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There are some in the A.I. community who argue that Google and/or Facebook will win the A.I. space, and that it is futile for anyone else to try. The argument leans on the fact that most machine learning algorithms perform better when they have larger data sets, and Google and Facebook have the most data, by far. I'm not convinced, and want to give you three reasons this may not happen.
1. More data may not always be the key.
If you think about how human brains work, we can't process nearly as much data as a computer can. In fact, too much data often overwhelms us and makes us poorer decision makers rather than better decision makers. If our ultimate generalized intelligence machines work more like the human brain, they may be able to use induction on much smaller data sets, and having massive data sets will cease to be an advantage. Certainly for some types of machine learning, more data will always be better, but it is easy to see a future where, for many applications and some A.I. architectures, that isn't the case.
2. Google and Facebook are mostly consumer focused.
Google and Facebook are focused on advertising, photos, and other common consumer needs. There is currently no company heavy in the B2B space that is focused on A.I. (Microsoft would be the closest, but most of their A.I. initiatives seem to be consumer) Workday has been public about their machine learning ambitions. But no one is really the clear leader yet. As that emerges, and we get an Oracle or Salesforce type of company with a heavy A.I. bent, that company could end up being the leader in the overall A.I. space.
3. Path dependence will matter for A.I.
Path dependence is one of the most important concepts in technology but is often misunderstood. Colloquially, it means "history matters." There is a more precise mathematical definition though, that is worth discussing.
Consider a jar filled with 50% red marbles and 50% black marbles. Every time you take a marble out of the jar, a new marble falls into the jar. The probability that the new marble is red or black is based on the probability distribution of red to black marbles currently in the jar. So, we know that at some point, the jar will eventually go all red or all black, but we don't know which. Why? Because if we are drawing random marbles out, eventually we will hit a patch where we draw 5 or 6 marbles out of the same color. When that happens, it shifts the probability distribution of marbles coming in enough that we also get several marbles in a row of the same color. And once the probability distribution tips over in the 60ish percent range, its a very difficult trend to reverse to not end up with a single color.
So the question is, will A.I. be path dependent? Or, more specifically, will the path dependence affect the companies and/or architectures that become dominant in the A.I. field? I believe it will. At the moment, the field is new enough, and the community of people who are deeply involved is small enough, and the applications that are deployed are few enough, that it is easy to change things, incorporate new ideas, and keep up with all the innovation. As things like machine learning become more prevalent and architectures become more entrenched in key applications, they will become more difficult to rip out and replace.
But more importantly, as techniques for unsupervised learning grow, and temporal aspects of training data become modeled more and more into A.I. architectures, the training history of a given A.I. will matter much more than before. Training history, particularly if it is for one of these mega-projects like Viv or Vicarious. As knowledge accumulation in a A.I. starts to matter, those with a head start may end up winning big.
I'm not saying Facebook and Google won't do well in the space. They definitely will. But it is far from certain that they will be as dominant in 5 years in A.I. as they are now, and picking the winners at this stage is still very difficult. So, don't be afraid to get out and start a company in the A.I. space and, if you are looking for a job in A.I., don't just go to Google or Facebook. There are plenty of other interesting opportunities.
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