Light Reading: Space Elevators, Brain Uploading and more

I’ve been meaning to get this blog more active, “at least one post a week” I tell myself. But every time I want to write something it always end up being about Canadian Politics. I often start jotting down ideas and then get too picky about being sure and I end up never writing anything. So new rule, if I can’t think of a single new idea to write about I’ll post a short list of interesting articles I read this week and what’s interesting about them:

Reality is all Math : this is a really interesting side topic of philosophy of Math and Science that I think about in my spare time. What is the nature of the universe in relation the mathematics. Why is math so good at describing the universe? Is the universe a computer or is there some beyond computation in the way physics behaves? This article has some interesting news on the latest thoughts from quantum physics about the relation of information and computation to the nature of the universe.

It sounds like there is debate about how central information theory is to explaining the equations of quantum physics. The opinions seem to range from important to central to quantum physics is nothing but information theory. I like that last one, but we’ll have to see what they find. It looks like there is no risk of physicists completely explaining everything before we get workable large scale quantum computers. I am glad to see there is more discussion about why the universe we live in adheres to quantum weirdness rather than just accepting the highly accurate math without any explanation.

Upcoming Technology and Your Job : Andrew Leigh wrote this piece on his experience being that rare thing, a politician who pays attention to science (we could sure use more of those). He presents his list of five technologies that could revolutionize politics in the near future, but really they are disruptive technologies that would widely affect everyone in society. It’s a bit fast and loose but they are genuinely important technologies to keep an eye on, always being wary to look for wide agreement before believing any claims. I’m in favour of anyone who brings up space elevators as a viable technology. Apparently NASA has made some significant advances to powering remote devices with laser that could reduce the weight load for a space elevator cable dramatically.

His discussion of Machine Intelligence is also worth thinking about even if it is jumping the gun a bit. We aren’t exactly near to creating self aware machines or being able to upload our minds into computers. But it is becoming possible to think about computers with the complexity of a human mind so it’s worth thinking through the implications.

He makes the point that replicated minds would be a threat to many people’s job’s as a single person who’ve very good at what they do could farm themselves out to available in many places at once. I suppose this is true but I think there is a much more relevant short term concern of people being made redundant by technological advances before we get to the point of copied human consciousness.

This is one of the topics I’m hoping to blog about here in the future: understanding scientific and technological change from the point of view of the job loss metric. What is the long term viability of your current career? Could it be done by machines or through crowdsourcing the skills of many people? As Leigh points out, typists and human computers in the early 20th Century had a career which would not exist a few decades later. Many factory workers have already found that what they do is fully automatable. Many educators are now starting to wonder if everything they do really needs a live human being present. What is the value of repeating lectures when you could have videos of the best teachers in the world which can be reused over and over?

This is a simple way to make discussion of new advances concrete for the layman and also very relevant. Most people don’t really care about quantum computers, machine vision or robotics. But if you explain how what’s going on in these fields of research could affect people’s jobs down the road, or the jobs which may or may not be viable for their children in the future, then they’ll be more interested in gaining a high level understanding.

If you have other good examples of ongoing research that could make entire jobs obsolete that people should be more aware of let me know.

Women in Computer Science : this article is couple weeks old but it’s worth a repost. Maria Klawe used to be department head at my alma mater doctorum (yes, I made up that phrase, if you know latin then correct me) which has a strong focus on CS Education and making it accessible to womens. She’s done some amazing things changing the CS program at Harvey Mudd to make it more accessible and focussed on solving problems rather than programming for its own sake. They have got their graduation number of female students up to 40% which is stratospheric in Computer Science program terms.

I am not just a machine

When discussing Artificial Intelligence I often hear people use the phrase “just a machine” to justify their belief that full AI is impossible or why computers could never be creative or have emotions. If no matter what we do, no matter how complex they become a machine will always be “just” a machine then this implies that there is something else that an agent could be? If humanity’s last defense against accepting true AI is the word just then what do human’s have that is beyond being a machine?  My belief, and I think the belief of many AI researchers, is that there is nothing else that a thinking, acting, exploring, living mind can be other than a machine. That means that our computers will always be just machines, but so are we.  The human mind is “just” a machine, that is, it is a wonderful, immensely powerful computational engine.

We aren’t “just” machines. Rather, we are machines, full stop.

We should be proud of it or at least accept it.  That way, when the machines that we’ve built can do everything we can we won’t feel any  more threatened or surprised than when our children grow up to be normal adults capable of everything we are.

For a great debate about the state of AI research and where it is (or isn’t) going you must take a read of this post by Peter Norvig.

