Artificial intelligence is increasingly being used as an unbiased judge, for matters ranging from insurance to economic efficiency. But can it ever truly be unbiased? When Remy Descartes first wrote the phrase cogito ergo sum –'I think therefore I am'– in the 1600s, he could not have been aware of the philosophical questioning that would erupt with the onset of artificial intelligence (AI) in the 20th and 21st century. Every Google search, every video suggested on YouTube and every Siri recommendation is built on machine learning algorithms designed to learn everything about your online habits, in a bid to offer targeted content that you might like. Even outside of consumer-level decisions, AI and algorithms are increasingly being used to root out hidden meanings in billions of lines of genetic code, in the hope of finding a cure to a disease or building machines that can talk for themselves.

Uber has suspended its self-driving car operations after one of its vehicles was involved in a crash in Arizona. The accident left one of the company's driverless Volvos on its side, but fortunately led to no serious injuries. A picture of the crash scene shows two other damaged cars sitting next to the Volvo, one of which has smashed windows and particularly bad dent marks, suggesting the accident happened at some speed. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Use our LinkedIn Login to download this post to PDF or save it to MyLibrary! Everyone has been hearing about AI, some have been hearing about NLP - and everyone has an opinion, belief, or thought about AI. However, that opinion, belief, or thought about AI (and/or NLP) is fully dependent upon the voices of whom everyone has chosen to listen. We are at a stage in our rate of change of technology where we have to let go of how we learned things in the past -- and step into a new stage of keeping ourselves always available to learn, no matter what we believe and/or think. We cannot, more than ever before, solve our problems with the same thinking we used to create them (said Albert Einstein nearly 100 years ago!).

The Bonassama District Hospital in Douala, Cameroon and six other African hospitals are adopting SOPHiA to – no matter their experience in genomic testing – get up to speed and analyze genomic data to identify disease-causing mutations in patients' genomic profiles, and decide on the most effective care. A release from the global leader in Data-Driven Medicine, Sophia Genetics, says in addition to the Bonassama district hospital, the modern technology is being adopted by Pharma Process in Casablanca, Morocco; ImmCell in Rabat, Morocco; The Al Azhar Oncology Center in Rabat, Morocco; The Riad Biology Center in Rabat, Morocco; The Oudayas, Medical Analysis Laboratory, Morocco;and The Center for Proteomic & Genomic Research (CPGR) in Cape Town, South Africa. As new users of SOPHiA, they become part of a larger network of 260 hospitals in 46 countries that share clinical insights across patient cases and patient populations, which feeds a knowledgebase of biomedical findings to accelerate diagnostics and care. Speaking about the adoption of SOPHiA in Africa, Jurgi Camblong, Sophia Genetics' CEO and co-founder, declared: "Since inception, our vision has been to develop innovative technological solutions that analyze patients' genomic profiles to offer better diagnosis and care to the greatest number of patients, wherever they live. Today, I am very proud that SOPHiA is triggering a technological leapfrog movement in healthcare across Africa."

One evening last November, a fifty-four-year-old woman from the Bronx arrived at the emergency room at Columbia University's medical center with a grinding headache. Her vision had become blurry, she told the E.R. doctors, and her left hand felt numb and weak. The doctors examined her and ordered a CT scan of her head. A few months later, on a morning this January, a team of four radiologists-in-training huddled in front of a computer in a third-floor room of the hospital. The room was windowless and dark, aside from the light from the screen, which looked as if it had been filtered through seawater. The residents filled a cubicle, and Angela Lignelli-Dipple, the chief of neuroradiology at Columbia, stood behind them with a pencil and pad. She was training them to read CT scans. "It's easy to diagnose a stroke once the brain is dead and gray," she said. "The trick is to diagnose the stroke before too many nerve cells begin to die." Strokes are usually caused by blockages or bleeds, and a neuroradiologist has about a forty-five-minute window to make a diagnosis, so that doctors might be able to intervene--to dissolve a growing clot, say. "Imagine you are in the E.R.," Lignelli-Dipple continued, raising the ante. "Every minute that passes, some part of the brain is dying. Time lost is brain lost." She glanced at a clock on the wall, as the seconds ticked by. "So where's the problem?" she asked. The blood supply to the brain branches left and right and then breaks into rivulets and tributaries on each side. A clot or a bleed usually affects only one of these branches, leading to a one-sided deficit in a part of the brain. As the nerve cells lose their blood supply and die, the tissue swells subtly.

