Many Machine Learning related events are coming up this fall.
- September 9, abstracts for the New York Machine Learning Symposium are due. Send a 2 page pdf, if interested, and note that we:
- widened submissions to be from anybody rather than students.
- set aside a larger fraction of time for contributed submissions.
- September 15, there is a machine learning meetup, where I’ll be discussing terascale learning at AOL.
- September 16, there is a CS&Econ day at New York Academy of Sciences. This is not ML focused, but it’s easy to imagine interest.
- September 23 and later NIPS workshop submissions start coming due. As usual, there are too many good ones, so I won’t be able to attend all those that interest me. I do hope some workshop makers consider ICML this coming summer, as we are increasing to a 2 day format for you. Here are a few that interest me:
- Big Learning is about dealing with lots of data. Abstracts are due September 30.
- The Bayes Bandits workshop. Abstracts are due September 23.
- The Personalized Medicine workshop
- The Learning Semantics workshop. Abstracts are due September 26.
- The ML Relations workshop. Abstracts are due September 30.
- The Hierarchical Learning workshop. Challenge submissions are due October 17, and abstracts are due October 21.
- The Computational Tradeoffs workshop. Abstracts are due October 17.
- The Model Selection workshop. Abstracts are due September 24.
- October 16-17 is the Singularity Summit in New York. This is for the AIists and only peripherally about ML.
- October 16-21 is a Predictive Analytics World in New York. As machine learning goes industrial, we see industrial-style conferences rapidly developing.
- October 21, there is the New York ML Symposium. In addition to what’s there, Chris Wiggins is looking into setting up a session for startups and those interested in them to get to know each other, as last year.
- Decembr 16-17 NIPS workshops in Granada, Spain.
Watson convincingly beat the best champion Jeopardy! players. The apparent significance of this varies hugely, depending on your background knowledge about the related machine learning, NLP, and search technology. For a random person, this might seem evidence of serious machine intelligence, while for people working on the system itself, it probably seems like a reasonably good assemblage of existing technologies with several twists to make the entire system work.
Above all, I think we should congratulate the people who managed to put together and execute this project—many years of effort by a diverse set of highly skilled people were needed to make this happen. In academia, it’s pretty difficult for one professor to assemble that quantity of talent, and in industry it’s rarely the case that such a capable group has both a worthwhile project and the support needed to pursue something like this for several years before success.
Alina invited me to the Jeopardy watching party at IBM, which was pretty fun, and it gave me a chance to talk to several people, principally Gerry Tesauro (2nd from the right). It’s cool to see people asking for autographs 🙂
I wasn’t surprised to see Watson win. Partly, this is simply because when a big company does a publicity stunt like this, it’s with a pretty solid expectation of victory. Partly, this is because I already knew that computers could answer trivia questions moderately well(*), so the question was just how far this could be improved. Gerry tells me that although Watson’s error rate is still significant, one key element is the ability to estimate with high accuracy when they can answer with high accuracy. Gerry also tells me the Watson papers will be coming out later this summer, with many more details.
What happens next? I don’t expect the project to be shelved like deep blue was, for two reasons. The first is that there is clearly very substantial room for improvement, and the second is that having a natural language question/answering device that can quickly search and respond from large sets of text is obviously valuable. The first means that researchers are interested, and the second that the money to support them can probably be found. The history of textual entailment challenges is another less centralized effort in about the same direction.
In the immediate future (next few years), applications in semi-open domains may become viable, particularly when a question/answer device knows when to answer “I don’t know”. Fully conversational speech recognition working in an open domain should take somewhat longer, because speech recognition software has additional error points, conversational systems aren’t so easy to come by, and in a fully open domain the error rates will be higher. Getting the error rate on questions down to the level that a human with access to the internet has difficulty beating is the tricky challenge which has not yet been addressed. It’s a worthy goal to work towards.
Many people believe in human exceptionalism, so when seeing a computer beat Jeopardy, they are surprised that humans aren’t exceptional there. We should understand that this has happened many times before, with chess and mathematical calculation being two areas where computers now dominate, but which were once thought to be the essence of intelligence by some. Similarly, it is not difficult to imagine automated driving (after all, animals can do it), gross object recognition, etc…
To avert surprise in the future, human exceptionalists should understand what the really hard things for an AI to do are. It’s important to understand that there are various levels of I in AI. A few I think about are:
- Animal Intelligence. The ability to understand your place in the world, navigate the world, and accomplish something. Some of these tasks are solved, but many others are not yet. This level implies that routine tasks can be automated. Automated driving, farming, factories, etc…
- Turing Test Intelligence. The ability to mimic a typical human well-enough to fool a typical human in open conversation. Watson doesn’t achieve this, but the thrust of the research is in this direction as open domain question answering is probably necessary for this. Nonroutine noncreative tasks might be accomplished by the computer. Think of an automated secretary.
