Sirius [1] is an open end-to-end standalone speech and vision based intelligent personal assistant (IPA) similar to Apple's Siri, Google's Google Now, Microsoft's Cortana, and Amazon's Echo. Sirius [1] implements the core functionalities of an IPA including speech recognition, image matching, natural language processing and a question-and-answer system. Sirius [1] is developed by Clarity Lab at the University of Michigan. Sirius [1] is published at the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2015. If you're interested in contributing to Sirius, fork our repo or post to sirius-users.

I've written previously about the way AI has become increasingly capable in creative tasks, whether it's playing jazz or cracking jokes. There have even been a couple of projects utilizing AI to help us develop smarter and more engaging video content. A Japanese company are promising to take this to a new level with the development of a robot that will provide creative advice for commercials and other marketing projects. The AI has been developed and deployed by the marketing agency McCann Japan, and will attempt to provide input on the various projects taken on by the agency. It will mine a large database of previous creative projects to suggest possibly new creative directions for an advert. It's a very similar approach to that taken by the robot jazz player mentioned above, which also mined a huge back catalog of past jazz performances to suggest creative new avenues the'player' could go down.

Before leaving Helsinki, we arXived [from the Air France lounge!] the paper Jean-Michel presented on Monday at ABCruise in Helsinki. This paper summarises the experiments Louis conducted over the past months to assess the great performances of a random forest regression approach to ABC parameter inference. I think the major incentives in exploiting the (still mysterious) tool of random forests [against more traditional ABC approaches like Fearnhead and Prangle (2012) on summary selection] are that (i) forests do not require a preliminary selection of the summary statistics, since an arbitrary number of summaries can be used as input for the random forest, even when including a large number of useless white noise variables; (b) there is no longer a tolerance level involved in the process, since the many trees in the random forest define a natural if rudimentary distance that corresponds to being or not being in the same leaf as the observed vector of summary statistics?(y); To the point that deriving a different forest for each univariate transform of interest is truly a minor drag in the overall computing cost of the approach. An intriguing point we uncovered through Louis' experiments is that an unusual version of the variance estimator is preferable to the standard estimator: we indeed exposed better estimation performances when using a weighted version of the out-of-bag residuals (which are computed as the differences between the simulated value of the parameter transforms and their expectation obtained by removing the random trees involving this simulated value). Another intriguing feature [to me] is that the regression weights as proposed by Meinshausen (2006) are obtained as an average of the inverse of the number of terms in the leaf of interest.

Apache Spark, the open-source cluster computing framework, will soon see a major update with the upcoming release of Spark 2.0. This update promises to be faster than Spark 1.6, thanks to a run-time compiler that generates optimized bytecode. It also promises to be easier for developers to use, with streamlined APIs and a more complete SQL implementation. Spark 2.0 will also include a new "structured streaming" API, which will allow developers to write algorithm for streaming data without having to worry about the fact that streaming data is always incomplete; algorithms written for complete DataFrame objects will work for streams as well. This update also includes some news for R users.

Unless you've been living under a rock (which will likely be affected by climate change soon, by the way), you know that between forest fires, illegal deforestation, poaching, and other crimes, an enormity of environmental issues puts our ecosystems in danger. According to the National Science Foundation, a century ago, more than 60,000 tigers roamed in the wild. Now, there are as few as 3,000 remaining. While human patrols can directly protect endangered animals, many protection agencies lack the resources necessary to cover the appropriate amount of ground, especially in large national parks where many of these illicit activities might occur. In 2011, Eve McDonald-Madden and her colleagues at the University of Queensland in Australia lamented that a lack of money limits the impact that management strategies can have on preventing the extinction of a species.

Musk and other tech giants are joining forces to fund research that will halt artificial intelligence from overtaking mankind. Elon Musk's contributions to society know no bounds: his latest scheme is intended to save humanity from being destroyed by artificial intelligence (AI). The billionaire, known for garnering a massive amount of wealth and attention with his revolutionary projects of PayPal, Tesla, and SpaceX, has consistently warned against AI, recently calling it humanity's greatest existential threat. His belief of the detriment AI may cause has led him to pool forces with other well-known tech entrepreneurs to establish an investment fund intended for researchers to pursue actions with a positive social impact. The 1 billion fund is slated to assist humans in staying at least one step ahead of technology.

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The National Telecommunications & Information Administration released Thursday a list of voluntary privacy best practices for commercial and non-commercial drone users, in the wake of concerns that drones could encroach on individual privacy and open a new front in the collection of personal data for commercial use. The privacy guidance, arrived at in consensus with drone organizations and companies like Amazon and Google's parent Alphabet, recommends that drone operators who collect personal data should have a privacy policy that explains what personally identifiable information they will collect, for what purpose the data is collected and if it will be shared with others, including in response to requests from law enforcement agencies. The guidelines also encourage drone operators to avoid using or sharing personal data for marketing purposes without consent of the individual. Drone operators should also not use personal data without consent for "employment eligibility, promotion, or retention; credit eligibility; or health care treatment eligibility other than when expressly permitted by and subject to the requirements of a sector-specific regulatory framework." Data collected should also not be held beyond a reasonable period, without the consent of the individual, or in exceptional circumstances, such as legal disputes or safety incidents.

There is so much doom and gloom associated with robots and jobs it is time to add some common sense to the misunderstandings created by so called experts opinions about robots and jobs – thankfully authors from the OECD may have added some clarity to the debate -- 'finding that on average, across the 21 OECD countries, '9% of jobs rather than 47%, as proposed by Frey and Osborne face a high automatibility.' Capitalism, the term for our global'free' markets, is a uniquely future-oriented economic system in which people invest, make innovations, apply for patents, and in other ways bet on the future. Behind all of this we find the hallmark of humanity, which is our creative intelligence. It is intelligence that drives these investments and innovations, and intelligence that forges within many of us an intense curiosity of what the future may hold. It is also intelligence that forges in others an anxiety over what the future holds. For many the future is no longer a promise but a threat!

The giant hedge fund, which manages 35 billion, is as much a technology company as it is a hedge fund. It uses advanced technologies to find investment opportunities, and it just hosted its annual artificial intelligence competition. One of those technological applications involves using natural-language-processing techniques to analyze the Fed minutes, such as those set for release Wednesday afternoon. "Historically, interpretations of those minutes required art, so Fed watchers pontificated and critiqued," the firm said in a note. "Now natural language processing techniques can translate those minutes into relatively objective data."