affects the concerned parties
2) Explain the data sources you use and how you audit outcomes
3) Explain how inputs in a model lead to outputs.
Explainability is motivated due to lacking transparency of the black-box approaches, which do not foster trust and acceptance of AI generally and ML specifically. Rising legal and privacy aspects, e.g. with the new European General Data Protection Regulations will make black-box approaches difficult to use in Business, because they often are not able to explain why a machine decision has been made.
Interpretability is about the extent to which a cause and effect can be observed within a system. Or, to put it another way, it is the extent to which you are able to predict what is going to happen, given a change in input or algorithmic parameters. It’s being able to look at an algorithm and go yep, I can see what’s happening here.
Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision.
XAI is an implemention of the social right to explanation. Transparency rarely comes for free and that there are often trade-offs between the accuracy and the explanaibility of a solution..
It is conceivable that a data scientist's version of 'explainable' is indecipherable to most people. Perhaps what people seek is not explainability but understanding. Explainability is a top-down method of speaking at people from the expert's perspective, while understanding seeks to understand how the listener interprets and adjusts the explanation according to the user's needs
By enabling the technology to help humans understand the nature of the algorithmic decision-making and learning as well as allowing humans to apply judgement that is incorporated into the model, trust can be built and models refined in a way that could deliver additional value and shift the perception of AI as a tool that makes decisions that no one can understand.
‘Understandable AI’ is different from ‘explainable AI’. Explainable AI is the domain of data scientists and AI engineers – the individuals who create and code these algorithms.
Understandable AI is the domain of UI/UX designers and product developers in collaboration with AI engineers and data scientists. AI-driven solutions should be developed with similar “user-first” principles in mind.
Understandable AI combines the technical expertise of engineers with the design usability knowledge of UI/UX experts as well as the people-centric design of product developers.
An understandable AI enables people to be a part of the decision-making process in an AI-driven enterprise.
Also critical to the Understandable AI process is the integration of non-data scientists to the development and design of AI products, illustrating the imperative of workforce upskilling for the future AI economy.
For example, an algorithm can be used to determine whether a credit card transaction is fraudulent. Given the millions of transactions that occur every day, an algorithm is the obvious solution to this problem.
There is a risk to incorrectly identifying a transaction as fraudulent (false positive), as you may frustrate and lose a customer. There is also a risk to missing a fraudulent transaction (false negative), as your risk losing a customer’s trust.
Most fraud can be identified with high certainty. But what do we do about the potentially fraudulent transactions that the AI has low confidence in? Enter understandable AI.
To help businesses and consumers alike better understand AI, Samsung has launched a new initiative called FAIR Future with the aim of involving everyone in AI by making it easier to understand.
In Unsupervised Learning the model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
An unsupervised learning algorithm explores collected data to find a structure. This works best for transactional data; for instance, it helps identify customer segments and clusters with specific attributes, often used in content personalization.
Popular techniques where unsupervised learning is used also include self-organizing maps, nearest-neighbor mapping, singular value decomposition, and k-means clustering. In other words: online recommendations, identification of data outliers, and segment text topics are examples of unsupervised learning.
In unsupervised ML , the algorithm doesn’t have correct answers or any answers at all, it is up to the algorithms discretion to bring together similar data and understand it.
Unsupervised machine learning is good at discovering underlying patterns and data, but is a poor choice for a regression or classification problem. Network anomaly detection is a security problem that fits well in this category
Unsupervised learning happens without the help of a supervisor just like a fish learns to swim by itself. It is an independent learning process.
Unsupervised learning does not rely on trained data sets to predict the outcomes but it uses direct techniques such as clustering and association in order to predict outcomes. Trained data sets mean the input for which the output is known.
The error will be corrected and this step will be performed iteratively till an acceptable level of performance is achieved.
Unsupervised methods help you to find features which can be useful for categorization. It is taken place in real time, so all the input data to be analyzed and labeled in the presence of learners.
It is easier to get unlabeled data from a computer than labeled data, which needs manual intervention.
As there are no known output values that can be used to build a logical model between the input and output, some techniques are used to mine data rules, patterns and groups of data with similar types. These groups help the end-users to understand the data better as well as find a meaningful output.
The fed inputs are not in the form of a proper structure just like training data is (in supervised learning). It may contain outliers, noisy data, etc. These inputs are together fed to the system. While training the model, the inputs are organized to form clusters.
When new data is fed to the model, it will predict the outcome as a class label to which the input belongs. If the class label is not present, then a new class will be generated.
