The search for Artificial General Intelligence

We already have some pretty fine examples of AI, but all of them are limited to performing a specific task. However, the ultimate search is for Artificial General Intelligence (AGI), which is effectively a complete ‘human-like’ AI system. This would be a system that basically can think like a human. But, as Dan Robitski wrote in futurism.com, “No amount of optimizing systems to get better at a particular task would ever lead to AGI.” In other words, he doesn’t think we’re going to just stumble across it. The reason being; companies working on AI systems have a narrow focus.

What would be the benefits of AGI?

If we had a system that was able to use abstract reasoning and everything that goes with that, including creativity, we would be able to make a major leap in solving the problems associated with aspects of space exploration, economics, healthcare and much more. There are lots of aspects of our lives that AGI could positively impact on.

AGI would also be a massively interesting investment and a very valuable one. However, Robitski believes that investment money would need to come from governments rather than private investors and venture capital, simply because the way private funds structure their businesses would mean that a development platform would never be able to raise enough money.

Why private investors are avoiding AGI

Marian Gazdik, Managing Director of Startup Grind Europe said: “Investors only fund something when they see the end of the tunnel, and in AI it’s very far.” Hence the need for government funds. Tak Lo, a partner at Zeroth.ai, an Asian accelerator that invests in technology startups, who was speaking alongside Gazdik at The Joint Multi-Conference on Human-Level Artificial Intelligence in Prague last week also commented: “I very much like General AI as an intellectual, but as an investor not as much.”

Venture capitalists like Lo prefer to “invest in companies with great business models that use AI to solve a big problem, or companies that got their hands on a large, valuable dataset for training algorithms.”

The problem with AGI is that a workable solution is so far away in the future that private investors just can’t see a time when they’ll see a return. They think in terms of five to ten years, and we may have to wait longer than that before AGI becomes a reality. On the other hand a government doesn’t have to be tied to the same time schedule: they can put money into a project that serves the greater good without being too concerned about when the end product is delivered. But, first they have to decide that AGI is a project they want to get behind and so far no major government has made that commitment. Until they do, AGI will remain a dream.

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