Research - AI in general - ICA talk

Abstract

I will argue that both of these positions are mistaken. That AI is in fact a branch of cognitive science, part of the understanding of ourselves and other animals. As such, AI is part of perhaps the hardest, most ambitious undertaking that science ever has or ever will undertake.

In response to the difficulty of this problem, I will describe the movement in AI that has essentially given up on human-level intelligence, and is working on animal-level intelligence - in the hope of producing properly grounded definitions of all the terms that are casually used at the human level - emotion, fear, love, consciousness, language, representations, memories - but whose precise definition remains elusive.

"Animal-AI" researchers would argue that we will only really know what these things mean when we understand their history and primitive origins.

AI is impossible?

• Roger Penrose, The Emperor's New Mind, Shadows of the Mind

• 2 strands:
  • Strand 1 - AI is impossible. A machine cannot be intelligent. Argument is based on Godel's result on the limits of formal systems. There are propositions that we can see are true that the AI logical system can't.
  • Strand 2 - Here's what you need to be intelligent / conscious - A type of "machine" which exploits quantum effects.

• AI rejects the 1st. Finds the 2nd interesting but unproven:
  • Strand 1 - Any working AI would not be a logical (truth-preserving) system. It would be probabilistic, statistical, learning by trying random actions out against the world and modifying itself. Full of contradictions. Just like us.
  • Strand 2 - Even if the quantum machine is built, it is not shown what (if any) relation this has to solving the problems of AI.

• I believe Penrose is looking for quantum effects in a vain attempt to explain the "unity" / apparent wholeness of the mind and of consciousness. He cannot believe that something that feels whole might not really be whole. That a decentralised Network of Mind or Society of Mind gives rise to a global level that feels unified.

• AI agrees that this is one of the big questions: How can something that is decentralised feel centralised? But it is hardly far-fetched that it could. I think we could feel any way that was evolutionarily useful.

AI is not a good enough model?

• Computers and robots are a rich model, but do we need a little more to really model the mind?
• Analog? Chemical? Quantum?

• AI is open to this. AI people want to use the best models and tools.
• If you show us how a quantum machine is good, we'll do AI on quantum machines.

• The paradigm may be flawed/incomplete, but the paradigm only changes when something better comes along. This is not narrow-minded. This is a sensible way to behave.

• Finally, the computer/robot paradigm is remarkably resilient. A "program" is a lot more than something like Microsoft Word. Genetic Programming. Neural Networks. Cellular Automata.

• The Church/Turing thesis postulates that the computational paradigm can model everything. "If you can write it down, it's an algorithm".

AI is easy? (and AI is dangerous?)

• Kevin Warwick, March of the Machines
• Full human-level intelligence will happen in 10-50 years. Humans will be enslaved / go extinct.

• Makes claims:
  1. Building full AI machines is possible in theory.
  2. This will happen soon.
  3. All the major problems of AI are near solution. The main problem is just to "scale up".
  4. Humans will remain stuck at the H. sapiens level while this is happening.

• Most AI people agree with 1, disagree with 2, disagree vehemently with 3. We don't even know what questions to ask yet. Warwick presents examples of only relatively simple machines. Such enthusiasm about scaling up could have been written at any time during the last 30 years of AI (and was).

• But let's not quibble about the timescale. Someday we'll make AI's. Is the AI project really about extinction of humanity in the long run?
• I think not. Because of the unspoken assumption 4, that humans stay the same.

• First we consider 3 - Why AI is hard.
• Then we consider 4 - What will happen to humans?

Why AI is hard

• In Warwick's book there is no sense of unsolved problems in AI. The challenge is expressed merely as "scaling up" existing work to larger numbers of neurons - "Brain Building", as if something will then magically happen.

• AI was like this before - "Faster machines and we will have AI", "More memory and we will have AI".

• It's not true - we need theoretical breakthroughs in understanding the systems we are talking about, their origin, evolution, development and dynamics.

• Some open issues follow.

Open Issues

1. Architectures of Mind - What does the whole mind look like? Network, Hierarchy or Society? Does I/O link to many brains or one? Who is in charge? Where am I? What is consciousness?

2. Action Selection - As a more specific example of the above. We know how to solve 1 problem. How does the creature deal with multiple problems at once?

3. "Learning to Learn" - How does the creature generate goals for itself in the first place?

4. Symbol-grounding, Evolution of language. - What is language? How do creatures processing numerical sensory data end up processing symbolic "words" with meanings? What does "chair" mean, internally? Is it a meaningless token #5099 being passed around, or is it a whole specialised sub-system, firing away? Do parts of the brain talk to each other? Do we have an internal language? Is it English, or is it something more messy? Will sub-symbolic AI plug in neatly to symbolic AI?