While undergoing the process of discovering patterns in the data, the model adjusts its parameters by itself hence it is also called self-organizing. The clusters will be formed by finding out the similarities among the inputs.
Types Of Unsupervised Algorithms--
Clustering Algorithm: The methods of finding the similarities between data items such as the same shape, size, color, price, etc. and grouping them to form a cluster is cluster analysis.
Outlier Detection: In this method, the dataset is the search for any kind of dissimilarities and anomalies in the data. For example, a high-value transaction on credit card is detected by the system for fraud detection.
Association Rule Mining: In this type of mining, it finds out the most frequently occurring itemsets or associations between elements. Associations such as “products often purchased together”, etc.
Autoencoders: The input is compressed into a coded form and is recreated to remove noisy data. This technique is used to improve image, and video quality.
Semi-supervised learning is a hybridization of supervised and unsupervised techniques. Two of the main methods used in unsupervised learning are principal component and cluster analysis.
Unsupervised or semisupervised approaches reduce the need for large, labeled data sets. Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the learnt model.
Semi-supervised learning uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets – the programmer can just label and a small subset of the data and let the machine figure the rest out based on this.
This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.
In a typical scenario, the algorithm uses a small amount of labeled data with a large amount of unlabeled data. Semi-supervised type of Machine Learning for classification, regression, and prediction.
Examples of semi-supervised learning are face- and voice-recognition applications. It is primarily used to improve the quality of training sets. For exploit kit identification problems, we can find some known exploit kits to train our model, but there are many variants and unknown kits that can’t be labeled. Semisupervised learning can address the problem.
Reinforcement machine learningis the science of decision making.
In reinforcement learning, systems are trained by receiving virtual “rewards” or “punishments,” often through a scoring system, essentially learning by trial and error. Through ongoing work, these techniques are evolving.
On the one hand it uses a system of feedback and improvement that looks similar to things like supervised learning with gradient descent. On the other hand, datasets are not used in solving reinforcement learning problems.
Reinforcement learning works well in situations where we don’t know whether a specific action is “good” or “bad” ahead of time, but we can measure the outcome of the action and figure that out after the fact. These kinds of problems are surprisingly common, and computers are well suited to learning this kind of pattern. Reinforcement learning is still a learning algorithm
Three major components make up reinforcement learning: the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent does.
Reinforcement learning occurs when the agent chooses actions that maximize the expected reward over a given time. This is easiest to achieve when the agent is working within a sound policy framework.
The goal of reinforcement learning in this case is to train the dog (agent) to complete a task within an environment, which includes the surroundings of the dog as well as the trainer. First, the trainer issues a command or cue, which the dog observes (observation). The dog then responds by taking an action.
If the action is close to the desired behavior, the trainer will likely provide a reward, such asa food treat or a toy; otherwise, no reward or a negative reward will be provided. At the beginning of training, the dog will likely take more random actions like rolling over when the command given is “sit,” as it is trying to associate specific observations with actions and rewards. This association, or mapping, between observations and actions is called policy.
From the dog’s perspective, the ideal case would be one in which it would respond correctly to every cue, so that it gets as many treats as possible. So, the whole meaning of reinforcement learning training is to “tune” the dog’s policy so that it learns the desired behaviors that will maximize some reward.
After training is complete, the dog should be able to observe the owner and take the appropriate action, for example, sitting when commanded to “sit” by using the internal policy it has developed. By this point, treats are welcome but shouldn’t be necessary (theoretically speaking!).
In Reinforcement Learning, there are rewards given to the algorithm upon every correct prediction thus driving the accuracy higher up.
When we look at the core loop of reinforcement learning we have: Make a decision (action), get feedback (scoring), use that feedback to improve the logic. Compare that to supervised learning where we have: Make a decision (prediction), get feedback (error metric), use that feedback to improve the logic.
We have an agent and a reward, with many hurdles in between. The agent is supposed to find the best possible path to reach the reward
A reinforcement learning algorithm, or agent, learns by interacting with its environment. The agent receives rewards by performing correctly and penalties for performing incorrectly. The agent learns without intervention from a human by maximizing its reward and minimizing its penalty
In supervised learning we have a dataset of examples, labeled with the correct outputs. The model uses those examples and labels to find trends and patterns that can be used to predict the response value.
Everything a supervised learning model “knows” comes from this training dataset. Training is also entirely passive: there is no notion of the model needing to do anything in order to generate or access new training data.
