🚗 Department of Driverless Cars
a. Large incumbents
lawsuit finally drew to a close 12 months after Waymo originally filed their lawsuit claiming damages over $1B. The two companies agreed to a settlement
in which Waymo will acquire a 0.34% equity position in Uber that is worth $245M at the $72B valuation. Uber must also sign an agreement to ensure that Waymo confidential information is not incorporated into Uber technology, which is otherwise given the greenlight to move forward in its development of self-driving technology. The original settlement offer was $500M, which Uber’s board rejected. To be honest, given the huge financing rounds being raised for self-driving car companies these days and the size of the prize for those companies who win local markets, it feels like paying off Waymo with equity to distance itself from future claims is actually a very good deal for Uber. It’s another sign that Dara is doing a top job at Uber’s CEO. He gave a strong impression at Goldman’s Internet Research conference in SF a few weeks ago, where he said that Uber’s ultimate mission to so give users A to B transport through whatever combination of mobility mediums possible.
Fun sidenote: Anthony Levandowski
, the man at the center of the trail, had entered a plan for a self-driving offroad motorbike
into the 2004 DARPA Challenge (see video in the article). IEEE pieced together the story, which shows a project quite ahead of its time. The bike used an AMD 64-bit CPU with 512 MB of RAM that was able to process only about one frame every 4 seconds! The main sensors were optical, with a range of 40 meters. While it did not manage to finish the qualification course, GhostRider earned the attention of Sebastian Thrun in 2006. His car, Stanley, won the DARPA challenge and he’d invited Levandowski to work on a mapping project called VueTool. It’s team members were then hired by Google, which ultimately led Levandowski and Thrun working on Google’s self driving car project. The rest is history :)
Street level mapping
, as we know, is one of the major war fronts in self-driving. Most companies have built their self-driving stack in a way that requires high-dimensional (HD) maps of the roads on which their cars will drive. Because these maps are the core infrastructure for autonomy, serious contenders want to build and own this part of the stack to reduce third party dependency on a core competency. This piece runs through the current state of affairs
in the HD mapping arena, involving Google, HERE, TomTom, Uber, DeepMap, Mapbox, Intel/Mobileye
and a ton more. What’s interesting is that human drivers do not have HD maps of the worlds they navigate, but are still able to generalise, learn quickly and adapt to diverse environmental situations. The HD mapping game implies that we’re not making necessary technology advances to progress towards human-level scene understanding and reasoning.
, an open dataset that includes RGB videos with millions of high-resolution image sequences for scene parsing. The company also dropped its lawsuit
against JingChi, another self-driving car company formed by ex-Baidu employees, as the two companies team up on technology development via Baidu Apollo. Eyebrows raised…
and Tier 1 OEM Magna
have signed a deal
for the co-development of autonomous vehicle software and joint ownership over IP. This deal gives Magna an exclusive agreement to outfit Lyft’s fleet with autonomous driving equipment, while still allowing the Lyft’s open marketplace to grow. In addition, Magna will purchase $200M worth of equity in Lyft, which pushes their recent financing round to $1.7B. This gives Lyft access to automotive-grade equipment and relationships with the sector.
The California DMV
are expecting to receive state approval
on new rules that would let autonomous car companies take the human safety driver out of the driver’s seat, and also apply for permits to take passengers.
have published their 2018 self-driving safety report
, which is really more of a descriptive account of their build processes, technology components, vehicle design and safety procedures that it is a quantitative account of their safety results. The company also announced a $100M investment
to upgrade two factories such that they are fit to manufacture production versions of the autonomous Chevy Bolt EV.
has increased the size
of its autonomous car fleet in California from 3 to 27. It’s still unclear what the company’s efforts are, however.
is pushing forward with their trucking business, which is is already making $100M’s a year according to their CEO. The company published their economic modelling exploring various modalities for launching self-driving trucks
. This is what the analysis showed: “In our baseline projections without self-driving trucks, the number of trucking jobs nationwide increased 766,000 by 2028. When we add self-driving trucks into the scenario above, truck driving jobs increase even more, with many long haul jobs shifting to local haul to support growing freight volume moving in and out of transfer hubs. Why? The deployment of self-driving trucks improves efficiency on long haul routes, lowering the overall cost of trucking and reducing the total cost of the goods being shipped. When goods are cheaper, consumers buy more of them. And when consumers buy more, more new goods need to be shipped than before, which drives truck freight volume up. In this scenario, when 1 million self-driving trucks are operating on highways, we would expect to see close to 1 million jobs shift from long haul to local haul, plus about 400,000 new truck driving jobs will be needed to keep up with the higher demand.”
