The relevance of sunglasses within the fashion industry has included prominent fashion editors' reviews of annual trends in sunglasses as well as runway fashion shows featuring sunglasses as a primary or secondary component of a look. Post Hand Download Login. Retrieved August 17, The practice of datamining observing games without playing in order to build up a database of hand histories for future reference is prohibited. Gambling dates back to the Paleolithic period, before written history. They allow wearers to see into water when only surface glare would otherwise be seen, and eliminate glare from a road surface when driving into the sun. The original frames were black; frames in many different colors were later introduced.
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Up until a few years ago Machine Learning systems applied to understanding human speech usually had as their front end programs that had been written by people to determine the fundamental units of speech that were in sound being listened to. Those fundamental units of speech are called phonemes, and they can be very different for different human languages. Different units of speech lead to different words being heard.
In earlier speech understanding systems the specially built front end phoneme detector programs relied on some numerical estimators of certain frequency characteristics of the sounds and produced phoneme labels as their output that were fed into the Machine Learning system to recognize the speech.
It turned out that those detectors were limiting the performance of the speech understanding systems no matter how well they learned. Getting the front end processing right for an ML problem is a major design exercise. Getting it wrong can lead to much larger learning systems than necessary, making learning slower, perhaps impossibly slower, or it can make the learning problem impossible if it destroys vital information from the real domain. Unfortunately, since in general it is not known whether a particular problem will be amenable to a particular Machine Learning technique, it is often hard to debug where things have gone wrong when an ML system does not perform well.
Perhaps inherently the technique being used will not be able to learn what is desired, or perhaps the front end processing is getting in the way of success. Just as MENACE knew no geometry and so tackled tic-tac-toe in a fundamentally different way than how a human would approach it, most Machine Learning systems are not very good at preserving geometry nor therefore are they good at exploiting it.
Geometry does not play a role in speech processing, but for many other sorts of tasks there is some inherent value to the geometry of the input data. The engineers or researchers building the front end processing for the system need to find a way to accommodate the poor geometric performance of the ML system being used.
The issue of geometry and the limitations of representing it in a set of numeric parameters arranged in some fixed system, as was the case in MENACE, has long been recognized.
While people have attributed all sorts of motivations to the authors I think that their insights on this front, formally proved in the limited cases they consider, still ring true today. Fixed structure stymies generalization. The fixed structures spanning thousands or millions of variable numerical parameters of most Machine Learning systems likewise stymies generalization.
We will see some surprising consequences of this when we look at some of the most recent exciting results in Machine Learning in a later blog post—programs that learn to play a video game but then fail completely and revert to zero capability on exactly the same game when the colors of pixels are mapped to different colorations, or if each individual pixel is replaced by a square of four identical pixels. Furthermore, any sort of meta-learning is usually impossible too.
A child might learn a valuable meta-lesson in playing tic-tac-toe, that when you have an opportunity to win take it immediately as it might go away if the other player gets to take a turn. Machine Learning engineers and researchers must, at this point in the history of AI, form an optimized and fixed description of the problem and let ML adjust parameters. All possibility of reflective learning is removed from these very impressive learning systems.
This greatly restricts how much power of intelligence and AI system with current day Machine Learning systems can tease out of their learning exploits. Humans are generally much much smarter than this. There have been some developments in reinforcement learning since , but only in details as this section shows.
Reinforcement learning is still an active field of research and application today. It is commonly used in robotics applications, and for playing games. It was part of the system that beat the world Go champion in , but we will come back to that in a little bit.
Without resorting to the mathematical formulation, today reinforcement learning is used where there are a finite number of states that the world can be in. For each state there are a number of possible actions the different colored beads in each matchbox corresponding to the possible moves. The policy that the system currently has is the probability of each action in each state, which for MENACE corresponds to the number of beads of a particular color in a matchbox divided by the total number of beads in that same matchbox.
Reinforcement learning tries to learn a good policy. The structure of states and actions for MENACE, and indeed for reinforcement learning for many games, is a special case, in that the system can never return to a state once it has left it.
That would not be the case for chess or Go where it is possible to get back to exactly the same board position that has already been seen. In some cases they are probabilities, and for a given state they must sum to exactly one.
For many large reinforcement learning problems, rather than represent the policy explicitly for each state, it is represented as a function approximated by some other sort of learning system such as a neural network, or a deep learning network. The steps in the reinforcement process are the same, but rather than changing values in a big table of states and actions, the parameters of MENACE, a learning update is given to another learning system.
MENACE, and many other game playing systems, including chess and Go this time, are a special case of reinforcement learning in another way. The learning system can see the state of the world exactly. In many robotics problems where reinforcement learning is used that is not the case. There the robot may have sensors which can not distinguish all the nuances in the world e. But in reality it could be that an early move was good, and just a dumb move at the end was bad.
The Q function that he learns is an estimate of what the ultimate reward will be by taking a particular action in a particular state. This is how they built their Alpha Go program which recently beat both the human Korean and Chinese Go champions. As a side note, when I visited DeepMind in June this year I asked how well their program would have done if on the day of the tournament the board size had been changed from 19 by 19 to 29 by I estimated that the human champions would have been able to adapt and still play well.
My DeepMind hosts laughed and said that even changing to an 18 by 18 board would have completely wiped out their program…this is rather consistent with what we have observed about MENACE. Alpha Go plays Go in a way that is very different from how humans apparatently play Go.
In English, at least, ships do not swim. Ships cruise or sail, whereas fish and humans swim. However in English planes fly, as do birds. By extension people often fly when they go on vacation or on a business trip.
