The next Machine Intelligence Summit will take place in New York on 9-10 November, to explore how AI will impact transport, manufacturing, healthcare, retail and more. Early Bird tickets are now available for this event - for more information and to register, visit the event page here. See the full events list here for events focused on AI, Deep Learning and Machine Intelligence taking place in London, Amsterdam, Boston, San Francisco, New York and Singapore.

In Munich, Germany, the technology conglomerate Siemens AG is betting 1.1 billion on digital technologies, such as the proposition that blockchain data can be leveraged by machine learning to improve the secure transmission of data used in energy trading. Siemens is welcoming its employees and independent firms to bid for the money if they are willing to research how Siemens can develop businesses that use artificial intelligence. Siemens is just one of a number of companies embracing machine learning--the combination of artificial intelligence and deep analytics that enables enterprises to make predictions on large amounts of data and allows developers to experiment by incorporating features like speech and pattern recognition, as well as statistical techniques, into their analysis. And HPE's recent announcement of "machine learning as a service" (MLaaS) is aimed at helping the process really take off. Speaking at HPE Discover Las Vegas 2016, HPE Executive Vice President Robert Youngjohns called machine learning and deep analytics the most fundamental change we're ever going to see.

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As we continue to chart the evolution of humanoid robots, humanity seems to be bypassing ethics over practicality. Robots already have advanced to a point that is leading to serious concern about the economic impact of humans being outsourced to robots for tasks as diverse as service, manufacturing, nursing, housework, yard maintenance and full-fledged agricultural duties. Some are predicting that robots of all types could fully replace humans by 2045. Meanwhile, humanoid robots filled with the latest artificial intelligence could lead to the outsourcing of future soldiers, leading to the literal possibility of robot wars. All of this is occurring as ethicists, governments and citizens scramble to make sense of exactly what type of future we should be looking forward to, and if any boundaries to this type of progress need to be made universal.

Google announced today that it has acquired Paris-based Moodstocks, a startup that has developed machine learning technology to bolster the image recognition features on smartphones. "We continue to pursue our machine learning and research efforts," wrote Vincent Simonet, head of the research and development team for France Google, "and Moodstocks is the latest proof of our commitment to this area." Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription. The terms of the deal were not disclosed.

Among other things, CareSkore wants to use machine learning to anticipate mortality. However, the newly endowed platform is more than just a Facebook poll that tells you how you'll meet your end this Christmas by being squashed by a falling piano. Storm ventures, Cota Capital, Rising Tide Fund and Liquid 2 Ventures are rallying behind the Y Combinator graduate with today's 4.5 million seed round. CareSkore is combining clinical, socio-economic, demographic, and behavioral data to paint a holistic picture of patients that doctors and insurance companies can use to provide better preventative care. The platform is leveraging Google's TensorFlow and Hadoop to cut through massive third party data sets and generate insights by finding relationships between environmental and clinical data.

As a UK-focused venture capital investor, Oxford Capital is fortunate that the UK is a world leader in AI (although to avoid topical accusations of British exceptionalism, it's definitely worth mentioning that there are centres of excellence in AI and machine learning (ML) throughout Europe). We have just completed an as-yet undisclosed investment in the AI space, and we're evaluating several other opportunities. Along with taking an interest in AI companies that approach us for funding, we've mapped out recent activity in the sector across the UK. Innovation starts with talented individuals, and AI is no exception. Many of the UK's most respected universities have AI research groups, such as the Intelligent Systems group within UCL and the joint Oxford-Cambridge Strategic AI Research Centre.

It's a non-traditional application of neural networks where the network is used to'hallucinate' a desirable image,

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

The following is a condensed and edited interview with Ginni Rometty, CEO, IBM. You joined IBM 35 years ago. What was the company like back then? What struck me was the seriousness of the kind of things we did. We were building complex back-office banking systems.