Update: readers of the post have also pointed out this critique by Ernest Davis and this response to Davis by Rob Bensinger. Update 2: Both Rob Bensinger and Michael Tetelman rightly pointed out that my intelligence definition was sloppily defined. I've added a clarification that the defintion is'for a given task'. This post is a discussion of Nick Bostrom's book "Superintelligence". The book has had an effect on the thinking of many of the world's thought leaders. In that light, and given this series of blog posts is about the "Future of AI", it seemed important to read the book and discuss his ideas. In an ideal world, this post would certainly have contained more summaries of the books arguments and perhaps a later update will improve on that aspect. For the moment the review focuses on counter-arguments and perceived omissions (the post already got too long with just covering those). Bostrom considers various routes we have to forming intelligent machines and what the possible outcomes might be from developing such technologies. He is a professor of philosophy but has an impressive array of background degrees in areas such as mathematics, logic, philosophy and computational neuroscience. So let's start at the beginning and put the book in context by trying to understand what is meant by the term "superintelligence" In common with many contributions to the debate on artificial intelligence, Bostrom never defines what he means by intelligence. Obviously, this can be problematic. On the other hand, superintelligence is defined as outperforming humans in every intelligent capability that they express.

The list of featured speakers includes innovators, business leaders, and entrepreneurs from the Allen Institute for Artificial Intelligence, Amazon Robotics, Baidu, Facebook, GE Software Research, Google, IBM, Pinterest, Tesla, and more. About MIT Technology Review Founded at the Massachusetts Institute of Technology in 1899, MIT Technology Review is a digitally oriented independent media company whose analysis, features, reviews, interviews, and live events explain the commercial, social, and political impact of new technologies. MIT Technology Review readers are curious technology enthusiasts--a global audience of business and thought leaders, innovators and early adopters, entrepreneurs and investors. Every day, we provide an authoritative filter for the flood of information about technology. We are the first to report on a broad range of new technologies, informing our audiences about how important breakthroughs will impact their careers and their lives.

Facebook CEO, Mark Zuckerberg says that within five to 10 years, artificial intelligence could advance to the point where computers can see, hear and understand language better than people. Zuckerberg stated this yesterday during the company's earnings call for the first quarter of 2016. Zuckerberg has already been focussing on AI through his company which already has research groups dedicated to advancing the company's capabilities in artificial intelligence, machine learning, computer vision and natural language processing and speech. Earlier this month, for example, it introduced an iOS feature called "automatic alternative text" that uses object recognition technology to provide spoken descriptions of Facebook photos to people who are visually impaired. Facebook has also unveiled new bot and chatbot technology as part of its Messenger Platform.

DATAVERSITY Education, LLC announced the agenda and opened registration for the company's newest online conference, Smart Data Online (SDO). The event will be held online at smartdataweek.com on July 13th, 2016 from 8:00 am to 2:20 pm Pacific Time. Registration is free and attendees will receive access to the on demand recordings, slides, and materials following the event. SDO is the newest event to be added to DATAVERSITY's educational programs, and is designed to provide guidance on executing and implementing a successful data strategy using new technologies in the fields of machine learning, cognitive computing, and artificial intelligence. Throughout the day on July 13th there will be six, 40-minute presentations, each followed by a 10 minute "Q & A" discussion with the presenter(s).

Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties, according to a recent paper published in Nature Communications. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," said Turab Lookman, a physicist and materials scientist in the Physics of Condensed Matter and Complex Systems group at Los Alamos National Laboratory. Lookman is the principal investigator of the research project. "Finding new materials has traditionally been guided by intuition and trial and error," said Lookman."But with increasing chemical complexity, the combination possibilities become too large for trial-and-error approaches to be practical." To address this, Lookman, along with his colleagues at Los Alamos and the State Key Laboratory for Mechanical Behavior of Materials in China, employed machine learning to speed up the process. They developed a framework that uses uncertainties to iteratively guide the next experiments to be performed in search of a shape-memory alloy with very low thermal hysteresis (or dissipation).

Adjusted R² measures the goodness of fit of a regression model. Higher the R², better is the model.

This story was delivered to BI Intelligence "E-Commerce Briefing" subscribers. To learn more and subscribe, please click here. The online marketplace has acquired AI company Expertmaker to enhance the way products display on eBay's pages, according to Internet Retailer. This purchase is part of eBay's structured data push for sellers, which utilizes eBay's standard way of categorizing and displaying products for sale on its marketplace. The use of structured data is not mandatory, but eBay has been strongly encouraging sellers to adopt it. As of the first quarter of 2016, 60% of listed items on eBay used the structured data rules, according to the company's earnings report.

Celaton's inSTREAM software streamlines the handling of unstructured unpredictable (and structured) content such as correspondence, claims, complaints and invoices that organizations receive by email, social media, fax and paper. This minimizes the need for human intervention and ensures that only accurate, relevant and structured data enters business systems. Unique to inSTREAM is its ability to learn through the natural consequence of processing information and collaborating with people. Capgemini's extensive knowledge and experience in business process services will also enable Celaton to accelerate and improve inSTREAM's capabilities. The cooperation will enable Capgemini to increase efficiency, shorten turnaround times and enhance quality in areas where incoming documents and queries need to be processed, improving overall customer satisfaction.

Traditionally, materials scientists have used a combination of trial-and-error and intuition to discover and perfect new materials with advantageous properties. Increasing chemical complexities make this strategy prohibitively time-consuming. To speed up the process, researchers at Los Alamos National Laboratory attempted to marry machine learning with targeted experiments. The team's "informatics-based adaptive design strategy" successfully accelerated the materials discovery process. "What we've done is show that, starting with a relatively small data set of well-controlled experiments, it is possible to iteratively guide subsequent experiments toward finding the material with the desired target," Turab Lookman, a physicist and materials scientist at Los Alamos, said in a news release.

Troparé has developed the ability for its clients to discover unbiased patterns, insights, and trends in their data; perform thorough analysis; and use this information to make accurate, micro-targeted predictions, all without having to write a single line of code. As a result, marketing and sales departments can attract, retain, and grow their most profitable customers and maximize campaign spending, without having to rely on IT. "Data has become the new life-line of modern day marketing. However, unless marketing departments have the tools to unify data and perform correct analysis on a large scale, simply having enormous amounts of data is useless without tremendous reliance on IT," said Greg Carpenter, CEO of Troparé. The second platform addition, (fuzzy) data matching & appending, is developed as an extension to the industry's widely-acknowledged struggle of data integration. The solution allows customers to easily combine, match, and append their own data with second and/or third party data derived from disparate sources.