Dr. David Ferrucci is one of the few people who have created a benchmark in the history of AI because when IBM Watson won Jeopardy we reached a milestone many thought impossible. I was very privileged to have Ferrucci on my podcast in early 2012 when we spent an hour on Watson's intricacies and importance. Well, it's been almost 8 years since our original conversation and it was time to catch up with David to talk about the things that have happened in the world of AI, the things that didn't happen but were supposed to, and our present and future in relation to Artificial Intelligence. All in all, I was super excited to have Ferrucci back on my podcast and hope you enjoy our conversation as much as I did. During this 90 min interview with David Ferffucci, we cover a variety of interesting topics such as: his perspective on IBM Watson; AI, hype and human cognition; benchmarks on the singularity timeline; his move away from IBM to the biggest hedge fund in the world; Elemental Cognition and its goals, mission and architecture; Noam Chomsky and Marvin Minsky's skepticism of Watson; deductive, inductive and abductive learning; leading and managing from the architecture down; Black Box vs Open Box AI; CLARA – Collaborative Learning and Reading Agent and the best and worst applications thereof; the importance of meaning and whether AI can be the source of it; whether AI is the greatest danger humanity is facing today; why technology is a magnifying mirror; why the world is transformed by asking questions.

Many financial institutions are rapidly developing and adopting AI models. They're using the models to achieve new competitive advantages such as being able to make faster and more successful underwriting decisions. However, AI models introduce new risks. In a previous post, I describe why AI models increase risk exposure compared to the more traditional, rule-based models that have been in use for decades. In short, if AI models have been trained on biased data, lack explainability, or perform inadequately, they can expose organizations to as much as seven-figure losses or fines.

IBM recently announced several new Watson technologies designed to help organizations identify, understand, and analyze some of the most challenging aspects of the English language with greater clarity and insights. These new features are considered the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater. There is a new advanced sentiment analysis feature defined to identify and analyze idioms and colloquialisms for the first time. So it can recognize phrases such as "hardly helpful" or "hot under the collar." Phrases like those have been challenging for artificial intelligence systems since they are difficult for algorithms to spot.

In previous posts we explored what analysts want to discover about their virtual assistant and some building blocks for building analytics. In this post I will demonstrate some common recipes tailored to Watson Assistant logs. First we extract raw log events and store on the file system. This requires the apikey and URL for your skill. For a single-skill assistant you will also need the workspace ID (extractable from the "Legacy v1 Workspace URL"), for a multi-skill assistant there are other IDs you can use to filter on (described in the Watson Assistant list log events API).

Artificial intelligence researchers at IBM have introduced a major upgrade to the famed Watson computer, allowing it to understand idioms and colloquialisms for the first time. IBM says the update makes it the first commercial AI system capable of identifying, understanding and analysing some of the most challenging aspects of the English language. Phrases like "hardly helpful" and "hot under the collar" are tricky for algorithms to spot, meaning AI is unable to debate complex topics or have nuanced conversations with humans. "Language is a tool for expressing thought and opinion, as much as it is a tool for information," said Rob Thomas, a general manager at IBM Data and AI. "This is why we believe that advancing our ability to capture, analyse, and understand more from language with NLP will help transform how businesses utilise their intellectual capital that is codified in data." Russia has launched a humanoid robot into space on a rocket bound for the International Space Station (ISS).

IBM is announcing several new IBM Watson technologies designed to help organizations begin identifying, understanding and analyzing some of the most challenging aspects of the English language with greater clarity, for greater insights. The new technologies represent the first commercialization of key Natural Language Processing (NLP) capabilities to come from IBM Research's Project Debater, the only AI system capable of debating humans on complex topics. For example, a new advanced sentiment analysis feature is defined to identify and analyze idioms and colloquialisms for the first time. Phrases, like'hardly helpful,' or'hot under the collar,' have been challenging for AI systems because they are difficult for algorithms to spot. With advanced sentiment analysis, businesses can begin analyzing such language data with Watson APIs for a more holistic understanding of their operation.

The supply chain industry is in a state of transition and transformation. New technology such as AI, Big Data and machine learning is making life easier for industry executives as an ever-increasing number of companies begin to digitise their offerings. In order to stay ahead in a dynamic and continuously evolving industry, businesses must trial technology to increase efficiency. The technology giants, IBM Watson, understands the challenge that supply chains face. The company has announced Watson Supply Chain Insights, an AI-based solution that enables supply chain professionals to get through a data overload for enhanced visibility throughout the entire supply chain.

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We want our AI models to be as accurate as they can be. That's one of the selling points of AI -- that we can encode the best version of our past knowledge and have an automated model infer and apply our judgement. How can we tell when the model is accurate enough to trust? More importantly how can we tell if our efforts to improve accuracy are actually making the model worse? This situation can happen through a training problem called overfitting.

At the IBM Watson Experience Center, digital and physical worlds meet in a futuristic-looking lounge overlooking San Francisco's Financial District. "Regardless of the industry you're in, there's likely an application for AI … even as a chef," said IBM's data and AI engagement lead Euniq Nebo, as he stood before a 32-foot digital screen displaying human-size images of various professionals. A chef on the screen stepped forward and came to life. Nebo spoke of questions facing a restaurant chef, such as which cutting-edge tools to invest in, or whether to incorporate local produce into a cuisine. But IBM is betting its AI can "extract the insights" from data to help its clients stay ahead of the curve, Nebo said.