Now barely one year later I have seen a few demonstrations that signal that artificial intelligence has taken another leap towards mainstream acceptance:
- Yann LeCun demonstrated a computer vision system that could learn to recognize objects from his pocket after being shown a few examples, under a talk about learning feature hierarchies for computer vision
- Andrew Hogue demonstrated Google Squared and Google Sentiment Analysis at Google Tech Talk, those systems both show rudimentary understanding of web pages and use word association
- IBM Watson super computer is competing against the best human players on Jeopardy
IBM WatsonIBM Watson is a well executed factoid extraction system, but it is a brilliant marketing move, promoting IBM's new POWER7 system and their Smart Planet consulting services. It gives some people the impression that we already have human-like AI, and in that sense it could serve as a catalyst for investments in AI. This post is not about human-like artificial intelligence, but about the spread of shallow artificial intelligence.
Applications For Shallow Artificial IntelligenceBoth people and corporations would gain value from having AI systems that they could ask free form questions to and get answers from in very diverse topics. In particular in these fields:
- Medical science
Many people, me included, are concerned about a big brother state and military use of AI, but I do not think that is going to stop adaption. These people play for keeps.
There are signs that the financial service industry is starting to use sentiment analysis for their pricing and risk models. Shallow AI would be a good candidate for more advanced algorithmic trading.
Bottom Up vs. Top Down ApproachesHere is a very brief simplified introduction to AI techniques and tools. AI is a loosely defined field, with a loose collection of techniques. You can roughly categorize them it top down and bottom up approaches.
Top down or symbolic techniques
- Automated reasoning
- Many forms of tree search
- Semantic networks
- Neural networks, computer with similar structure to the brain
- Machine learning
The top down systems are programmed by hand, while the bottom up systems learns themselves based on examples without human intervention, a bit like the brain.
What Is Causing This Sudden Leap?Many top down techniques were developed by the 1960s. They were very good ideas, but they did not scale; they only worked for small toy problems.
Neural networks are an important bottom up technique. They started in 1950s, but fell out of favor; they came roaring back in 1980s. In the 1990 the machine learning / statistical approaches to natural language processing beat out Chomsky's generative grammar approach.
The technology that is needed for what we are doing now have been around for a long time. Why are these systems popping up now?
I think that we are seeing the beginning of a combination machine learning with top down techniques. The reason why this have taken so long is that it is hard to combine top down and bottom up techniques. Let me elaborate a little bit:
Bottom up AI / machine learning are black boxes that you give some input and expected output and it will adjust a lot of parameter numbers so it can mimic the result. Usually the numbers will not make much sense they just work.
In top down / symbolic AI you are creating detailed algorithms for working with concepts that make sense.
Both top down and bottom up techniques are now well developed and better understood. This makes it easier to combine them.
Other reasons for the leap are:
- Cheap, powerful and highly parallel computers
- Open source software, were programmers from around the world develop free software. This makes programming into more of an industrial assembly of parts.
Who Will Benefit From An AI Boom?Here are some groups of companies that made a lot of money during the Internet boom:
- Cisco and Oracle the tool makers
- Amazon and eBay small companies that grew to become domineering in e-commerce
- Google and Yahoo advertisement driven information companies
Initially big companies like IBM and Google that can create the technology should have an advantage, whether it will be in the capacity of tool makers or domineering players.
It is hard to predict how high the barrier to entry in AI will be. AI programs are just trained on regular text found on or off the Internet. And today's super computer is tomorrow's game console. The Internet has a few domineering players, but it is generally decentralized and anybody can have a web presence.
New York is now filled with startups using machine learning as a central element. They are "funded", but it seems like they got some seed capital. So maybe there is room for smaller companies to compete in the AI space.
Job Skills That Will Be Required In An AI BoomDuring the Internet boom I met people with a bit of technical flair and no education beyond high school who picked up HTML in a week and next thing they were making $60/hour doing plain HTML. I think that the jobs in artificial intelligence are going to be a little more complex than those 1990s web developer jobs.
In my own work I have noticed a move from writing programming to teaching software based on examples. This is a dramatic change, and it requires a different skill set.
I think that there will still be plenty of need for programmers, but cognitive science, mathematics, statistics and linguistics will be skills in demand.
My work would benefit from me having better English language skills. The topic that I am dealing with is, after all, the English language. So maybe that English literature degree could come in handy.
Currently I feel optimistic about the field of artificial intelligence; there is progress after years of stagnation. We are wrestling a few secrets away from Mother Nature, and are making progress in understanding how the brain works. Sill, introduction of such powerful technology as artificial intelligence is going to affect society for better and worse.