Many nations are racing to achieve a global innovation advantage in artificial intelligence (AI) because they understand that AI is a foundational technology that can boost competitiveness, increase productivity, protect national security, and help solve societal challenges. This report compares China, the European Union, and the United States in terms of their relative standing in the AI economy by examining six categories of metrics—talent, research, development, adoption, data, and hardware. It finds that despite China’s bold AI initiative, the United States still leads in absolute terms. China comes in second, and the European Union lags further behind. This order could change in coming years as China appears to be making more rapid progress than either the United States or the European Union. Nonetheless, when controlling for the size of the labor force in the three regions, the current U.S. lead becomes even larger, while China drops to third place, behind the European Union. This report also offers a range of policy recommendations to help each nation or region improve its AI capabilities. Read More
Daily Archives: August 20, 2019
To power AI, this startup built a really, really big chip
COMPUTER CHIPS ARE usually small. The processor that powers the latest iPhones and iPads is smaller than a fingernail; even the beefy devices used in cloud servers aren’t much bigger than a postage stamp. Then there’s this new chip from a startup called Cerebras: It’s bigger than an iPad all by itself.
The silicon monster is almost 22 centimeters—roughly 9 inches—on each side, making it likely the largest computer chip ever, and a monument to the tech industry’s hopes for artificial intelligence. Cerebras plans to offer it to tech companies trying to build smarter AI more quickly. Read More
5 ways to fast-track your next AI implementation
Preparing for and implementing AI projects can be a multi-year journey. According to the latest figures, only 28% of respondents reported getting past the AI planning stage in the first year. This is due to several factors including the relative maturity of the technology (at least in the ever-expanding set of industry use cases), the level of complexity involved such as extensive integration requirements, limited enterprise experience and lack of internal skill sets, concerns with AI bias as well as governance, risk and compliance concerns, extensive change management requirements and more.
How do you gain quick wins around this important enabling technology? Five ways to fast-track your next AI implementation. Read More
Removing Coordinated Inauthentic Behavior From China — 8/19/2019
Today, we removed seven Pages, three Groups and five Facebook accounts involved in coordinated inauthentic behavior as part of a small network that originated in China and focused on Hong Kong. The individuals behind this campaign engaged in a number of deceptive tactics, including the use of fake accounts — some of which had been already disabled by our automated systems — to manage Pages posing as news organizations, post in Groups, disseminate their content, and also drive people to off-platform news sites. They frequently posted about local political news and issues including topics like the ongoing protests in Hong Kong. Although the people behind this activity attempted to conceal their identities, our investigation found links to individuals associated with the Chinese government.
— Presence on Facebook: 5 Facebook accounts, 7 Pages and 3 Groups.
— Followers: About 15,500accounts followed one or more of these Pages and about 2,200 accounts joined at least one of these Groups.
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Ah the life of the Machine Learning Engineer…
Machine Learning for Everyone
Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.
Now there’s a simple introduction for those who always wanted to understand machine learning, explained using real-world problems, practical solutions, simple language, and no high-level theorems. Read More
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