Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce, are competing in the race to acquire private AI companies, with Samsung emerging as a new entrant this month with its acquisition of startup Viv Labs, which is developing a Siri-like AI assistant. In 2014 Google acquired British company DeepMind Technologies for some 600M (Google DeepMind's program recently beat a human world champion in the board game "Go"). This year, it acquired visual search startup Moodstock, and bot platform Api.ai. Salesforce, which joined the race last year with the acquisition of Tempo AI, has already made two major acquisitions this year: Khosla Ventures-backed MetaMind and open-source machine-learning server PredictionIO.
Nearly 140 private companies working to advance artificial intelligence technologies have been acquired since 2011, with over 40 acquisitions taking place in 2016 alone (as of 10/7/2016). Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce, are competing in the race to acquire private AI companies, with Samsung emerging as a new entrant this month with its acquisition of startup Viv Labs, which is developing a Siri-like AI assistant. Google has been the most prominent global player, with 11 acquisitions in the category under its belt (follow all of Google's M&A activity here through our real-time Google acquisitions tracker). In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.
Many data scientists are getting excited about advances in large-scale machine learning, particularly recent success stories in computer vision and speech. While correctly identifying meaningful patterns in data sets is the promise of machine learning, AI in the data center currently is the buzz for big tech companies like Google, Amazon, Facebook AI Research, Twitter and many more startups with shared goals of making useful machine learning software. For researchers looking at the scientific and engineering challenges of understanding the brain and building computers, Neural Computation highlights common problems and techniques in modeling the brain, and in the design and construction of neurally-inspired information processing systems. H2O is open source (AI for Business, deep learning with H20), Spark is open source (SparkNet batch processing framework). It's very easy to see the algorithm development and the value-add.
Whether it's opening Google Maps for directions, shopping on Amazon, or watching movies on Netflix, 2016 was the year that bots truly hit mainstream. This seismic development -- which has critical implications for businesses, consumers, and, indeed, humankind -- is the result of three developments that came to a head in 2016: First, an unprecedented capacity to analyze data; second, a backlash (limited though it was) to protect what data can be analyzed; and third, a welcoming of automation into our lives that we couldn't have fathomed even a few years ago. At this moment, we have more data than ever before. We're creating information at a bewildering pace -- approximately 2.5 quintillion bytes of data daily, which is enough to fill 57.5 billion iPads (at 32 gigabytes apiece). Big data is now a given, affecting every industry and function.