A cyber attack on the Internet infrastructure company Dyn on October 21 hindered internet browsing for hours while the company scrambled to restore service. The as-yet unidentified attackers were helped by a millions-strong army of Internet of Things devices, including enterprise webcams and DVRs, that were quietly conscripted into a botnet to launch the denial-of-service attack. The incident is the latest reminder that many IoT devices aren't adequately secured. These types of attacks will continue as long as a large enough number of vulnerable devices exists. So the question facing the security industry is how to shrink that number.

On June 14, 2016, four researchers at the Jet Propulsion Laboratory were preparing to ship a waist-high, ape-like robot named RoboSimian off-site. They had built the bot to rescue people from dangerous situations that human rescuers can't hack. The scientists swapped one lithium-ion battery for a fresh one, then left for lunch to let the new power supply charge. Left alone in the lab, RoboSimian's battery did what such batteries famously do: went boom. Plumes of smoke vented from the robot's exposed torso, followed by a burst of flame.

With Halloween just around the corner, what better way to mark the day than telling stories of wail and woe about the technology that wants to kill, terrify, or bring about the end of society as we know it. ZDNet's bloggers, writers, and editors have come up with a list enough to scare the wits out of you. Cars that will drive you off this mortal coil. Phones that will blow up in your pocket (yes, we're looking at you, Samsung). Why not ask your iPhone, "Siri, are you giving all of my data to the government?"

Not everyone is pleased with the emergence of hobbyist drones. Drones, otherwise known as unmanned aerial vehicles (UAVs), can be used for a variety of purposes -- such as land surveys and military applications -- and there is now a wide range of affordable devices on the market for enthusiasts. While these devices can be great for photographers and videographers, as with most new consumer technology, they can also irritate. We've heard of cases when members of the public have resorted to taking pot-shots at drones flying around their property without consent. Drone use is becoming such an issue in some areas that aviation regulators have issued edicts for prison terms when flight rules are disobeyed, putting commercial flights at risk.

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."

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.