Amazon, Baidu, Facebook, Google and Microsoft, among other technology companies, have been investing heavily in artificial intelligence and related disciplines like machine learning because they see the technology enabling services that become a source of revenue. Consultancy Accenture earlier this week quantified this enthusiasm, predicting that AI "could double annual economic growth rates by 2035 by changing the nature of work and spawning a new relationship between man and machine" and by boosting labor productivity by 40 per cent. Certainly things could work out well for Accenture, which a day later announced a partnership with Google to help companies deploy Google technology like machine learning. It's as if the global services firm has a stake in the future it foresees. But the machine learning algorithms underpinning this harmonious union of people and circuits aren't secure. In a paper [PDF] presented in August at the 25th Annual Usenix Security Symposium, researchers at École Polytechnique Fédérale de Lausanne, Cornell University, and The University of North Carolina at Chapel Hill showed that machine learning models can be stolen and that basic security measures don't really mitigate attacks.

Rebuilding fine motor skills after a stroke takes intensive therapy involving repeated attempts to use the affected hand, several times a day, day after day. Some involve moving the affected fingers with the other hand until new brain pathways for hand control develop. Zain Samdani, a 16-year-old from Saudi Arabia and a finalist in the 2016 Google Science Fair demonstrated a different approach at the finalist showcase on Tuesday. Samdani, who says he'd seen family members struggling with hand rehab, built an exoskeleton out of 3D-printed segments. He connected that to a glove, wired to control the robotic device.

It's unavoidable: the Internet of Things will kill many jobs. Self-driving cars alone could put millions out of work. And the manufacturing sector, already reeling from decades of job losses, could see millions of more jobs replaced by machines. The convergence of IoT and cognitive computing could also threaten many prestigious jobs as computers learn to perform thinking tasks rather than solely mechanical ones. "We will soon be looking at hordes of citizens of zero economic value," write venture investor William H. Davidow and technology writer Michael S. Malone in Harvard Business Review. "Figuring out how to deal with the impacts of this development will be the greatest challenge facing free market economies in this century."

The Senior Computer Vision Expert will also very closely work with Subject Matter Experts and Business Analysts to rapidly understand a specific business domain and iteratively refine the analyses and the learning models to create high-fidelity automated analytics solutions.

In the burgeoning field of computer science known as machine learning, engineers often refer to the artificial intelligences they create as "black box" systems: Once a machine learning engine has been trained from a collection of example data to perform anything from facial recognition to malware detection, it can take in queries--Whose face is that? Is this app safe?--and spit out answers without anyone, not even its creators, fully understanding the mechanics of the decision-making inside that box. But researchers are increasingly proving that even when the inner workings of those machine learning engines are inscrutable, they aren't exactly secret. In fact, they've found that the guts of those black boxes can be reverse-engineered and even fully reproduced--stolen, as one group of researchers puts it--with the very same methods used to create them. In a paper they released earlier this month titled "Stealing Machine Learning Models via Prediction APIs," a team of computer scientists at Cornell Tech, the Swiss institute EPFL in Lausanne, and the University of North Carolina detail how they were able to reverse engineer machine learning-trained AIs based only on sending them queries and analyzing the responses.

At the Hotel Parco dei Principi in Rome, in September of 1973, the Hungarian mathematician Paul Erd?s approached his friend Richard Guy with a request. He said, "Guy, veel you have a coffee?" It cost a dollar, a small fortune to a professor of mathematics at the hinterland University of Calgary who was not much of a coffee drinker. Yet, as Guy later recalled--during a memorial talk following Erd?s's death at age 83 two decades ago--he was curious why the great man had sought him out. Guy and Erd?s were in the Eternal City for an international colloquium on combinatorial theory, so Erd?s--who sustained himself with espresso and other stimulants, worked on math problems 19 hours a day, and in his lifetime published in excess of 1,500 papers with more than 500 collaborators--most likely had another problem on the go.

Google announced a number of updates to its cloud computing services at a small event in San Francisco this morning. These updates touch Google's machine learning services, as well as its database and analytics services, and include an update to how it supports its users. The company's focus today, though, was clearly on machine learning. Google launched the private alpha of its machine learning services a few months ago and today, Google's infrastructure chief Urs Hoelzle announced that Cloud Machine Learning is now available to all businesses as a public beta. The service allows you to train machine learning models and promises that you can train models with terabytes of data within only a few hours.

It's an exciting time for innovations in ed tech, and chatbots are at the forefront. Mobile apps are still compelling and there are many use cases where an app can provide the richest experience. However, the downside is that you still need to download them, log in, keep them updated, and make sure they work well with your devices. When it comes to sheer speed and convenience, nothing can beat a chatbot. So, what is a chatbot, anyway?

Given the votes on this post so far I foresee at best a lukewarm reaction to this on this sub; which kind of makes sense because this is more of a technical/research sub than a discussion sub. For general discourse I'd recommend /r/AIethics or /r/ControlProblem (although I prefer the former even if it's less active). To answer your question though. Harris talks about us not being able to marshal the appropriate response to an AI armageddon, but anyone familiar with the AI Winter (which probably includes many on this sub, which probably explains the dissatisfaction with AI-focused dystopian musings) knows that if anything, we've had a cultural overreaction to the threat of AI vs. what harm -- if any -- it's actually caused. Lots of people talk about how dangerous AI could be, and there are lots of movies made that stir up a lot of fear of the implications of AI, but what intelligent systems have actually done is help diagnose illness, learn to play video games, and give us a platform for doing tons of new and exciting things in the world.

Amazon, Google, Facebook, IBM, and Microsoft have announced they are forming a non-for-profit organisation to educate the public about artificial intelligence (AI) technologies, as well as alleviate anxieties around its application. The collective, which includes Google's AI subsidiary DeepMind, also plans to develop best practices on the challenges and opportunities within the field of AI. The organisation, called Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI), will address legal and ethical challenges that AI presents, encourage public discourse, and identify opportunities to use AI to bring improvements to society. The organisation does not intend to be a regulatory body, with a statement saying it does "not intend to lobby government or other policymaking bodies." Members of the Partnership on AI will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology.