The IoT continued its toddler-like growth and stumbles in 2016. Here are five trends to look for in 2017 as the IoT enters its adolescence and how to benefit from them. Filling out a whole product value proposition through partnerships has repeatedly proven its importance across B2B and enterprise software sectors. In the IoT, they will be even more critical. As an example, the Industrial Internet Consortium (IIC) is driving the definition of platforms and test beds and should show results in 2017.

The Huawei Mobile Broadband Forum in Tokyo this week had tongues wagging about all the possibilities in a new 5G world. While the Chinese tech company would be behind the infrastructure that delivers 5G, the emphasis was on the next generation of apps that such pipes could bring. Whether it's driverless cars or simply cars with artificial intelligence to help humans drive safer and smarter, the amount of data involved makes a 5G mobile network imperative. The showfloor had several examples of this, including a Toshiba concept which had the car talking to the driver to advise on safety, navigation and fuel stops. Virtual and augmented reality was mentioned liberally during the forum.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. Another thing she loves is data science. The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time. So, she decided to marry these two loves in as productive a fashion as possible.

A new artificial intelligence tool created by Google and Oxford University researchers could significantly improve the success of lip-reading and understanding for the hearing impaired. In a recently released paper on the work, the pair explained how the Google DeepMind-powered system was able to correctly interpret more words than a trained human expert. The tool is called Watch, Listen, Attend and Spell (WLAS), and the paper describes it as a "network that learns to transcribe videos of mouth motion to characters." Using videos from the BBC, the team trained the system with a dataset of more than 100,000 natural sentences. While similar attempts in the past have focused on a narrow set of words, the report said, Google and Oxford wanted to address lip reading through "unconstrained natural language sentences, and in the wild videos."

It's easy for a small business to get lost in the center of the universe. In the big apple, small business is big business – but the competition is as fierce as the fashion. According to New York's Small Business Development Center, small businesses make up 99 percent of all New York businesses. Getting neighbors to like you, and come back often, is the lifeblood of small retailers everywhere, but New Yorkers are an especially tough crowd. We found one NYC retailer looking to grow exponentially over the next two years – turning to technology to literally lead the way.

Nintendo Reports Second Quarter Losses But 3DS Sales Are Up Thanks To'Pokmon GO' At Inbound 2016, HubSpot's co-founders Brian Halligan and Dharmesh Shah entertained 19,000 attendees with their take on the past and future of marketing. Here's what I learned from their keynote presentation and a brief interview. So predicts Halligan, adding "in five years, you will do a lot less navigating through apps and more just asking questions and chatting back and forth with bots… the next thing you know, we like it and it's easier and more efficient than waiting for the sales rep to call you back." Shah notes that businesses started building websites in the 1990s so they can answer customer questions 24/7. "Soon," he says, "they will start building bots. They won't replace the websites, but they will power them. The shortest time between a customer question and the answer will be a bot. It's not human vs. bot, it's human to the bot powered."

Ben Dickson is a software engineer and the founder of TechTalks. As artificial intelligence and machine learning mature and manifest their potential to take on complicated tasks, we've become somewhat expectant that robots can succeed where humans have failed -- namely, in putting aside personal biases when making decisions. But as recent cases have shown, like all disruptive technologies, machine learning introduces its own set of unexpected challenges and sometimes yields results that are wrong, unsavory, offensive and not aligned with the moral and ethical standards of human society. While some of these stories might sound amusing, they do lead us to ponder the implications of a future where robots and artificial intelligence take on more critical responsibilities and will have to be held responsible for the possibly wrong decisions they make. At its core, machine learning uses algorithms to parse data, extract patterns, learn and make predictions and decisions based on the gleaned insights.

When medics apply radiotherapy to a cancer patient, they have to carefully determine which parts of the body should be exposed to radiation in order to kill the tumor while ensuring that as much healthy surrounding tissue as possible is preserved. "Clinicians will remain responsible for deciding radiotherapy treatment plans, but it is hoped that the segmentation process could be reduced from up to four hours to around an hour," explains DeepMind. It's currently drawing on 600,000 medical evidence reports and 1.5 million patient records and clinical trials to help doctors develop better treatment plans for cancer patients. After coming under fire earlier in the year when an app project appeared to provide DeepMind with free access to 1.6 million patients' records, the research outfit recently announced that it was helping to spot the early signs of visual degeneration by sifting through a million eye scans.

Chatbots are on the rise. Startups are building chatbots, platforms, APIs, tools, analytics. Microsoft, Google, Facebook introduce tools and frameworks, and build smart assistants on top of these frameworks. Multiple blogs, magazines, podcasts report on news in this industry, and chatbot developers gather on meetups and conferences. I have been working on chatbot software for a while, and I have been looking on what is going on in the industry. In this article, I will dive into architecture of chatbots.

Deep Learning in Retail & Advertising Summit London The Deep Learning in Retail & Advertising Summit is a multidisciplinary event bringing together data scientists, engineers, CTOs, CEOs & leading retailers to explore the impact of deep learning and AI in the retail and advertising sector. Applications include computer vision for sizing; image analysis for shopping efficiency; and natural language processing for personalised shopping experiences. The Deep Learning in Retail & Advertising Summit is a multidisciplinary event bringing together data scientists, engineers, CTOs, CEOs & leading retailers to explore the impact of deep learning and AI in the retail and advertising sector. Applications include computer vision for sizing; image analysis for shopping efficiency; and natural language processing for personalised shopping experiences.