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It's like saying, "I can lift weight. Should you use a good objective for a resume or a summary? The most important feature of each of the tribe is that they firmly believe that theirs is the only way to model and predict. Unfortunately, this thinking hinders their ability to model a broad set of problems. For example, a Bayesian would find it extremely difficult to leave the probabilistic inference method and look at the problem from a evolutionary point of view. His thinking is forged based on priors, posteriors and likelihood functions.
If a Bayesian were to look at an evolutionary algorithm like a genetic algo, he might not critically analyze it and adapt it to the problem at hand. This limitation is prevalent across all tribes. Analogizers love support vector machines but there are limited because they look for similarities of inputs across various dimensions; i. The same serpent,"the curse of dimensionality" that the author talks about in the previous chapters comes and bites each tribe, depending on the type of problem being solved.
Indeed the tribe categorization is not a hard categorization of the algorithms. It is just meant as a starting point so that you can place the gamut of algos in separate buckets. The chapter starts with a discussion of "Rationalism vs. The rationalist likes to plan everything in advance before making the first move.
The empiricist prefers to try things and see how they turn out. There are philosophers who strongly believe in one and not in the other. From a practical standpoint, there have been productive contributions to our world from both the camps. David Hume is considered to be one of the greatest empiricist of all time. In the context of Machine Learning, one of his questions has hung like a sword of Damocles over all the knowledge, which is,. The author uses a simple example where you have to decide to ask someone out for a date or not.
So, a safe way out of the problem, at least to begin with, is to assume that future will be like the past. In the ML context, the real problem is: One might think that by amassing huge datasets, you can solve this problem. However once you do the math, you realize that you will run out of data that covers all the cases needed to carry the inductive argument safely. Each new data point is most likely unique and you have no choice but to generalize.
According to Hume, there is no way to do it. You have no choice but to try to figure out at a more general level what distinguishes spam from non-spam. The "no free lunch" theorem: If you have been reading some general articles in the media on ML and big data, it is likely that you would have come across a view on the following lines:. With enough data, ML can churn out the best learning algo.
The theorem says that no learner can be better than random guessing. Are you surprised by this theorem? Here is how one can reconcile to it,. Pick your favorite learner. All I have to do is flip the labels of all unseen instances. And therefore, on average over all possible worlds, pairing each world with its antiworld, your learner is equivalent to flipping coins.
How to escape the above the random guessing limit? If we know something about the world and incorporate it into our learner, it now has an advantage over random guessing. What are the implications of "free lunch theorem" in our modeling world? Data alone is not enough. Starting from scratch will only get you to scratch. Machine learning is a kind of knowledge pump: Unwritten rule of Machine learning: The author states that the principle laid out by Newton in his work, "Principia", that serves as the first unwritten rule of ML.
One of the ways to think about creating a form is via "conjunctive concepts", i. The problem with "conjunctive concepts" is that they are practically useless. Real world is driven by "disjunctive concepts", i. One of the pioneers in this approach of discovering rules was Ryszard Michalski, a Polish computer scientist.
After immigrating to the United States in , he went on to found the symbolist school of machine learning, along with Tom Mitchell and Jaime Carbonell. The author uses the words "blindness" and "hallucination" to describe underfitting and overfitting models.
Carnegie Mellon University. Resume · bahana-line.comll@bahana-line.com, , Can we make everybody a programmer (by letting people teach computers. Computer science, machine learning, artificial intelligence, cognitive neuroscience, Honorary Doctor of Laws degree, Dalhousie University Track how Technology is Transforming Work, Tom M. Mitchell and Erik Brynjolfsson, Nature.
By using ton of hypothesis, you can almost certainly overfit the data. On the other hand, being sparse in your hypothesis set, you can fail to see the true patterns in the data. This classic problem is obviated by doing out-of-sample testing. Is it good enough? Induction as inverse of deduction: Symbolists work via the induction route and formulate an elaborate set of rules. Since this route is computationally intensive for large dataset, the symbolists prefer something like decision trees.
Decision trees can be viewed as an answer to the question of what to do if rules of more than one concept match an instance. How do we then decide which concept the instance belongs to? Decision trees are used in many different fields. In machine learning, they grew out of work in psychology. Ross Quinlan, later tried using them for chess.
His original goal was to predict the outcome of king-rook versus king-knight endgames from the board positions. From those humble beginnings, decision trees have grown to be, according to surveys, the most widely used machine-learning algorithm. Quinlan is the most prominent researcher in the symbolist school.
An unflappable, down-to-earth Australian, he made decision trees the gold standard in classification by dint of relentlessly improving them year after year, and writing beautifully clear papers about them. A mathematician solves equations by moving symbols around and replacing symbols by other symbols according to predefined rules. The same is true of a logician carrying out deductions.
