At Rise of AI 2018 in Berlin, Stefan Wess, CEO of Empolis, argues that today's AI hype is only the latest wave of a much older story. He opens with two predictions of human-level machine intelligence, made by Herbert A. Simon in 1965 and Marvin Minsky in 1970, and shows on Google Trends that current interest is still below the 2004 peak. Drawing on his own path from case-based reasoning research to building Empolis, a company spun off from the German Research Center for Artificial Intelligence in 1991 that now serves about 700,000 professional users and 40 million customers, he insists there is no general artificial intelligence today and treats the term mainly as a marketing label for machine learning. He walks through where classic, knowledge-driven AI and newer, data-driven sub-symbolic AI each work well, and names data availability, data quality and bias, and explainability as the three real obstacles to deploying AI in enterprises. He closes by calling for hybrid, "grey box" AI and redefining AI as augmented intelligence that helps humans decide better.
Key takeaways
- Predictions of human-level machine intelligence date back to Herbert A. Simon in 1965 and Marvin Minsky in 1970.
- Current AI interest on Google Trends is still below the hype peak reached in 2004, according to Wess.
- Wess treats artificial intelligence mainly as a marketing term for machine learning, not general intelligence.
- He identifies data availability, data bias and explainability as the three main obstacles to enterprise AI adoption.
- He advocates hybrid, "grey box" AI that combines knowledge-driven and data-driven approaches, and calls AI augmented intelligence.
Detailed notes
AI hype in historical perspective
Wess opens by noting that artificial intelligence is part of pop culture and an old dream of humanity, which is why it always makes headlines and triggers strong emotions. He cites two predictions: Herbert A. Simon's 1965 claim that machines would be capable, within twenty years, of doing any work a man can do, and Marvin Minsky's 1970 prediction that within three to eight years a machine would have the general intelligence of an average human being. Looking at Google Trends, he says the current hype level is surprisingly still only about 75 percent of the peak reached in 2004.
From case-based reasoning to Empolis
Wess describes starting to program computer games in the 1980s and wanting, without success, to build a program that could beat him at the board game Risk. He later did a PhD in computer science and worked academically in case-based reasoning, which he explains through the example of a car mechanic who solves a new problem by recalling and adapting a similar case seen before. In 1991 he co-built a company as a spinoff of the German Research Center for Artificial Intelligence based on that idea; today it has about 220 employees and is used by roughly 700,000 professional users serving about 40 million customers worldwide, including, he says, most people in the audience without their knowledge.
"AI is the new smart": from decision support to decision automation
Wess traces a progression in enterprise technology from business intelligence to big data, predictive analytics, machine learning and now artificial intelligence. He states plainly that there is no revolutionary, general artificial intelligence today, calls AI a marketing word that he personally translates to machine learning, and compares it to how everything became "smart" in the last ten years, from smartphones to smart TVs and smart data. He then describes how enterprises currently use data through a decision-support system that produces recommendations a human reviews before acting, and argues AI is moving this toward decision automation, where routine cases run automatically and only exceptions go to humans. Plotting business problems by frequency and complexity, he shows a large middle zone of problems that are neither simple enough to automate today nor complex enough to require full human judgment, and argues AI's opportunity is to shrink that zone.
Classic versus sub-symbolic AI applications
Wess contrasts classic, knowledge-driven (symbolic) AI, which he says has proven business value and reliable, explainable results but is expensive to build and maintain and works best for high-volume, well-defined tasks, with newer, data-driven sub-symbolic approaches (deep learning), which reach superhuman performance on sensory tasks such as voice recognition but are hard to reason about, hard to integrate with existing domain knowledge, and data-hungry.
Three obstacles to enterprise AI
He names three practical barriers to deploying AI in companies. First, data availability: he describes a paradox where consumer-facing systems have abundant data about people but little understanding of them, while enterprises have deep knowledge of their machines but little usable, labeled data. Second, bias: ordinary business data reflects routine cases, while real value lies in the exceptions, so models trained on regular data tend to repeat past behavior rather than create value. Third, and most important in his view, explainability: he asks whether an organization is comfortable with black-box decisions, contrasting an ABS braking system, where no explanation is needed, with a court setting, where liability, accountability and comprehensibility require a decision to be explained even when it was wrong.
Hybrid AI and augmented intelligence
Wess argues the way forward is hybrid AI that combines knowledge-driven representation with data-driven learning, which Gartner calls "grey box" AI between black-box and white-box systems; he places case-based reasoning in this hybrid category. He shows a modified Gartner hype cycle in which he believes the plateau of productivity is closer than commonly expected, describes today's AI as being simultaneously science-fiction general intelligence and a practical engineering tool, and says he dislikes the term artificial intelligence because it invites too much emotion. He concludes that for him AI means augmented intelligence: helping humans make better decisions.
Quotes
Artificial intelligence is a marketing word, and I translated it always to machine learning, because that's where we are at that point in time.
Stefan Wess
Are you happy with black box decisions?
Stefan Wess
AI for me is augmented intelligence, if we talk about decisions helping humans really to make better decisions.
Stefan Wess
Questions answered in this talk
What are the main obstacles to deploying AI in the enterprise?
According to Stefan Wess at Rise of AI 2018, the three main obstacles are data availability, data quality and bias, and explainability, with explainability the most important because black-box decisions are hard to defend in contexts like court proceedings or workers council reviews.
What is case-based reasoning?
According to Stefan Wess at Rise of AI 2018, case-based reasoning is an AI approach in which a system solves a new problem by recalling a similar past case and adapting its solution, much like a car mechanic diagnosing a new problem from experience with similar past repairs.
What is hybrid or 'grey box' AI?
According to Stefan Wess at Rise of AI 2018, hybrid AI combines knowledge-driven, symbolic representation with data-driven machine learning, a combination Gartner calls 'grey box' AI, positioned between fully explainable white-box systems and opaque black-box systems.