In reinforcement learning that is not the case. With reinforcement learning instead of labeled training data what we get (oftentimes) is a set of rules. The agent has to explore by choosing an action, transforming the state, and receiving feedback. In other words the learning process requires the agent to be actively doing thing, unlike any of the other learning algorithms we’ve seen so far.
Reinforcement Learning employs the use of rewarding systems that achieve objectives in order to strengthen (or weaken) specific outcomes. This is frequently used with agent systems.
Reinforcement learning shows how flexible the mechanism of feedback and improvement can be at generating a logic.
Reinforcement learning is an unsupervised technique allows algorithms to learn tasks simply by trial and error.
The methodology hearkens to a “carrot and stick” approach: for every attempt an algorithm makes at performing a task, it receives a “reward” (such as a higher score) if the behavior is successful or a “punishment” if it isn’t. With repetition, performance improves, in many cases surpassing human capabilities—so long as the learning environment is representative of the real world.
Reinforcement learning can help AI transcend the natural and social limitations of human labeling by developing previously unimagined solutions and strategies that even seasoned practitioners might never have considered.
In reinforcement learning, the aim is to weight the network (devise a policy) to perform actions that minimize long-term (expected cumulative) cost. At each point in time the agent performs an action and the environment generates an observation and an instantaneous cost, according to some (usually unknown) rules.
The rules and the long-term cost usually only can be estimated. At any juncture, the agent decides whether to explore new actions to uncover their costs or to exploit prior learning to proceed more quickly.
Multi-Agent Reinforcement Learning(MARL) is the deep learning discipline that focuses on models that include multiple agents that learn by dynamically interacting with their environment
There are four types of reinforcement: positive, negative, punishment, and extinction. Positive reinforcement is the delivery of a reinforcer to increase appropriate behaviors whereas negative reinforcement is the removal of an aversive event or condition, which also increases appropriate behavior
Reinforcement learning allows machines and software agents to automatically determine the ideal behaviour within a specific context, in order to maximize its performance.
Extinction is a procedure in which reinforcement of a previously rewarded behavior is stopped. Extinction of positively reinforced behaviors does not allow the learner to access positive reinforcers after a problem behavior.
In reinforcement learning, instead of youtube videos you have an agent which interacts with an environment (an animal in an ecosystem, a robot in a house, an AI player in a videogame, etc.) during an extended time, and we design the problem so that doing some specific actions in some specific states yields a numerical value we call reward.
We set this value to be positive for states and actions we want the system to do, and negative (in which case we sometimes call it punishment) for states and actions we want it to avoid. What you are searching for in your optimization problem is then a function to tell the agent what to do in a given situation (a policy) that maximizes the long term reward, that is, the sum of the reward it gets over a long period of time.
“Punishment” is just negative terms in that sum; since the goal of your optimization algorithm is to maximize it the solution will avoid them (or make sensible compromises - some good solutions may require to take a bit of punishment before reaching higher reward).
There is no “understanding” of any kind. You use an optimization algorithm to find a function that maximizes a measurement which is the sum of many terms which can have various positive or negative values; the solution will be a function that tends to favor high, positive terms and avoid negative ones.
It’s a simple consequence of the way you described the problem and the mathematical and computational machinery you are using to solve it.
Reinforcement learning is a goal-oriented learning approach inspired by behavioral psychology that allows you to take inputs from the environment. As such, reinforcement learning implies that the agent will get better as it is in use: it learns while in usage.
When we humans learn from our mistakes, we are actually functioning through a reinforcement learning approach. There is no actual training phase; instead the agent learns through trial-and-error using a predetermined reward function that sends back the input about how optimal a specific action it took turned out to be.
Technically, reinforcement learning does not need to be fed with data, but instead generates its own as it goes.
Reinforcement learning that requires no mathematical model is Q-learning. Q-learning is reinforcement learning technique which tries to maximize rewards. These rewards are for example inning a game or when learning to walk any forward movement is a reward.
Basically doing well at the task you are performing is a reward and a Q-learning algorithm tries to maximize these rewards and thus in turn maximise performance. Of course the Q-learning algorithm is just one algorithm among many and has pros and cons in different situations, but the point is that there are today computer programs capable of this ability to at least some extent.
Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.
Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.
Again,Reinforcement learning, is a type of dynamic programming that trains algorithms using a system of reward and punishment. A reinforcement learning algorithm, or agent, learns by interacting with its environment
Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. .
Some of the most popular reinforcement learning training algorithms rely on deep neural network policies. The biggest advantage of neural networks is that they can encode really complex behaviors, which opens up the use of reinforcement learning in applications that are otherwise intractable or very challenging to tackle with alternative methods, including traditional algorithms.