According to this hypothesis, intelligence is independent of the substrate. Symbolist machine learning is an offshoot of the knowledge engineering school of AI. The use of computers to automatically learn the rules made the work of pioneers like Ryszard Michalski, Tom Mitchell, and Ross Quinlan extremely popular and since then the field has exploded. An interesting example of a success from Symbolists is Eve, the computer that discovered malaria drug. There was a flurry of excitement a year ago, when an article, titled, Robot Scientist Discovers Potential Malaria Drug was published in Scientific American.
This is the kind of learning that Symbolists are gung-ho about. This chapter covers the second tribe of the five tribes mentioned in the book. This tribe is called "Connectionists". Connectionists are highly critical about the way Symbolists work as they think that describing something via a set of rules is just the tip of iceberg.
In a sense, there is no one to one correspondence between a concept and a symbol. Instead the correspondence is many to many. Each concept is represented by many neurons, and each neuron participates in representing many different concepts. In a non-math way, it says that "Neurons that fire together stay together". The other big difference between Symbolists and Connectionists is that the former tribe believes in sequential processing whereas the latter tribe believes in parallel processing.
To get some basic understanding of the key algos used by connectionists, it is better to have a bit of understanding of the way neuron is structured in our brain. The branches of the neuron connect to others via synapses and basic learning takes place via synaptic connections. It looked a lot like the logic gates computers are made of.
The problem with this model was that the model did not learn. It was Frank Rosenblatt who came up with the first model of learning by giving variable weights to the connections between neurons. The following is a good schematic diagram of the perceptron:. This model generated a lot of excitement and ML received a lot of funding for various research projects. However this excitement was short lived. Marvin Minsky and few others published many examples where perceptron failed to learn.
One of the most simple and dangerous example that perceptron could not learn was XOR operator. Perceptron was mathematically unimpeachable, searing in its clarity, and disastrous in its effects. Machine learning at the time was associated mainly with neural networks, and most researchers not to mention funders concluded that the only way to build an intelligent system was to explicitly program it. For the next fifteen years, knowledge engineering would hold center stage, and machine learning seemed to have been consigned to the ash heap of history.
Fast forward to John Hopfield work on spin glasses, there was a reincarnation of perceptron. Hopfield noticed an interesting similarity between spin glasses and neural networks: Each such state has a "basin of attraction" of initial states that converge to it, and in this way the network can do pattern recognition: Suddenly, a vast body of physical theory was applicable to machine learning, and a flood of statistical physicists poured into the field, helping it break out of the local minimum it had been stuck in.
The author goes on to describe "Sigmoid" function and its ubiquitous nature. If you think about the curve for sometime, you will find it everywhere.
Sigmoid functions in that book are used to describe various types of phenomenon that show an exponential slow rate of increase in the beginning, then a sudden explosive rate of increase and subsequently with an exponential rate of decrease. Basically if you take the first derivative of the Sigmoid function, you get the classic bell curve. I think the book,"The Age of Paradox" had a chapter with some heavy management gyan that went something like — "you need to create another Sigmoid curve in your life before the older Sigmoid curve starts a downfall" or something to that effect.
Well, in the context of ML, the application of Sigmoid curve is more practical. It can be used to replace the step function and suddenly things become more tractable. A single neuron can learn a straight line but a set of neurons, i. Agreed there is a curse of dimensionality here, but if you think about it, the hyperspace explosion is a double edged sword. On the one hand, there objective function is far more wiggly but on the other hand, there is a less scope that you will stuck at a local minimum via gradient search methods.
With this Sigmoid input and multi layer tweak, Perceptron came back with vengeance. There was a ton of excitement just like the time when perceptron was introduced. The algorithm by which the learning takes place is called "back propagation", a term that is analogous to how human brains work. This algo was invented by David Rumelhart in It is a variant of gradient descent method.
The backprop solves what the author calls "credit assignment" problem. In a multi-layered perceptron the error between the target value and the current value needs to be propagated across all layers backward. The basic idea of error propagation, i. Comparing this output with the desired one yields an error signal, which then propagates back through the layers until it reaches the retina. Based on this returning signal and on the inputs it had received during the forward pass, each neuron adjusts its weights.
As the network sees more and more images of your grandmother and other people, the weights gradually converge to values that let it discriminate between the two. Sadly the excitement phase petered down as learning with dozens of hundreds of hidden layers was computationally difficult. In the recent years though, backpropagation has made a comeback thanks to huge computing power and big data. It now goes by the technique "Deep Learning". The key idea of deep learning is based on auto encoders that is explained very well by the author. However there are many things that need to be worked out for deep learning to be anywhere close to the Master algorithm.
