Collaborating Authors


Humans and AI: Future Best Friends


It is not that hard to believe, how just two decades ago Deep Blue a computer beat a chess grandmaster Gary Kasparov. AI is enhancing itself and is becoming better at numerous "human" jobs -- diagnosing disease, translating languages, providing customer service -- and it's improving fast. This is raising reasonable fears amongst workers and upcoming students. According to The Guardian, 76% of Americans fear that their job will be lost to AI. While it's speculated AI will take over 1.8 million human jobs by the year 2020, however, the technology is also expected to create a 2.3 million new kinds of jobs, many of which will involve the collaboration between humans and AI.

How AI Learns to Play Games


Over the past few years, we've seen computer programs winning games which we believe humans were unbeatable. This belief held considering this games had so many possible moves for a given position that would be impossible to computer programs calculate all of then and choose the best ones. However, in 1997 the world witnessed what otherwise was considered impossible: the IBM Deep Blue supercomputer won a six game chess match against Gary Kasparov, the world champion of that time, by 3.5 – 2.5. Such victory would only be achieved again when DeepMind's AlphaGo won a five game Go match against Lee Sedol, 18 times world champion, by a 4-1 score. The IBM Deep Blue team relied mostly in brute force and computation power as their strategy to win the matches.

The Moravec Paradox -


"Focusing on your strengths is required for peak performance, but improving your weaknesses has the potential for the greatest gains. This is true for athletes, executives and entire companies." As parents, we get to see our kids growing, trying, falling and learning in the process. First steps, first words, first drawings leave us amazed. As our children become adults, they continue to learn, choose a career and become athletes, surgeons, plane pilots, journalists, teachers… and we're proud.

Weighing the Trade-Offs of Explainable AI


In 1997, IBM supercomputer Deep Blue made a move against chess champion Garry Kasparov that left him stunned. The computer's choice to sacrifice one of its pieces seemed so inexplicable to Kasparov that he assumed it was a sign of the machine's superior intelligence. Shaken, he went on to resign his series against the computer, even though he had the upper hand. Fifteen years later, however, one of Deep Blue's designers revealed that fateful move wasn't the sign of advanced machine intelligence -- it was the result of a bug. Today, no human can beat a computer at chess, but the story still underscores just how easy it is to blindly trust AI when you don't know what's going on.

Leveraging Rationales to Improve Human Task Performance Artificial Intelligence

Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning methods. In this work, we consider a question not previously explored within the XAI and ML communities: Given a computational system whose performance exceeds that of its human user, can explainable AI capabilities be leveraged to improve the performance of the human? We study this question in the context of the game of Chess, for which computational game engines that surpass the performance of the average player are widely available. We introduce the Rationale-Generating Algorithm, an automated technique for generating rationales for utility-based computational methods, which we evaluate with a multi-day user study against two baselines. The results show that our approach produces rationales that lead to statistically significant improvement in human task performance, demonstrating that rationales automatically generated from an AI's internal task model can be used not only to explain what the system is doing, but also to instruct the user and ultimately improve their task performance.

Why We Must Unshackle AI From the Boundaries of Human Knowledge


Artificial intelligence (AI) has made astonishing progress in the last decade. AI can now drive cars, diagnose diseases from medical images, recommend movies, even whom you should date, make investment decisions, and create art that people have sold at auction. A lot of research today, however, focuses on teaching AI to do things the way we do them. For example, computer vision and natural language processing – two of the hottest research areas in the field – deal with building AI models that can see like humans and use language like humans. But instead of teaching computers to imitate human thought, the time has now come to let them evolve on their own, so instead of becoming like us, they have a chance to become better than us.

3 Steps to Implement Artificial Intelligence.


Artificial Intelligence (AI) could increase global GDP by 14 percent, or an astounding $15.7 trillion by 2030. This is due, in large part, to productivity gains from AI automation and workforce augmentation. AI will change the world, but it takes time to implement and train it. It's important for your business to understand how, when, and where to implement Artificial Intelligence, and it's often best to start small. The world at large is still learning how best AI can be used to benefit society.

Artificial Intelligence, Deep Learning, and How it Applies to Entertainment


In 1955, computer scientist John McCarthy coined the term artificial intelligence. Just five years before, English Mathematician Alan Turing had posed the question, "Can Machines Think?" Turing proposed a test: could a computer be built which is indistinguishable from a human? This test, often referred to as the Turing Test, has sparked the imagination of AI researchers ever since and been a key idea in the field. In the late 1990s artificial intelligence made its mark again, when IBM's Deep Blue beat the world chess champion Gary Kasparov. Since then, advances in computing power and data accumulation have led to a proliferation of new technologies driven by artificial intelligence.

Is AI About to Outpace Human Intelligence?


Considering the public awareness of artificial intelligence and the speed new breathtaking progress is taking place, it seems to be just a matter of time when AI will surpass the human intelligence level. And yes, the headlines AI is writing are stunning! While the victory of the IBM chess computer Deep Blue over the former chess world champion Garry Kasparov in 1996 (and again in 1997) was something like the eighth wonder of the world, the victory of Googles AlphaGo over Lee Sedol in 2016 in the strategy board game "Go" was seen as predictable for many. AI development has undergone a vast acceleration during the last decade. Assuming a stable growth rate of AI development: Is AI supposed to surpass the human intelligence level over the next few years?

How a New AI Breakthrough Could Undermine the Financial Industry's Entire Foundation


While robots have taken many of the jobs of the manually skilled, Pluribus and its future generations are coming for the jobs at the other end of the spectrum--the brilliant, the cunning, the creative. Have we reached an artificial intelligence (AI) milestone overload? Are we so jaded about momentous breakthroughs in AI capabilities that we no longer acknowledge them with the appropriate awe AI demands? One would think so after the July performance of Carnegie Mellon and Facebook's Pluribus went virtually unnoticed. You should, because this valedictorian of machine learning is a serious threat to your livelihood.