Wired ran a long piece on Didi Chuxing,
the immense Chinese ridesharing company with 400M registered customer who take 25M rides a day in more than 400 Chinese cities. This volume is almost 2x Uber and all global ridesharing apps combined. The company that famously beat Uber out of China is working with carmakers to redesign vehicles for sharing, as well as projects on electric and autonomous vehicle too.
have announced their first commercial partner
, Volkswagen Group, with the goal of creating a new mobility-as-a-service solution that integrates Aurora full-stack technology into VW cars. A few weeks later, Aurora announced a whopping $90M Series A
round led by Greylock and Index.
have launched a Level 4 autonomous transport service
to 125,000 residents of a private residential area called The Villages. The area includes 750 miles of road, 100 dining options, 8 major grocery stores, 80 tennis courts, a regional hospital, a school and more. Residents will be able to summon door-to-door transport through Voyage. This go-to-market strategy is the absolute right one in my view - it lets you test a fully autonomous transport system in the real world without throwing cars in the deep end of city traffic. In doing so, Voyage will be able to measure all the benefits of self-driving in a more controlled environment. Mapping of the streets is conducted using Carmera
came out of stealth with two big announcements
: a) they’ve designed and built a delivery focused self-driving vehicle that kind of looks like a shopping basket on wheels, and b) they’ve raised $92M already to get there. The vehicle will navigate on roads instead of pavements (like Starship’s
delivery robot). It’s quite an impressive product.
💪 The giants
Bloomberg ran a long profile on SoftBank’s
Masayoshi Son, citing his vision of “a trillion connected devices, generating data that will be analyzed by artificial intelligence to supposedly make the world a better place.” Their investor presentations
are really worth a flick through, especially to understand the high level connectedness of the assets they’re acquiring stakes in.
In this widely watched presentation about the future for WeChat
, founder Allen Zhang said that they will still not interfere or curate service providers who reach users through the app. In an age where Chinese users conduct most of their lives through WeChat, I would have thought that automated curation would be the next step (much like it has been for products led by US technology companies).
opened their first Go-powered grocery and convenience store
in Seattle in January, letting anyone with an Amazon account, the Amazon Go app to collect and purchase goods without needing to go through a checkout line. Customers scan a QR code at the entrance and are followed by cameras and weight sensors on the shelves to track what they “buy”. The company now plans to open up to 6 more stores in 2018, powered by technology that’s been under development for four years. Several startups have since followed suit, include Bodega.
hostile $142B takeover bid for Qualcomm, which could have been the largest tech deal ever, has been blocked by the Trump administration
. It believes there is credible evidence that Broadcom, which is based in Singapore, might take action via Qualcomm that ‘threatens to impair the national security of the United States”. The Europeans worry that Broadcom will have access to data about German citizens given that Qualcomm’s subsidiary makes chips for their passports.
The story of Apple’s
investments into in-house semiconductor design and production
is a rich one. Jobs long believed that the company should own the technologies within their products instead of procuring them from third parties. The company has chip building and testing facilities in California and Israel, originally from its acquisition of P.A. Semi in 2008. Apple has also been hiring talent from Qualcomm, with whom it been engaged in legal battles over licensing fees. Apple also made news for their Neural Engine and custom GPUs for iPhone 8 and X.
Palliative care clinics
are centers that care for patients for whom there are no further treatment options to cure their declining health state. The NYT ran a story about developing an AI model for predicting the time window during which patients would die.
Quite a morose thought for sure, but the motivation was to optimally allocate very strained resources to deliver an overall improved quality of life for all patients. The model ascribed low probabilities of death for 95% of patients who ended up living longer than 12 months.
Two papers came out applying deep learning models to diagnostic images of the eye
(optical coherence tomography or retinal fundus). The use cases were to 1) predict cardiovascular risk factors
(e.g. age, gender, smoking status, systolic blood pressure) and 2) classify age-related macular degeneration and diabetic macular edema
. There was already lots of evidence that non-deep learning methods (such as logistic regression with standard risk factors) can be used to infer cardiovascular risk from retinal images. Patients in the UK Biobank (those used in this study) are still largely healthy to begin with. It’ll be super interesting to see how these predictions pan out as patients age. The second study, however, faces a few issues. The authors use a model that analyses a 2D image, whereas an OCT scan is 3D (i.e. it’s built from multiple slices to create a volume). They in fact use slices taken from the same eye (i.e. the same volume scan) in the training and test set, which means the model is actually allowed to ‘cheat’ by overfitting the data. Both studies also use attention mechanisms to draw heatmaps on their images to claim that the neural networks focus on the right parts of the image to make their inferences. It is, however, great to see cross-disciplinary machine learning and life sciences studies published in journals read by biologists (rather than the standard arxiv), as its through this channel that more life scientists will become familiar with machine learning tools. The key question in all of this is whether the diagnostic task is clinically-validated such that we can be confident neural network models can indeed learning clinically-relevant representations of data instead of spurious overfitting of training data. Thanks to Pearse Keane for comments here!
