BPM 31510 is just another cancer drug in human development trials, except for one thing. Scientists didn't toil away in labs to come up with it; artificial intelligence did. The cancer drug development process is costly and time-consuming. On average, it takes 24 to 48 months and upwards of 100 million to find a suitable candidate. Add that to the fact that 95% of all potential drugs fail in clinical trials, and the inefficiencies of the whole drug-discovery machine really become apparent.

ConnectX-5 introduces smart offloading engines that enable the highest application performance while maximizing data center return on investment. Furthermore, ConnectX-5 is the first PCI Express 3.0 and 4.0 compatible adapter, enabling greater flexibility and future-proofing for the data center. With the exponential growth of data and the increase in businesses that takes advantage of real-time data processing for high performance computing (HPC), data analytics, machine learning, national security and'Internet of Things' applications, the market needs not only the fastest interconnect available, but also interconnect intelligence that can perform data algorithms as the data moves throughout the data center. The new intelligent ConnectX-5 100G adapter enables the most advanced real-time in-network computing engines to unleash business opportunities and new technological developments. "The new ConnectX-5 100G adapter further enables high performance, data analytics, deep learning, storage, Web 2.0 and more applications to perform data-related algorithms on the network to achieve the highest system performance and utilization," said Gilad Shainer, vice president, marketing at Mellanox Technologies.

NEW YORK, NY--(Marketwired - Jun 15, 2016) - Datorama, a global leader in marketing analytics innovation, today announced the company has added an office in Europe. The latest addition to Datorama's global footprint is located in Hamburg, Germany and marks a critical milestone as the company expands into the German, Austrian and Swiss (DACH) region. Datorama's Hamburg office further strengthens a robust EMEA presence, which includes: Amsterdam, Barcelona, London and Paris. Designed for marketers, Datorama's Marketing Integration Engine helps leading enterprises, agencies and publishers centralize all of their marketing data across silos for cross-channel visualization, analysis and data-driven insight generation. By analyzing inputs from unlimited data sources, including online and offline marketing channels, and first- and third-party applications across CRM, billing, call centers, and more, the company's patent-pending artificial intelligence (AI)-based software delivers a single source of truth at the data layer to drive tactical and strategic marketing performance optimization.

We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences.

Ronny Kohavi is a Microsoft Distinguished Engineer and the General Manager for Microsoft's Analysis and Experimentation team at Microsoft's Applications and Services Group. He was previously Partner Architect at Bing, part of the Online Services Division at Microsoft. He joined Microsoft in 2005 and founded the Experimentation Platform team in 2006. His papers have over 27,000 citations and three of his papers are in the top 1,000 most-cited papers in Computer Science. In short, Ronny is the big kahuna of experimentation and I am very happy he took time to have this discussion with me.

There's been a lot written the past couple days about Microsoft acquiring LinkedIn, the business focused social network. The business rationale is focused on Microsoft combining its Office products together with the information available on LinkedIn to create new services for its users. For example, as you head into a meeting that's on your Outlook calendar, Microsoft's Cortana (its digital assistant) can provide you background on each of the attendees that it has gleaned from their LinkedIn profiles. Another example used is the potential opportunity to embed Office tools into the LinkedIn platform to make collaboration on Word documents, spreadsheets, Powerpoint presentations, and so on, easier. On another front, Facebook has recently announced partnerships with POS solution providers to gain access to transaction data which it is combining with location services to understand what stores a shopper goes to and creating a link between Facebook advertising and products purchased.

Hawking, Berners-Lee and Zuckerberg have all given their warnings. The fear is of a future where artificial intelligence (AI) drives the decisions in major corporations, and machines become so competent and clever that they take up our jobs. Already we're seeing how AI is impacting several businesses including IBM, Google, Amazon and Apple - through image recognition, speech understanding and the mining of large data sets. The sophistication of AI is even being leveraged to make decisions on what messages to show consumers, and creating bespoke messaging depending on what it thinks will be interesting, relevant and useful. Clearly the next frontier of brand and consumer interaction is upon us.

Every job has its more boring components. In libraries, one of those jobs is scanning the shelves, looking for missing and misplaced books, and taking stock of what's available. For human workers, this is time-consuming, repetitive and boring, all of which can contribute to wandering attention. The autonomous robotic shelf-scanning platform, or AuRoSS, is in development by the Institute for Infocomm Research of Singapore's Agency for Science, Technology and Research. It uses laser mapping to navigate a library, and RFID tags placed on books to scan the collection.

My own research into ways dangerous AI systems might emerge suggests that the science fiction trope of AIs and robots becoming self-aware and rebelling against humanity is perhaps the least likely type of this problem. Much more likely causes are deliberate actions of not-so-ethical people (on purpose), side effects of poor design (engineering mistakes) and, finally, miscellaneous cases related to the impact of the surroundings of the system (environment). Because purposeful design of dangerous AI is just as likely to include all other types of safety problems and will probably have the direst consequences, that is the most dangerous type of AI, and the one most difficult to defend against.

Welcome to Mossberg, a weekly commentary and reviews column on The Verge and Recode by veteran tech journalist Walt Mossberg, now an Executive Editor at The Verge and Editor at Large of Recode. As usual, there were scores of feature changes and announcements to Apple's four software platforms -- iOS, watchOS, tvOS, and the newly renamed macOS -- at this year's Worldwide Developer's Conference. Just listing them all here would tax the patience of all but the most Apple-obsessed readers. But there were two big themes, and a few significant moves consistent with them, that I believe stood out in the cascade of slides and demos the company showed at WWDC. They show how stubbornly Apple clings to both its principles and its profit centers.