>In 1966, the MIT AI lab famously assigned Gerald Sussman the problem of solving vision in a summer; as we all know, machine vision still hasn't been solved over five decades later.
It certainly took five extra decades, but it would be a massive shift of goalposts to say the problem of vision hasn't been sufficiently solved today.
>In November 2016, in the pages of Harvard Business Review, Andrew Ng, another well-known figure in deep learning, wrote that “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.” A more realistic appraisal is that whether or not something can be automated depends very much on the nature of the problem, and the data that can be gathered, and the relation between the two. For closed-end problems like board games, in which a massive amount of data can be gathered through simulation, Ng’s claim has proven prophetic; in open-ended problems, like conversational understanding, which cannot be fully simulated, Ng’s claim thus far has proven incorrect. Business leaders and policy-makers would be well-served to understand the difference between those problems that are amenable to current techniques and those that are not; Ng’s words obscured this. (Rebooting AI gives some discussion.)
It takes significantly longer than a second to actually understand spoken conversation (rather than provide a conditioned response or match against expected statements, both of which computers are fully capable of doing).
>I just wish that were the norm rather than the exception. When it’s not, policy-makers and the general public can easily find themselves confused; because the bias tends to be towards overreporting rather than underreporting results, the public starts fearing a kind of AI (replacing many jobs) that does not and will not exist in the foreseeable future.
Robotic manufacturing has already eliminated massive swaths of high paying jobs. Likewise, software has eliminated massive swaths of data entry and customer service jobs (with software being a particularly poor replacement for the latter, but still being put into widespread use to cut costs). And contrary to beliefs that new jobs will be created in IT, software is able to massively eliminate low skilled tech jobs as well, as e.g. automated testing did to India's IT industry.
As with existing jobs that have been automated away, companies won't need generalized AI to eliminate many more jobs. Many jobs don't rely on unconstrained complex deduction and thinking, and will be ripe for replacement with deep learning algorithms. And we can be reliably assured that corporations will engage in such replacements even when the outcomes are not up to par.
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