Innovation in the West vs the East
: In the West, we’re witnessing a rather large number of healthcare startups developing doctor-on-demand services, medical imaging technology, and health monitoring solutions via wearable devices. While I’m bullish on these opportunities, the regulatory hurdles and clinical trial processes to establish these companies as part of the mainstay are significant. On the one hand, it feels like the risk/reward discussion is skewed towards the system slowing down progress. On the other hand, careful evaluation and study of any new technology, especially if it concerns human health, is paramount. Reading about medical innovation in the East, however, gives me the sense that the West is seriously falling behind. In this piece about Tencent’s medical ecosystem in China
, we’re given a window into the company’s WeChat Intelligent Healthcare product suite. Through WeChat, consumers can book appointments (in person or online), make payments, obtain medical insurance, access their medical data, and benefit from AI-driven diagnostics that improve turnaround time. All of this occurs within the Tencent network. The company is also heavily investing in a 3rd party ecosystem of medical technology startups, as well as launching its own clinic’s, Tencent Doctorwork. I’m curious to hear any of your opinions on the ground! Interestingly, Alibaba has recently bid $1.4B for iKang
, a chain of 110 private healthcare clinics in China, which indicates another push into an Alibaba technology-driven healthcare network.
, an independent, non-party think tank focused on public services and economic prosperity, produced a report on AI in the UK’s National Health Service
. They suggest that “AI can help reduce the“AI could help address the health and wellbeing gap by predicting which individuals or groups of individuals are at risk of illness and allow the NHS to target treatment more effectively towards them….AI could help address the efficiency and funding gap by automating tasks, triaging patients to the most appropriate services and allowing them to self-care.”
The report recommends the NHS to fully digitise its data in a machine-readable format, implement a clear AI software procurement process, require IT suppliers to build interoperability of their systems right from the start, and provide a list of secure, training datasets. Also mentioned is the need for a framework of AI explainability, which includes tests of bias and making data preprocessing procedures and training data available to the regulatory agency.
Applying ML to human genomes
to discern the mechanisms of disease: This piece profiles Barbara Engelhardt’s work
in developing new statistical tools that search for the expected biological patterns to map our the genome’s real but elusive “ground truth”. What’s interesting here is the how she uses unsupervised techniques as feature extractors, then rationalises which features make biological sense, then uses ‘classical’ latent factor models to glean interpretable mechanistic insights into biology. This strategy lends itself well to the fact that genomics datasets have huge amounts of noise and very little signal, in addition to enormous feature spaces from very few samples.
🔮 Where AI is heading
of MIT and Rethink Robotics fame shares his predictions
about the next 32 years of AI progress, what to expect and when. He focuses on self-driving cars, AI research and robotics achievements, as well as space travel. Short summary of a great piece: “It always takes longer than you think. It just does.”
use video data from their Clips product to understand how a human-centered design process elevates AI
. I like the emphasis they place on anchoring user expectations for AI-driven systems. In particular, they note: a) that ML won’t figure out what problems to solve and thus we need to align ML systems with human ned, b) the goals of an ML system should be clear to a user such that their expectations are calibrated, c) the field has to be multi-disciplinary.
of the Oxford Robotics Institute penned a tremendous review of the subsystems required to build autonomous platforms
, as well as the state of learning based methods for these, including uncertainty and introspection. In particular, I like how he makes the point that it is “often more efficient simply in terms of human effort to port our prior knowledge into algorithmic prior structure, which will be the most practical way for short to medium term research with potential for real world impact.”
Using both learned approaches and prior knowledge based on geometry, dynamics and kinematics is suggested as a healthy hybrid approach.
Why is machine learning (and thus progress towards general AI) hard to achieve? One reason is that machine learning is a fundamentally hard debugging problem
. Compared to regular software engineering that requires awareness of the trade offs of competing frameworks, tools and techniques and judicious design decisions, machine learning adds two additional dimensions along which bugs are common: the model itself and the data.
is an increasingly large deal in the ML world - in this piece
, Google researchers We explore the powerful interfaces that arise when you combine them — and the rich structure of this combinatorial space.