Information and communications technology (ICT) stands at an evolutionary inflection point as 3rd Platforms comprised of cloud, Big Data and social technologies are being adopted on a massive scale, according to a digital transformation forecast from IDC FutureScape report. Enterprise-scale businesses are assembling and making use of these 3rd Platforms more rapidly than initially anticipated, and that's driving a digital transformation of their businesses and the economy, according to IDC. The market research provider forecasts that this digital transformation, or DX, will be macroeconomic in scale and reshape the global economy over the next three to four years. Digital Transformation Forecast To wit, IDC makes the following 10 predictions in the digital transformation forecast in the company's new FutureScape report: By 2020, 50% of the Global 2000 will see the majority of their business depend on their ability to create digitally-enhanced products, services, and experiences. For industry leaders, the fastest revenue growth will come from information-based products and services.

He studied English Literature and History at Sussex University before gaining a Masters in Newspaper Journalism from City University. Businesses are often data-rich but information-poor. Machine learning (ML) is changing that. The use of artificial intelligence (AI) to let computers learn independently through algorithms without being explicitly programmed can help companies process vast quantities of complex data to improve analytics, predictive accuracy and decision-making. Machine learning is already being used in everything from fraud detection to self-driving cars, and in sectors from marketing to government.

The new, hippest technology product comes in innovative packaging, is very expensive and comes with an advertising campaign that boasts of how its obsessive engineering makes it the best in the world. Casper is the company at the forefront of a technology (and marketing) revolution that's seeing perhaps one of the most domestic and boring of products – bedding – become this year's must-have tech product. And that's entirely on purpose: the company is being advertised on tech podcasts, and promotes its mattresses with the kind of fun marketing that would usually be reserved for a phone or a computer. It hasn't come easily, and it hasn't been as much of a trick as it might seem. Instead, the company says that it really is a tech company – and that it has the product to prove it.

The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."

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." The professional human lip reader whom the researchers compared the results against had roughly 10 years of experience in the field and had deciphered videos for the royal wedding and in court for trials as well, the report said.

Each year brings the same amount of innovation as the previous several years -- and the human brain finds it hard to imagine the consequences. For perhaps the first time in history, technology is evolving faster than we expect it to. For example, experts estimated that it would take another decade before a computer would beat a world champion at the ancient and complex game of Go -- yet AlphaGo triumphed earlier this year. It's not hard to find parallels between how new technologies are changing the world today, and how they will affect our business systems in the future. For example, every Tesla car in hands-free driving mode gathers huge amounts of information about current conditions.

What does the Chinese Room problem attempt to demonstrate?

A study published last Monday, heralded as an historic achievement by Microsoft, details a new speech recognition technology that's able to transcribe conversational speech as well as humans -- or at least, as best as professional human transcriptionists (which is better than most humans). The technology scored a word error rate (WER) of 5.9%, which was lower than the 6.3% WER reported just last month. "[I]t's the lowest ever recorded against the industry standard Switchboard speech recognition task," Microsoft reports. The rate is the same as (or even lower than) the human professional transcriptionists who transcribed the same conversation. "We've reached human parity," says Xuedong Huang, Microsoft's chief speech scientist.

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It is a massive trial-and-error process. From time to time, you will hear about a new fantastic piece of computer science. For example, right now deep learning is the hot new thing. Some years ago, people were very excited about MapReduce. As an ecosystem changes, some tools become less likely to be useful while others gain dominance in common use cases.