Patrick Schwerdtfeger is a regular speaker for Bloomberg TV and a leading authority on global business trends including big data, artificial intelligence and machine learning. After a number of years of limited progress, artificial intelligence has seen astonishing developments in recent years and machine learning is part of that process. Patrick covered “Monetizing Big Data” and “Will Artificial Intelligence Destroy Us?” at the 2016 NASSCOM conference in Hyderabad, India. He described the difference between narrow or weak AI and how it differs from general purpose AI, with AlphaGo and the recent recreation of the Bose-Einstein Condensate (BEC) by an AI as examples. These platforms are “general purpose” because they do not start with a set of rules. Instead, they are given access to data and are given an objective to achieve, that’s all. They can then process the data as they wish in an effort to achieve the objective. In all of these cases, the developers of the platform were stunned at the path chosen by the AI. It turns out that machines “learn” very differently than humans, and Patrick can present that reality at your next event.
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The State of Artificial Intelligence (AI)
The recent years have seen tremendous progress in the area of deep learning and neural networks. Various AIs have surpassed expectations and demonstrated the potential of machine learning technologies. Examples include AlphaGo (via Deep Mind, acquired by Google in 2014) and an AI designed to recreate the Bose-Einstein Condensate (BEC) in less than an hour, developed by Australian researchers in 2016. And of course, there are countless other applications (by IBM Watson and Vicarious among others) that have not made the headlines but have dramatically improved the performance of everyday tasks (like spam filtering and image recognition). Some have referred to these recent developments as an AI Spring, following an AI Winter which saw little progress over recent years.
The strength of these AIs grows with the additional layers of analysis, adding context to simpler versions. Each additional layer dramatically improves the results, allowing the overall capabilities to compound on top of each other. The inevitable result will be an increasingly complex and integrated digital infrastructure with modules interpreting different aspects but then contributing to a larger analysis. The requirement to host such an infrastructure is enormous computing power. In 2016, the two fastest supercomputers in the world were both located in China. They are investing heavily in the enabling technologies behind artificial intelligence. Google is also investing heavily. Naturally, we can expect significant innovations coming from those two places.
The possible dangers of artificial intelligence emerge when there is one single monolithic AI that is far more advanced than any other platform. If such an AI were allowed to develop in isolation (without any material competition), later AIs would never be able to catch up. In fact, their relative capabilities would only grow further apart. Once a truly “general purpose” machine learning infrastructure is in place, it could easily be replicated, allowing the core capabilities to grow exponentially. Particular areas of future development include virtual and augmented reality interfaces.
Patrick’s Machine Learning Keynote Speech
Patrick regularly customizes his keynote program to best complement the event objectives of his clients. As such, his standard artificial intelligence and machine learning keynote is only a basic framework. As a matter or process, he builds his keynotes in modules, where each module is between 5 and 8 minutes long. The opening module discusses the concept of leverage and how technology offers leverage to those who capitalize on emerging capabilities. After that, Patrick provides a strategic overview of the topic, covering recent developments in neural networks and deep learning as well as implications for business executives and investors.
The keynote then moves into a series of case histories and success stories. These use cases are incredibly important which explains why the Harvard Business School uses the case history method in their MBA program. Patrick has accumulated dozens of case histories and examples that are both entertaining to hear and insightful to analyze. Depending on your event objectives, he can select the use cases that are most appropriate for the occasion. Finally, after demonstrating the breadth of the field, Patrick usually closes with his signature ‘think bigger’ message, encouraging attendees to expand their thinking and consider possibilities that might seem implausible at first.