Afew years ago the cosmologist Max Tegmark found himself weeping outside the Science Museum in South Kensington. He'd just visited an exhibition that represented the growth in human knowledge, everything from Charles Babbage's difference engine to a replica of Apollo 11. What moved him to tears wasn't the spectacle of these iconic technologies but an epiphany they prompted. "It hit me like a brick," he recalls, "that every time we understood how something in nature worked, some aspect of ourselves, we made it obsolete. Once we understood how muscles worked we built much better muscles in the form of machines, and maybe when we understand how our brains work we'll build much better brains and become utterly obsolete." Tegmark's melancholy insight was not some idle hypothesis, but instead an intellectual challenge to himself at the dawn of the age of artificial intelligence. What will become of humanity, he was moved to ask, if we manage to create an intelligence that outstrips our own?

Are your colleagues lousy at communicating with each other? A chatbot could help, specifically one called CoachBot. Developed by the London-based HR company Saberr, it asks about workplace dynamics and provides the team with reports. A unit within the UK's National Health Service is trialling it, as are 10 companies, including Unilever and Logitech. When Coachbot arrives in the workplace, staff have to introduce themselves to it.

There are increasing calls for government oversight of artificial intelligence development. Artificial intelligence (AI) promises to have a huge and positive impact on our world – but, it also brings with it complex issues that we have never had to face as a society before. AI sounds alarm bells for some people, who are frightened that AI will bring about a real threat to humanity, learning our worst traits, intensifying inequalities and triggering weapons of mass destruction. Others believe AI will take people's jobs and discriminate against the vulnerable in society. Kevin Kelly, author and founder executive editor of Wired believes these anxieties are deep rooted because they link our intelligence to our identity, but that they can be overcome.

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The field of AI has reached an inflection point today, where it is on the cusp of revolutionising areas as diverse as security, finance, transport, healthcare and government service delivery. Availability of massive volumes of data, relatively inexpensive computational capabilities and improved training techniques, such as deep learning, have led to significant leaps in AI capabilities and will only continue to do so for the foreseeable future. The pace is accelerating and governments need to figure out how to deal with this era of AI 2.0, where AI is becoming all-pervasive. Where if they want to unlock the potential of the data being generated at an ever-increasing velocity, government departments need AI at their fingertips, in the here and now. On September 14, senior executives from a wide range of key public sector agencies in Singapore and institutes of higher learning gathered for a vibrant, insightful discussion on the next stage of artificial intelligence.

One of the activities of AI-Ethics.com is to monitor and report on the work of all groups that are writing draft principles to govern the future legal regulation of Artificial Intelligence. Many have been proposed to date. Click here to go to the AI-Ethics Draft Principles page. If you know of a group that has articulated draft principles not reported on our page, please let me know. At this point all of the proposed principles are works in progress.

It was October 2001, one month after the tragic terrorist attacks on 9-11-2001. I was sitting in my NASA office when the phone rang. The voice on the other end of the call said, "We would like you to brief the President tomorrow on data mining." I remember clearly my response: "Do you mean THE President?" Yes, they did mean the President of the United States.

An aspect of artificial intelligence that's sometimes overlooked is just how good it is at creating fake audio and video that's difficult to distinguish from reality. The advent of Photoshop got us doubting our eyes, but what happens when we can't rely on our other senses? The latest example of AI's audiovisual magic comes from the University of Washington, where researchers have created a new tool that takes audio files, converts them into realistic mouth movements, and then grafts those movements onto existing video. The end-result is a video of someone saying something they didn't. It's a confusing process to understand by just reading about it, so take a look at the video below: You can see two side-by-side clips of Barack Obama.

We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesign or approximate existing networks, we propose two schemes that adaptively utilize networks. We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example. By allowing examples correctly classified using early layers of the system to exit, we avoid the computational time associated with full evaluation of the network. We extend this to learn a network selection system that adaptively selects the network to be evaluated for each example. We show that computational time can be dramatically reduced by exploiting the fact that many examples can be correctly classified using relatively efficient networks and that complex, computationally costly networks are only necessary for a small fraction of examples. We pose a global objective for learning an adaptive early exit or network selection policy and solve it by reducing the policy learning problem to a layer-by-layer weighted binary classification problem. Empirically, these approaches yield dramatic reductions in computational cost, with up to a 2.8x speedup on state-of-the-art networks from the ImageNet image recognition challenge with minimal (<1%) loss of top5 accuracy.

The outcome is the result of two powerful forces in the evolution of information retrieval: artificial intelligence--especially natural language processing--and crowdsourcing. Computer algorithms interpret the relationship between the words we type and the vast number of possible web pages based on the frequency of linguistic connections in the billions of texts on which the system has been trained. But that is not the only source of information. The semantic relationships get strengthened by professional annotators who hand-tune results--and the algorithms that generate them--for topics of importance, and by web searchers (us) who, in our clicks, tell the algorithms which connections are the best ones. Despite the incredible, world-changing success of this model, it has its flaws.