In recent years, deep learning approaches have come to dominate discriminative problems in many sub-areas of machine learning. Alongside this, they have also powered exciting improvements in generative and conditional modeling of richly structured data such as text, images, and audio. This talk, led by indico's Head of Research, Alec Radford, will serve as an introduction to several emerging application areas of generative modeling and provide a survey of recent techniques in the field.

Mr Toyoda Toyota president announced that the car company would be launching a research company in silcon valley to develop artificial intelligence for use in cars and robotics. The Toyota research institute according to the company press release would make drive accessible to everyone regardless of inability. However the new company, which will have 200 employees and launch in 2016, will be looking to go "beyond" autonomous cars. Health, mobility and personal well being robotics to improve all aspects of human life. Toyota has previously said its first self-driving car will be out by 2020 and it has produced some robots which includes a series of nursing bots to help those with physical impairments.

It might not happen anytime soon, but then again, it might. In Japan, a short novel co-written by an artificial intelligence program (its co-author is human) made it past the first stage of a literary contest, the Japan News reports. The Nikkei Hoshi Shinichi Literary Award is named after Hoshi Shinichi, a Japanese science fiction author whose books include "The Whimsical Robot" and "Greetings from Outer Space." Judges for the prize weren't told which novels were written by humans and which were penned by human-computer teams. The award is unique in that it accepts entries from "applicants who are not human beings (AI programs and others)."

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?" I guess it is about time to write my script. In the following paragraphs, I really don't mean to tell you why you or anyone else should use Python. To be honest, I really hate those types of questions: "Which * is the best?" (* insert "programming language, text editor, IDE, operating system, computer manufacturer" here).

Specifically designed in the context of big data in our research lab, the new and simple strong correlation synthetic metric proposed in this article should be used, whenever you want to check if there is a real association between two variables, especially in large-scale automated data science or machine learning projects. Use this new metric now, to avoid being accused of reckless data science and even being sued for wrongful analytic practice. In this paper, the traditional correlation is referred to as the weak correlation, as it captures only a small part of the association between two variables: weak correlation results in capturing spurious correlations and predictive modeling deficiencies, even with as few as 100 variables. In short, our strong correlation (with a value between 0 and 1) is high (say above 0.80) if not only the weak correlation is also high (in absolute value), but when the internal structures (auto-dependencies) of both variables X and Y that you want to compare, exhibit a similar pattern or correlogram. Yet this new metric is simple and involves just one parameter a (with a 0 corresponding to weak correlation, and a 1 being the recommended value for strong correlation).

This article was first published in the April 2016 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. In November 2013, more than 100 patients with cancer - including pancreatic, breast, liver and brain tumours - embarked on clinical trials involving BPM 31510, a drug discovered by an algorithm. The story of BPM 31510 begins with the extraction of biological data from healthy and cancerous tissue samples from over 1,000 patients. This data was then processed by artificial intelligence algorithms, which analysed it and suggested possible drug treatments. "We've essentially reversed the scientific method," says Niven R Narain, the 38-year-old president and co-founder of Berg, the Boston pharma startup which makes BPM 31510. "Instead of a preconceived hypothesis that leads us to do experiments and generate a particular type of data, we allowed the biological data from the patients to lead us to the hypotheses." Making an effective cancer-fighting drug is a notoriously difficult process: according to Narain, development and production can cost pharmaceutical companies up to 2.6 billion ( 1.8bn) and take 12 to 14 years to complete. "Only one per cent of the cancer drugs that make it to clinical trials prove to be effective. It's expensive and the development process is inexcusably long," Narain says.

Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation. Edit: Some folks have asked about a followup article, and I'm planning to write one. Feel free to follow if you'd be interested in reading it and thanks for all the feedback! However, this is a bit terse…. A neural network trained with backpropagation is attempting to use input to predict output.

Shape-shifting robots could soon be morphing their way from the realms of science fiction into reality. Researchers have developed a metal-based material that can alter its shape and can even heal after it is damaged, conjuring up images of the T-1000 from the Terminator films. The group behind the material said it could have a number of applications, such as for the wings of an aircraft which could change their shape and even flexible skin for robots. Scientists have developed a metal-based material which can alter its shape, combining the stiffness of a metal alloy with the flexibility of a soft, porous rubber foam. The new composite material (pictured) has a stiff scaffold structure which'melts away' when needed By combining a stiff metal with soft, porous rubber foam, the team was able to combine the properties from both into a new composite material which has a stiff scaffold structure that'melts away' when needed.

Andy Grove, the refu gee from Hungary who became one of the pillars of Silicon Valley and, as both scientist and executive, was a principal figure in the rise of the Intel Corp. and a symbol of the world-wide computer revolution, died Monday. The death was announced on the company's website. It did not give a cause or a location. He had been diagnosed with prostate cancer and Parkinson's disease. In a statement, Intel chief executive Brian Krzanich said that Mr. Grove "made the impossible happen, time and again, and inspired generations of technologists, entrepreneurs, and business leaders."

Stop-start journeys punctuated with annoying queues at traffic lights could become a thing of the past. In the city of tomorrow, traffic lights will be replaced by intelligent intersections enabling cars with sensors embedded to automatically weave around each other, town planners have claimed. Such smart junctions, where lanes of cars merge harmoniously from one to the next, would cut traffic jams while enabling twice as many vehicles to use a road. The concept was developed by researchers at MIT's Senseable City Lab who believe such junctions will become a reality with the take up of cars fitted with sensors that enable them to'talk' to one another. Connected and self-driving vehicles are predicted to lead to safer and more efficient cities, for example.