London-based BenevolentAI, which uses AI to develop drugs and energy solutions has raised $115M at $2B valuation. They are building, to quote founder Ken Mulvaney, a ‘brain…to ingest and compute billions of data points in specific areas such as health and material science, to help scientists better determine combinations that might finally solve persistently difficult problems in fields like medicine’.
We’re looking forward to having them at CogX London 2018 (11-12 June) to discuss the exciting future which is in store. You can grab your ticket here. Tweet with this link for your chance to win a free ticket.
In case you haven’t heard, the Mayor of London has commissioned CognitionX to develop a stronger evidence base for the Mayor’s AI policy. If you are an AI company, deploying AI, or have an opinion about AI in London then have your say in our 10 min census: https://info.cognitionx.com/londons-ai-innovation-census. Share the word here.
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For this briefing, Notes from the AI frontier: Insights from hundreds of use cases, McKinsey mapped both traditional analytics and newer “deep learning” techniques and the problems they can solve to more than 400 specific use cases in companies and organisations. Drawing on McKinsey Global Institute research and the applied experience with AI of McKinsey Analytics,
they assess both the practical applications and the economic potential of advanced AI techniques across industries and business functions.
In more than 2/3 of their use cases, AI can improve performance beyond that provided by other analytics techniques
AI’s impact is likely to be most substantial in marketing and sales, as well as supply-chain management and manufacturing
Former Yahoo Chief Executive Officer Marissa Mayer is starting a technology business incubator, Lumi Labs, with longtime colleague Enrique Munoz Torres. The venture will
focus on consumer media and AI, according to the company’s website, which is set against a backdrop of snow-covered peaks. Lumi means snow in Finnish, Mayer told the New York Times, which reported the news earlier Wednesday.
The next project for Mayer, who was an early employee at Google and worked there until leaving to run Yahoo in 2012, had been a matter of considerable speculation in Silicon Valley. She left Yahoo, once a leading search engine and web destination, after it was sold to Verizon Communications Inc. last year.
The impact of AI and machine learning on organisations is huge: according to a new study from SAP 48% of the companies who say they have already benefited from machine learning cite increased profitability as the top benefit they have realised.
We’ve seen some significant news pieces over the past couple of days on how AI is effecting one specific business function, HR. It will be interesting to see the
ROI organisations will be able to get from these AI solutions:
Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity.
MIT researchers have shown that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. They propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN.
Stripe is updating Radar, its machine learning-powered fraud prevention tool, with a new set of features expressly designed for the enterprise. Radar is also getting a performance boost, with new machine learning models that Stripe says should help businesses reduce fraud by an additional 25 percent.
The new bundle of features, packaged as Radar for Teams, is designed to help larger businesses keep granular control over their fraud prevention methods. For instance, Radar for Teams lets fraud prevention
teams set custom risk thresholds at which to block payments. It also gives them a way to create lists of attributes (such as card numbers, emails or IP addresses) to consistently block or allow. The product also lets teams use custom rules to influence Radar logic; for instance, a team could block all transactions above $1,000 when the IP country does not match the card’s country.
Microsoft Translator first allowed users to download entire languages for offline translations starting in 2016. But this new update focuses on AI-powered “neural translation technology,” which the company says produces translations that are 23 percent more accurate than the previously available offline packs.
The technology is also open to third-party developers, allowing them to integrate AI translations into their apps. Offline capabilities are available now on Android devices and iOS devices by the end of the week. Ironically, the update is available on the Amazon Fire devices made by Microsoft’s cross-town rival, but it is not on Windows devices yet.
With the rise of AI, sophisticated trash-sorting robots are now turning up at recycling plants across the nation. Guided by cameras and computer systems trained to recognise specific objects, the robots’ arms glide over moving conveyor belts until they reach their target. Suction cups or oversized tongs attached to the arms snag cans, glass, plastic containers, and other recyclable items out of the rubbish and flick them into nearby bins.
ZenRobotics has installed its AI recycling systems at two
U.S. locations and in 10 other countries. Its largest robotic, called Heavy Picker, can lift 60-pound objects with its tong-tipped arm, making it especially useful for sorting construction debris.
The Subcommittee on IT’s three-part series has identified several potential challenges for industries affected by the widespread adoption of AI technology, focusing on one central question: what is the appropriate role of the government related to these challenges and opportunities?
The session from yesterday featured below includes Terah Lyons (Partnership for AI) and Jack Clark (OpenAI) as witnesses.
Engineers at Nanyang Technological University used a 3D camera and two industrial robot arms fitted with grippers and force sensors to take on the challenge of building an £18 “Stefan” chair from the furniture company.
Working together, the robots completed the job in 20 minutes and 19 seconds after having the parts placed in front of them. More than half of the time was spent planning moves, with the execution taking nine minutes in total. Typically, the job takes a person 10 to 15 minutes, an Ikea spokesperson said.