The future isn't ready to arrive just yet. Apple has announced that its AirPods – the wireless earphones that the company were set to release this month – aren't "ready". The AirPods were first unveiled along with the iPhone 7 at an event last month. And the two were part of the same pitch – just as Apple had done with dropping the headphone jack, the AirPods were meant to signal that the company was ready for the wireless future. The AirPods built from the ground up for Apple's new vision of listening.

Whether it's Google's headline-grabbing DeepMind AlphaGo victory, or Apple's weaving of "using deep neural network technology" into iOS 10, deep learning and artificial intelligence are all the rage these days, promising to take applications to new heights in how they interact with us mere mortals. To go deeper (yes, I went there) on the subject, I reached out to the team at the deep learning-focused company Skymind, creators of Deep Learning For Java (DL4J), and authors of the recently released O'Reilly book Deep Learning: A Practitioner's Approach, Josh Patterson and Adam Gibson. Josh and Adam offer us a gentle introduction to the subject in this interview, as well as insight into how they are building an open source-based business around deep learning. For the uninitiated, what is deep learning (DL) and why should I care about it? Adam Gibson (AG): Deep learning is just another term for neural networks, a set of algorithms that have been around for decades. For a long time people were skeptical about them, but as chips got more powerful and as we gathered more data to train them on, deep neural nets started breaking records. We're hitting expert human accuracy on a lot of problem sets, with accuracy rates in the high 90s, which is a quantum leap over other algorithms. So if you have a problem that matters to your business, you can probably attach a dollar value to that improvement in accuracy, and if you're a large business, that value can be huge. It's a competitive edge with a big impact on margins.Josh Patterson (JP): To build on what Adam said, with deep learning we're moving from manual feature creation to automated feature learning. The trick with deep learning is to recognize the input data type and match it to the correct deep network architecture to enable robust automated feature learning. An example is how automatically learn the features in complex image data, where historically this was harder for other machine learning methods. What problems are DL best suited for?

No one can predict the future of technology with 100 percent accuracy. But these four pillars are certainly at the forefront of innovation in the years ahead. Speaking at a machine learning and artificial intelligence event hosted by Madrona Venture Group in Seattle on Wednesday, Joseph Sirosh, corporate VP of the Data Group at Microsoft, outlined five trends to watch in a world he described as "ACID": Algorithm, Cloud, IoT, and Data. "We live in a time of great change in computing, where unreasonable effectiveness of algorithms, cloud, IoT, and data are changing how applications are built, period," he said. "Even if you are on the right track, if you don't hop on this bandwagon and actually build things and deploy them and take advantage of their strength, you won't be very effective."

If you're interested in learning about, investing in, or building a company involved in machine learning, Toronto should be on your radar. If you've been following the industry, you likely know all about Geoff Hinton's lab based out of the University of Toronto. But even if you haven't been following the industry, you may have witnessed some of the excitement surrounding the 2012 Imagenet challenge, which represented a breakout moment for the lab -- and the industry as a whole: Hinton's lab demonstrated, for the first time in history, that computers could recognize images more accurately than humans could. This achievement was not the product of an epiphanic moment but rather a result of the incredible talent coming through the lab year after year, with students building off each other's ideas and breakthroughs. Many of these talented students are now considered world leaders in the field, and they hold important posts at the world's most cutting edge organizations.

The hype surrounding artificial intelligence has made AI a buzzword among policymakers and companies. But Andrew Ng, chief scientist at Chinese internet giant Baidu, says AI not only justifies the hype but predicts it will have the same kind of impact on society that the widespread introduction of electricity did. Ng was speaking at the Nikkei Innovation Forum: The Future of AI, Robots and Us, held in Silicon Valley on Wednesday local time and hosted by Nikkei Inc. "I make this analogy that AI is the new electricity. A lot of years ago, as we started to electrify the U.S., that transformed industry after industry," Ng said. "Everything from factories [to] agriculture, transportation and communications was transformed by electricity. I think that we now see a clear path for AI to transform multiple industries as well."

Artificial intelligence, and technology in general, will have a crucial role to play in society's progress, that is the view of the UK and American governments anyway. This Thursday President Obama announced at the first White House Frontier Conference that more than $300 million in funding, through partnerships, will be released for tech innovations that will improve healthcare, develop smart cities and enhance America's space ambition. "We may be in a slightly different period now, simply because of the pervasive applicability of AI and other technologies," said President Obama in a video shown at the start of the conference. President Obama believes AI, in particular, will be able to help solve the biggest crises that face the world, such as disease, famine, climate change and economic inequality. Others, like Tesla CEO Elon Musk, suggest AI's rise will be the biggest threat to the survival of the human race.

Should judges begin to feel fearful for their job security? According to a team of researchers, a new AI tool has been found to be highly accurate at predicting the outcome of European court cases. An AI judge that computes outcomes instead of a human jury has been the dystopian creation of science fiction writers for a number of years, but new developments suggest that they could be used in some capacity in future courts. According to University College London, a joint team of researchers from the US and the UK has developed a new AI method that is the first to predict the outcomes of a major international court, by automatically analysing case text using a machine-learning algorithm. The international court in this case is the European Court of Human Rights (ECtHR), and by churning the data obtained during these court cases from the cases brought by defendants and prosecutors, the AI was able to predict their outcomes 79pc of the time.

This year, artificial intelligence has taken center stage as a technology to watch. From Salesforce to IBM to Oracle to Microsoft, all technology providers have been honing some version of AI and machine learning technologies. Microsoft rolled out its latest iteration of services for AI at the Microsoft Ignite conference in September. At Ignite, the company outlined Microsoft artificial intelligence in four areas of technology: agents, applications, services and infrastructure. By bringing AI to these four areas, Microsoft is hoping to make applications like Outlook more intelligent, helping workers to find documents relevant to their meetings more easily.

IBM Chief Executive Ginni Rometty told CNBC on Wednesday that the company's artificial intelligence platform, Watson, will reach more than 1 billion consumers by the end of 2017. "This is a celebration of Watson really scaling," she said. "Through others, Watson is touching hundreds of millions of consumers, and by the end of next year it'll be a billion." Rometty was on site at IBM's World of Watson 2016 event in Las Vegas where the legacy technology company demonstrated how Watson can influence businesses in several different industries. General Motors CEO Mary Barra was at the event for the announcement of Watson joining On-Star in millions of cars in 2017.

CapitaLand unveil virtual concierge services for customers that allows them to hail rides, book restaurants, and browse retailer offerings by chatting with Sparkle – the Group's fully automated artificial intelligence chatbot. This is the first chatbot piloted by a real estate developer in Asia. The chatbot is among a series of new features released on CapitaStar, Singapore's multi-mall, multi-store card-less rewards programme. It is available for download from the App Store and Google Play. Grab, ride-hailing app company, has also signed on as a launch partner, to provide ride hailing services.