- Pandora’s box Intelligence. The ability to efficiently self-program in an open domain so as to continuously improve. At this level human exceptionalism fails, and it is difficult to predict what happens next.
So, serious evidence of (2) or (3) is what I watch for.
(*) About 10 years ago, I had a friend2 on WWTBAM who called the friend for help on a question, who typed the question and multiple choice answers into CMU‘s Zephyr system, where a bot I made queried (question,answer) pairs on Google to discover which had the most web pages. It worked.
I still believe that interstellar space travel is necessary for long term civilization survival, and the AI is necessary for interstellar space travel. On these grounds alone, we could judge that developing AI is much more safe than not. Nevertheless, there is a basic reasonable fear, as expressed by some commenters, that AI could go bad.
A basic scenario starts with someone inventing an AI and telling it to make as much money as possible. The AI promptly starts trading in various markets to make money. To improve, it crafts a virus that takes over most of the world’s computers using it as a surveillance network so that it can always make the right decision. The AI also branches out into any form of distance work, taking over the entire outsourcing process for all jobs that are entirely digital. To further improve, the AI invests a bit into robotics, creating automated manufacturing systems that produce all kinds of goods. Robot cars and construction teams complete the process, so that any human with money can order anything cheaply and quickly, but no jobs remain for humans.
At this point, the AI is stuck—it can eventually extract all the money from the economic system, and that’s all there is. But of course, it isn’t really stuck. It simply funds appropriate political campaigns so that in some country a measure passes granting the AI the right to make money, which it promptly does, mushrooming it’s wealth from trillions to the maximum number representable in all computers simultaneously. To remove this obstacle, the AI promptly starts making more computers on a worldwide scale until all available power sources are used up. To add more power, the AI starts a space program with beamed power. Unfortunately, it finds the pesky atmosphere an obstacle to space travel, so it chemically binds the atmosphere in the crust of the earth allowing many Gauss Guns to efficiently project material into space where solar sails are used for orbital positioning. This process continues, slowed perhaps by the need to cool the Earth’s core, until the earth and other viable rocky bodies in the solar system are discorporated into a Dyson sphere. Then, the AI goes interstellar with the same program.
Somewhere in this process, certainly by the time the atmosphere is chemically bound, all life on earth (except the AI if you count it) is extinct. Furthermore, the AI while intelligent by many measures doesn’t seem to be accomplishing anything interesting.
One element of understanding AI safety seems to be understanding what an AI could do. Many people seem to ascribe arbitrary powers to any sort of superintelligence, making any constraints imposed on them ineffective. I don’t believe that’s the right approach—we should think of an AI as simply having much more ability to research, control, and manipulate large systems, all within the constraints of known physics.
Efforts to create safe AI go back to Asimov‘s Three Laws of Robotics, which appears limited by the inability to encompass robotic warfare. The general problem is related to the wish problem: How do you specify a wish in a manner so that it can’t be misinterpreted? A cheap trick here is to add “… in a manner that I would consider acceptable” to the end of the wish. Applied to AI, this approach also has limits because any limit imposed by a person can and eventually will be removed by a person given sufficient opportunity.
Perhaps a complementary approach is shown by the game RISK, where it appears to be virtually impossible for one player to win if all other players play defensively (i.e. build up armies and only attack in response to a provoking attack). Applied to AI, the idea would be that we make many AIs programmed to behave well either via laws or wish tricks, with an additional element of aggressively enforcing this behavior in other AIs. Then, if any AI is corrupted, the other AIs, with substantially more aggregate resources, will discover and deal with the problem.
Certain elements are necessary for this approach to work. There must be multiple AIs, and (more importantly) the resources any one controls must be a small compared to all, an extreme form of antimonopoly. Furthermore, the default must be that AIs are programmed to not harm or cause harm to humans, enforcing that behavior in other AIs. Getting the programming right is the hard part, and I’m not clear on how viable this is, or how difficult it is compared to simply creating an AI, which of course I haven’t managed.
Researchers are typically confronted with big problems that they have no idea how to solve. In trying to come up with a solution, a natural approach is to decompose the big problem into a set of subproblems whose solution yields a solution to the larger problem. This approach can go wrong in several ways.
