A teary-eyed Mala Sharma felt vindicated as she stood outside a school for impoverished children in India. A student had snatched the Adobe Systems executive's iPad and had a go with the company's simplest video editing program. He nailed it, creating a quick video that Sharma said amused his teacher and peers. For years, Adobe has been the dominant provider of expensive editing tools to professional content producers. But the recent experience in India with her company's newer, consumer-oriented app showed Sharma firsthand the value of expanding efforts to make tools approachable to anyone. "I stood there with tears in my eyes because I felt like in that minute Adobe touched and changed that child's life," Sharma said.

In 2016, people have witnessed the digital and physical worlds continuing to merge as everyday objects, commercial and industrial equipment, and entire cities become smart and connected to the cloud. Analysts expect that by 2020 more than 50 billion devices – wearables, store inventory sensors, autonomous vehicles, medical equipment, city infrastructure and more – will be connected to the internet and each other.1 And people's relationship with technology – how it is used, the experiences it enables and what benefits people derive from it – are beginning to shift dramatically. Not since the transition from analog to digital has there been the potential for transformation of this magnitude: Networks are evolving faster than ever to accommodate the intense bandwidth demands created by the ever-growing number of always-on, connected devices to access, analyze and share data in real time. In 2016, Intel has been working with policymakers, the industry and industrial leaders to focus on four areas that will help spur innovation and enable more rapid transformation, leading to greater and new opportunities for industries and consumers: artificial intelligence, 5G networks, automated driving and virtual reality/merged reality.

Ambar presses her hand to her forehead, nose crinkled in concentration as she considers the question on her screen: how many sevens in 91? The ten-year-old has been grappling with it for about a minute when she smiles: "13!". Her tutor responds by posting a large smiley cat picture on her screen – the virtual equivalent of a pat on the back. He is sitting on the other side of the world in an online tutoring centre in India. Ambar, who attends Pakeman primary school in north London, is one of nearly 4,000 primary school children in Britain signed up for weekly one-to-one maths sessions with tutors based in India and Sri Lanka.

Latest KDnuggets poll identifies the list of top algorithms actually used by Data Scientists, finds surprises including the most academic and most industry-oriented algorithms. Latest KDnuggets Poll asked Which methods/algorithms you used in the past 12 months for an actual Data Science-related application? . Here are the results, based on 844 voters. The top 10 algorithms (and methods) and their share of voters are: Figure 1: Top 10 algorithms & methods used by Data Scientists. See full table of all algorithms and methods at the end of the post.

With automation and IT working together to take over routine tasks, IT workers can devote more time to innovation. Don't forget, though, that adding automation to the mix doesn't mean that IT and automation work in silos. As KPMG told Network World, the co-existence between human employees and cognitive systems is creating a new class of digital labor that can enhance human skills and expertise, allowing employees to innovate constantly. Before diving into what this means to IT, let's examine the underlying concepts. Machine learning is a branch of artificial intelligence (AI) that uses data, algorithms, and known outcomes to build systems that learn and adapt without human input.

Primarily the Googles, Amazons, Microsofts and IBMs of the world who are very strong at that, but I think increasingly so we're going to be focusing on the holders of data which essentially is the feed stock that goes into things like artificial intelligence." Soon, investors will be assessing companies and sectors based on how well they can collect data and how they can use this data for their core business, according to Ma. Daniel Hegarty, CEO and founder of digital mortgage broker habito, says AI in the form of chatbots (a program which consumers engage with through messaging services) will become mainstream next year. "Expect them to become more polished and human-like, and even more ingrained in the fabric of our daily lives – whether you're ordering groceries from across the kitchen using Amazon's Alexa or transferring money to a family member using a Facebook chatbot," he said in a press statement. "Bots offer a comfortable conversational paradigm that allows us to get directly to what we need, yet still allow us to interact with them passively.

Yeah well, i watched the first few minutes then quit because it is mainly fiction (movies, fantasy), no facts. Btw., just a few days ago i watched Metropolis, the b/w-movie the first scenes are from. I suggest we define intelligence, sentience, consciousness and self-awareness on a supra-wikipedia level. And get an understanding of what the soft voiced speaker means by "human level". How do humans perceive, understand, what are the cognitive processes and can they be modeled into (very complex but still) algorithms?

At the Amazon re:Invent conference, Amazon announced Lex, a deep learning service that is based upon the technology used by Alexa in Amazon's portable Bluetooth and Wi-Fi enabled Echo speaker. Shortly after Amazon's announcement, Google introduced Actions on Google which allow developers to build Google Assistant-based conversational apps, including integration with the Google Home device. You as a developer to integrate your services with the Google Assistant. Conversation actions which enable you to fulfill a user request action through a two-way dialog. When users request an action, the Google Assistant processes this request, determines the best action to invoke, and invokes your Conversation Action if relevant.

AI has been a hot topic for legal in blogging and thought leadership. Outlets like Legaltech news and Forbes have reported on the emerging use of AI in the legal industry and how it has helped many firms find success in the changing market of a client-demand based business model. The billable hour is beginning to put a strain on many firms, with clients evaluating legal services to meet their value-based, consultative needs. The Center for the Study of the Legal Profession at the Georgetown University Law Center and Thomson Reuters Peer Monitor 2016 Report addresses these trending changes in the industry, stating "What once was a seller's market has now clearly become a buyer's market, and the ramifications of that change are significant." This new buyers-mindset has been consistently evolving since 2008: "[The] market for law firm services has changed in significant and permanent ways. Clients who previously deferred to their outside firms on virtually all key decisions regarding the organization, staffing, scheduling, and pricing of legal matters are now, in most cases, in active control of all of those decisions."

Today's tech buzzword, artificial intelligence (AI), can be a difficult concept to wrap your head around. AI technology, which holds vast potential to revolutionize the way the world works, is often used to paint an apocalyptic view of the future. We now live in a world where talking computers and self-driving cars are no longer a thing of the future -- but are we also at risk of developing computers so smart that they can replace humans? Influential tech figures such as Elon Musk and Stephen Hawking have cautioned the industry against diving head-on into AI because the technology's implementation could do more harm than good if we aren't careful. While there's no arguing that some of the cautionary tales around AI hold merit, our current reality is far from the robot-run society we've seen in the movies.