"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years. Since early 2013, IBM's Watson has been used in the medical field, and after winning an astounding series of games against with world's best living Go player, Google DeepMind's team decided to throw their weight behind the medical opportunities of their technologies as well. Many of the machine learning (ML) industry's hottest young startups are knuckling down significant portions of their efforts to healthcare, including Nervanasys(recently acquired by Intel), Ayasdi (raised $94MM as of 02/16), Sentient.ai With all the excitement in the investor and research communities, we at TechEmergence have found most machine learning executives have a hard time putting a finger on where machine learning is making its mark on healthcare today. We've written this article, not to be a complete catalogue of possible applications, but to highlight a number of current and future uses of machine learning in the medical field, with relevant links to external sources and related TechEmergence interviews.
Netradyne, a leader in Artificial Intelligence (AI) technology focusing on driver and commercial fleet safety, today announced that its Driveri platform has been selected as the winner of the "Best AI-based Solution for Transportation" award from AI Breakthrough, an independent organization that recognizes the top companies, technologies and products in the global Artificial Intelligence (AI) market today. "Netradyne's recognition by the Artificial Intelligence Awards as being the best AI-based solution for transportation reiterates our company's belief that we are driving forward the potential for AI in all aspects of the transportation space including commercial trucking and autonomous vehicles," said Sandeep Pandya, Netradyne President. "This organization recognizes cutting edge AI technology at the highest levels and to have the Driveri platform mentioned alongside so many other impactful companies such as Google, NVIDIA and IBM is a tremendous honor. We are elated by the recognition and will continue to look for innovative ways to showcase AI's unique and impactful capabilities within transportation." The mission of the AI Breakthrough Awards is to honor excellence and recognize the innovation, hard work and success in a range of AI and machine learning related categories, including AI platforms, Deep Learning, Smart Robotics, Business Intelligence, Natural Language Processing, industry specific AI applications and many more.
Wave Computing, a Silicon Valley AI startup specializing in data flow processing of Deep Neural Networks, has acquired MIPS Technologies for an undisclosed amount. Wave projects that the acquisition will be immediately cash-flow positive and accretive to its balance sheet and valuation. The deal logic is pretty sound, adding new markets such as edge AI computing while giving the company in-house RISC cores it can use for its next-generation DataFlow Processing Unit datacenter AI chip. Who is Wave Computing, and why does it need MIPS? Wave is an early innovator in AI silicon geared towards datacenter use, to train deep neural networks (DNNs) and run those networks for predictions and classifications.
The longevity and biotechnology industries are focusing on aging in a big way, and it's beginning to show. The fields of Artificial Intelligence (AI) and regenerative medicine are putting their money on combating aging and age-related diseases, and the benefits are likely to be immense. While biotechnology and AI are relatively new concepts, the announcements of funding and collaboration yesterday by and between three companies are bringing those concepts that much closer to the forefront of medicine. Insilico Medicine, a Baltimore-based next-generation AI company specializing in the application of deep learning for target identification, drug discovery and aging research, yesterday announced a collaboration agreement with WuXi AppTec, a leading global contract research outsourcing provider based in Shanghai, China, serving the pharmaceutical, biotech, and medical device industries. "It's a big step not only for Insilico Medicine but for AI and the pharmaceutical industries," said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.
Zebra Medical Vision, a machine and deep learning start-up, has raised $30 million (€25.47 million) in C round funding, bringing the total investment in the company to $50 million (€42.45 million). The company is also unveiling its Textray chest X-Ray research, which it claims is the most comprehensive AI research conducted on chest X-Rays to date, providing a glimpse into a future automated chest X-Ray analysis product being developed by the company. This round of investment is led by aMoon Ventures with the participation of strategic healthcare investors Aurum, Johnson & Johnson Innovation JJDC Inc. and Intermountain Healthcare and leading global AI experts Fei Fei Lee and Richard Socher. These new investors are joining a list of top existing investors Khosla Ventures, NVIDIA, Marc Benioff, OurCrowd and Dolby Ventures who also participated in this C round. The chest X-Ray AI analytics product was trained using nearly 2 million images to identify 40 different common clinical findings.
