Reuters – Apple unveiled a series of improvements to its Siri digital assistant last Thursday, but the tech giant still trails rivals such as Google and Amazon in the red-hot field of artificial intelligence, experts say. Apple's biggest move was to open up the talking iPhone assistant to third-party developers for inclusion in their apps, paving the way for users to hail a ride from Uber or send a message with Tencent's WeChat using voice commands. Experts in artificial intelligence applauded the move as an important step forward, in part because the more people use an artificial intelligence system, the better it becomes. But some wondered why Apple had not made Siri an open platform much sooner, noting that competing products including Amazon.com's Alexa, Microsoft's Cortana and the Google app are already open to developers. "Is it too little too late?"

Intelligent computers that can make decisions like humans may someday be on Hewlett Packard Enterprise's product roadmap. The company has been showing off 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.

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow. Pattern classification and machine learning are very hot topics and used in almost every modern application: Optical Character Recognition (OCR) in the post office, spam filtering in our email clients, barcode scanners in the supermarket … the list is endless. In this article, I want to give a quick overview about the main concepts of a typical supervised learning task as a primer for future articles and implementations of various learning algorithms and applications. Predictive modeling is the general concept of building a model that is capable of making predictions. Typically, such a model includes a machine learning algorithm that learns certain properties from a training dataset in order to make those predictions.

David: I've gone through this myself in the past couple of years. There is so much out there right now that if you have any interest in machine learning or data science topics, you can buy any number of good books that will give you overviews and get you started. There are online courses and tons of blogs that will cover a lot of this stuff. I would say the best thing to do is to get started and just try some stuff. Honestly, for basic machine learning, a good start is to take a look at our presentation.

Google announced in a blog post on Thursday that it has set up a new AI research group in Europe to focus on machine learning (ML). Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Google Research, Europe -- as the group is known -- is based out of Google's office in Zurich, Switzerland, which is home to Google's largest engineering office outside the US. Google said the group, which is expected to grow to over 100 people in the coming years, will focus on three key areas: machine intelligence, natural language processing and understanding, and machine perception. Companies like Amazon, Facebook, and Microsoft are all investing heavily in these areas as they look to make their platforms and services more intelligent.

"In 2016, selecting the top achievers was extremely difficult," said Alex Vieux, publisher and CEO of Red Herring. "The variety, depth, disruption and traction we saw from the early stage companies to those with significant scale made it one of the toughest vintages to judge. The North America winners are representative of the amazing ecosystem that never ceases to astound, with new and experienced entrepreneurs continuing to push the barriers of innovation. As one of the winners, Armorway should be proud of its accomplishment under such strong competition." Red Herring's editorial staff evaluated companies on both quantitative and qualitative criteria, such as financial performance, technological innovation and intellectual property, DNA of the founders, business model, customer footprint and market penetration.

In the future, people - certain people - may be able to download information directly into a device embedded in their skull and linked to their brain. These people will also be capable of improved physical performance, courtesy of pods embedded in their bodies and controlled by a link to the brain that can release chemicals or hormones to enable performance of all kinds of tasks, even in extreme conditions. That's what awaits us as biology and technology blend together, says business software developer MYOB Group in its latest Future of Business report entitled, appropriately, 'The Augmented Human'. MYOB chief technical officer and futurist Simon Raik-Allen says technology will move from mobile, wearable technological devices such as Fitbits to tiny embeddable devices that can provide real-time data or move parts of the body. Artificial intelligence (AI) will enable people to enhance their brain or personality.

Are you more clever than your fridge? There are two types of artificial intelligence, but not all artificial intelligence is created equal. There is narrow AI, which is painstakingly designed to compute just one thing, but it does it very well. And there is general AI that can adapt to solve different tasks by learning and changing. But who do general AIs learn from?

Zebra Medical Imaging is working with Intermountain to create a neural network that will study and compare x-rays with its own database. This means that, instead of handing your x-ray to a doctor, where human error could come into play, the image would instead be given to a computer equipped with deep learning to sift through a massive database of possible anomalies. The development of this technology also means that the AI can more efficiently interpret fresh x-rays and offer suggestions to radiographers, thus reducing the possibility of misdiagnosis. As deep learning technology evolves to become stronger and cheaper, the opportunities for artificial intelligence (AI) to continue assisting medical practitioners becomes more apparent. Eventually, the team behind this work is hoping that it can lend itself to better access to medical diagnostics, especially in countries where there is a shortage of medical experts.

Tesla CEO Elon Musk reckons humans will need to implant "neural laces" in their brains to keep up with AI advancement. While Musk's description of an injectable human-computer link may sound like science fiction, top tech executives repeatedly said artificial intelligence (AI) was on the verge of changing everyday life, during discussion at a conference by online publication Recode this week. It is no secret that tech companies are diving into AI analytics research, an industry that will grow to 70 billion by 2020 from just 8.2 billion in 2013, according to a Bank of America report citing IDC research. AI, which combs through large troves of raw data to predict outcomes and recognise patterns, is already used in Web search systems, marketing recommendation functions, and security and financial trading programs. The technology will spread to driverless cars and service robots in the future, the Bank of America report said.