Alleged criminals might not be the only ones to benefit from pleading the Fifth. By falling silent during the Turing test, artificial intelligence (AI) systems can fool human judges into believing they're human, according to a study by machine intelligence researchers from Coventry University. Alan Turing, considered the father of theoretical computer science and AI, devised the Turing test in an attempt to outline what it means for a thing to think. In the test, a human judge or interrogator has a conversation with an unseen entity, which might be a human or a machine. The test posits that the machine can be considered to be "thinking" or "intelligent" if the interrogator is unable to tell whether or not the machine is a human.

Amazon's annual competition for picking perfect robots that could one day work in the company warehouses crowned the latest champion in its robotic picking challenge. Competitors were asked to handle a wide range of products from toiletries to clothes and then evaluated on the basis of speed and precision in stocking shelves. The contest was won by a joint team from the TU Delft Robotics Institute in the Netherlands and the company Delft Robotics. The robot developed by Team Delft managed to pick items from a mock Amazon warehouse shelf at a rate of 100 per hour. The failure rate noted was 16.7%.

WIMBLEDON fans are being served a mash up of machine learning and advanced analytics in a bid to capture viewers' attention on social media and digital platforms. Statistics and analytics have been a big feature of grand slam tennis for some years now. But what's new this year is Watson. IBM's flagship AI-driven analytics platform has been tasked with crunching through the hundreds of thousands of social media and online posts which the event generates. It's mission will be to find the stories that fans are most engaged with, and drive creation of the sort of content that they most want to see.

Google's DeepMind is teaming up with NHS-funded Moorfields Eye Hospital to research whether machine learning can help the fight against blindness. The NHS said AI could play a "big role in tackling avoidable sight loss" and the partnership is intended to explore "how cutting edge technologies can help medical research into eye diseases". This includes macular degeneration, which generally affects the elderly, as well as diabetes-related sight loss. Machine learning processes will be applied to around a million eye scans to help search for early symptoms of sight loss. The number of people with sight loss is set to double by 2050, with around two million people in the UK currently living with sight loss – around one in 30.

Automation has everyone on the edge. According to a recent report by US-based research firm'HfS Research', India's IT services industry, which employs around 3.7 million people, will lose 6.4 lakh jobs to automation in the next five years. The report further adds that the IT industry worldwide would see a net decrease of 9% in headcount, or about 1.4 million jobs, with countries such as the Philippines, the United Kingdom and the United States also taking hits by 2021. But that is just one part of the story. The wave of'technological unemployment' will have a broader spectrum, say experts.

The NHS is pairing up with Google's artificially intelligent image software DeepMind for a new medical research partnership that could play a "big role in tackling avoidable sight loss." Specialists at the NHS-funded Moorfields Eye Hospital in London will use DeepMind, the internet giant's machine learning project, to research whether the technology can help detect and prevent eye diseases and blindness. DeepMind will be applied to one million anonymous eye scans to look for early signs of eye conditions that humans might miss such as macular degeneration and retinal conditions caused by diabetes. The end goal of the research is to create a more efficient method by which to analyse data and come to an earlier diagnosis for patients. The number of people suffering from sight loss in the UK is predicted to double by 2050, and the project marks Google's first machine learning collaboration with healthcare specialists.

Google announced today that it has acquired Paris-based Moodstocks, a startup that has developed machine learning technology to bolster the image recognition features on smartphones. "We continue to pursue our machine learning and research efforts," wrote Vincent Simonet, head of the research and development team for France Google, "and Moodstocks is the latest proof of our commitment to this area." "Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription."

In my first article, I touched on the problem of information overload in Market Intelligence (MI). It's a term that has been popularized by the media. It's often described as when you are dealing with more information that you are able to process to make good decisions. And they are 3 things CEO must be aware of. The technologies we have been using up until now, and that we are familiar with, are building blocks we can use to perceive the environment (the data) and infer meaning (semantic relationships, cause and consequence, and basically everything that enables the system to interpret its surroundings).

Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive). In contrast, we use the (standard) Logistic Regression model in binary classification tasks. Now, let me briefly explain how that works and how softmax regression differs from logistic regression. As the name suggests, in softmax regression (SMR), we replace the sigmoid logistic function by the so-called softmax function?: Now, this softmax function computes the probability that this training sample x(i) belongs to class j given the weight and net input z(i). So, we compute the probability p(y j x(i); wj) for each class label in j 1, ..., k.

SAN FRANCISCO: Google on Wednesday announced a deal to buy Moodstocks, a French startup behind technology that helps smartphones recognize whatever they are aimed at. Moodstocks caught the US technology giant's eye for its work in computer vision and machine learning, as well for accomplishments in enabling smartphones or other mobile devices to recognize images and objects. Google is among Silicon Valley titans investing in ways to get computers to see and understand the world around them the way people do. Machine learning has been woven into an array of Google offerings, such as its free language translation and photo services. "There's a lot more to be done to improve machine vision," Google France tech site lead Vincent Simonet said in a blog post.