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Many definitions of artificial intelligence include a comparison to the human mind or brain, whether in form or function. Alan Turing wrote in 1950 about "thinking machines" that could respond to a problem using human-like reasoning. His eponymous Turing test is still a benchmark for natural language processing. Later, Stuart Russell and John Norvig observed that humans are intelligent, but we're not always rational. Russell and Norvig saw two classes of artificial intelligence: systems that think and act like a human being, versus those that think and act rationally.

Welcome to eSimplicity's Tech Talks blog series! Tech Talks is a series launched by eSimplicity's technical writing interns to discuss various topics within the tech industry. From personal experiences within the company to emergent innovative technologies, eSimplicity aims to gauge diverse perspectives and shed light on engaging topics within the tech sector! In a recent interview, eSimplicity's Lead AI Scientist Bin Shao shared with us his thoughts on the prominence of artificial intelligence, as well as its place in the future. Bin has over 20 years of professional experience in the areas of artificial intelligence, machine learning, computer vision and cybersecurity.

Although learning from data is effective and has achieved significant milestones, it has many challenges and limitations. Learning from data starts from observations and then proceeds to broader generalizations. This framework is controversial in science, yet it has achieved remarkable engineering successes. This paper reflects on some epistemological issues and some of the limitations of the knowledge discovered in data. The document discusses the common perception that getting more data is the key to achieving better machine learning models from theoretical and practical perspectives. The paper sheds some light on the shortcomings of using generic mathematical theories to describe the process. It further highlights the need for theories specialized in learning from data. While more data leverages the performance of machine learning models in general, the relation in practice is shown to be logarithmic at its best; After a specific limit, more data stabilize or degrade the machine learning models. Recent work in reinforcement learning showed that the trend is shifting away from data-oriented approaches and relying more on algorithms. The paper concludes that learning from data is hindered by many limitations. Hence an approach that has an intensional orientation is needed.

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow.

Here's a question scientists might ask more often: what if we succeed? That is, how will the world change if we achieve what we're striving for? Tucked away in offices and labs, researchers can develop tunnel vision, the rosiest of outlooks for their creations. The unintended consequences and shoddy misuses become afterthoughts – messes for society to clean up later. Today those messes spread far and wide: global heating, air pollution, plastics in the oceans, nuclear waste and babies with badly rewritten DNA.

In 1960, physicist Eugene Wigner pondered "The Unreasonable Effectiveness of Mathematics in the Natural Sciences," wondering why it was that mathematics provided the "miracle" of accurately modeling the physical world. Wigner remarked, "it is not at all natural that'laws of nature' exist, much less that man is able to discover them." Fifty years later, artificial intelligence researchers Alon Halevy, Peter Norvig, and Fernando Pereira paid homage to Wigner in their 2009 paper "The Unreasonable Effectiveness of Data," an essay describing Google's ability to achieve higher quality search results and ad relevancy not primarily through algorithmic innovation but by amassing and analyzing orders of magnitude more data than anyone had previously. The article both summarized Google's successes to that date and presaged the jumps in "deep learning" in this decade. With sufficient data and computing power, computer-constructed models obtained through machine learning raise the possibility of performing as well if not better than human-crafted models of human behavior.

Do we still need humans to power customer experiences? In its Digital CX Trends 2018 report (fee charged) released today, Forrester researchers found that "while AI, intelligent agents, and chatbots were central to the business conversation in 2017, most companies discovered they lack the design acumen and technical chops to seize the opportunities." This, researchers found, has led to widespread struggles with the basics and few leaders "innovating the way forward." It's fair to say not everyone is excited about artificial intelligence's invasion into customer experience, even those who profit from it. Yet many organizations are still turning to AI to power customer experiences.

Can a new technique known as deep learning revolutionize artificial intelligence, as yesterday's front-page article at the New York Times suggests? There is good reason to be excited about deep learning, a sophisticated "machine learning" algorithm that far exceeds many of its predecessors in its abilities to recognize syllables and images. But there's also good reason to be skeptical. While the Times reports that "advances in an artificial intelligence technology that can recognize patterns offer the possibility of machines that perform human activities like seeing, listening and thinking," deep learning takes us, at best, only a small step toward the creation of truly intelligent machines. Deep learning is important work, with immediate practical applications.

I initially dismissed David Weinberger's report of alien knowledge as tabloid sensationalism. But as the recommendations for his essay accumulated, it gave me pause. Weinberger's post rewards a close reading. My intent here is to present a more incremental, less revolutionary perspective on AI and machine learning. I believe the historical antecedents paint a far more earthly, but perhaps no less sensational, picture. I also believe Weinberger is accurately expressing the concerns (and possibly even hopes) of many within the community, expert and layperson alike.