Team Human vs. Team Watson Round III

Well it seems the machines have won, select your pod early, you’ll want to get a good view of the energy harvesting machines.

We were talking about Watson around the ol’ AI research lab today and someone pointed out that Watson is yet another highly tailored solution to a particular problem just like DeepBlue (click it, its Arcade Fire, just CLICK IT) was for chess. It’s using a lot of brute force and some reasoning but it’s still not solving the same problem humans are and the domain is somewhat restricted.

Now the interesting difference is that whereas chess is a deterministic game where you can search for an optimal strategy, jeopardy has layers of uncertainty hidden behind human language and the behaviour of other players. So while it’s not a very realistic setting for general AI, and doesn’t claim to be, it has stepped over an important threshold from deterministic, logic based problems to ones that require reasoning under uncertainty and statistics.

This is very fitting as the field of AI research itself has gone through the same change in focus in the past 20 years as outlined very well by Peter Norvig recently. When I took my undergraduate AI classes in the 90s I fell in love with prolog and logical planning. That’s why I went into AI research later.

When I got to grad school I found out that during my undergrad AI courses I had been missing a renaissance that had been occurring which led to modern machine learning and probabilistic AI. Watson’s achievement is only possible with these new methods and the raw computing power increases we have had over the same period.

But  apparently it did also have one other advantage. As many people have speculated, the machine did seem to have a buzzer advantage. According to op-ed by Ken Jennings himself, Watson’s speed with the buzzer was decisive in making up for questions it got wrong.  Is this just sour grapes? Maybe just a little, you need some ego to be an intense competitor like Jennings, but I think he has a point. As I pointed out yesterday the quick reaction time between making the decision to buzz and registering a button press is something a machine can clearly be faster at. Is this what winning at Jeopardy means?

It shouldn’t be.

Winning should mean the ability to answer complex questions, with ambiguous meanings, under time pressure while making the best strategic betting choices.  That is the task Watson performed admirably at. It could have had a buzzer delay and read the screens with computer vision rather than receiving a text file to parse and perhaps it still would have won.

But we’ll never know now.

So you won this round Watson. And you’re impressive (well, the engineering team that built ‘you’ is impressive actually). Hopefully everyone has learned a bit about AI and hopefully some young girls or boys will be inspired to consider computer science or engineering that otherwise wouldn’t have.

But next year…next year you should come back and put it all the table.  Play it our way, the human way, you have the capability to at least try. And may the best machine, be they biological or electronic, win.

Team Human vs. Team Watson : Round II

Since Watson is doing so well there has been some confusion about what is actually going on as we watch the game. There’s been some talk that the Jeopardy challenge is unfair, and that’s true it is, but both sides have some unfair advantages.  Here’s how it is as far as I understand it.

Team Human Advantages (#teamhuman)

  • Using the most advanced computational system ever encountered, which has been under intense development for millions of years, the Human Brain. It has more raw computational power than Watson, can handle almost infinitely more parallelization and dynamic linking, is incredibly robust to new information and has pattern recognition heuristics which we are only barely beginning to comprehend. Its hard to underestimate how big an advantage this is and its hard to judge it, this is why it seems like a good problem for AI research.
  • They understand language – Watson does not understand language at all. It knows some things about language patterns and has learned how to match words and phrases for answers to other words and phrases which are questions for Jeopardy, and only Jeopardy. If a topic shows up which is has not seen much then it does not know what to do. Since it doesn’t understand language it can’t make the kind of leaps of reasoning that the humans can. This is why the topics are restricted to types of topics that have shown up on Jeopardy before, nothing that requires Trebek to explain the meaning of the question.

Watson Advantages (#teamwatson)

  • A huge memory database of facts which are relevant to jeopardy questions – its hard to say if this is more facts than Ken Jennings has in his head, it’s represented very differently but humans have amazing heuristics for accessing data quickly and linking it together. But perhaps Watson has an edge here.
  • A totally focussed system designed and optimized for years just to play Jeopardy (against most of us this is an advantage, against Ken Jennings and … its questionable who has spent more time training for these games)
  • Question sent as a text file – this really could be a bit of an advantage, the computer still needs to scan the text, parse it and analyze it.  The humans need to analyse the visuals and simultaneously listen to Alex Trebek describe the question, of course humans are very good at that kind of thing, Watson is not.
  • No video questions – ya, that’s just not on, you want to see a machine fail at something? I’ll show you my toaster trying to cook lasagna, we’re just not there yet.
  • Button pressing – it’s not clear exactly how Watson’s button actuator works. And how does Watson know it is allowed to press the button if its not listening to Trebek? It’s possible it has an ‘unfair’ fast reaction time to the humans, I don’t know.

