Not a day goes by when we do not hear about the threat of AI taking over the jobs of everyone from truck drivers to accountants to radiologists. An analysis coming out of McKinsey suggested that "currently demonstrated technologies could automate 45 percent of the activities people are paid to perform." There are even online tools based on research from the University of Oxford to estimate the probability that various jobs will be automated. This concern that progress in AI will make most human labor obsolete has led some to call for a (universal) basic income, in which all citizens periodically and unconditionally receive money from the state (see "Basic Income: A Sellout of the American Dream"). Y Combinator, a prominent startup incubator in Silicon Valley, will run a pilot study of basic income in Oakland, California, and its president has stated that "at some point in the future, as technology continues to eliminate traditional jobs and massive new wealth gets created, we're going to see some version of this at a national scale."

Cutting edge Artificial Intelligence and Natural Language Processing has been used to analyze over 75,000 opinions from across the web to determine a comprehensive list of the most popular online retailers in the US. Collecting text opinions from across the web – including from reviews, forums, social media, YouTube etc. – the new report found that Barnes & Noble was ranked the best eCommerce site in the US. In the first study of its kind, AI company Aspectiva, which analyzes UGC from across the web to generate insights for retailers & shoppers, applied its technology to determine exactly what shoppers liked and disliked about key US retailers. The online bookstore came top in a list of over 40 retailers. Aspectiva's analysis found that Barnes & Noble shoppers rated highly their "fast service", "prices" and "easiness" when using their website.

Experts with the Word Economic Forum (WEF) say that there will be seven million less jobs available in 2020 than now. Oxford University experts warn that 20 years from now 39 percent of people will lose their jobs. Industrial workers will be the next to be phased out by the onslaught of intelligent robots. WEF experts insist that the loss of jobs in some sectors will made up for by new jobs available on other economic sectors. According to them, there will be 2 million more high-tech jobs available four to five years from now.

Two crucial questions in discussions about the risks of artificial superintelligence are: 1) How much more capable could an AI become relative to humans, and 2) how easily could superhuman capability be acquired? To answer these questions, I will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how an AI could improve on humans in two major aspects of thought and expertise, namely mental simulation and pattern recognition. I find that although there are very real limits to prediction, it seems like an AI could still substantially improve on human intelligence, possibly even mastering domains which are currently too hard for humans. In practice, the limits of prediction do not seem to pose much of a meaningful upper bound on an AI's capabilities, nor do we have any nontrivial lower bounds on how much time it might take to achieve a superhuman level of capability. Takeover scenarios with timescales on the order of mere days or weeks seem to remain within the range of plausibility. As AI systems become more advanced, there is the possibility of them reaching superhuman levels of intelligence, eventually breaking out of human control (Bostrom 2014). The answers to these questions will influence the urgency of dealing with questions of superintelligent AI, as well as the correct means of it. If AI systems can rapidly achieve strong capabilities, becoming powerful enough to take control of the world before any human can react, then that implies a very different approach than one where AI capabilities develop gradually over many decades, never getting substantially past the human level (Sotala & Yampolskiy, 2015). Views on these questions vary. Authors such as Bostrom (2014) and Yudkowsky (2008) argue for the possibility of a fast leap in intelligence, with both offering hypothetical example scenarios where an AI rapidly acquires a dominant position over humanity. On the other hand, Anderson (2010) and Lawrence (2016) appeal to fundamental limits on predictability – and thus intelligence – posed by the complexity of the environment. 'Practitioners who have performed sensitivity analysis on time series prediction will know how quickly uncertainty accumulates as you try to look forward in time. There is normally a time frame ahead of which things become too misty to compute any more. Further computational power doesn't help you in this instance, because uncertainty dominates. Reducing model uncertainty requires exponentially greater computation. We might try to handle this uncertainty by quantifying it, but even this can prove intractable.

Microsoft has released a new software update for the Xbox One console, and it comes with fixes that improve the overall gaming experience. Unfortunately, this update is only available to preview members. According to DualShockers, the system software update has a build code of rs1_xbox_rel_1610.161103-1900. When it comes to Messaging, users who are unable to send messages will now see a dialog that contains information on why the message cannot be sent. The dialog will also direct users to Xbox Support.

Though scientists have long known that mice can pick out scents -- the smell of food, say, or the odor of a predator -- they have been at a loss to explain how they are able to perform that seemingly complex task so easily. But a new study, led by Venkatesh Murthy, professor of molecular and cellular biology, suggests that the means of processing smells may be far simpler than researchers realized. Using a machine-learning algorithm, Murthy and colleagues were able to "train" a computer to recognize the neural patterns associated with various scents, and to identify whether specific odors were present in a mix of smells. The study is described in a Sept. 1 paper in the journal Neuron. Along with Murthy, the paper was co-authored by Alexander Mathis, Dan Rokni, and Vikrant Kapoor, postdoctoral fellows working in Murthy's lab, and Professor Matthias Bethge from the Werner Reichardt Centre for Integrative Neuroscience & Institute of Theoretical Physics in Germany.

When the potential of intelligent alien civilisations comes up in conversation, it's usually about the search. How will we find them? Are they there at all? What actions should we take if – or when – we find them, or they find us? Well, according to physicist Stephen Hawking, we should probably stop trying to contact them at all, because reaching out to advanced civilisations could put humanity and Earth in a pretty risky situation.

DeepMind, the AI research lab in London that was acquired by Google in 2014 for a reported £400 million, faces one big problem, according to Professor Yann LeCun, who heads up Facebook's AI research group. Notably, LeCun believes that DeepMind, which employs over 250 people and today sits under Alphabet (Google's parent company), is too far away from California. "The challenge I think that DeepMind has is that it's geographically separated from the mothership in California and that makes it very difficult to build technology that can be used in products," LeCun told Business Insider during an interview in London last week. "So it pushes DeepMind to some extent to try to survive on its own." DeepMind declined to comment on this story but it would likely argue that being based in the UK is not a barrier when it comes to working with product and research teams across Google and the rest of the Alphabet group.

Imagine a typical day in 2020: Your personal AI assistant wakes you up with a friendly greeting before preparing your favorite breakfast. During your morning workout, it plays new songs that perfectly match your musical tastes. For your driverless commute to work, it has pre-selected a few articles based on the duration of your commute and what you've read in the past. You read the news and realize the presidential election is coming up. Based on a predicted model that takes into account your previously expressed views and data on other voters in your state, your AI assistant recommends you vote for the Democratic candidate.

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace($\lambda$), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from any behaviour policy, whatever its degree of "off-policyness"; and (3) it is efficient as it makes the best use of samples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy policy evaluation and control settings and derive online sample-based algorithms. We believe this is the first return-based off-policy control algorithm converging a.s. to $Q^*$ without the GLIE assumption (Greedy in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins' Q($\lambda$), which was an open problem since 1989. We illustrate the benefits of Retrace($\lambda$) on a standard suite of Atari 2600 games.