AI startups received $1.8bn from investors in the first half of 2017, according to CB Insights. The second half of the year has seen a continuation of the trend, as Graphcore and SenseTime secured large capital financing rounds. This said, AI remains a trend with question marks hanging over it, with many of the industry\u2019s leading researchers still feeling that we are a long way off solving critical issues with AI \u2013 like the frame problem, and inference.
\nTypically, when most consider AI, they think of the Googles and Baidus of this world, who spent between $20 to $30bn internally on the area in 2016, according to McKinsey. Much of this effort has been focused on a race to achieving patents and intellectual property, in the hope that they will enable these companies to own the future of software and hardware.
\nMachine-learning has already been implemented by Facebook, which uses its own internally developed algorithms to read user posts, or Netflix\u2019s algorithms to make better recommendations to its subscribers \u2013 increasing the time spent in their ecosystems.
\nThe sector is yet to really face down challenges from researchers involved with the well-financed AI efforts of \u2018big tech.\u2019 Google\u2019s Ray Kurzweil, makes the point that today \u201cmachine-learning is today very brittle, requiring a lot of preparation by humans in the form of special-purpose coding, special-purpose sets of training data, and a custom learning structure. Today\u2019s machine-learning really fails to imitate anything like the sponge-like learning structure that humans engage in.\u201d
\nThis form of intuitive learning still presents significant market opportunities, in the form of autonomous driving, voice recognition, video recognition, and data processing in business applications. On the back of this potential, investments have continued to flow.
\nSenseTime announced it will collaborate with Qualcomm on developing proprietary AI algorithms, to be deployed in smart devices. Although the two companies did not disclose the size of the investment, SenseTime is currently trying to raise $500m in a new funding round, in what would be the biggest ever fundraising by an AI startup \u2013 which values the company at $2bn.
\nSenseTime focuses on developing facial recognition technology, and is just one of a number of startup efforts that are competing to develop an AI to quickly identify and analyze identities using cameras. SenseTime\u2019s algorithms have to date been used in limited tests by Chinese authorities to track and capture suspects in public spaces such as airports and festivals.
\nThe startup already has 40 Chinese local authorities as clients, and is now seeking to expand overseas. SenseTime raised $410m in July, in a funding round led by its main backer, Chinese firm CDH Investments, and China\u2019s state-backed fund Sailing Capital.
\nElsewhere, Graphcore has raised $50m in new funding from Sequoia Capital. Graphcore had reportedly been chasing a $1bn valuation, but couldn\u2019t quite convince investors of that potential. The funding comes on top of the $60m the group has already raised in its first two funding rounds \u2013 taking total investments in the group to $110m.
\nGraphcore was founded in 2016 in the UK, and focuses on building intelligence processing units (IPU) \u2013 chips that are specifically designed to assist programmers in creating machine-learning systems, that can be used in fields such as autonomous cars, data centers, and medical detection devices. The target applications for the IPU are those that already run machine-learning algorithms on standard graphics and processing units \u2013 hoping to entice them onto the dedicated silicon.
\nThe company claims its IPU accelerators and Poplar software framework deliver \u201cthe fastest and most flexible platform for current and future machine intelligence applications, lowering the cost of AI in cloud data centers and improving performance by between 10x to 100x.\u201d
\nGraphcore is underpinned by the belief that Efficient AI processing power is rapidly becoming the most sought-after resource in the world, and that the current CPU and GPU market can\u2019t serve machine-learning type applications anywhere near as effectively as a dedicated processing unit.
\nHowever, it\u2019s still unclear as to which use cases are really going to drive this AI dedicated processor market \u2013 given that most autonomous driving programs are currently partnered with Nvidia, to use its GPU, and that other applications are still quite nascent in their development.
\nGraphcore CEO, Nigel Toon, stresses that the companies IPU processor will fulfil two critical elements \u2013 high computing power, so that a high number of calculations can be achieved at relevant speeds, and interconnect functions. As more processors are required for the high computing capacity, the number of interconnections between processor chips becomes increasingly important, to network both processing cycles and data.
\nToon, claims Graphcore is also focusing on the software to achieve this interconnect function in its IPU chips, through its Poplar software programing framework and application libraries. The libraries can be ported to Google\u2019s TensorFlow machine learning software framework. In 2015, Google open-sourced the TensorFlow software library, aiming to set a de facto standard for ML systems.
\nOne of the software features that Graphcore is working on is the ability to generate noise or random numbers, to improve its effective decision making. For instance, there may be multiple valid answers or responses to a given situation, like when driving, and the addition of noise in the processing unit apparently helps a better decision to be made.
\nGraphcore has stiff competition in the AI chip market, which although still an emerging area has seen a lot of activity. Google launched Tensor Processing Units (TPUs) and Intel revealed its Nervana Neural Network Processor (NNP) family.
\nGraphcore investor Sequoia Capital has been the early private investment partner behind Apple, Oracle, Nvidia, Yahoo, Google, YouTube, PayPal, Instagram, WhatsApp, and Airbnb. Capital Group has experience in the processor market, having worked with Nvidia. Graphcore has already received investments in previous rounds from Samsung, Bosch, DeepMind\u2019s (now part of Google) co-founder Demis Hassabis and Amadeus Capital (a venture capital group ran by ARM co-founder Herman Hauser).
\nCEO Toon sold Icrea, his previous company and a maker of 3G and 4G baseband chips, to Nvidia for $367m in 2011. Before Graphcore, the electrical engineering graduate ran Picochip, a Bath-based semiconductor group, which is now owned by Intel, and XMOS, another Bristol-based chip company. Although this form in the chip market suggests that Toon is a man chasing trends, he has stressed that he believes Graphcore has the potential to go public.
\nSource: copyright 2017 Rethink Research, Inc.
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