A trained deep neural network policy is often treated as a “blackbox,” meaning that the internal structure of the neural network is so complex, often consisting of millions of parameters, that it is almost impossible to understand, explain, and evaluate the decisions taken by the network .
This makes it hard to establish formal performance guarantees with neural network policies. Think of it this way: Even if you train your pet, there will still be occasions when your commands will go unnoticed.
Reinforcement Learning is a multi-decision process. Unlike the “one instance, one prediction” model of supervised learning, an RL agent's target is to maximize the cumulative rewards of a series of decisions — not simply the immediate reward from one decision..
Unsupervised learning is where you only have input data (X) and no corresponding output variables. The unsupervised learning in convolutional neural networks is employed via autoencoders. The autoencoder structure consists of two layers, an encoding and a decoding layer.
Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer.
The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.
Reinforcement learning differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) .
What makes deep learning and reinforcement learning functions interesting is they enable a computer to develop rules on its own to solve problems. Deep learning is essentially an autonomous, self-teaching system in which you use existing data to train algorithms to find patterns and then use that to make predictions about new data.
Reinforcement learning is an autonomous, self-teaching system that essentially learns by trial and error. It performs actions with the aim of maximizing rewards, or in other words, it is learning by doing in order to achieve the best outcomes. when computers use reinforcement learning, they try different actions, learn from the feedback whether that action delivered a better result, and then reinforce the actions that worked, i.e. reworking and modifying its algorithms autonomously over many iterations until it makes decisions that deliver the best result.
A good example of using reinforcement learning is a robot learning how to walk. The robot first tries a large step forward and falls. The outcome of a fall with that big step is a data point the reinforcement learning system responds to. Since the feedback was negative, a fall, the system adjusts the action to try a smaller step. The robot is able to move forward. This is an example of reinforcement learning in action.
Deep learning and reinforcement learning are both systems that learn autonomously. The difference between them is that deep learning is learning from a training set and then applying that learning to a new data set, while reinforcement learning is dynamically learning by adjusting actions based in continuous feedback to maximize a reward.
An example of reinforcement learning is a generative adversarial network (GAN)
Reinforcement learning is defined by characterising a learning problem and not by characterising learning methods. Any method which is well suited to solve the problem, we consider it to be the reinforcement learning method.
Reinforcement learning assumes that a software agent i.e. a robot, or a computer program or a bot, connect with a dynamic environment to attain a definite goal. This technique selects the action that would give expected output efficiently and rapidly.
Ensemble machine learning is a technique that combines several base models in order to produce one optimal predictive model.
An ensemble method is a technique that combines the predictions from multiple machine learning algorithms together to make more accurate predictions than any individual model
Voting and averaging are two of the easiest ensemble methods. ... Voting is used for classification and averaging is used for regression. In both methods, the first step is to create multiple classification /regression models using some training dataset.
It is done to decrease variance (bagging), bias (boosting), or improve predictions (stacking). The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner, thus increasing the accuracy of the model.
When we try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias. Ensemble helps to reduce these factors (except noise, which is irreducible error).
Another way to think about Ensemble learning is Fable of blind men of Hindoostan and the elephant. All of the blind men had their own description of the elephant. Even though each of the description was true, it would have been better to come together and discuss their undertanding before coming to final conclusion.
This story perfectly describes the Ensemble learning method.
Using several models to predict the final result actually reduces the likelihood of giving weightage to decisions made by a poor models.
Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
An ensemble contains a number of hypothesis or learners which are usually generated from training data with the help of a base learning algorithm..
Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance. The main causes of error in learning models are due to noise, bias and variance.
Ensemble methods help to minimize these factors. These methods are designed to improve the stability and the accuracy of Machine Learning algorithms.
The more diverse these base learners are, the more powerful will the final model be. In any machine learning model, the generalization error is given by the sum of squares of bias + variance + irreducible error. Irreducible errors are something that is beyond us! We cannot reduce them.
However, by using ensemble techniques, we can reduce the bias and variance of a model. This reduces the overall generalization error.
The bias-variance trade-off is the most important benchmark that differentiates a robust model from an inferior one. In machine learning, the models which have a high bias tend to have a lower variance and vice-versa.
1. Bias: Bias is an error which arises due to false assumptions made in the learning phase of a model. A high bias can cause a learning algorithm to skip important information and correlations between the independent variables and the class labels, thereby under-fitting the model.
