🚗 Department of Driverless Cars
Intel CEO, Brian Krzanich, announced that Intel Capital will invest $250m
in the next two years in the autonomous vehicle (AV) ecosystem, focused on problems in connectivity, communication, context awareness, deep learning, security and safety. When viewed in the context of the fund’s short-lived intention to sell $1bn worth of portfolio holdings
in March this year (it was cancelled in May
), I think this shows Intel is serious on going long with AI. Indeed, the company purchased recently Nervana Systems and Movidius, which could help it’s larger AV program
and the race against NVIDIA.
, the startup offering a direct to consumer network of cloud-connected dashboard cameras applied to car insurance, inked a data sharing agreement
and investment from Toyota Research Institute, BMW iVentures and Allianz Ventures (thanks Moritz for sharing!). One of the reasons for the immense progress in AI is data crowdsourcing. Collaborations between startups and incumbents who have mature products and distribution scale makes a lot of sense. Others to watch: Nexar
(dashboard camera for road safety) and Mapillary
(crowdsourced street maps).
It’s been a phenomenal Q3 for NVIDIA
, which blew public market analysts away by recognising $2bn in revenue, up 54% from last year. The share price popped 30% in a single day, which tells me that public markets have yet to truly appreciate the impact of AI. While the lionshare of the company’s revenue came from gaming
, it launched the Drive PX 2 platform, a partnership with Baidu for their AVs and a data collection partnership with TomTom. So much more to come I’m sure!
University of Michigan, which has a simulated urban and suburban environment for testing automated and connected vehicles, launched open-access automated cars
for academics and industry partners to advance driverless research. The cars are powered by PolySync
middleware (check out the open source project here
After an audacious talk at TC Disrupt announcing a driverless car kit, Comma.ai founder George Hotz received a special order from the National Highway and Traffic Safety Administration to answer detailed questions on safety, testing and performance. Instead of complying, George stated that “dealing with regulators and lawyers… isn’t worth it”
and pulled the product entirely. Unsurprisingly cowboy-like!
🏥 Healthcare and life sciences
HBR walks through how a visitor to an AI-infused hospital might be like
. It helps frame how AI introduces a new user experience paradigm centered around contextual awareness, personalisation and seamless interaction with the physical and digital world.
A Harvard research group has developed a system that uses generative deep learning models trained on chemical structures to output novel structures
using representations of learned chemical knowledge. This is an exciting means to supercharge exploration of a complex search space.
📱 Digital assistants and context awareness
Slack, the business messaging platform used by 4 million people every day, will be stepping up its game with AI-driven productivity tools
. Stuart Butterfield made the case for building an application that draws from enterprise resource planning, marketing, sales, business intelligence and other enterprise systems to answer complex queries that otherwise require painfully inefficient searching through troves of data. This will probably be the fruits of Noah Weiss’s “Search, Learning and Intelligence”
team set up in January, which now counts two dozen machine learning engineers.
🎓 Academic poaching tracker
, Associate Professor of Machine Learning at CMU, whose group publishes widely in deep learning was hired by Apple as their inaugural Director of AI Research. His work has explored transfer learning (the ability for models to train on one task/data type and be used on a different one), reinforcement learning and unsupervised learning. The team is growing!
Fei Fei Li
, Director of the Stanford Artificial Intelligence Lab and Stanford Vision Lab, was hired by Google
to lead their Google Cloud Machine Learning group along with Jia Li
, who is Head of Research at Snap Inc. and did her PhD work with Fei Fei Li. Both were involved in building ImageNet, the large-scale image database that helped catalyse breakthroughs in computer vision.
