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Chinese AI Players Face Blacklist Roadblocks Enterprise IT News

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In widening its punishment of China in the ongoing US-China trade war, the US has extended its blacklist to directly handicap its biggest competition in a much higher stakes race, for world domination in AI. Recently, almost ten more AI companies – including providers of video surveillance, facial and speech recognition and data recovery; were added to US trade black list. The reasons cited were related to the violation of human rights by the supposed usage of AI technology in China's repression the Muslim ethnic minority groups of the Uygur region. Here is an interesting digression; that one of the most notable Chinese AI companies in this most recent US blacklist is SenseTime Group (known for its facial recognition AI tech), whose founder Tang Xiao'ou was appointed as the foreign national to Malaysia's sovereign wealth fund, Khazanah Nasional. SenseTime is the top AI'unicorns' startup from China with a valuation of over USD7 billion.


3 Top Artificial Intelligence Stocks to Watch in October The Motley Fool

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Some investors may know that NVIDIA's shares have taken a hit over the past year, but the company's second-quarter results have helped share tick up 11% over the past few months. NVIDIA's revenue decline in the quarter wasn't as bad as analysts or the company's management had expected and earnings per share of $1.24 easily outpaced expectations of $1.14 per share. With the company's recent performance and its long-term play with AI chips, investors would be wise to consider the company's stock as a solid AI bet.


3 Top Artificial Intelligence Stocks to Watch in October The Motley Fool

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Some investors may know that NVIDIA's shares have taken a hit over the past year, but the company's second-quarter results have helped share tick up 11% over the past few months. NVIDIA's revenue decline in the quarter wasn't as bad as analysts or the company's management had expected and earnings per share of $1.24 easily outpaced expectations of $1.14 per share. With the company's recent performance and its long-term play with AI chips, investors would be wise to consider the company's stock as a solid AI bet.


Review latest investments to AWS' machine learning platform

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AWS is always adding services and features, but it's been particularly active with its AI services of late. For example, AWS rolled out 13 machine learning products at re:Invent 2018 alone. AWS' massive investment in product development is good for users, but this pace of change makes it difficult for IT professionals to keep up. The Deep Learning AMIs and Containers target developers who build sophisticated custom models with the AWS machine learning platform and DevOps teams charged with deploying them on cloud infrastructure. In contrast, the managed services, which join similar products for image recognition, speech transcription and interactive chatbots, are designed for data scientists and non-specialists who want to analyze large and complicated data sets using techniques that are more advanced than standard statistical analysis.


UCSF Launches Artificial Intelligence Center to Advance Medical Imaging

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UC San Francisco is launching a new center to accelerate the application of artificial intelligence (AI) technology to radiology, leveraging advanced computational techniques and industry collaborations to improve patient diagnoses and care. The Center for Intelligent Imaging, or ci2, will develop and apply AI to devise powerful new ways to look inside the body and to evaluate health and disease. Investigators in ci2 will team with Santa Clara, Calif.-based NVIDIA Corp., an industry leader in AI computing, to build infrastructure and tools focused on enabling the translation of AI into clinical practice. "Artificial intelligence represents the next frontier for diagnostic medicine," said Christopher Hess, MD, PhD, chair of the UCSF Department of Radiology and Biomedical Imaging. "It is poised to revolutionize the way in which imaging is performed, interpreted and used to direct care for patients. "The Center for Intelligent Imaging will serve as a hub for the multidisciplinary ...


There is No Such Thing as a Free Lunch: Part 1 - KDnuggets

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Almost every day we read about companies and their Artificial Intelligence (AI) strategies. Sometimes it feels like an arms race where businesses feel they will get left behind if they can't claim to have AI and (usually) deep learning embedded somewhere in their product. We have seen this type of thing before, reminiscent of the social media and big data hypes of years gone by. It used to be that companies were tripping over themselves to be seen as "big data" now the focus is on "AI " as they try to position themselves as appealing to customers and investors – one report estimates as much as 40% of European startups classified as "AI" don't actually use AI in any material way! The hype around AI has largely been driven by substantial recent progress in the sub field of deep learning.


Global Computer Vision Market To Witness Massive Growth By 2025 oogle, Facebook, Microsoft, NVIDIA, Texas Instruments - Market Research Scoop

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This Computer Vision report contains a chapter on the global market and all its associated companies with their profiles, which gives valuable data pertaining to their outlook in terms of finances, product portfolios, investment plans, and marketing and business strategies. The report helps you achieve your dream of an outshining and winning business. This Computer Vision market research report helps in answering many business challenges more quickly and saves your lot of time. Moreover, the report consists of all the detailed profiles for the Computer Vision market's major manufacturers and importers who are influencing the market. Market research report improves your professional reputation and adds integrity to the work you do such as refining your business plan, preparing a presentation for a key client, or making recommendations to an executive.


AI Hardware: Harder Than It Looks

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The second AI HW Summit took place in the heart of Silicon Valley on September 17-18, with nearly fifty speakers presenting to over 500 attendees (almost twice the size of last year's inaugural audience). While I cannot possibly cover all the interesting companies on display in a short blog, there are a few observations I'd like to share. Computer architecture legend John Hennessy, Chairman of Alphabet and former President of Stanford University, set the stage for the event by describing how historical semiconductor trends, including the untimely demise of Moore's Law and Dennard scaling, led to the demand and opportunity for "Domain-Specific Architectures." This "DSA" concept applies not only to novel hardware designs but to the new software architecture of deep neural networks. The challenge is to create and train massive neural networks and then optimize those networks to run efficiently on a DSA, be it a CPU, GPU, TPU, ASIC, FPGA or ACAP, for "inference" processing of new input data.


Pssst.... build your own machine learning computer, it's cheaper and even faster than using GPUs on cloud

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If you've been thinking about building your own deep learning computer for a while but haven't quite got'round to it, here's another reminder. Not only is it cheaper to do so, but the subsequent build can also be faster at training neural networks than renting GPUs on cloud platforms. When you start trying small side projects like, say, building little autonomous drones or crafting a bot to spit out random snippets of poetry, you begin to realise how much compute power is really needed to get interesting results. So you can either fork out money to rent hardware via cloud services like AWS or Google Compute Platform or build your own server. Jeff Chen, an AI engineer and entrepreneur, drew up a handy shopping list for all the different parts needed to craft your own deep learning rig.


If AI is the best, where should we invest?

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As I sat down to write the predecessor to this article I has no idea that I would possibly turn these into a series - I don't know if any will follow this but maybe, just maybe I will have some ideas for engaging (hopefully) and useful content in the coming weeks/months!! However, I didn't really drill into specific hardware/infrastructure hazards. What does it look like? Developer PCs - A PC - Has a Graphics card (GPU) installed, fast processor, plenty of memory and some high-speed cache drives (SSD) to enable smaller datasets to be processed on the system for Proof of concept (POC) work whilst minimising the initial outlay.r Training machines - If you don't want to make the dev machines'heavy lifters' you'll need to invest in dedicated machines to handle the training workloads.