In an effort to distribute the challenge of building an automated safety and awareness processing stack, Udacity
, which now has a permit to test AVs in California, and leading Chinese ride-sharing startup DiDi
announced a $100k self-driving car challenge
. The dataset includes Velodyne LiDAR point clouds, radar objects and camera image frames (here).
, the self-driving car spin-out from Google X, is proving far more effective
on Californian roads than its competitors. Data shows that Waymo logged 30x more autonomous miles in 2016 than others and only required human intervention 0.2x per thousand miles for safety reasons. Waymo also made the news for pursuing a federal civil lawsuit
. It focuses on Anthony Levandowski, who was a key engineer at Waymo and co-founder of Otto
, a 7-month old driverless software company for trucks that Uber purchased for $700m. Waymo claims
that Levandowski stole 14,000 confident files from Otto servers that describe key self-driving software and LiDAR IP before selling the company to Uber. It has subsequently transpired that Levandowski consulted to Uber on self-driving technology six month before he started Otto. Putting Uber further into the spotlight, one of its AVs was involved in a serious crash in Arizona
, where the company’s cars retreated post their testing ban from SF streets.
has spent the better part of a $2.9bn R&D budget on AI
over the last 2.5 years - an endeavor run by 1,300 researchers. The company was also a victim of an attempted cyber attack
directed against its autonomous driving IP. High stakes at play here.
Tesla announced it was close to releasing version 2.0 of Autopilot
(AP2) based on NVIDIA Drive PX2 and in-house software (vs. 1.0 Mobileye system). While the speed limit for Autosteer has been upped, only 1 out of 8 camera sensors on the new hardware stack is being used. As such, a US law firm is seeking to sue Tesla
for selling AP2 to customers before it’s ready. On the other hand, an Ohio car insurance provider is offering reduced premiums
for Tesla owners with Autopilot.
, in contrast to BMW/VW/Mercedes-Benz, is said to consider removing all driving controls
from their self-driving cars that are set to debut in 2021. They don’t buy that resting drivers can react sufficiently fast to intervene when needed, thus meaning Ford would skip from Level 3 to 5 autonomy.
announced DRIVE PX platform collaborations with Bosch
, the world’s largest automotive supplier, and PACCAR
, a leading global truck manufacturer.
b. The startups
, a US self-driving startup authorised to test on public Californian roads, released an impressive video demo
of a 2017 Lincoln MKZ navigating autonomously in day, night, light rain and cloudy darkness using only a front-mounted cameras. The company’s stated go-to-market model is to provide the self-driving OS to OEMs instead of selling aftermarket or operating their own service. The team draws roots from Princeton’s vision group.
, the Oxford spinout led by Paul Newman and Ingmar Posner that has quietly built impressive mobile autonomy software, won an FT award
. This team tightly couples fundamental research at the University’s Oxford Robotics Institute with real-world applications for self-driving. In 3 years, it’s accomplished significant feats without venture financing, releasing an autonomous control system, Selenium
. #LongUKAI! A prime CMU/Uber-style acquisition target here…
Comma.ai announced the Panda
, a circuit board that extracts granular driving data from a vehicle and can issue accelerator and brake commands to the car. The only way to get your hands on one is by accumulating sufficient points on the company’s Chffr
dashcam video recording app. The (updated) longer term goal being to aggregate worldwide driving data, presumably as a pseudo-Mobileye REM product.
Slightly more information emerged
, another startups that can test on public roads in California. The company retrofits a roof-mounted rig equipped with nine HD cameras, two radars and six Velodyne Puck LiDAR sensors and uses sensor fusion with deep learning to translate inputs to driving instructions. Current limitations include altering the vehicle path on the fly to compensate for obstructions that suddenly appear. The company is also said to focus on logistics in dense geographic areas as opposed to transporting people.
The big boys
joined the Partnership on AI to Benefit People and Society,
appointing Siri co-founder/CTO Tom Gruber to the board along with representatives from DeepMind, Amazon, Microsoft, Facebook and IBM. Apple is also building out its engineering and AI research
footprint in Seattle, following its acquisition of Turi last year. This includes a $1m endowed professorship in ML
at the University of Washington. The company also released a new app, Clips
, for native iOS video editing empowered by computer vision, NLP and AR tools. Furthermore, the new iOS 10.3 update includes a consent for Apple to read user iCloud data
(following differential privacy manipulation) to improve predictive features in Siri.
made lots of announcements
at Cloud NEXT 2017 including the acquisition of data science community Kaggle
, GA release of a) Cloud ML Engine for training and deploying proprietary models to the cloud, and b) Cloud Vision API. There were also releases to help data scientists visually explore and prepare data (Cloud Dataprep) as well as integrate data from BigQuery and Commercial Datasets, and the fully-managed data processing pipeline for batch and streamed data (Dataflow). This shows that ML infrastructure is indeed still a native space where opportunities exist for specialised startups. Separately, YouTube
announced that it had reached 1 billion machine-generated video captions
for their audio content. More on video understanding later!
