The Dark Secret at the Heart of AI

["Dave Farber" <farber () gmail com>]

Begin forwarded message:

> From: Dewayne Hendricks <dewayne () warpspeed com>
> Date: May 28, 2017 at 1:41:09 PM EDT
> To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>
> Subject: [Dewayne-Net] The Dark Secret at the Heart of AI
> Reply-To: dewayne-net () warpspeed com
>
> The Dark Secret at the Heart of AI
> No one really knows how the most advanced algorithms do what they do. That could be a problem.
> By Will Knight
> Apr 11 2017
> <https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/>
>
> Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The 
> experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous 
> cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of 
> artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, 
> it relied entirely on an algorithm that had taught itself to drive by watching a human do it.
>
> Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely 
> clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of 
> artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the 
> brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one 
> day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be 
> difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to 
> isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so 
> that it could always explain why it did what it did.
>
> The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car’s underlying AI 
> technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been 
> widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that 
> the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless 
> other things to transform whole industries.
>
> But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more 
> understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures 
> might occur—and it’s inevitable they will. That’s one reason Nvidia’s car is still experimental.
>
> Already, mathematical models are being used to help determine who makes parole, who’s approved for a loan, and who 
> gets hired for a job. If you could get access to these mathematical models, it would be possible to understand their 
> reasoning. But banks, the military, employers, and others are now turning their attention to more complex 
> machine-learning approaches that could make automated decision-making altogether inscrutable. Deep learning, the most 
> common of these approaches, represents a fundamentally different way to program computers. “It is a problem that is 
> already relevant, and it’s going to be much more relevant in the future,” says Tommi Jaakkola, a professor at MIT who 
> works on applications of machine learning. “Whether it’s an investment decision, a medical decision, or maybe a 
> military decision, you don’t want to just rely on a ‘black box’ method.”
>
> There’s already an argument that being able to interrogate an AI system about how it reached its conclusions is a 
> fundamental legal right. Starting in the summer of 2018, the European Union may require that companies be able to 
> give users an explanation for decisions that automated systems reach. This might be impossible, even for systems that 
> seem relatively simple on the surface, such as the apps and websites that use deep learning to serve ads or recommend 
> songs. The computers that run those services have programmed themselves, and they have done it in ways we cannot 
> understand. Even the engineers who build these apps cannot fully explain their behavior.
>
> This raises mind-boggling questions. As the technology advances, we might soon cross some threshold beyond which 
> using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we 
> find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make 
> decisions differently from the way a human would? We’ve never before built machines that operate in ways their 
> creators don’t understand. How well can we expect to communicate—and get along with—intelligent machines that could 
> be unpredictable and inscrutable? These questions took me on a journey to the bleeding edge of research on AI 
> algorithms, from Google to Apple and many places in between, including a meeting with one of the great philosophers 
> of our time. 
>
> [snip]
>
> Dewayne-Net RSS Feed: <http://dewaynenet.wordpress.com/feed/>



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