5. Robots or simulation? - Robots are more real, may solve symbol-grounding. But experiments in simulation are often more practical. e.g. Sims evolved a world of beautiful 3-D creatures from scratch. Large-scale experiments involving the Web?

Open Issues / Why AI is hard

1. Societies. Culture. - Maybe you need a whole interacting society and culture to be intelligent. Idea that we aren't that intelligent individually - most of our intelligence resides in our culture, in other people, in our support systems, the way our world is structured for us, our books, our machines.

   We didn't arise alone in a lab like Cog. There were always millions of us.

   We didn't arise in a planet that was full. There was no other intelligent life. We had "peace and quiet" for 2 million years, during which we could slowly evolve our own languages and cultures. And it was a process of trial and error. Most societies fail - Jared Diamond, Guns, Germs and Steel: A Short History of Everybody for the Last 13,000 years - Evolutionary History.

   How will AIs do this? Their cultures will be under massive pressure from the outside world, right from the start. Their societies will fail much more easily.

The planet is full of humans

• There is such a thing as an answer to the question "How do they work?"

• They have a culture, a language, etc. already.

• What will we be able to do with these in the future?

Mid-3rd millennium - We will become AI's (if we want, in a free society)

• AI is part of Cognitive Science - the understanding of minds in nature.
• Long-term, AI is about us.

• Once you understand, you can change. You can copy. You can transplant to a new medium. All the rest that we are familiar with from non-brain human biology. Do what you want to. In a free society no one forces you to.

• Over the next few hundred years, we will understand ourselves, and so become, in effect, AI's.

• No evidence that:

  Solving the unsolved problems in AI.
  Socialising the AI machines in a culture that doesn't fail.

  is harder than:

  Reading brains into a new material without understanding them.

• The former needs theoretical understanding. The latter doesn't.
• The former needs the new culture to work. The latter doesn't. The culture already exists.

• One big advance in a technology for reading the mind without destroying it, and the latter would happen immediately. Without the need for a proper Theory of the Mind at all! Transplant to new components, and trust the brain's ability to adapt and re-organise to smooth out the details.

Early 21st cent. - Animal-level AI

• Researchers gone back to basics since the 1980s.

• Trying to define: emotion, fear, love, consciousness, language, representations, memories - by looking at the animal, the infant.

• Build a pre-linguistic creature that learns from its environment and "understands" it in a physical sense. That tries to learn language rather than has language already inbuilt.

• Rather than a creature that uses high-level human language that it doesn't understand to fool us into thinking there is someone at home. - Eliza.

• Warwick comes from the "Animal-AI" world, but thinks it has actually solved these issues.

Early 21st cent. - 2 types of AI systems

• Pre-linguistic robots and non-verbal learning systems. Autonomous robots in unpredictable environments (e.g. home, street, building site, farm) as opposed to predictable (factory).
  • Learning and adaptability key to survival in unpredictable domain.
  • Constant interruptions. Modify plans on the fly.
  • Make judgements on incomplete data.
  • Have to learn some tasks from scratch, because human literally can't tell you how to do them.

• Specialised learning and search systems for processing data that it doesn't "really" understand. Search engines.

Early 21st cent. - Animal-like robots

• Jobs in physical world requiring adaptation to unpredictability but not requiring symbolic intelligence.
• Dangerous. Tedious. Impossible (underwater etc.). Just cheaper.

• Engineer the environment - factories.
• e.g. Engineer the traffic so cars communicate. Don't need sensors.
• Tele-robotics - Remote control. No need to give it a brain. But can you hire enough remote controllers? 24 hours a day. NASA - 8 light minutes.

• Major cause of early death in the world today.
• Once autonomous cars are safer, it would be criminal to allow humans to drive.
• Liberate old people, disabled, children, busy business people.
• Liberate the drunk! And sleepy people.

• Robots are already in the home. Washing machine. Vacuum cleaner. Car. We often have strange definitions of what we call a robot.
• Autonomous lawn mowers, vacuum cleaners exist.
• Network them. A house full of animals, guarding you and your property.
• Can it get up the stairs?

Does this begin to answer the unsolved problems? Do we get a robotic chimpanzee by 2100? Does this then begin to converge with the rest of Cognitive Science?

It will be an interesting century.

Conclusion - Intelligence flows on

He imagines a totalitarian society but never explains where this totalitarian society is supposed to come from. So like, we just give up on human freedom and democracy just because a new technology exists?

• There is another view - Humans are smart, we'll understand the mind, but we'll be civilized about it. We'll value everybody, whether they want to be part of the new experiments or not.

• Intelligence will be liberated into humans (enhancements, full transplants), machines, networks, software agents, robots.

• Intelligence / civilization will carry on, flowing into new bodies (as it has always done).

• (Like all technology) It will find that it is far, far easier to adapt to and work with what already exists - a planet packed with humans and cultures and societies and languages - rather than reinvent the wheel (and, literally, everything else).