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Model with high bias pays very little attention to the training data and oversimplifies the model. It always leads to high error on training and test data.
2. Variance: Variance tells us how sensitive a model is to small changes in the training data. That is by how much the model changes. High variance in a model will make it prone to random noise present in the dataset thereby over-fitting the model.
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but has high error rates on test data.
In supervised learning, underfitting happens when a model unable to capture the underlying pattern of the data. These models usually have high bias and low variance. It happens when we have very less amount of data to build an accurate model or when we try to build a linear model with a nonlinear data. Also, these kind of models are very simple to capture the complex patterns in data like Linear and logistic regression.
In supervised learning, overfitting happens when our model captures the noise along with the underlying pattern in data. It happens when we train our model a lot over noisy dataset. These models have low bias and high variance. These models are very complex like Decision trees which are prone to overfitting.
You can think of ensemble learning analogous to the board of directors in a company, where the final decision is taken by the CEO. Instead of taking a decision all by himself, the CEO takes inputs ( brainstorming ) from each of the board members before arriving at a final conclusion.
The CEO, in this case, is the final model and the board members are the base learners which provide independent inputs to the CEO. This drastically reduces the chance of committing an error when the CEO makes his final decision.
We use this approach regularly in our daily lives as well — for example, we ask for the opinions of different experts before arriving at conclusions, we read different product reviews before buying a product, a panel of judges consult among them to declare a winner.
In each of the above scenarios what we are actually trying to achieve is to minimize the likelihood of an unfortunate decision made by one person (in our case a poor model).
In each of the above scenarios what we are actually trying to achieve is to minimize the likelihood of an unfortunate decision made by one person (in our case a poor model).
The goal of any machine learning problem is to find a single model that will best predict our wanted outcome. Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods take a myriad of models into account, and average those models to produce one final model.
Ensemble learning is used to combine the predictions from multiple separate models. It reduces the model complexity and reduces the errors of each model by taking the strengths of multiple models. Out of multiple ensembling methods, two of the most commonly used are Bagging and Boosting.
Typically, ensemble learning can be categorized into four categories:--
1. Bagging: Bagging is mostly used to reduce the variance in a model. A simple example of bagging is the Random Forest algorithm.
2. Boosting: Boosting is mostly used to reduce the bias in a model. Examples of boosting algorithms are Ada-Boost, XGBoost, Gradient Boosted Decision Trees etc.
3. Stacking: Stacking is mostly used to increase the prediction accuracy of a model.
4. Cascading: This class of models are very very accurate. Cascading is mostly used in scenarios where you cannot afford to make a mistake. For example, a cascading technique is mostly used to detect fraudulent credit card transactions, or maybe when you want to be absolutely sure that you don’t have cancer.
Decision Trees are not the only form of ensemble methods, just the most popular and relevant in DataScience today. Voting and averaging are two of the easiest ensemble methods. They are both easy to understand and implement. Voting is used for classification and averaging is used for regression.
Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.
Ensemble Methods allow us to take a sample of Decision Trees into account, calculate which features to use or questions to ask at each split, and make a final predictor based on the aggregated results of the sampled Decision Trees.
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.
Strengths and Weakness of Decision Tree approach
The strengths of decision tree methods are:--
Decision trees are able to generate understandable rules.
Decision trees perform classification without requiring much computation.
Decision trees are able to handle both continuous and categorical variables.
Decision trees provide a clear indication of which fields are most important for prediction or classification.
The weaknesses of decision tree methods :--
Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute.
Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.
Decision tree can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting field must be sorted before its best split can be found. In some algorithms, combinations of fields are used and a search must be made for optimal combining weights. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and compared.
Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
Machine learning (ML) refers to algorithms that autonomously improve their performance, without humans directly encoding their expertise. Usually, ML algorithms improve by training themselves , hence 'data-driven' AI.
The major recent advances in this field are not due to major breakthroughs in the techniques per se but, rather, through massive increases in the availability of data. In this sense, the tremendous growth of data-driven AI is, itself, data-driven. Usually, ML algorithms find their own ways of identifying patterns, and apply what they learn to make statements about data.
Different approaches to ML are suited to different tasks and situations, and have different implications. . Machine learning falls under the umbrella of AI, that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
In the field of machine learning there is an incredibly important problem is known as the bias-variance dilemma. It’s entirely possible to have state-of-the-art algorithms, the fastest computers, and the most recent GPUs, but if your model overfits or underfits to the training data, its predictive powers are going to be terrible no matter how much money or technology you throw at it.
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