How we might steer AI towards a more utopian future. There’s no question that AI will redefine humanity’s future. The only question is how. When a recent article in the MIT Technology Review claimed AI researchers had little interest in applied AI, I felt a twinge of concern.
There’s no question that AI will redefine humanity’s future.
The only question is how.
When a recent article in the MIT Technology Review claimed AI researchers had little interest in applied AI, I felt a twinge of concern.
While it doesn’t immediately seem like a big deal, the 2nd order consequences of this are vast.
Below are my thoughts on the divergence of academia from application in AI, its effects, and some possible solutions.
A few academic datasets come up again and again in AI research. MNIST, ImageNet, CIFAR…
This makes benchmarking algorithms against each other easy.
But it also comes with problems.
Popular face data sets, such as the AT&T Database of Faces, contain primarily light-skinned male subjects, which leads to systems that struggle to recognize dark-skinned and female faces.
In theory, companies “productionizing” the tech would test it like crazy to prevent negative side-effects. But we know this isn’t happening due to highly publicized failures.
Anecdotally, I also know how easy it is to achieve great results when evaluating a model, only to have it flop in production.
By default, algorithms are biased. And bias is only removed via extreme proactiveness. That may require creating a new standardized dataset without bias.
In ML, bias is opt-out, not opt-in.
Assuming that a future general AI will be benevolent and compassionate is a huge leap of faith.
Personally, I fall into the Elon Musk camp — we must do everything we can to ensure that AI will be friendly to humanity.
If today’s “simple” AI is divorced from real human problems, there’s no reason to believe future “advanced” AI will be different.
*A good start may be more interdisciplinary research on the effect of AI on humans and society, *while we continue pushing the boundaries of what is possible.
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