and regulatory levels.m The second is that while function-based systems have been an enabling and positive development, we do need to be acutely aware of the reasons behind their success to better understand the implications. A key finding here is that some tasks in perception and cognition can be emulated to a reasonable extent without having to understand or formalize these tasks as originally believed and sought, as in some text, speech, and vision applications. That is, we succeeded in these applications by having circumvented certain technical challenges instead of having solved them directly.n This observation is not meant to discount current success but to highlight its nature and lay the grounds for this question: How far can we go with this direction? I revisit this issue later in the article.

Human-Level or Animal-Level?

Let me now get to the thoughts that triggered the title of this article in the first place. I believe human-level intelligence is not required for the tasks currently conquered by neural networks, as such tasks barely rise to the level of abilities possessed by many animals. Judea Pearl cited eagles and snakes as having vision systems that surpass what we can build today. Cats have navigation abilities that are far superior to any of those in existing automatous-navigation systems, including self-driving cars. Dogs can recognize and react to hu- m Eric Horvitz of Microsoft Research brought up the idea of subjecting certain AI systems to trials as is done to approve drugs. The proper labeling of certain AI systems should also be considered, also as is done with drugs. For example, it has been suggested that the term “self-driving car” is perhaps responsible for the misuse of this AI-based technology by some drivers who expect more from the technology than is currently warranted.

n For example, one can now use learned func-

tions to recognize cats in images without having to describe or model what a cat is, as originally thought and sought, by simply fitting a function based on labeled data of the form: (image, cat), (image, not cat). While this approach works better than modeling a cat (for now), it does not entail success in “learning” what a cat is, to the point where one can recognize, say, deformed images of cats or infer aspects of cats that are not relayed in the training dataset.

the camera of a self-driving car (the vulnerability of these systems to mistakes remains controversial in both its scope and how to deal with it at the policy and regulatory levels).

The significance of these observations stems from their bearing on our ability to forecast the future and decisions as to what research to invest in. In particular, does the success in addressing these selected tasks, which are driven by circumscribed commercial applications, justify the worry about doomsday scenarios? Does it justify claims that AI-based systems can now comprehend language or speech or do vision at the levels that humans do? Does it justify this current imbalance of attitudes toward various machine learning and AI approaches? If you work for a company that has an interest in such an application, then the answer is perhaps, and justifiably, yes. But, if you are concerned with scientific inquiry and understanding intelligence more broadly, then the answer is hopefully no.

In summary, what has just happened in AI is nothing close to a breakthrough that justifies worrying about doomsday scenarios. What just happened is the successful employment of AI technology in some widespread applications, aided greatly by developments in related fields, and by new modes of operation that can tolerate lack of robustness or intelligence. Put another way—and in response to headlines I see today, like “AI Has Arrived” and “I Didn’t See AI Coming”— AI has not yet arrived according to the early objective of capturing intelligent behavior. What really has arrived are numerous applications that can benefit from improved AI techniques that still fall short of AI ambitions but are good enough to be capitalized on commercially. This by itself is positive, until we confuse it with something else.

Let me close this section by stressing two points: The first is to reemphasize an earlier observation that while current AI technology is still quite limited, the impact it may have on automation, and hence society, may be substantial (such as in jobs and safety). This in turn calls for profound treatments at the technological, policy,

We succeeded in
these applications
by having
circumvented
certain technical
challenges instead
of having solved
them directly.