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.

Artificial Intelligence is a technology that uses human-like learning to perform tasks. The idea of Artificial Intelligence or AI is nothing new. As a concept it has been in our literature and art for centuries. But these ideas had no foundation other than as philosophies of nature and science fiction. Connectionist paradigms of Artificial Intelligence have a somewhat more flexible approach than their rule-based cousins, but both have applications in modern technologies. Machine learning is based on artificial neural networks, which are a much-simplified version of how our brains work.

A robotic employee will be deployed instead of human council workers to answer customer queries. IPsoft said Amelia, its technology platform, will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context, applies logic, learns, resolves problems and even senses emotions. IPsoft said Amelia will be deployed to work for Enfield Council in North London. Capable of analyzing natural language, she understands context and even senses emotions.

MIT researchers have developed a computer system that independently adds realistic sounds to silent videos. Although the technology is nascent, it's a step toward automating sound effects for movies. In a series of videos of drumsticks striking things - including sidewalks, grass and metal surfaces - the computer learned to pair a fitting sound effect, such as the sound of a drumstick hitting a piece of wood or rustling leaves. The findings are an example of the power of deep learning, a type of artificial intelligence whose application is trendy in tech circles. With deep learning, a computer system learns to recognize patterns in huge piles of data and applies what it learns in useful ways.