MIT Tech Review run a piece on Goldman Sachs
’ efforts to breathe automation into their business
. Starting with replacing 4 currency traders with 1 software engineer, the firm has mapped the 146 steps required to take a company public to identify many that are “begging to be automated”. On their side, JP Morgan
has made significant investments
to develop an internal cloud infrastructure and environment to build and run machine learning applications. This includes their Contract Intelligence software, which interprets commercial loan agreements. The product cuts down on the 360k human hours required to analyse 12k contracts a year.
Facebook “today cannot exist without AI”,
says Joaquin Candela (head of applied ML group) in this Backchannel piece on the group’s genesis and its impact on Facebook, Instagram and Messenger over the last two years.
British chip maker ARM
, which was acquired by SoftBank
for $32bn last year, announced their DynamIQ technology
. It applies to ARM’s Cortex-A CPUs and enables custom configurations of large and small CPUs in a single cluster. It also provides a shared memory subsystem, faster data transfer with accelerators and power savings that collectively focus on delivering performance and efficiency for running AI applications at the edge. ARM recently passed the 100 billion chips sold milestone since 1991.
announced a first product with their 3D XPoint memory technology
that is positioned to replace hard drives or SSDs by providing greater density and performance.
keeps expanding the universe of cloud providers offering their Pascal architecture-based Tesla GPUs. They’ve just added Tencent Cloud
, followed by a collaboration with Microsoft
to develop a new hyperscale GPU accelerator powered by 8x Tesla P100 GPUs for AI cloud computing.
AI research in production
Innovation in AI, whether it occurs in the real world or research lab, builds upon the shoulders of published research. There are two fundamental flaws
in the implementation of research: Papers a) seldom contribute much time to solving and openly discussing engineering problems, and b) are fraught with a lack of rigor and reproducibility. These are important problems that we must work to correct as a community. DistillPub
, a new open-source publication for the machine learning, can help here. It provides new data visualization opportunities, transparency over methods and cash prizes for clearly communicated work.
It’s clear that talent is a bottleneck in software engineering and even more in AI. In order to deliver on promises for AI, we need to drive more talent from diverse backgrounds into the field and do so by sustaining the institutions
that educate future generations.
Ben Medlock argues that the missing link
between current AI systems and true AGI is an embodied system for the AI agent. He points to AI systems as only replicating one of the many layers of human cognition, where the others are the biological substrates and complexity of eukaryotic systems.
NYT runs a piece on Santiago Ramon y Cajal
, a 20th century Spanish neuroscientist who published fundamental on how information flowed through the neurons
and synapses in the brain. Equipped with a microscope, he painstakingly sketched these neural structures and quite incredibly built up his reasoning from there.
Last issue we talked about a new frontier in training AI agents: complex simulation environments. At Google NEXT 2017, Improbable founder Herman Narula presented a quick talk on their SpatialOS distributed computation infrastructure for simulation that you can watch here
Policy and governance
Researchers in Cambridge published a sharply critical piece
on the collaboration between Google DeepMind
and the UK’s National Health Service (NHS).
Based on analysis of information reported last year by the New Scientist, the authors argue that the breadth of patient data shared between parties was far greater than originally announced and concerns more than patients under direct care for acute kidney disorder. They claim that plans for a consolidated and canonical data infrastructure for the NHS is beyond the original stated remit of the collaboration (see DeepMind Blockchain project
). More importantly, the authors state that minimal consultation was had with public bodies governing data privacy, health research and medical device regulation. The NHS and DeepMind responded saying that this paper misrepresents the use of data and makes both factual mistakes and analytical errors. While the tone of this piece is also unfairly harsh, it highlights the careful balance that needs to be struck between sufficiently complying with incumbent regulatory frameworks and streamlining these procedures to catalyse necessary upgrades to core NHS services.
