Rethinking AI with SAS’s Oliver Schabenberger

At last week’s AnalyticsX with SAS Software, I had the pleasure of interviewing Oliver Schabenberger Executive Vice President, Chief Operating Officer and Chief Technology Officer at SAS.  Schabenberger had some very insightful thoughts on on Artificial Intelligence, and how customers approach this topic. I want to thank Mr Schabenberger for his valuable time and it was my privilege to meet him and spend time with him to discuss these topics.

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Herbert Simon in Red By Richard Rappaport [3] [4] – Own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=4138586
Schabenberger commented that the original ambition of artificial intelligence was regarded as easily solvable. He pointed back to Herbert Simon’s assertion that The machine will be capable of doing any work that a man [or a woman!] could do’. Now, we smile at the optimism, but, at the time, the industry entered the first winter of AI since expectations were not met.

The AI industry started again with the work of people like Rosenblatt(1958) in perceptrons, and then adoption of neural nets after the work of Rumelhart and McClelland(1986) which, despite a lot of efforts, brought about more disappointment and wrought another winter for AI.

Nowadays, however, things have changed drastically and AI is having another boom. As Schabenberger noted, at one point, working on AI would get you ridiculed at a cocktail party. Now, you don’t get invited to the party unless you are working in AI. AI is back, and suddenly the market has a slew of AI experts.

So what changed? Why has AI come out of hiberation and back into the sunlight?

Today we have a different source of knowledge throughout the world: Data. Data-driven is an adjective that we hear everywhere, and businesses are rethinking their data, which has led to rethinking Artificial Intelligence.

Today, we are building better algorithms than ever. At the heart of today’s Artificial Intelligence revolution is data science. We create models using a combination of data science,  artificial intelligence and Analytics. The models impact many arenas, from healthcare, insurance, education, medicine, finance and so on.

Artificial intelligence is no longer about rules or backpropagation. It’s all about processing data and imbuing human-like intelligence in a system, with varying degrees. After some time, the system develops its own logic and there are plenty of examples where programs have learned to program themselves through being programmed implicitly.

Fundamentally, the artificial intelligence boom is an analytics boom.

As it stands now, Artificial Intelligence is enabled by massive data volumes, cloud technology and digital transformation, empowered by advances in computing.

AI impacts people who manage organizations. Previously, managers would ask the question tell me how you did that. Now, this question is now replaced by show me the data.

This data driven approach to artificial intelligence has caused very powerful transformation in the industry, but people are still confused by what Artificial Intelligence really means. Schabenberger cuts a neat and disciplined distinction between Narrow and General Artificial Intelligence.

Rethinking Narrow and General Artificial Intelligence

Shabenberger’s insight is that people’s expectations on Artificial Intelligence can be distinguished by the tasks it is expected to do, as well as its autonomy. Does it really think and work in a real environment, or is it dedicated to a narrow task, which it does well?

Artificial General Intelligence

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The goal of AGI is to create a general thinking machine. It is a machine that has a thinking capacity. In Artificial General Intelligence, the definition includes self-sufficiency and broad human-like general intelligence, where the intelligence can be extrapolated from one situation to another and the system can learn over time. Perhaps, if it is to be human-like, it can forget over time, too, and choose areas to focus?

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Schabenberger calls this approach Artificial General Intelligence (AGI). In this interpretation of Artificial Intelligence, the goal is to have the machines behave, think and be like humans. It is to attempt to realise human intelligence in hardware and software. It is distinct from the narrow form of Artificial Intelligence because it focuses on a breadth of activities, rather than a very focused attempt of using human-like intelligence in one domain with a high degree of expertise.

 

 

Narrow Artificial Intelligence

In contrast to this perspective, there’s another approach to artificial intelligence, which Schabenberger calls Artificial Narrow Intelligence. In contrast to AGI, these artificial intelligence systems do not think. These systems are applying and executing algorithms, not thinking. They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way.

For example, let’s take Siri, Alexa and Cortana. This is a narrow form of artificial intelligence which solves very specific tasks. These systems are purpose built systems, aimed at a specific objective. These systems are executing algorithms that are programmed implicitly.

 

What does the distinction mean for industry?

The precise distinction will allow businesses to formalize and understand what they mean when they talk about AI. With AI, everyone has an opinion, and they can mean different things. By focusing on outcomes, this means that business ideas can be generated that can be actionable and achievable by focusing on an understanding of AI that is Narrow, rather than the general, sweeping definition of Artificial Intelligence.

At present, data specialists and developers are the ones developing the system in silos, with no ethics discussion, or values imbued. However, these decisions cannot be made in a vacuum, with no form of ethics discussion.  We are all impacted by data, and we will all be impacted by Artificial Intelligence. We cannot assume to have the ethical superiority of the uninvolved, pointing fingers when things go wrong. If we are putting God in the machine, let’s make it one that we can and want, to live with.

And what do all these Artificial General Systems have in common? They do not exist, and we have absolutely no clue have to build them, according to Schabenberger. We should have a conversation about what if we could get there. How does that impact ethics? How does it impact jobs? Our planet?

 

To summarise, Schabenberger’s distinction is very clear for businesses to understand, and to direct their discussions on artificial intelligence. Bringing this clarity is essential when people use the same term to mean different things, and the neat distinction will facilitate successful discussions and outcomes for business.

References

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. http://dx.doi.org/10.1037/h0042519

Rumelhart, D.E; McClelland, James (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge: MIT Press. ISBN 978-0-262-63110-5.

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