Researchers from Adobe and University of California, Berkeley developed software that automatically generates images inspired by the color and shape of the digital brushstroke. The software uses deep neural networks to learn the features of landscapes and architecture, like the appearance of grass or blue skies. Drawing a dark-colored, upside-down V triggers the AI to conjure a mountain or church steeple, where its seen the shape and color before. A blue line above that becomes a sky with various hues of blue, and a green line below becomes textured grass. Using CUDA, TITAN X GPU and the cuDNN version of the Theano deep learning framework, the researchers trained their models on more than 275,000 images of churches and landscapes.

When it comes to artificial intelligence, we may all be suffering from the fallacy of availability: thinking that creating intelligence is much easier than it is, because we see examples all around us. In a recent poll, machine intelligence experts predicted that computers would gain human-level ability around the year 2050, and superhuman ability less than 30 years after.1 But, like a tribe on a tropical island littered with World War II debris imagining that the manufacture of aluminum propellers or steel casings would be within their power, our confidence is probably inflated. AI can be thought of as a search problem over an effectively infinite, high-dimensional landscape of possible programs. Nature solved this search problem by brute force, effectively performing a huge computation involving trillions of evolving agents of varying information processing capability in a complex environment (the Earth). It took billions of years to go from the first tiny DNA replicators to Homo Sapiens.

For the past two months, the Guardian has been testing a Facebook Messenger bot, "Sous-chef," that provides recipe suggestions to people based on what they had in their fridges. Now, it's using the learnings to shape its main news bot, which launched a week ago. It's too early to get data on how many people are actively using the news app, though the Guardian's 6.2 million Facebook followers all have access to the bot. We spoke to Martin Belam, The Guardian's social and new formats editor; and Chris Wilk, group product manager for off-platform, about what they've learned. Keep it simple The bot has a simple setup: Users enter specific ingredients like "salmon" or types of cuisine, dietary requirements, and specific dishes, to get started.

A new press release reports, "Today Tractica announced the launch of its new Artificial Intelligence Advisory Service, a subscription-based market research and analysis suite that provides independent and objective market intelligence and strategy insights for companies engaged in the rapidly evolving artificial intelligence (AI) market. As part of the service, Tractica's global industry analyst team provides strategic and quantitative analysis focused on the market opportunity for AI technologies in enterprise, consumer, and government markets." Managing director Clint Wheelock commented, "Artificial intelligence technologies are already beginning to have a disruptive effect on established business models across virtually every industry, while simultaneously enabling new business processes that were not previously possible… Rapid advances in AI technologies like deep learning, machine learning, computer vision, and natural language processing (NLP) are being enabled by more powerful hardware, sophisticated algorithms, and a virtually limitless ocean of data to analyze and interpret." The release goes on, "As part of its Artificial Intelligence service, Tractica's industry analysts offer timely and actionable market insights, covering specific technology and industry sectors as well as overall market conditions and trends. Research reports include an in-depth examination of AI business models, use cases, technology issues, and key industry players in addition to detailed market sizing, segmentation, and forecasts. Tractica's Artificial Intelligence Advisory Service examines use cases and business models for the application of artificial intelligence technologies in enterprise, consumer, and government markets."

A report that was released by Forrester last month predicts that cognitive technologies will take over some 7% of jobs in the United States in less than a decade (by 2025). Notably, the report asserts that the trend will make itself felt five years from now. "By 2021, a disruptive tidal wave will begin. Solutions powered by AI/cognitive technology will displace jobs, with the biggest impact felt in transportation, logistics, customer service, and consumer services," says Forrester VP Brian Hopkins. Forrester estimates around 6% of jobs will be eliminated by as early as 2021.

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If you believe everything you read, you are probably quite worried about the prospect of a superintelligent, killer AI. The Guardian, a British newspaper, warned recently that "we're like children playing with a bomb," and a recent Newsweek headline reads, "Artificial Intelligence Is Coming, and It Could Wipe Us Out." Numerous such headlines, fueled by comments from as the likes of Elon Musk and Stephen Hawking, are strongly influenced by the work of one man: professor Nick Bostrom, author of the philosophical treatise Superintelligence: Paths, Dangers, and Strategies. Bostrom is an Oxford philosopher, but quantitative assessment of risks is the province of actuarial science. He may be dubbed the world's first prominent "actuarial philosopher," though the term seems an oxymoron given that philosophy is an arena for conceptual arguments, and risk assessment is a data-driven statistical exercise. So what do the data say?

In the age of the customer, the next generation customer experience will be powered by artificial intelligence. When everyone and everything is connected to the Internet, companies must leverage information and digital technologies including cloud computing, mobile, social, Internet of Things (IoT) and AI to transform how they connect with customers in a whole new way. Per Gartner, 89% of marketers expect to compete primarily on the basis of customer experience. Customer experience is a top priority and managed as a team sport. Digital business transformation will require an experimental and technology-led mindset that must be inclusive of the entire business - marketing, sales, services, IT, R&D and customer and partner communities.

Starting today, Google Drive features Natural Language Processing to make it even easier to find that buried spreadsheet or long-lost docs. Taking a page from its Google Assistant playbook, the search box in Drive now allows for easy, human-oriented search queries like "find my budget spreadsheet from last December" or "show me presentations from Anissa." In typical Google style, the search bar will translate the query to a more robot-like string (as in: "budget Type:Spreadsheet" in that first example) and present you with autocomplete suggestions before presenting the results. According to Google Drive Product Manager Josh Smith, the natural language processing in Drive will get smarter the more you search. Finally, the Drive team added a couple more often-requested features to the product today, including: autocorrect for misspelled search terms, the ability to split documents into multiple columns and an auto-save feature that creates a copy whenever importing and converting non-Google formats.

Both hxd1011 and Frank are right ( 1). Essentially resampling and/or cost-sensitive learning are the two main ways of getting around the problem of imbalanced data; third is to use kernel methods that sometimes might be less effected by the class imbalance. Let me stress that there is no silver-bullet solution. By definition you have one class that is represented inadequately in your samples. Having said the above I believe that you will find the algorithms SMOTE and ROSE very helpful.