So while this is a fascinating challenge and should demonstrate to everyone how far Artificial Intelligence research has come, everyone should keep in mind that Watson is not Data or Skynet, in fact it’s not even Wall-E.

Watson has been trained to play Jeopardy. It has the ability to answer questions and find data in a much more natural manner than was possible even 5 years ago. But it is not playing the same game that Ken Jennings and Brad Rutter are. Maybe next year.

My advice for next year’s challenge

Oh you know there will be one, Jeopardy ratings are through the roof! The engineers at IBM should make efforts to remove these complaints of unfairness in the following ways:

  • (1) Let us see Watson’s button – Ahem, you know what I mean. Put a robot out there or something more visual to let Watson press the button. Also, do some work with people who understand the human body to make sure it isn’t unfairly fast. How long does it take for a human being to physically press a button from the moment they ‘decide’ to press it? It may seem like a handicap to add this delay to Watson but it really would seem more fair. After all, we want to know that Watson is winning on the questioning answering part of the game, not these physical details, so remove them as issues.
  • (2) Give Watson a camera – Watson really could visually parse the questions to know what they are. At the beginning of the round it would scan the topics visually and build a database to start planning.  This shouldn’t be hard as visual text analysis is quite advanced, it wasn’t added because it was a needless complication of an engineering problem. But the optics, excuse the pun, are not good in terms of fairness.
  • (3a) Get rid of all talking – Lock each player in a room, they wouldn’t hear Alex Trebek, they’ll all just read the questions.  When another contestant answers this should be sent via text file to all the other players. Then it would be fair…but boring and weird.
  • OR
  • (3b) Give Watson speech recognition – This will be a real problem as speech recognition is one of those areas that really has turned out to be harder than anyone imagined. Vision? No Problem. Text analysis? Give me enough text and I will move the Earth. Robotic control? Are you kidding? Easy. But understanding human speech transmitted through a vibrating air column, damn that’s hard.

    But they should do it, they could use the latest technology available for this, which IBM is generally recognized to lead anyways, and just let it be the machine’s achilles heal.  It could train on Alex Trebek’s Canadian accent (no French Alex!) It could train on its opponents and it could nail the common and simple prompts that the host gives when it is it someone’s turn to play or to break for commercials. It could just ignore Trebek reading the question out except for the cue that it is time to press the button. It may still not do much better at taking advantage of  wrong answers by opponents and it would likely make some entertaining mistakes, but it would make it more realistic. Paradoxically it might get more credit for losing in this way than it will in winning the way it is currently set up.

So if humanity loses tonight, don’t fret. This machine is just a step along the way and sometimes things aren’t as smart as they first seem. Then again, you could also say its holding by not even trying to do everything at once. Would you be more scared/impressed if Watson really did everything its human opponents did and still faired well?

On to round three.








Team Human vs Team Watson : Round I

Just in case watching a computer play humans on Jeopardy wasn’t your idea of a romantic evening last night, here’s my summary of what happened.

They spent a lot of time explaining the way Watson was built and even gave a high level discussion of the fact that the system maintains a belief distribution over possible answers.  When the computer answers a question we see its top three picks with the probability weight on each answer and the threshold for buzzing in.

The first round was impressive with Watson dominating the first few minutes.  It answered quickly and flawlessly until the first commercial break. It seemed that the humans just weren’t fast enough.

But after the commercials it got interesting. Ken Jennings seemed to modify his strategy to simply press the button as early as possible.  It was clear he buzzed in several times having no idea what the answer was, then stalled for a moment and guessed, usually correctly.  This was a smart adaptation to Watson’s algorithm.  The computer won’t answer unless it is confident in its answer. But a human can keep thinking after they buzz in and gamble that they can come up with something.

Interestingly when Ken buzzed in very early the answers showing on the screen for Watson seemed to be lower quality, it still hadn’t converged on a good answer and froze at the buzzer.

At one point Ken buzzed in and got it wrong, then Watson buzzed in. We could see that its top answer was the same wrong answer Ken just gave, but it repeated it anyways.  This seems to indicate the algorithm does not keep computing after the buzzer and can’t take into account the answers of other players. A minor thing to change really.

The round ended with Watson answering the last question, correctly identifying the Event Horizon of a black hole. Fitting I think.

We’ll see what happens tonight.

What do you think about the challenge, leave your observations in the comments. If you are on twitter make sure to pick a side: Are you rooting for #teamhuman or #teamwatson?

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