Artificial intelligence (AI) technology is progressing at a rapid pace, as is the application of the technology to solve real-world problems. While the market for chipsets to address deep learning training and inference workloads is still a new one, the landscape is changing quickly – in the past year, more than 60 companies of all sizes have announced some sort of deep learning chipset or intellectual property (IP) design. A new report from Tractica finds that virtually every prominent name in the technology industry has acknowledged the need for hardware acceleration of AI algorithms and the semiconductor industry has responded by offering a wide variety of solutions. Tractica forecasts that the market for deep learning chipsets will increase from $1.6 billion in 2017 to $66.3 billion by 2025. System-on-a-chip (SoC) accelerators such as those found in mobile devices will lead the market in terms of sheer volumes by the end of the forecast period, followed by application-specific integrated circuits (ASICs) and graphics processing units (GPUs).
June 04, 2018, Taipei: Tata Elxsi, a global design and technology services company, is showcasing solutions and services for digital technologies including IoT, AI and Extended Reality at Computex Taipei between June 5-9, 2018. "Tata Elxsi is working with leading operators, OEMs and ODMs across industries such as automotive, communications, broadcast, consumer electronics and healthcare for digital transformation", said Nitin Pai, Senior Vice President - Marketing, Tata Elxsi. "We look forward to collaborating with Taiwanese companies to help them create smarter, connected products and services for the global market." Edge IoT platform: Tata Elxsi's powerful software platform allows easy integration, management and control of diverse devices and sensors for applications such as smart home and connected healthcare. Edge AI solution: Tata Elxsi's Deep Neural Network (DNN) edge compiler allows AI algorithms to be embedded on low power, low compute and unconnected devices, transforming them into smart edge devices.
Intel today announced plans to release Nervana Neural Net L-1000, code named Spring Crest, to make it easier for developers to test and deploy AI models. Intel first introduced the Neural Network Processor (NNP) family of chips last fall. Spring Crest will be 3-4 times faster than Lake Crest, its first NNP chip, said Intel VP and general manager of the AI product group Naveen Rao. The Nervana Neural Net L-1000 will be Intel's first commercial NNP chip and will be made broadly available in late 2019. The news was announced today at Intel's first-ever AI Dev Con being held at the Palace of Fine Arts in San Francisco.
WIRE)-- 8x8, Inc. (NYSE:EGHT), a leading provider of cloud phone, meeting, collaboration and contact center solutions, today announced the acquisition of MarianaIQ (MIQ), a high-growth Silicon Valley startup, as part of the strategic investments it has been making in AI and Machine Learning. MIQ brings deep learning capabilities to the newly announced X Series to transform both employee and customer experience. The MIQ team, including founders Soumyadeb Mitra and Venkat Nagaswamy, have been leaders in applying AI and deep learning to practical business problems since 2013, and join 8x8 to strengthen AI capabilities for enterprise communications. "8x8 has continuously evolved itself to provide best-in-class enterprise communications to our customers. With the acquisition of MarianaIQ, we are fundamentally transforming how customers and employees interact through one system of engagement, and how companies optimize valuable moments of customer engagement with one set of data in one system of intelligence," said Dejan Deklich, Chief Product Officer, 8x8.
Intel announced today that it is forming a strategic research alliance to take artificial intelligence to the next level. Autonomous systems don't have good enough ways to respond to the uncertainties of the real world, and they don't have a good enough way to understand how the uncertainties of their sensors should factor into the decisions they need to make. According to Intel CTO Mike Mayberry the answer is "probabilistic computing", which he says could be AI's next wave. IEEE Spectrum: What motivated this new research thrust? Mike Mayberry: We're trying to figure out what the next wave of AI is.