Ask the Google search app "What is the fastest bird on Earth?," and it will tell you. "Peregrine falcon," the phone says. "According to YouTube, the peregrine falcon has a maximum recorded airspeed of 389 kilometers per hour." That's the right answer, but it doesn't come from some master database inside Google. When you ask the question, Google's search engine pinpoints a YouTube video describing the five fastest birds on the planet and then extracts just the information you're looking for.

Britain's intelligence services have officially been given the "most extreme spying powers ever seen". The Investigatory Powers Act has now been given royal assent, meaning that those surveillance rules will pass into law. The bill was officially unveiled a year ago and passed through the House of Lords earleir this month, but the act of being signed off means that those powers now go into effect. It adds new surveillance powers including rules that force internet providers to keep completely records of every website that all of their customers visit. Those will be available to a wide range of agencies, which includes the Department for Work and Pensions as well as the Food Standards Agency.

Farming is the oldest industry out there, but it's one that's far from being perfect. Some parts of the world suffer from major cases of over-farming, whereas other places can't get enough. It's tricky trying to get the right balance between supply and demand when it comes to food, but with thanks to machine learning techniques, we may just be one step closer to achieving this. The world's population is steadily increasing, and in order to continue to feed everyone, farming techniques need to become more efficient and farmers smarter. One new technique that has been developed to tackle this problem is vertical farming.

The AtlaSense team has been working hard on our machine learning and AI modules, and we are pleased to present you with our newest features: data categorization, topic modeling, and document linking across your enterprise data. These new features are meant to provide greater insight into your data and improve the way teams and organizations classify information. Data Categorization Using our document categorization workflow, you can identify responsive documents, find semantically similar documents, and build a classifier to make predictions on the data found in a project. Topic Modeling With our topic modeling workflow, you can learn about the prevalent and abstract topics in a particular project or collection of information. Document Linking Our document-to-document linking workflow allows you link related files and also suggests similar documents on the file viewer.

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The goal of genome-wide association studies (GWAS) (e.g. the WTCCC study1) is to examine the relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and individual traits, which are usually complex diseases or behavioral characteristics. Generally, a large number of statistical tests are performed in parallel, each SNP being individually tested for association2,3,4. The standard approach consists of computing individual, SNP-specific p-values corresponding to a statistical association test and comparing these p-values against some given significance threshold (say t*), meaning that precisely those SNPs with p-values smaller than t*are declared to be associated with the trait4,5,6. We refer to this approach as raw p-value thresholding (RPVT) and review some standard methods for choosing t*for the purpose of controlling multiple type I error rates (in particular, the family-wise error rate (FWER) and the expected number of false rejections (ENFR)) in the Methods Section. According to the GWAS catalog7,8 (last accessed 03-07-2015), the more than 1,400 GWAS published so far have led to the identification of more than 11,000 SNPs associated with about 800 human diseases and anthropometric traits with p-values using t* 1 10 5.

A team of researchers at MIT's Computer Science and Artificial Intelligence Lab (CSAIL) have created a deep-learning algorithm that is able to generate its own videos and predict the future of a video based on a single frame. As detailed in a paper to be presented next week at the Conference on Neural Information Processing Systems in Barcelona, the CSAIL team trained their algorithm by having it watch 2 million videos which would last for over a year if played back to back. These videos consisted of banal moments in day to day life to better accustom the machine to normal human interactions. Importantly, these videos were found "in the wild," meaning they were unlabeled and thus didn't offer the algorithm any clues as to what was happening in the video. Drawing from this video data set, the algorithm would attempt to generate videos from scratch that mimicked human motion based on what it had observed in the 2 million videos.

Keep your recruiting and business development vision clear by understanding the direction and growing impact of artificial intelligence (AI) on the economy. We talked earlier about innovations in AI that will certainly displace jobs. In October, a report from the White House took that conversation further, laying the groundwork for regulatory and industry support of rapidly coalescing advancements in AI. Developed with interagency effort, the findings follow an outreach effort by the National Science and Technology Council (NSTC), including requests for information and public workshops hosted by research and other institutions. AI is defined in several ways.

The term "data science" was first used by the statistician William H. Cleveland in his 2001 paper entitled, "Data Science: An Action Plan for Expanding the Technical Areas of t...". Cleveland emphasized that the "[results in] data science should be judged by the extent to which they enable the analyst to learn from data". The scientific discipline of learning from data has been happening for centuries before the term data science ever came into being. Statisticians have been collecting, processing, analysing, visualising and interpreting vast amounts of diverse data to generate models. In doing so, they developed many algorithms that are used for regression and classification such as GLM (Generalised Linear Modeling) and embedded in statistical packages such as SAS and SPSS that are used extensively to this day.

The House of Commons has passed a Bill that forces people to ask to be allowed to see porn and bans many sex acts from appearing in adult videos. The Digital Economy Bill received an unopposed third reading from MPs, meaning that it is on its way to becoming law. As such, it will force pornographic websites to add age verification checks that won't let people watch videos until they sign up to a special verification programme. It will also put into law new rules that will allow videos depicting unusual practices from adult websites. That clause, which provoked anger this week, bans anything from being made available online in the UK that wouldn't be allowed on a commercially available DVD.