Birds move from one place to another by traveling through the air. These days, so too can people. But really people do not fly at all like birds fly. Birds who can fly that far non-stop and there are some certainly take a lot longer than a day to do that. If humans could fly like birds we would think nothing of chatting to a friend on the street on a sunny day, and as they walk away, flying up into a nearby tree, landing on a branch, and being completely out of the sun.
If I could fly like a bird then when on my morning run I would not have to wait for a bridge to get across the Charles River to get back home, but could choose to just fly across it at any point in its meander. We do not fly like birds. Human flying is very different in scope, in method, and in auxiliary equipment beyond our own bodies. Arthur Samuel introduced the term Machine Learning for two sorts of things his computer program was doing as it got better and better over time at and through the experience of playing checkers.
A person who got better and better over time at and through the experience of playing checkers would certainly be said to be learning to be a better player. Thus, in his first sentence of his paper, again, does Samuel justify the term learning: What I have tried to do in this post is to show how Machine Learning works, and to provide an argument that it works in a way that feels very different to how human learning of similar tasks proceeds.
Thus, taking an understanding of what it is like for a human to learn something and applying that knowledge to an AI system that is doing Machine Learning may lead to very incorrect conclusions about the capabilities of that AI system. These are words that have so many different meanings that people can understand different things by them. Even for humans it surely refers to many different sorts of phenomena. Learning to ride a bicycle is a very different experience from learning ancient Latin.
And there seems to be very little in common in the experience of learning algebra and learning to play tennis. So, too, is Machine Learning very different from any sort of the myriad of different learning capabilities of a person.
I think we are in that same position today in regard to Machine Learning. The papers in conferences fall into two categories. One is mathematical results showing that yet another slight variation of a technique is optimal under some carefully constrained definition of optimality. A second type of paper takes a well know learning algorithm, and some new problem area, designs the mapping from the problem to a data representation e.
This would all be admirable if our Machine Learning ecosystem covered even a tiny portion of the capabilities of human learning. And, I see no alternate evidence of admirability. They have neither any understanding of how their tiny little narrow technical field fits into a bigger picture of intelligent systems, nor do they care.
They think that the current little hype niche is all that matters, are blind to its limitations, and are uninterested in deeper questions. I recommend reading Christopher Watkins Ph. It revitalized reinforcement learning by introducing Q-learning, and that is still having impact today, thirty years later. But more importantly most of the thesis is not about the particular algorithm or proofs about how well it works under some newly defined metric.
Instead, most of the thesis is an illuminating discussion about animal and human learning, and attempting to get lessons from there about how to design a new learning algorithm.
And then he does it. A Probabilistic Perspective, Kevin P. Murphy, MIT Press, Born in he was certainly the oldest person in the lab at that time. He was the principal author of the full screen editor a rarity at that time that we had, called Edit TV, or ET at the command level.
He was still programming at age 85, and last logged in to the computer system when he was 88, a few months before he passed away. Watkins was unable to tell exactly from reading the paper. H Surcombe, and D. Hobbs, Cambridge University Press, Many people have since built copies of MENACE both physically and in computer simulations, and all the ones that I have found on the web report matchboxes, virtual or otherwise. Note that in total there are , different legal ways to play out a game of tic-tac-toe.
If we consider only essentially different situations by eliminating rotational and reflective symmetries then that number drops to 31, I really appreciate your insightful discussion of machine learning. I have always been interested in the problem of how humans learn to change the ontology with which they describe the world.
Implementing this computationally, including on robots, is a good way to test the viability of such models and incidentally can create artifacts of great value.
But for me, the interesting question is how to learner can go from one level of description of the world, to another in which both learning and problem-solving are vastly easier. Some years ago, I wrote an essay on this, especially focused on spatial knowledge: You ground it in getting around and interacting with the real world, not in playing games. I found it far more admirable than this Christopher Watkins and his angle on reinforcement learning — although you are both inspired by animal behavior, he seems to take exactly the wrong parts from it, in a way that works much better for games than for the real world.
Many are focusing on very specialized techniques and feel more like technical reports than actual research. How a discovery was made, how it relates to other approaches in a qualitative sense, and how progress could be made, also structurally, is often just mentioned very briefly. More often than not, a result is presented, without much deduction or context besides some hand-waving references to entire papers , and then some statistical validation or mathematical proof is presented.
How an idea was obtained, the reasoning or inspiration behind it is not mentioned, eventhough it gives a better understanding of how to interpret the results, or would ease the transfer of that insight to other domains. A lot of publications would benefit from presenting ideas from various points of views, and various abstraction levels.
Most often, the only description provided is pretty technical, specialized, and low level. As such it often feels similar to reverse engineering programs written in assembly language, to extract higher level concepts and non-machine specific terms or concepts that are actually usable. Even if a topic is pretty technical and specific in nature, it would be possible to lift it from its implementation or optimization details.
Especially, providing a framework that allows for experimentation and validation to explore alternatives most experiments lack the amount of detail necessary, to be reproduced without filling major holes with guess work. Instead of describing a technical method with words, source code should be available, or some other formal executable form. Your email address will not be published. Rodney Brooks Robots, AI, and other stuff. O Some board positions may not result in so many different looking positions when rotated or reflected.
How well do matchboxes learn? Alan would find it, simply by matching character for character, in the following part of the table for the first and second moves by MENACE: Summary of What Alan Must Do With these modifications we have made the job of Alan both incredibly simple and incredibly regimented. When Donald gives Alan a string of nine characters Alan looks it up in a table, noting the matchbox number and transform number.
He opens the numbered matchbox, randomly picks a bead from it and leaves it on the table in front of the open matchbox. He looks up the color of the bead in the numbered transform, to get a number between one and nine.
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