The researcher hired by Toyota Motor Corp. to spearhead its robotics and artificial intelligence efforts says the automaker's production principles can be applied to build affordable helper robots for rapidly aging societies. Robot makers are struggling with the same scale challenges that the auto industry overcame with the "miracle" that occurred when Henry Ford developed the assembly line, according to Gill Pratt, the chief executive officer of Toyota Research Institute. Toyota's vaunted production system later showed how to make cars both more cheaply and reliably, despite mistake-prone humans' role in manufacturing, he said. "My thought is, if the Toyota production system can be applied to cars, maybe it can also be applied to robots, because they're quite similar," Pratt told reporters Friday in Tokyo. He's particularly sanguine about the prospects for devices that would help the elderly age where they live.

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 percent of the time, explained Khosla, adding, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "But the truly exciting thing was when we combined the pathologist's analysis with our automated computational diagnostic method, the result improved to 99.5 percent accuracy," said Beck.

According to the Urban Dictionary, a dadbot is a guy with a dad bod so controlled by his spouse he resembles a robot. Now, just in time for Father's Day, comes the Dad Joke Bot. It has the sort of corny, pun-packed punch lines you'd expect from, well, a father. The bot was released today by Fatherly, a parenting advice site for dads created by early Thrillist employee Mike Rothman. The company was formed last year and has raised 2 million in seed funding.

Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Cognitive computing comes from a mashup of cognitive science -- the study of the human brain and how it functions -- and computer science, and the results will have far-reaching impacts on our private lives, healthcare, business, and more. The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works. While computers have been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language, or recognizing unique objects in an image.

Sitting on the Fintech panel at today's ASIFMA capital markets conference in Hong Kong, I had a small epiphany. By "we" I mean anyone involved in Finance or Fintech. If you work in a field with real A.I. applications such as image processing, robotics, industrial automation or such, keep pretending like you know what you're talking about. Why are we even talking about A.I. in the first place? To the lay man, which let's face it most investors are, A.I. sounds magical.

Learning needs to be less like memorization, and more like… Angry Birds. Half of school dropouts name boredom as the No. 1 reason they left. The blog is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A." And every time you get something wrong, your score gets lower and lower.

Ever since humans invented technology, those new developments came along with fears about the unknown consequences of their impact. Consistently, one of those fears has been whether technology would replace humans in certain places. A great example was last month's Legal Service Jam, where I was lucky to mentor a group of legal workers in the adoption of service design tools to re-invent their profession. I couldn't help but notice a certain level of anxiety around the topic of professional uncertainty in the face of technological advancement. Many of us were there that day to think about how technology could disrupt a stereotypically old-fashioned industry, but many raised concerns: Are we not working towards replacing ourselves? Naturally, change is inevitable and those who realize this and adapt to it early enough, will reap the rewards.

Intelligent computers that can make decisions like humans may some day be on Hewlett Packard Enterprise's product roadmap. The company has been showing a prototype computer designed to emulate the way the brain makes calculations. It's based on a new architecture that could define how future computers work. The brain can be seen as an extremely power-efficient biological computer. Brains take in a lot of data related to sights, sounds and smell, which they have to process in parallel without lagging, in terms of computation speed.

Goal Autoencoder have long been proposed to tackle the problem of unsupervised learning. In this week's summary we have a look at their capabilities of providing a features that can be successfully used in supervised tasks and sketch their framework architecture. Motivation In supervised learning, back in the days, deeper architectures need some kind of pretraining of layers before the actual supervised tasked could be pursued. Autoencoder came in handy for this and allowed to train one layer after the other and were able to find useful features for the supervised learning. Steps Let us start by looking at the general architecture.

Artificial Intelligence (A.I.) is shaping up to be an important element of customer service in the next few years. Beyond that, many businesses will rely on A.I. to provide all of their customer service. Fully automated customer service is just great business. A.I. promises consistent customer service, instantly and 24x7 - for a fraction of the cost of a traditional service desk. Customers will love them too; enjoying efficient service without having to sit on hold for 40 minutes.