🌐 AI is everywhere, for everyone
Google announced a many updates to their Cloud Platform
offering. First, they launched a Cloud Jobs API which uses machine learning to understand how job titles and skills relate to one another and what job content, location, and seniority are the closest match to a jobseeker’s preferences. Second, developers will be able to access NVIDIA and AMD GPUs in the cloud starting from 2017! Third, the company dropped pricing for the Cloud Vision API as a result of running the models on their proprietary TPU hardware. Fourth, the Cloud Natural Language API is pushed publicly and the Translation API is live with their state of the art Neural Translation Machine
. The company also announced a $4.5m grant
to the Montreal Institute for Learning Algorithms
, which notably includes Yoshua Bengio. The Montreal office will also open a deep learning and AI research group.
The Backchannel features a piece on Google’s Assistant product
, which runs across multiple of the company’s products including Home and the Pixel phone. Namely, it talks about The Transition - a two year period of AI training that will help Google “move from systems that are explicitly taught to ones that implicitly learn.” Fernando Pereira, who leads Google’s projects in natural language understanding, likens the launch of the Assistant to that of Search: “It’s going to be way more fluent, more able to help you do what you want to, understand more of the context of the conversation, be more able to bring information from different sources together.”
Bryan Johnson, founder of Braintree and OSFund, opined a piece
on his newest venture, Kernel
, which seeks to (wait for it…) build the world’s first implantable neural prosthetic for human intelligence enhancement. This will be a long but fascinating journey: Bryan suggests that “each market approved product we create will require approximately $200M and 7–10 years”. Without any details on roadmap and how it works, hard to say much! Watch this space.
Google DeepMind and Blizzard announced a collaboration
to open up StarCraft II as a complex testing environment for AI research. The game requires exploration of partially observable environments, long-term planning, memory and multi-agent collaboration, making it rather fascinating. More resources on StarCraft in AI are available on GitHub here
🔮 Preparing for the future
The US National Science and Technology Council’s Subcommittee on Machine Learning and Artificial Intelligence publish a whitepaper entitled “Preparing for the future of artificial intelligence”
. It explores the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by the progress in AI. Here are some important recommendations for US Federal agencies (page 40):
- Prioritize open training data and open data standards in AI.
- Explore the potential to create DARPA-like organizations to support high-risk, high-reward AI research and its application.
- Draw on appropriate technical expertise at the senior level when setting regulatory policy for AI-enabled products.
- Prioritize basic and long-term AI research.
- Ensure the efficacy, fairness and evidenced-based explainability of consequential decisions made by AI-based systems about individuals.
On the topic of accountability, this piece sets out five key principles
for technologists to onboard: responsibility, explainability, accuracy, auditability and fairness. Nature magazine run two pieces (here
) exploring this black box problem.
In the EU, the General Data Protection Regulation that will come into effect in 2018 prohibits any automated decision that “significantly affects” EU citizens
. This includes techniques that evaluate a person’s “performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.” What’s more, the rule gives the right to EU citizens to review how a particular service made a particular algorithmic decision.
Outgoing President Obama sat down with MIT Media Lab Director
, Joi Ito, to discuss AI. The technology, in his words, “promises to create a vastly more productive and efficient economy. If properly harnessed, it can generate enormous prosperity and opportunity.”
British Prime Minister Theresa May announced an ambitious Industrial Strategy Challenge Fund
to help Britain “capitalise on its strengths in cutting-edge research like AI and biotech”
, as well as further tax credits and government investment worth £2 billion per year by 2020 for R&D.
Here’s a chart
from the World Economic Forum on the change in share of jobs from 1980 to 2012, which shows that many of the jobs that AI is suggested to automate away have indeed already fallen.
Facebook’s News Feed came under significant scrutiny over the proliferation of fake content
and the echo chambers that it can create, namely in the context of the Trump/Clinton election campaign (see Blue Feed, Red Feed
). Tim O’Reilly explores the problem of editorial curation
in a world of infinite information and limited attention. In the piece, Tim and Matt Cutts, former head of the web spam team at Google, rightly state that while Facebook’s pursuit of engagement on its content (vs. link quality for Google search) might optimise for revenue, it ends up producing “shady stories, hoaxes, incorrect information, or polarizing memes as an unintended consequence.”