Apple CEO Tim Cook has for the first time confirmed that the company is working on autonomous driving technology, Bloomberg reports. When asked about the tech giant's intentions in the automotive field during a recent Bloomberg Television interview, Cook said that the company is "focusing on autonomous systems." Cook hinted that there are applications for Apple's technology besides powering self-driving cars: "Clearly one purpose of autonomous systems is self-driving cars," he said. The revelation comes after rumors have circulated for years about Apple's plans to expand more deeply into the car market. The initiative, said to be called Project Titan, was reportedly launched in 2014.

Your first impressions on meeting a new person are likely to be wrong, according to one leading scientist. The assumptions we make when meeting new people are based largely on their facial expressions and appearance, but this rarely matches up to their personality. And these hang-ups may spoil our chances of finding a life partner or landing the perfect job, according to Professor Alex Todorov, from Princeton University in New Jersey. Faces that look happy, even if they're not smiling, (left) are commonly rated as more trustworthy than faces that appear angry (right). First impressions are likely to be wrong as they are based on shallow assumptions about appearances, according to one leading expert.

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.

The number of non-Japanese children at public schools who are lacking in Japanese language skills and who need remedial lessons hit a record 34,335 as of May last year, the latest survey by the education ministry showed Tuesday. The number, up 17.6 percent from the previous biennial survey conducted in 2014, accounted for 42.9 percent of the 80,119 non-Japanese children at public elementary schools, high schools and other public facilities across the country, according to the survey. The Ministry of Education, Culture, Sports, Science and Technology conducted the survey covering about 35,000 public schools. The survey looks at children who cannot hold simple daily conversations in Japanese and/or those who have difficulty learning at school due to poor secondary language skills. "We have taken various measures, such as training teachers and allocating Japanese-language lecturers at schools. But the number of (foreign) children is growing so fast that we have been unable to catch up with it," Yasuhiro Obata, head of at the International Education Division of the ministry, said in a phone interview with The Japan Times.

Machine learning is a form of artificial intelligence by which algorithms are "trained" to analyze new information using existing data. Researchers are using it to identify individuals with a genetic condition known as fragile X premutation. How much information can we extract from a five-minute recording of someone talking? Enough to tell whether that individual may be genetically predisposed to some health complications, according to researchers at the University of Wisconsin–Madison's Waisman Center and Wisconsin Institute for Discovery. In a new study published this month in Scientific Reports, the researchers used machine learning to analyze hundreds of voice recordings and accurately identify individuals with a genetic condition known as fragile X premutation, which increases the risk of developing neurodegenerative disorders, infertility or having a child with fragile X syndrome.

IT decision makers are divided about the impact of disruptive technologies such as Artificial Intelligence (AI) and automation - the so called'Fourth Industrial Revolution' - on the labour market, according to new research from BT. Contrary to many reports which speculate about widespread job losses, one third of organisations surveyed who plan to implement AI and automation within the next two years believe it will create more jobs within the workplace. This reflects the view that AI will generate new opportunities for programmers, algorithm designers and software engineers and create new job categories such as AI trainers, ethicists and lawyers. However, the same proportion predict that these technologies could result in job losses in their organisation, given concerns that innovations in robotics and intelligent computer systems may eventually replace jobs traditionally done by humans, particularly those of a manual, repetitive nature. Against this uncertainty surrounding the impact of these technologies on the jobs market, the survey of 1,501 IT decision makers across UK organisations of all sizes revealed that AI and automation is already being implemented by over a third of all respondents. For example, one in four organisations are using automation technologies like drones, robots or autonomous vehicles, with almost two thirds (63%) describing AI or automation technologies as being'very beneficial' to their organisations.

When talking about machine learning algorithms, Amazon definitely comes to mind. The e-commerce giant has (arguably) the most sophisticated ML toolset around that is uses for drone deliveries, online tailors, or its AWS offering. Turns out, Amazon's lesser known operation – small business lending – is also powered by machine learning, Bank Innovation has learned. Since the launch of its SME lending program in 2012, the company has issued more than $3 billion in total loans, with more than $1 billion issued in the past 12 months, Amazon announced today. In total, more than 20,000 "invited" businesses received those loans.

No matter how great a surgeon is, robotic assistance can bring a higher level of precision to the operating table. The ability to remotely operate a robot that can hold precision instruments greatly increases the accuracy of surgical procedures like thoracoscopic surgery, which is used to treat lung cancer. Of the two most common types of lung cancer, non-small cell lung cancer (NSCLC) is a good candidate for surgery because the tumors spread slowly and are more localized. Since more than 80 percent of people with lung cancer have NSCLC, surgery is a common treatment. Lung cancer usually starts when epithelial cells that line the inside of the lungs rapidly reproduce into cancerous cells, creating tumors inside the lungs.

BERLIN--(BUSINESS WIRE)--Today at GE's Minds Machines Europe event, GE Ventures (NYSE:GE) announced the launch of Avitas Systems, a new company that will use predictive data analytics, robotics, and artificial intelligence to deliver advanced inspection services to the oil and gas, transportation, and energy industries. Routine inspections can be slow and costly, and often include humans performing high-risk tasks. Data is manually collected and processed, and can take weeks to analyze. By reducing high-risk tasks through robotics, Avitas Systems can make inspection processes safer and more efficient through data automation, decreasing costs by up to 25%. By performing inspections based on anticipated risk, instead of regular time intervals, Avitas Systems can also help to increase asset longevity.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios. I will then provide examples of applications of transfer learning before delving into practical methods that can be used to transfer knowledge.