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Expert Interview (Part 2): Piatetsky-Shapiro on Self-Driving Artificial Intelligence and How Business Should Approach It

In the first half of our two-part conversation with data scientist and KDnggets founder Gregory Piatetsky-Shapiro, he provided examples of advances in deep learning continuing to push the field of AI full steam ahead. In today’s Part 2, Piatetsky-Shapiro notes some artificial intelligence concerns as it continues to advance and how business should approach incorporating AI.

Artificial Intelligence: Some Causes for Concern

The period of time when humans and computers collaborate to solve problems might not last very long. It’s not a matter of if, but when computers will be able to do jobs better than us. The question we should be asking now is What will humans do?

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In the short term, Piatetsky-Shapiro says he’s concerned about the use of technology to automate repetitive tasks previously done by humans. Even devices with limited intelligence will be able to complete jobs that are structured and require a lot of repetition. For example, toll booths on Mass Pike were removed and the job of collectors replaced by EZ-pass radio technology and taking photos of license plates and recognizing the numbers – a limited form of computer vision.

In regards to the developments in the field of Artificial General Intelligence (AGI) – machine learning that is able to perform the same intellectual tasks that humans can – Piatetsky-Shapiro tends to side with entrepreneur Elon Musk and physicist Stephen Hawking. It could put humanity at risk.

“I think we are not likely to have AGI in the next 10 years, but people, in general, have very poor track record of predicting long-term events.”

Even if the probability of AGI is small, its impact could be huge. A program like AlphaGo Zero demonstrates that computers can achieve super human ability in a relatively narrow field and that once they do it, they are probably using a different logic than we do, Piatetsky-Shapiro says.

“What if AGI values are not aligned with what we want to do? That’s a serious problem.”

While the AI Now Institute was founded this year at NYU to address the problem of incorporating values training in artificial intelligence, Piatetsky-Shapiro says he doesn’t think we should pretend there would be any guarantees about the way programs behave. Just like a parent can’t guarantee their children won’t rebel against the values they’re raised with, we shouldn’t assume machines would always follow the rules we put in place.

“If it is really intelligent, it will have its own values.”

Just like a parent can't guarantee their children won't rebel against the values they're raised with, we shouldn't assume machines would always follow the rules we put in place.

How Businesses Should Approach Artificial Intelligence 

There are no best practices yet for companies wanting to incorporate AI and machine-learning into their business strategies today because the technology has only been viable for a few years. With that in mind, brands need to be aware of both the capabilities of these tools, but also the limitations.

He shared three guidelines to follow or be aware of when using artificial intelligence:

  1. In order to use these tools effectively, companies need large sets of data – at least 100,000 examples. The more recent the data and the more frequent the data, the more effective the predictions will be.
  2. Make sure there are people in the organization who understand the technology and know what will lead to development.
  3. Have realistic expectations. There’s a lot of randomness when it comes to predicting human behavior. If you build a model that gives you perfect predictions, chances are you probably have false predictors.

To better manage and leverage all the data they’re collecting, Piatetsky-Shapiro recommends enabling more interactive access.

“I think the approach of dumping everything together in one Data Lake and hoping you’ll discover something is probably not very useful,” he says.

Instead, have specific goals you want to answer and look at the data with the goals in mind. Look at what gives the best return on investment and what gives value. Many of the Big Data projects that create big data lakes are not able to show ROI.

“Start with business value and proceed from there,” he says.

Finally, invest in good quality data visualization. Humans are still the best at interpreting data, so the visuals should clearly present patterns that allow business stakeholders to make better decisions.

For a more Big Data insights, check out our report, 2018 Big Data Trends: Liberate, Integrate & Trust, to see what every business needs to know in the upcoming year, including 5 key trends to watch for in the next 12 months!


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