Hey Siri, call my date and tell them I'm just not that into them.

I have trained a SVM and logistic regression classifier on my dataset for binary classification. Both classifier provide a weight vector which is of the size of the number of features. I can use this weight vector to select the 10 most important features. For doing that I have turned the weights into t-scores by doing a permutation test. I did 1000 permutations of the class labels and at each permutation I calculated the weight vector.

With an ever-increasing volume of electronic data being collected by the healthcare system, researchers are exploring the use of machine learning--a subfield of artificial intelligence--to improve medical care and patient outcomes. An overview of machine learning and some of the ways it could contribute to advancements in plastic surgery are presented in a special topic article in the May issue of Plastic and Reconstructive Surgery, the official medical journal of the American Society of Plastic Surgeons (ASPS). "Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making," write Dr. Jonathan Kanevsky of McGill University, Montreal, and colleagues. They highlight some key areas in which machine learning and "Big Data" could contribute to progress in plastic and reconstructive surgery. Machine Learning Shows Promise in Plastic Surgery Research and Practice Machine learning analyzes historical data to develop algorithms capable of knowledge acquisition.

With an ever-increasing volume of electronic data being collected by the healthcare system, researchers are exploring the use of machine learning--a subfield of artificial intelligence--to improve medical care and patient outcomes. An overview of machine learning and some of the ways it could contribute to advancements in plastic surgery are presented in a special topic article in the May issue of Plastic and Reconstructive Surgery, the official medical journal of the American Society of Plastic Surgeons (ASPS). "Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making," write Dr. Jonathan Kanevsky of McGill University, Montreal, and colleagues. They highlight some key areas in which machine learning and "Big Data" could contribute to progress in plastic and reconstructive surgery. Machine learning analyzes historical data to develop algorithms capable of knowledge acquisition.

The UK's AI scene is the talk of the town at the moment, with a number of significant startup exits happening over the last few years. Evi was acquired by Amazon for a reported 18 million in 2013, DeepMind was bought by Google for around 400 million in 2014, VocalIQ was acquired by Apple for an unknown amount in 2015, and SwiftKey was bought by Microsoft for 175 million in 2016. Saul Klein, a venture capitalist at London-based LocalGlobe, believes there are a number of factors that have led to a general surge in AI. "Clearly this [AI] has been decades in the making," said Klein during an interview with Business Insider at LocalGlobe's office in King's Cross. "There are conditions that exist now that make mainstream AI and the application of AI possible. In terms of what makes the UK so special, Klein believes the Oxbridge-London triangle is playing an important role in the creation of the UK's best AI companies. Oxford, Cambridge, Imperial, and UCL all have deep expertise in applied mathematics, computer science, and machine learning, according to a blog post by two AI investors. As a result, several of Britain's best-known AI companies started off as research projects within these institutions before being spun out. Evi and VolalIQ began at Cambridge, for example, while DeepMind has close ties to all four institutions. There are also a number of organisations in the UK that incubate AI startups in their early days. Entrepreneur First in London, for example, helps deeply technical people to find cofounders to launch a tech startup with; at least half of their last cohort focused on applying machine learning to different challenges. LocalGlobe, which Klein founded with his father Robin, is using its 45 million fund to make a number of investments into UK AI startups, as are VCs like Playfair Capital and White Star Capital. "There are really amazing AI-driven businesses that are emerging and some of the companies that we will announce investments in are squarely focused in and around that," said Klein. In terms of whether AI could one day pose a threat to humanity, as famous scientist Stephen Hawking predicts, Klein said: "I guess the way I would look at it is that there are lots of technologies that we have created over time, including nuclear weapons, that have existential risk.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Infosys has launched a machine learning platform and strengthened its partnerships with Microsoft. The platform is called Mana and aims to drive automation and bring in innovation amongst businesses, the company said at its annual conference in San Fracisco. Mana, with the Infosys Aikido service offerings, lowers the cost of maintenance for both physical and digital assets; captures the knowledge and know-how of people in an organisation; simplifies core business processes and enables businesses to bring new user experiences leveraging technology, the company said in a statement. Addressing the gathering, Vishal Sikka, CEO & Managing Director, Infosys, said that purposeful AI is about leveraging technology to amplify people. "We can automate the repetitive, mechanisable tasks; we can capture the knowledge and know-how across people and long-lived systems and bring this knowledge back inside the systems to drive more value; and in doing these things we can free people to put all of our creativity, passion, and imagination into thinking about the bigger opportunities ahead of us," he added.

Twelve years ago, Robert McEliece, a mathematician and engineer at Caltech, won the Claude E. Shannon Award, the highest honor in the field of information theory. During his acceptance lecture, at an international symposium in Chicago, he discussed the prize's namesake, who died in 2001. Claude Shannon: Born on the planet Earth (Sol III) in the year 1916 A.D. Generally regarded as the father of the information age, he formulated the notion of channel capacity in 1948 A.D. Within several decades, mathematicians and engineers had devised practical ways to communicate reliably at data rates within one per cent of the Shannon limit. As is sometimes the case with encyclopedias, the crisply worded entry didn't quite do justice to its subject's legacy. That humdrum phrase--"channel capacity"--refers to the maximum rate at which data can travel through a given medium without losing integrity.

In the absence of unobserved confounders, matching and weighting methods are widely used to estimate causal quantities including the Average Treatment Effect on the Treated (ATT). Unfortunately, these methods do not necessarily achieve their goal of making the multivariate distribution of covariates for the control group identical to that of the treated, leaving some (potentially multivariate) functions of the covariates with different means between the two groups. When these "imbalanced" functions influence the non-treatment potential outcome, the conditioning on observed covariates fails, and ATT estimates may be biased. Kernel balancing, introduced here, targets a weaker requirement for unbiased ATT estimation, specifically, that the expected non-treatment potential outcome for the treatment and control groups are equal. The conditional expectation of the non-treatment potential outcome is assumed to fall in the space of functions associated with a choice of kernel, implying a set of basis functions in which this regression surface is linear. Weights are then chosen on the control units such that the treated and control group have equal means on these basis functions. As a result, the expectation of the non-treatment potential outcome must also be equal for the treated and control groups after weighting, allowing unbiased ATT estimation by subsequent difference in means or an outcome model using these weights. Moreover, the weights produced are (1) precisely those that equalize a particular kernel-based approximation of the multivariate distribution of covariates for the treated and control, and (2) equivalent to a form of stabilized inverse propensity score weighting, though it does not require assuming any model of the treatment assignment mechanism. An R package, KBAL, is provided to implement this approach.