In 2017 firms worldwide spent around $21.8B on M&A related to AI, according to PitchBook, a data provider, about 26x more than in 2015
Around 85% of companies think AI will offer a competitive advantage, but only one in 20 is “extensively” employing it today
In Q4 2017, public companies across the world mentioned AI and machine learning in their earnings reports more than 700 times, seven times as often as in the same period in 2015
John Hagel of Deloitte: many bosses are more interested in the potential cost and labour savings than in the broader opportunities AI might bring
A longer-term concern is the way AI creates a virtuous circle or “flywheel” effect, allowing companies that embrace it to operate more efficiently, generate more data, improve their services, attract more customers and offer lower prices.
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Slack CEO Stewart Butterfield says Slack is pioneering products that will provide individual Slack users with data on whether their digital communication changes when they speak with people of different demographics. He says this data will help promote more equal, inclusive workplace cultures, and make employees more efficient and effective.
“These are analytics that no one else has access to you except for you,” he said. “And they don’t present you with any real moral value either way, but [they answer questions like], do you talk to men differently than you talk to women? Do you speak to support groups differently than you speak to superiors? Do you speak in public differently than you speak in private?
Global admission platform INTCAS has raised £4.1M to develop an artificial intelligence platform to help students make informed decisions when choosing a career path or a university course. The UK company is funded by a syndicate of investors around that world and it plans to draw a further £36m in autumn 2018, INTCAS chief executive, Zakaria-Saqib Mahmood told The PIE News.
We are essentially developing an artificial intelligence platform, very much around predicting algorithms on the future success of a student,” he said, adding that the cost of compiling the volume of data is the reason for the funding.
Chemists have a new lab assistant: artificial intelligence. Researchers have developed a ‘deep learning’ computer program that produces blueprints for the sequences of reactions needed to create small organic molecules, such as drug compounds. The pathways that the tool suggests look just as good on paper as those devised by human chemists.
The new AI tool, developed by Marwin Segler, an organic chemist and artificial-intelligence researcher at the University of Münster in Germany, and his colleagues, uses deep-learning neural networks to imbibe essentially all known single-step organic-chemistry reactions — about 12.4 million of them. This enables it to predict the chemical reactions that can be used in any single step. The tool repeatedly applies these neural networks in planning a multi-step synthesis, deconstructing the desired molecule until it ends up with the available starting reagents.
DeepMind returns to Paris: They have decided to open their first lab in continental Europe, in another of the world’s great cultural and scientific capitals: Paris. The lab will be headed up by Remi Munos, one of DeepMind’s principal research scientists, author of 150 research papers, and former professor at École Polytechnique.
Mayor on London sets up London Office of Data Analytics: Supported by the GLA’s Intelligence Unit, and using the London Datastore as a ‘front door’ LODA is each of a collaborative and convening environment in which data science projects are tested, formed and executed; a place to learn (through our City Data Academy); and somewhere to find practical advice and proactive support.
What’s cooler than Amazon’s Alexa voice assistant being able to get you a beer? Having it delivered via a toy tank. Software developer Balázs Simon posted his newest project, called WalaBeer Tank, to Hackster earlier this week and you’ll just have to see the results to believe it.
Adobe and Nvidia have announced a partnership that will see both companies deliver new artificial intelligence (AI) and deep learning services for Adobe Creative. Making the announcement during the Adobe Summit keynote in Las Vegas on Wednesday, Adobe CEO and president Shantanu Narayen was joined by Nvidia founder and CEO Jensen Huang.
The CEOs said the partnership will see both companies work to optimise the Adobe Sensei AI and machine learning framework for Nvidia GPUs. It is expected that the new announcement will help Adobe extend the availability of Sensei APIs and to broaden the Sensei offering to a new audience of developers, data scientists, and partners.
Just ahead of Spotify’s public debut planned for early April, Pandora is punching its rival where it hurts: personalized playlists. Spotify’s playlists customized to its individual users – like Discover Weekly, Release Radar and Daily Mix – have been a key draw for its service, but Pandora believes it can do even better. Yesterday, they launched its own take on personalization with playlists built to fit users’ moods, activities, and genres, which are all powered by Pandora’s Music Genome.
“We’ve been building out, for many years, a collection of well over 75 machine learning algorithms and techniques to help drive content discovery and delivery,” explains Chris Phillips, Pandora’s Chief Product Officer, of the personalization technology. “What we’re doing is what we believe is the bleeding edge of deep learning algorithms,” he says.
One of China’s top tech companies is trying to push the frontiers of artificial intelligence by teaching computers to understand scenes from the 1990’s romance-disaster epic Titanic. The technology, from China company SenseTime, is supposed to distinguish Titanic’s romantic scenes from disaster scenes. Although most humans would have no problem distinguishing Jack and Rose’s blooming love from the Titantic’s sinking, the feat is highly complex for computers.
In a demonstration at MIT Technology Review‘s EmTech Digital conference in San Francisco on Monday, the technology performed well and was able to classify the scenes correctly. It highlights the advancement of artificial intelligence, but also how far it still has to go before becoming able to understand more complex movie scenes outside of public demonstrations.