IBM Aims Watson at Embodied Cognition By Darryl K. Taft Posted 2016-11-05 Print Q&A: IBM is focusing its Watson cognitive computing technology on the area of embodied cognition, according to Grady Booch, chief scientist of Watson/M. At the close of IBM's recent World of Watson conference in Las Vegas, eWEEK interviewed Grady Booch, Big Blue's chief scientist of Watson/M about the future of IBM's Watson cognitive computing platform and where IBM is taking the technology to benefit enterprise customers, consumers and developers alike. Among other areas, IBM is applying Watson to embodied cognition or putting artificial intelligence (AI) into the physical world. "This is embodied cognition: By placing the cognitive power of Watson in a robot, in an avatar, an object in your hand or even in the walls of an operating room, conference room or spacecraft, we take Watson's ability to understand and reason and draw it closer to the natural ways in which humans live and work," Booch said in a talk. "In so doing, we augment individual human senses and abilities, giving Watson the ability to see a patient's complete medical condition, feel the flow of a supply chain or drive a factory like a maestro before an orchestra."

Please can you give an overview of the current drug discovery paradigm? In what ways do you think it needs to be leaner? With the current drug discovery paradigm, it takes up to 15 years to translate an idea, such as hypothesizing a certain protein is important in a disease and testing this with targeting the protein with a drug, all the way through to proof of concept. The drug has to be filed with the regulatory authorities, having done all the safety and efficacy testing. Estimates vary, but it's currently reckoned to cost over 1 billion dollars per drug.

Patents are given to inventions, which are usually the product of a human mind. But what about inventions that come from not-so-human sources, like artificial intelligence (AI)? Should these patents be awarded to their computer inventors? Well, at least one expert patent attorney thinks so. Ryan Abbott is a professor of law and health sciences at the University of Surrey's School of Law, and he is a patent attorney at the United States Patent and Trademark Office (USPTO). He is also an adjunct assistant professor of medicine at the David Geffen School of Medicine at UCLA--quite the list of credentials, to be sure.

Yesterday I gave a super duper high level 12 minutes presentation about some issues of bias in AI. I should emphasize (if it's not clear) that this is something I am not an expert in; most of what I know is by reading great papers by other people (there is a completely non-academic sample at the end of this post). This blog post is a variant of that presentation. Structure: most of the images below are prompts for talking points, which are generally written below the corresponding image. I think I managed to link all the images to the original source (let me know if I missed one!). Automated Decision Making is Part of Our Lives To me, AI is largely the study of automated decision making, and the investment therein has been growing at a dramatic rate. The last time I taught this class was in 2012. The amount that's changed since there is incredible.

You know the moment when you go in for a yearly physical and the doctor asks, "So, how have you been?" I can't recall what I ate yesterday, let alone remember a pattern of headaches or the overall quality of my sleep. The problem is that I am human. I forget and can be lax when it comes to taking care of myself. Most health issues sneak up on us, and we're not inherently wired to remember patterns.

This was the title of a very popular book published in 2012, featuring several job interview questions (brain teasers) asked by Google's hiring managers to candidates. They apparently dropped all these questions, as they found out that they were not good indicators of career success. I had one phone interview with Google long ago, and was rejected right away. The interviewer was just focused on very technical details, and spent all her time arguing about Lasso regression, and was clearly looking for a specialist, dismissing people with a broad range of skills and non-standard approach to solving tech problems. Big companies do not value things like intuition, innovation, vision or a disruptive mindset (despite claiming the contrary), and for good reasons.

IAB (Interactive Advertising Bureau) and its Data Center of Excellence today announced the winners of the inaugural IAB Data Rockstar Awards, celebrating top industry leaders and practitioners who have demonstrated achievement in data science or technology. The top finalists were selected by the IAB Data Center of Excellence Board of Directors and were evaluated based on demonstrated excellence, creativity or forward-thinking approaches to solving problems in data science, as well as the impact their contributions have made to their company or industry. Chalasani developed a highly efficient, distributed, extreme-scale, single-pass online logistic regression learning system in Scala/Spark, using variants of Stochastic Gradient Descent, capable of handling hundreds of millions of sparse features and billions of training observations. His system incorporates a number of state-of-the-art techniques that do not exist together in any other machine learning system, including adaptive feature-scaling, adaptive gradients, feature-interactions and feature-hashing. Chalasani work is central to MediaMath's vision for every addressable interaction between a marketer and a consumer to be driven by Machine Learning optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes.

It's now a hired gun for thousands of companies in at least 20 industries. David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

Make it easy for your audience to quickly determine what they're about to digest. Use an abstract or introduction to recall your objectives and clearly state them for your readers. What is the problem that you've set out to solve? If you have a desired outcome or any expectations of your audience, say it, as this is the entire reason you're presenting them with your analysis. You then cover everything from your preamble in this section: the question you've been on a mission to answer, your hypothesis, and the methodology you've used.