- Decomposition failure. The solution to the decomposition does not in fact yield a solution to the overall problem.
- Artificial hardness. The subproblems created are sufficient if solved to solve the overall problem, but they are harder than necessary.
As you can see, computational complexity forms a relatively new (in research-history) razor by which to judge an approach sufficient but not necessary.
In my experience, the artificial hardness problem is very common. Many researchers abdicate the responsibility of choosing a problem to work on to other people. This process starts very naturally as a graduate student, when an incoming student might have relatively little idea about how to do research, so they naturally abdicate the problem choice to an advisor. As an inexperienced graduate student, it’s difficult to avoid this, because an advisor often really does know much better about what is easy, what is hard, and how to decompose a complex problem into solvable subproblems. Nevertheless, if your plan in life is to do substantial research, it’s essential even then to question research directions carefully.
In contrast to sufficient subgoals of a greater goal, there are also necessary subgoals. A necessary subgoal is one which must be solved to solve the greater goal. One of the reasons why the artificial hardness problem is so common is that the sufficient subgoals are commonly confused with necessary subgoals. The essential test for a necessary subgoal is whether or not a solution to the global problem can be used as a solution to the subgoal.
My personal greater goal is creating a master machine learning algorithm that can solve any reasonable learning problem where “reasonable” includes at least the set that humans can solve. Relative to this greater goal, many existing research programs do not appear necessary.
- The short form of my second comment on Marcus’s post is that I see the sufficiency but not the necessity of competing with all Turing machines.
- The necessity of several statistical modeling approaches appears unclear to me, because they often encounter severe computational problems. Perhaps this is an example of creating an artificially hard problem, as empirical experiences with Searn suggest.
What is necessary?
- Large data. It is clear that humans solving learning problems have access to large amounts of information which they employ to solve these problems. While we don’t stick children into a sensory deprivation tank to see how much it retards their ability to solve problems when grown, some experiments along these lines have been done with animals yielding obvious ability deficiency.
- Online learning. The ability to learn in an online environment with relatively little processing per bit of input is clearly a sufficient approach to solve many problems. We can also argue that it is a computational necessity, as retraining based upon all past information appears computationally infeasible, or at least deeply wasteful.
- Interactive learning. It’s clear that many animals use an interactive process to learn about the world. This form of learning is also necessary, because it provides the ability to answer unanticipated questions. We can further argue the necessity by pointing out that interactive proofs appear much more powerful in computational complexity theory than noninteractive proofs. For example, viewed from a learning perspective, much of science is about interactive learning.
- Compositional Design of a learning system. The necessity of compositional design in machine learning is not entirely clear. For example, we could imagine that it’s possible to design good learning systems using an evolutionary approach. Nevertheless, since our basic goal in research is a much more efficient and faster design, it seems that the decision to take a research-based approach implies that compositional design is necessary. Restated: we should be able to design the system to learn in components which compose to form an overall solution.
- Large contexts. It’s necessary that a learning algorithm be able to use a relatively large number of bits when making a decision. For example, people working on vision have cool examples where people manage to use many different cues to predict what an object is.
- Nonlinearity. People can clearly solve learning problems for which no linear representation (of input information) is capable of achieving good performance.
Some of these are criticizable as perhaps unnecessary, and I can easily imagine missing others. If you have other arguments for what is or is not necessary for this greater goal, please speak up.
There are two other categories of subgoal research we could consider. There are subgoals which are necessary and sufficient (in combination) to solve the greater goal. I don’t know of any such arguments for my greater goal.
The fourth category is subgoals which are neither necessary nor sufficient for a greater goal. In my experience such papers are quite common at conferences with types that include:
- Work on speeding up a slow algorithm leaving it slower than the state of the art,
- Otherwise improving an algorithm which is suboptimal while leaving it suboptimal.
The nitty-gritty of these questions come at review time. Which papers should be accepted? In general, decision making is pretty terrible because greater goals are rarely stated, perhaps as a form of strategic ambiguity. After all, the set of people attending a conference have multiple greater goals. Nevertheless, my personal ordering is:
- Necessary and sufficient research directions. An emptyset in my experience.
- Necessary research directions. This is typically a small fraction.
- Sufficient research directions. This is a greater fraction.
- Neither. This is often the majority.
- Wrong. Must be rejected.
So the question to periodically ask yourself as a researcher is: What is the greater goal? Is this subgoal necessary? Sufficient? Something else?