The list of signatories to the Asilomar AI Principles
run by the Future of Life Institute continues to grow. Videos from this year’s conference at which questions of ethics, values and longer-term goals are discussed can be found here
Following Bill Gates earlier this year, French Socialist Party candidate Benoit Hamon suggested a corporation tax on economic value generated as a result of AI (“robot tax”) that will go to fund universal basic income. Pro case
: it’s an effective way to prevent further wealth disparity
between the rich who can afford robots to work for them and the less well off who can’t. Con case
: a robot tax stifles innovation
and automation isn’t the only factor that affects the incentive to participate in labor markets (e.g. education, safety nets, trade) and thus shouldn’t be targeted in isolation.
Next frontiers for AI
: Developing systems that understand the contents of video in real-time remains a complex, unsolved problem. This is largely because current static image ML tools don’t go much beyond object recognition, semantic segmentation (labelling each pixel) and captioning. Facebook
, which has users consume over 100 million hours of video a day, has set it sights on this problem because “video understanding is going to be ridiculously impactful”. Google
launched a Kaggle competition using the YouTube-8M dataset, but that only focuses on predicting video labels from 4716 classes (e.g. “electric guitar”, “cuisine” and “talent show”). Meanwhile, a startup in Berlin called TwentyBN
is attacking video understanding from a unique angle
. First, they build a dataset of crowd-acting videos that depict short segments of objects interacting with one another (e.g. placing/pushing/dropping an object onto/on/off a table). Next, they train networks to accurately predict these correct action labels to learn common sense about the 3D world in which objects interact that can be transferred to new problems.
Learning to learn
: Several research groups
have shown that machine learning can be used to improve how learning systems learn (termed “learning to learn”). Jeff Dean of Google Brain stated that this “automated machine learning” is the most promising avenue his team are working on.
: WIRED features a piece on a few researchers and companies working on data efficient means of handling uncertainty
. This is key in the real world where there are only a few examples of driving accidents as a proportion of regular driving footage. AI systems must reason on this uncertainty to make the best (interpretable) decisions.
Hardware for computation
, the British semiconductor startup developing novel silicon optimised for intelligent applications, released beautiful teaser visualisations
of networks at work on their hardware. Watch this space as the company unveil aspects of its core technology this quarter!
received 510(k) clearance from the FDA to market it’s deep learning solution for automated ventricle segmentation
on cardiac MRI images. This is allegedly the first regulated implementation of cloud-based deep learning in the clinical setting and adds to a CE Mark received in December 2016.
announced a collaboration with IBM Watson Health
to integrate and market its deep learning-based non-contrast CT system to help assess patients suspected of head trauma or stroke and rule out brain bleeds. The company is conducting a clinical trial a working towards PMA Class III regulation with the FDA.
Eleven Two Capital
outline opportunities for data-driven health technology
. I do agree that there’s huge value to be created in diagnostics (imaging and physiological sensor), therapeutic discovery and development (see this piece by NVIDIA
), treatment & care monitoring, as well as clinical & administrative workflow optimisation. Recent examples include Grail and Freenome (liquid biopsies).
Researchers at the University of Toronto Scarborough
(commercialised via Structura Bio) have demonstrated they’re able to reconstruct the 3D structure of protein molecules
from tens of thousands of low-resolution 2D electron cryomicroscopy images. Existing methods require days to weeks and as much as 500,000 CPU hours and prior understanding of the target structure - new methods overcome these bottlenecks to speed up drug discovery. Paper here.
, Chief Scientist at Baidu and the original lead of Google Brain, announced his departure
from the Chinese search giant. Andrew remains a driving motivational and educational force behind the adoption of AI in companies and by students (e.g. via his Coursera ML lessons) worldwide. Wang Haifeng
steps up to lead AI at Baidu.
, Professor of Information Engineering at the University of Cambridge, steps up to Chief Scientist at Uber
in connection with the acquisition of Geometric Intelligence. Zoubin is a world-leader in probabilistic modelling and machine learning, focused on decision making under uncertainty and learning efficiently from limited data. Zoubin will move to the West Coast.
Professor of Cognitive Robotics at Imperial College London, took up an appointment at DeepMind
as a Senior Research Scientist. He moves to part time at Imperial. His early work focused on symbolic reasoning, cognitive robotics and increasingly on unifying symbolic reasoning with reinforcement learning.
Ian Goodfellow, formerly part of OpenAI’s founding team, has moved back to Google Brain.
, who co-founded Madbits (acq. Twitter) and then tech lead for Twitter’s Cortex AI team has left to join NVIDIA
as head of AI infrastructure.