Can machines think?" asked Alan Turing, known as the father of artificial intelligence (AI), in a seminal paper on the topic of computing machinery and intelligence in 1950. Turing did not coin the term'Artificial Intelligence' but his work laid the foundations for a new research area to be termed'Artificial Intelligence' by John McCarthy, one of the organizers of the 1956 conference held at Dartmouth College, UK to delve into the fundamental task of developing an electronic brain. However, by 1973, disappointed by the progress of work, funding dried up in the UK and USA and AI plunged into a long'winter'. In the 20th century, AI was an idea for the future. It needed much more computing power and a greater variety of digital data sources than was available at that time. Today, the picture has changed. Computing power has reached petaflops levels, distributed on the Cloud and accessible to personal devices like smartphones. As much as 90% of the existing data has been created in the last two years; 2.5 quintillion of data is generated per day from sensors, mobiles, online transactions and social media. The challenge is how to harness this huge data flow to return actionable information without storing this data for future analysis because the growth of storage capacity has long been surpassed by the growth of data volume and there is no possibility of the gap being covered. This is why AI has again gained prominence. Big Data Analytics enables the analysis of data as it streams and stores only the intelligence gathered for future reference. Big Data Analytics is one of the applications of Artificial Intelligence. Atanu Sinha, Director – India and SAARC, Hexagon Geospatial, says: "Big Data Analytics is more to do with past analysis and future trends based on which an organization can make informed decisions.

A mysterious self-driving car company has been quietly expanding in recent years in the world's largest country. Now, Moscow-based Cognitive Technologies has hired a slew of new recruits and is ready to move to the U.S. in the coming months. "The big R&D center will stay in Russia, but the main engineers and business guys will be sent to U.S. soil to set up a proper office," Roman Tarasov, the company's VP for global business, tells Inverse. Cognitive Technologies was founded in 1993 by the guys who created Kaissa, the world's first computer chess champion. For decades it worked on image and voice recognition applications, selling products to Intel, Yandex, and others.

It was just a friendly little argument about the fate of humanity. Demis Hassabis, a leading creator of advanced artificial intelligence, was chatting with Elon Musk, a leading doomsayer, about the perils of artificial intelligence. They are two of the most consequential and intriguing men in Silicon Valley who don't live there. Hassabis, a co-founder of the mysterious London laboratory DeepMind, had come to Musk's SpaceX rocket factory, outside Los Angeles, a few years ago. They were in the canteen, talking, as a massive rocket part traversed overhead. Musk explained that his ultimate goal at SpaceX was the most important project in the world: interplanetary colonization. Hassabis replied that, in fact, he was working on the most important project in the world: developing artificial super-intelligence. Musk countered that this was one reason we needed to colonize Mars--so that we'll have a bolt-hole if A.I. goes rogue and turns on humanity. Amused, Hassabis said that A.I. would simply follow humans to Mars. This did nothing to soothe Musk's anxieties (even though he says there are scenarios where A.I. wouldn't follow). An unassuming but competitive 40-year-old, Hassabis is regarded as the Merlin who will likely help conjure our A.I. children. The field of A.I. is rapidly developing but still far from the powerful, self-evolving software that haunts Musk. Facebook uses A.I. for targeted advertising, photo tagging, and curated news feeds. Microsoft and Apple use A.I. to power their digital assistants, Cortana and Siri. Google's search engine from the beginning has been dependent on A.I. All of these small advances are part of the chase to eventually create flexible, self-teaching A.I. that will mirror human learning. Some in Silicon Valley were intrigued to learn that Hassabis, a skilled chess player and former video-game designer, once came up with a game called Evil Genius, featuring a malevolent scientist who creates a doomsday device to achieve world domination.

I'm always amazed at people who tell me they would never trust a driverless car to take them somewhere but then happily get into a car driven by their teenager. Talk about preferring the devil you know. Driverless vehicles are likely to be much safer than those driven by humans. The safety differential is so large that insurance companies are already looking at alternative business models to make up for the fact that premiums will likely plummet once robots are driving us everywhere. The barriers to our transition to driverless vehicles, and to other forms of robot intervention into our daily lives, then, are not just technical but social, political and psychological.

Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR application. Even though DL-based approaches now outperform the state-of-the-art in a number of recognitions tasks of the field, yet substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.