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What AI labs should be doing now

 By now I guess it is clear that Deep Learning isn't the miracle solution that will finally bring us our dream of Artificial General Intelligence. Now we can leave the hype alone and focus on the reality of the situation, Deep Learning is a sophisticated tool that can help us solve many problems we can't solve through traditional software development.

We should continue its development with the aim of continuously reducing its computational footprint so that it is more accessible to the common programmer without multimillion-dollar resources. But we must stop that unproductive fantasy that somehow deep learning is that path to the biggest dream of humanity, which is Artificial General Intelligence (AGI).

Every AI Lab around the world should be devoting nothing less than 30% of its time and resources to the development of AGI. It is the most important goal humanity can ever achieve because it will be a goal of goals, enabling us to solve every other problem we are currently cracking our brains about. 

It will help us solve physics that will enable us to achieve interstellar travel. It will help us solve biology and enable us to live eternal healthy lives. It will literally give us solutions to the most confounding questions of our existence and enable us to become a truly universal species. 

So why devote only 30% of time and resources and not more, well we have to understand that it is a very risky pursuit with the possibility that we may never solve it within this century because no one knows for sure how to go about solving it. But it would be a disservice to humanity to devote fewer resources to its pursuit because of the possibility that we may solve it in the next year or in a decades time. It is as risky as powered flight and maybe we do not yet have the critical base technology or the math to even make reasonable attempts at the problem yet, but it will be a shame to not try because the payout will be enormous if we succeed. 

Many AI labs have abandoned many of the old research directions that AI took in the past, everybody is devoting too many resources to squeezing one last drop of juice from deep learning they are ignoring so many promising old school research direction.

It should be of note that current deep learning techniques were actually proposed in the 70s and it is only now that we have the computing ability to even try it, which we did with great success as you can see from all the achievements of deep learning from speech recognition to self-driving cars. The question I often ask is that what if there is some deep-buried research that could give us some headway towards AGI, but it is buried in some obscure journal from the 1950s?

On the one hand, it might seem like we should go back to the past and try out every idea ever published but this would be impractical. This is where the intuition comes to play, researchers should go to the past and pore over a lot of ideas and allow their intuition to guide them to what they find promising and they should be given appropriate resources to pursue the direction they have chosen exhaustively, who knows what we could discover. 

It is very important that we strive to build AGI no matter what our definition of the term means to us. We should try like the Wright Brothers, the future of humanity depends on it. 


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