Winter isn’t coming. At least not for the world of artificial intelligence. Jeff Bigham, program director of the Human-computer Interaction Institute at Carnegie Mellon, believes we’ll soon reap the bounties of an AI autumn instead.
An AI winter is a period in which research and funding freeze up and it becomes difficult to break new ground. Since the inception of artificial intelligence technology in the 1940s there’s been several of these periods. The last decade has brought new hope for AI – and with it all the hyperbole the modern media can muster. AI is in the public’s eye on a larger scale than ever before, this much is certain.
So it would then follow that greater hype would lead to a colder winter. Bigham seems to believe this won’t be the case, because we’re actually accomplishing stuff with modern AI. In a recent blog post he says:
AI hype is deflating all around us, and what will be left is a rich harvest of human-centered technical work applying Machine Learning to important problems.
If AI autumn is a period where we’ll start seeing some return on the hype cycle, then what exactly does that mean? Today’s neural networks are already saving lives, producing art, and making billions of dollars for big tech companies. Are we about to enter a golden-era of godlike AI super human symbiosis?
Nah, not really. Bigham says it’s all about human-computer interaction:
People who can apply ML to solve real human problems will become the most important tech people out there. Powerful ML is increasingly captured in easy-to-use libraries; if you want to stay ahead of the curve, you need the skills we teach in our HCI curriculum.
If your goal is to fight through the winter, with hopes of someday developing truly intelligent AI, then break free from the stranglehold of deep learning and practical application, and go forth bravely.
If your goal is to reap the rewards of the harvest, study HCI.
He goes on to point out that it’s become increasingly common for developers to consider the human factor too late into the production of a machine learning system. His argument for human-centric AI research predicts that useful AI will begin to rise to the top as consumers, the media, and the public at-large eventually lose interest in over-hyped pipe dreams from people and companies promising things beyond the ability of today’s current machine learning technologies.
According to Bigham, the next phase of modern AI – its autumn — will allow talented developers to prosper. To quote him just the slightest bit out of context, it’s become obvious this phase will be about uncovering the problems that are “both important enough to matter” and “narrow enough” that specific machine learning methods will work well. Unfettered by out-of-whack expectations during this modest-to-accurately-hyped season, researchers will finally have time to address the field’s lingering open questions.
Unless he’s wrong about the whole winter thing. Then we’ll all watch as the AI bubble bursts and thousands of startups and major technology companies go under amid investor panic.
H/t: Jack Clark, Import AI
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