World-leading tech company IBM is to acquire risk and compliance business Promontory and combine it with its AI division Watson to form a new offering to the corporate sector via Watson Financial Services. US company Promontory has around 600 staff and advises companies on compliance and risk, much as many large UK and US law firms already do. The combination with Watson and its AI capabilities will allow IBM to provide clients with a more automated approach to risk and compliance review, just as some legal AI companies have already been helping some law firms to develop similar capabilities to provide to their clients. Although the company is not branding this as a move into law firm territory, undoubtedly it will have some impact given the increasing focus from lawyers on helping their clients with risk and regulatory compliance. Moreover, since the financial crisis of 2008 the level of investment from large corporates and banks in risk and compliance has growth massively, something that lawyers were quick to target.

IBM Research and the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology have joined forces to further develop the scientific field of machine vision – a core aspect of artificial intelligence. Big Blue and MIT will build the IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension, or BM3C, in Cambridge, Mass. Together they plan to develop cognitive computing systems that mimic the human ability to understand and integrate input from multiple sources for use in a variety of computer applications in industries such as healthcare, education, and entertainment. MIT researchers will collaborate with IBM scientists and engineers who will provide technology expertise and advances from the IBM Watson platform. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish.

Over the last decade, the "data revolution" has touched every aspect of our work and personal lives. Today's business challenges have never been more complex, and the critical insights that can address these challenges are often buried in an avalanche of data. In today's marketplace, the business that wins, is the business that "thinks." The viability of a company in the marketplace now depends on its ability to use data and analytics to fuel a thinking business. Companies in industries as diverse as healthcare, retail, banking and manufacturing are already using cognitive technologies to reshape business and do things faster and more efficiently than ever before.

IBM's famous supercomputer has accomplished many, many things these past years, from making movie trailers to saving a person's life. Now, it's also helping teachers make lesson plans by powering Teacher Advisor, a program IBM developed with the American Federation of Teachers. If you're thinking "How hard could a grade school lesson plan be?" Well, have you seen Common Core mathematics? It's not the same math from back in the day, and teachers who didn't grow up with it might have a tough time conjuring up a way to make it more understandable.

IBM Watson is a Cognitive Computer that has opened up new avenues of human - computer collaboration. IBM Watson has a lot of human language processing capabilities and extreme scale information retrieval. It has found a lot of applications in the areas of Healthcare, Banking, Consumer Retail and Lifestyle, thanks to the ability to understand complex information architectures and human life scenarios. Its ability to make continuous inferences based on repeated question - answering problems is amazing. It is a continuously learning machine with cognitive information processing abilities.

Watson can create one-to-one experiences for brands and consumers. Next week, IBM will begin showing display ads for Campbell's on The Weather Company's website with personalized recipes created by Watson and based on a user's location, what the weather is in the area and which ingredients they want to cook with. Here's how it works: When a user sees an ad for Campbell's on The Weather Company's website, they'll be able to ask Watson to suggest dishes to make based on they ingredients they say into their microphones. Additional APIs could be added to the mix in the future, said Monica Fogg, The Weather Company's head of ad product and brand marketing.

The Weather Company is getting ready to roll out its first ad campaign since being acquired by IBM earlier this year. Next week, IBM will begin showing display ads for Campbell's on The Weather Company's website with personalized recipes created by Watson and based on a user's location, what the weather is in the area and which ingredients they want to cook with. Using a series of application program interfaces, or APIs--Speak and text, 'Chef Watson' API and a natural language classifier--Watson is able to ingest client data and then develop an experience based on a particular brand. According to Jeremy Steinberg, IBM's global head of sales for The Weather Company, Watson wasn't initially built for advertising. However, he said, Watson has the potential to create one-to-one experiences for brands and consumers.

They are the APAC and Australian leaders of some of the largest, or fastest rising marketing tech, adtech and ecommerce companies. And they are passionate about helping their clients understand their own consumers using data analytics. So we figured it was time to turn the lens around. We used IBM's Personality Insight services in the Watson Developer Cloud to tell us a little bit about the personality of each of the following executives; Karen Stocks from Twitter, Ben Sharp from AdRoll, Liam Walsh from Amobee, Jodie Sangster from ADMA, Paul Robson from Adobe, Derek Laney from Salesforce, Paul Cross from Oracle, Matt Barrie from Freelancer and Ruslan Kogan from Kogan. Given their commitment to the cause of data-driven marketing we are sure they won't mind at bit.

Yes, you read that right: an actual artificial intelligence created the advertisement for a movie about terrifying AI. To create the film, the company used experimental Watson APIs and machine learning techniques to comb through hundreds of movie trailers for horror and thrillers. "Let's send Watson to film school," as John Smith, an IBM fellow who helped work on the project, explained. The team behind Watson helped the AI learn how movie trailers work, and then analyzed every scene in the human-made movie to pick the best ones for the trailer. The AI was able to detect which of the movie scenes were cheerful and uplifting, versus which ones were sad or scary.

Modern search engines have made dramatic progress in answering questions about facts, such as those that might be retrieved or directly inferred from a knowledge base. However, many other real user questions are more complex, such as requests for opinions, explanations, instructions or advice for a particular situation, and are still largely beyond the competence of the computer systems. As conversational agents become more popular, QA systems are increasingly expected to handle such complex questions, and to do so in (nearly) real-time, as the searcher is unlikely to wait longer than a minute or two for an answer. One way to overcome some of the challenges in complex question answering is crowdsourcing. We explore two ways crowdsourcing can assist a question answering system that operates in (near) real time: by providing answer validation, which could be used to filter or re-rank the candidate answers, and by creating the answer candidates directly. In this paper we present CRQA, a crowd-powered, near real-time automatic question answering system for complex informational tasks, that incorporates a crowdsourcing module for augmenting and validating the candidate answers. The crowd input, obtained in real-time, is integrated into CRQA via a learning-to-rank model, to select the final system answer. Our large-scale experiments, performed on a live stream of real users questions, show that even within a one minute time limit, CRQA can produce answers of high quality. The returned answers are judged to be significantly better compared to the automatic system alone, and even are often preferred to answers posted days later in the original community question answering site. Our findings can be useful for developing hybrid human-computer systems for automatic question answering and conversational agents.