ARTIFICIAL intelligence has come a long way. In recent years, smart machines inspired by the human brain have demonstrated superhuman abilities in games like chess and Go, proved uncannily adept at mimicking some of our language skills and mastered protein folding, a task too fiendishly difficult even for us. But with various other aspects of what we might reasonably call human intelligence – reasoning, understanding causality, applying knowledge flexibly, to name a few – AIs still struggle. They are also woefully inefficient learners, requiring reams of data where humans need only a few examples. Some researchers think all we need to bridge the chasm is ever larger AIs, while others want to turn back to nature's blueprint.

Insurance is moving toward a world in which carriers will not be allowed to make decisions that affect customers based on black-box AI. Artificial intelligence (AI) holds a lot of promise for the insurance industry, particularly for reducing premium leakage, accelerating claims and making underwriting more accurate. AI can identify patterns and indicators of risk that would otherwise go unnoticed by human eyes. Unfortunately, AI has often been a black box: Data goes in, results come out and no one -- not even the creators of the AI -- has any idea how the AI came to its conclusions. That's because pure machine learning (ML) analyzes the data in an iterative fashion to develop a model, and that process is simply not available or understandable.

Dr. Francois Laborie is President of Cognite, supporting the full-scale digital transformation of asset-heavy industries in North America. Artificial intelligence has changed our lives as consumers. Why hasn't it changed our industries? AI in industry requires more than just big data to work, and the solution lies in the world of physics. If a predictive algorithm fails in the consumer industry, it's not the end of the world. Maybe an ad doesn't get clicked or a TV show doesn't get watched.

Deep reinforcement learning is having a superstar moment. Trouncing physicians at medical diagnoses and crushing humanity's best gamers at Go and Atari. While far from achieving the flexible, quick thinking that comes naturally to humans, this powerful machine learning idea seems unstoppable as a harbinger of better thinking machines. Except there's a massive roadblock: they take forever to run. Because the concept behind these algorithms is based on trial and error, a reinforcement learning AI "agent" only learns after being rewarded for its correct decisions.

In collaboration with BigML partner, INFORM Gmbh, we're pleased to bring the BigML community a new educational webinar: Machine Learning Fights Financial Crime. This FREE virtual event will take place on October 28, 2020, at 8:00 AM PDT / 9:00 AM PDT and it's the ideal learning opportunity for Financial institutions, banking sector professionals, credit professionals, risk advisers, crime fighters, fraud professionals, and anyone interested in finding out about the latest financial crime-fighting and risk analysis strategies and trends. Financial institutions must innovate to stop the onslaught of fraudulent transactions. The utilization of Machine Learning as a tool for fraud detection is trending. Combining Machine Learning with existing intelligent and dynamic rule sets produces a sustainable strategy to address this challenge.

Imagine you enter your neighbor's kitchen room for the first time. As soon as you step your foot inside, you can figure out where the essential food items are present if the refrigerator is having two doors or a single door, what do their plates look like, etc. We humans barely have any trouble identifying things. This is because we have evolved ourselves through reasoning, learning, and cognitive thinking. Even toddlers can do the same when left in a playroom full of colorful balls.

Artificial intelligence (AI) – i.e., algorithms and machine learning processes – are drivers of many of today's major innovations in software and hardware, from social media chatbots to highly automated customer relationship management applications. One of the key differences to look at in the years ahead will be traditional AI versus hybrid AI, which we can understand by comparing two AI-driven technologies: IBM Watson and the Tesla Model S. These two categories were put forward at the start of a September 2017 SC 20/20 Webcast discussion of the future of AI, to contrast traditional and hybrid AI. Hybrid AI goes further than the former category by integrating human expertise. Could it be the secret to making bots, machines and applications more intelligent than the ones that rely on the limited AI of today? IBM Watson, which was identified in the webinar as an example of conventional AI, has become an important exploratory tool in fields such as health care.

In brief: Ray Kurzweil, chief engineer for Google and famous futurist, spoke in a discussion held at the Council on Foreign Relations on Friday. He emphasized how AI would enhance humankind, despite the possibility of "difficult episodes." Amidst all the talk about how artificial intelligence (AI) is threatening society with great harm--beginning with taking over human-held jobs and then, eventually, becoming more intelligent and taking over the entire world--some experts believe that AI shouldn't be feared. Foremost among these experts is Google's director of engineering and notable "future teller" Ray Kurzweil, who has said time and again that the technological singularity won't necessarily go down as expected. Kurzweil discussed the future of AI at the Council on Foreign Relations (CFR) in Washington, D.C. on Friday.

Amidst all the talk about how artificial intelligence (AI) is threatening society with great harm--beginning with taking over human-held jobs and then, eventually, becoming more intelligent and taking over the entire world--some experts believe that AI shouldn't be feared. Foremost among these experts is Google's director of engineering and notable "future teller" Ray Kurzweil, who has said time and again that the technological singularity won't necessarily go down as expected. Kurzweil discussed the future of AI at the Council on Foreign Relations (CFR) in Washington, D.C. on Friday. And, while he agreed with Tesla CEO and founder Elon Musk who warned of the potential "existential risks" a super-intelligent AI could bring, Kurzweil said that humanity would be able to overcome these "difficult episodes," if they ever actually happen. He continued by noting that scientific and technological advancements always come with inherent risks and that AI should not be considered any more (or less) of a threat.