Feature: This Chinese robot could revolutionize journalism

#AI interviews impress us! This Chinese robot #Jiajia could revolutionize journalism

  • Chen Xiaoping (R), director of a robot research and development team, and Jia Jia, an interactive robot that looks like a real Chinese young woman in traditional outfit, talk through internet with Kevin Kelly on screen, founding executive editor of Wired magazine, in Hefei, capital of east China’s Anhui Province, April 24, 2017.
  • Jia Jia was unveiled in 2016 by Chen’s robot research and development team at the University of Science and Technology of China in Hefei.
  • “It’s something we could never have imagined,” said Jia Jia’s creator Professor Chen Xiaoping, director of the Robotics Laboratory at the University of Science and Technology of China (USTC) in Hefei, a city in east China’s Anhui Province.
  • The first interview conducted by Jia Jia as a special Xinhua reporter on Monday was merely a small step in the era of artificial intelligence (AI), said Chen, who has been long involved in the development of Jia Jia and honored as the “father” of the robot.
  • Jia Jia, did a live interview with Kevin Kelly, a U.S. journalist and technology observer, on Monday, which was hailed by scientists as “having symbolic significance” as it was the world’s first interactive conversation between an “AI reporter” and a human being.

Chen Xiaoping (R), director of a robot research and development team, and Jia Jia, an interactive robot that looks like a real Chinese young woman in traditional outfit, talk through internet with Kevin Kelly on screen, founding executive editor of Wired magazine, in Hefei, capital of east China’s Anhui Province, April 24, 2017. Jia Jia was invited as a special reporter of the Xinhua News Agency to conduct the man-machine dialogue with Kelly on Monday. Jia Jia was unveiled in 2016 by Chen’s robot research and development team at the University of Science and Technology of China in Hefei. It took the team three years to research and develop this new-generation interactive robot, which can speak, show micro-expressions, move its lips, and move its body. (Xinhua/Guo Chen) 
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AI for a Better World

Learn how #EinsteinAI and #accenturetech make artificial intelligence responsible:

  • Advancements in AI are creating new opportunities and long with it comes responsibilities.
  • How can advancements in Artificial Intelligence benefit society?
  • Join Salesforce Chief Scientist, Richard Socher and Nicola Morini Bianzino, Global Lead Artificial Intelligence at Accenture.

Advancements in AI are creating new opportunities and long with it comes responsibilities. How can advancements in Artificial Intelligence benefit society? W…
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AI as the new UI – Accenture Tech Vision

#AI will help companies improve the experience and outcome for every interaction. @Accenture

  • Moving beyond a back-end tool for the enterprise, artificial intelligence (AI) is taking on more sophisticated roles within technology interfaces.
  • From autonomous driving vehicles that use computer vision, to live translations made possible by machine learning, AI is making every interface both simple and smart–and setting a high bar for how future experiences will work.
  • AI is poised to act as the face of a company’s digital brand and a key differentiator – and become a core competency demanding of C-level investment and strategy.

See how Artificial Intelligence plays a wide range of increasingly sophisticated roles in creating better customer interactions at the user interface (UI).
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Book: Machine Learning Algorithms From Scratch

Book: #MachineLearning Algorithms From Scratch #abdsc

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
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scikit-learn-classifiers/sklearn-classifiers-tutorial.ipynb at master · mmmayo13/scikit-learn-classifiers · GitHub

Scikit-learn #MachineLearning classification algorithms - A quick overview with examples

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scikit-learn-classifiers – An introduction to implementing a number of scikit-learn classifiers, along with some data exploration
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Practical Deep Learning For Coders—18 hours of lessons for free

Practical #DeepLearning For Coders—18 hours of lessons for free

  • After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
  • I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
  • Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
  • But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

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Artificial intelligence could build new drugs faster than any human team

Artificial intelligence could build new drugs faster than any human team

  • Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis.
  • The company has invited academic and non-profit researchers from around the country to detail which diseases they’re trying to generate treatments for, so AtomNet can take a shot.
  • The academic labs will receive 72 different drugs that the neural network has found to have the highest probability of interacting with the disease, based on the molecular data it’s seen.
  • Atomwise’s system only generates potential drugs—the compounds created by the neural network aren’t guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market.
  • The company believes that the speed at which it can generate trial-ready drugs based on previous safe molecular interactions is what sets it apart.

Artificial intelligence could build new drugs faster than any human team
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Reference.AI: Automated reference checks powered by artificial intelligence – BetaList

Reference.AI: Automated reference checks powered by artificial intelligence

  • Reference.AI aims to be the world’s first artificially intelligent hiring manager that automates the reference check process.
  • The tool provides accurate and comprehensible reference check results plus suggested follow-up questions that hiring professionals can integrate into an efficient and effective decision-making process.

Automated reference checks powered by artificial intelligence
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Hashtag Artificial Intelligence – Katherine Bailey – Medium

Good read

Hashtag #ArtificialIntelligence 

 #fintech @katherinebailey @Acquia #AI

  • Here’s one that was presented recently at a talk by one of Amazon’s top Machine Learning people:Artificial Intelligence: A system or service which can perform tasks that usually require human intelligenceThis is a fairly common way to define it.
  • Here’s a similar formulation from Nathan Benaich in his post 6 areas of AI and machine learning to watch closely:The ultimate goal of AI […] is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence.One problem with this definition is that it means the state of being an instance of Artificial Intelligence is temporary.
  • Just look at all the “What is the difference between Artificial Intelligence and Machine Learning?”
  • Really, why should Machine Learning be defined in relation to AI?The slippery definition issue above can be looked at as follows: it is the term “Artificial Intelligence” looking for things to refer to.
  • This is no more true now than it was 50 years ago but many smart people are utterly convinced of it.The term “Artificial Intelligence” has been around since the early days of computer science, when “thinking machines” were seen as the natural next step after programming basic logic.

The internet is awash with stories about something called Artificial Intelligence. Confusion around what it is is prompting many to proffer definitions of it, or corrections of wrong definitions…
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