All said and done there are a few limitations to exclusively following a connectionist tribe. Firstly, the learning algo is difficult to comprehend. It comprises convoluted connections between various neurons. The other limitation is that the approach is not compositional, meaning it is divorced from the way a big part of human cognition works.
The chapter starts with the story of John Holland, the first person to have earned a PhD in computer science in Holland is known for his immense contribution to Genetic algorithms. His key insight lay in coming up with a fitness function that would assign a score to every program considered. Starting with a population of not-very-fit individuals-possibly completely random ones-the genetic algorithm has to come up with variations that can then be selected according to fitness.
How does nature do that? This is where the genetic part of the algorithm comes in. In the same way that DNA encodes an organism as a sequence of base pairs, we can encode a program as a string of bits. Variations are produced by crossovers and mutations. The next milestone came in , when the US Patent and Trademark Office awarded a patent to a genetically designed factory optimization system.
If the Turing test had been to fool a patent examiner instead of a conversationalist, then January 25, , would have been a date for the history books. He sees genetic programming as an invention machine, a silicon Edison for the twenty-first century. A great mystery in genetic programming that is yet to be solved conclusively is the role of crossover. There were other problems with genetic programming that finally made ML community at large divorce itself from this tribe.
Evolutionaries and connectionists have something important in common: But then they part ways. Evolutionaries focus on learning structure; to them, fine-tuning an evolved structure by optimizing parameters is of secondary importance. In contrast, connectionists prefer to take a simple, hand-coded structure with lots of connections and let weight learning do all the work. As in the nature versus nurture debate, neither side has the whole answer; the key is figuring out how to combine the two.
The Master Algorithm is neither genetic programming nor backprop, but it has to include the key elements of both: So, is this it? Have we stumbled on to the right path for "Master Algorithm"?
See Over the holidays 50 years ago, two scientists hatched artificial intelligence. Samuel's machine learning programs were responsible for the high performance of the checkers player. Ivan Sutherland's MIT dissertation on Sketchpad introduced the idea of interactive graphics into computing. Bert Raphael's MIT dissertation on the SIR program demonstrates the power of a logical representation of knowledge for question-answering systems.
Alan Robinson invented a mechanical proof procedure, the Resolution Method, which allowed programs to work efficiently with formal logic as a representation language. It was a popular toy at AI centers on the ARPA-net when a version that "simulated" the dialogue of a psychotherapist was programmed. First Machine Intelligence workshop at Edinburgh - the first of an influential annual series organized by Donald Michie and others.
First successful knowledge-based program for scientific reasoning. First successful knowledge-based program in mathematics. Richard Greenblatt at MIT built a knowledge-based chess-playing program, MacHack, that was good enough to achieve a class-C rating in tournament play. Roger Schank Stanford defined conceptual dependency model for natural language understanding.
Later developed in PhD dissertations at Yale for use in story understanding by Robert Wilensky and Wendy Lehnert, and for use in understanding memory by Janet Kolodner. Sometimes called the first expert system. The Meta-Dendral learning program produced new results in chemistry some rules of mass spectrometry the first scientific discoveries by a computer to be published in a refereed journal.
Subsequent work by Grosz, Bonnie Webber and Candace Sidner developed the notion of "centering", used in establishing focus of discourse and anaphoric references in NLP. David Marr and MIT colleagues describe the "primal sketch" and its role in visual perception. Randall Davis demonstrated the power of meta-level reasoning in his PhD dissertation at Stanford. Herb Simon wins the Nobel Prize in Economics for his theory of bounded rationality , one of the cornerstones of AI known as "satisficing".
The Stanford Cart , built by Hans Moravec, becomes the first computer-controlled, autonomous vehicle when it successfully traverses a chair-filled room and circumnavigates the Stanford AI Lab. First expert system shells and commercial applications. Later founds Thinking Machines, Inc. James Allen invents the Interval Calculus, the first widely used formalization of temporal events. Mid 80's Neural Networks become widely used with the Backpropagation algorithm first described by Werbos in Rod Brooks' COG Project at MIT, with numerous collaborators, makes significant progress in building a humanoid robot TD-Gammon, a backgammon program written by Gerry Tesauro, demonstrates that reinforcement learning is powerful enough to create a championship-level game-playing program by competing favorably with world-class players.
July 4, First official Robo-Cup soccer match featuring table-top matches with 40 teams of interacting robots and over spectators. Web crawlers and other AI-based information extraction programs become essential in widespread use of the world-wide-web.