AI Financing Is Seven Times Higher in US Than China

#AI Financing Is Seven Times Higher in #US Than #China

  • AI Financing Is Seven Times Higher in US Than China

    (Yicai Global) July 28 — Total financing in America’s artificial intelligence sector totaled USD20.7 billion from 2001 through 2016, making up 71.8 percent of AI financing worldwide, a new report from Chinese digital industry think tank Wuzhen Institute said.

  • Despite possessing great potential for AI development, China’s overall strength in the field significantly lags behind the US in terms of both the number of companies and the scale of financing.
  • AI in China has developed most rapidly during the last three years, with new firms formed during that time making up 55.38 percent of the total number of AI companies in China from 2000 through 2016, the report finds.
  • Financing of China’s AI companies over the last three years also accounts for 93.59 percent of total domestic financing since 2000.
  • Regionally, Beijing ranks first in China in terms of new AI companies and financing, representing 50 percent of the country’s total financing.

Total financing in America’s artificial intelligence sector totaled USD20.7 billion from 2001 through 2016, making up 71.8 percent of AI financing worldwide, a new report from Chinese digital industry think tank Wuzhen Institute said.
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This new AI can read your mind and predict your thoughts

This new AI can read your mind and predict your thoughts -   #ArtificialIntelligence  #AI

  • Today, various pieces of software can do everything from chat with us on Facebook Messenger to guiding the Mars rover Curiosity while its human engineers catch a nap.
  • The group’s new software takes a novel approach to guessing what is going on inside a human brain, using data gathered from brain scans via fMRI to predict human thoughts by seeing how the pattern of brain activity that produces them, then detecting it in reverse.
  • “One of the big advances of the human brain was the ability to combine individual concepts into complex thoughts,” lead researcher Marcel Just explains.
  • The discovery of this correspondence between thoughts and brain activation patterns tells us what the thoughts are built of.”
  • The algorithm was then trained using this data, and learned to detect the same patterns occurring again, accurately predicting what a person was about to say a stunning 90 percent of the time.

At one point in our history, the most impressive example of artificial intelligence was a computer that was really, really good at chess. Today, various pieces…
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This app uses artificial intelligence to turn design mockups into source code

This app uses artificial intelligence to turn design mockups into source code

  • While traditionally it has been the task of front-end developers to transform the work of designers from raw graphical user interface mockups to actual source code, this trend might soon be a thing of the past – courtesy of artificial startup UIzard Technologies has leveraged the latest developments in the field of machine learning to build a neural network that, once fed with raw screenshots of graphical user interface, proceeds to automatically generate code.
  • What is particularly intriguing is that the so-called Pix2Code model has the capacity to produce code for three different platforms, including Android and iOS as well as other web-based technologies.
  • As UIzard founder Tony Beltramelli explains in his research, the novel approach could potentially “end the need for manually-programmed” user interfaces altogether.
  • At present, the method generates code from screenshots with an impressive accuracy of over 77 percent, but the consistency of the algorithm is likely to improve in the future.
  • Watch this brief video demonstration to see the AI-powered app in action:

    While UIzard has already shared some of the principles behind the technology powering its software, the company notes that the source code for the actual app will become available later this year.

Designers might no longer have to rely on front-end developers to turn their user interface mockups into source code – courtesy of artificial intelligence.
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Accountants are embracing artificial intelligence

Accountants are embracing artificial intelligence

  • This is according to a new report by Sage, which says 96 percent of accountants are confident about the future of accountancy as well as their role in it.
  • Despite welcoming change, more than two thirds of respondents (68 percent) expect their roles to change through automation, in the future.
  • Here’s what accountants are expecting from automation: almost four in ten (38 percent) see number-crunching as their number one frustration.
  • Thirty-two percent still use manual methods for this work.
  • A quarter (25 percent) use Excel while seven percent still use handwritten notes.

Accountants are embracing artificial intelligence
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March Madness: A Swarm Intelligence Is Predicting the Future

#MarchMadness: A Swarm Intelligence Is Predicting the Future   #AI #IBMInterConnnect

  • When Unanimous AI developed UNU in 2015, the goal was to create artificial intelligence (AI) systems that “keep people in the loop,” amplifying human intelligence instead of replacing it.
  • Unanimous AI’s March Madness bracket was able to beat all but three percent of ESPN brackets across the country on the first day of the tournament.
  • The technology makes use of the collective intelligence of people — combining “knowledge, insights, and intuitions,” as Unanimous AI puts it — to develop a kind of artificial intelligence that’s inherently human.
  • As Unanimous AI explains, “We empower people to act as ‘data processors’ that come together online and form an intelligent system, connected by AI algorithms.
  • One day, perhaps we’ll be able to combine the intelligence of Watson with that of a swarm of medical professionals to improve healthcare, or combine the insights of an investment-making AI with a swarm of finance experts.

The collective intelligence is killing it on ESPN.
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Snapchat Is Beginning to Use Machine Learning to Improve Ad Targeting

Snapchat is beginning to use machine learning to improve ad targeting:

  • Snap’s expected IPO coming sometime next year will likely keep a fire lit under Snap to further innovate.
  • While GBB campaigns are still charged on a CPM basis, Snap says the campaigns end up being more effective.
  • Snap has also been making a big push to improve measurement.
  • Snapchat is adding ways to optimize campaign performance with the help of machine learning.
  • According to CBInsights , Snap Inc. acquired four patents in 2016, and while it’s nowhere near the 16 it received in 2015 or the 18 it received in 2014, it’ll likely need to keep innovating next year.

Snapchat is adding ways to optimize campaign performance with the help of machine learning.  Earlier this month, Snap Inc. began rolling out what it calls Goal-Based Bidding (GBB). The option, available to marketers buying ads through Snapchat’s API, uses machine learning to know which users are most likely to swipe a certain type of ad.
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This scary artificial intelligence has learned how to pick out criminals by their faces

This scary artificial intelligence has learned how to pick out criminals by their faces

  • This scary artificial intelligence has learned how to pick out criminals by their faces
  • The neural network could tell criminals apart from non-criminals with a stunning accuracy of 89.5 percent.
  • With the advent of photography, a tiny fraction of 19th-century scientists believed they could develop methods of accurately identifying criminals by their facial features.
  • If psychologists are right to suggest humans can make out criminals from non-criminals , machines should be capable of this too – especially, since neural networks are modeled after the human brain.
  • The photographic sample that was fed to the neural network is severely limited, to say the least – both when it comes to the quantity and quality of the photos that the AI parsed.

Researchers from Shanghai Jiao Tong University have developed an artificial intelligence algorithm that can supposedly pick out criminals by their faces.
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