🏥 Life (and) science
The Nobel Prize in Chemistry was awarded to Jennifer Doudna and Emmanuelle Charpentier for the discovery of CRISPR
, the popular gene-editing method. Keep your eyes out for machine learning-based improvements to the technology :-)
A new paper evaluated
the current regulatory frameworks for the development and evaluation of AI-based diagnostic imaging algorithms and identified several factors that limit the trustworthiness of the systems. These include conflating the diagnostic task with the diagnostic algorithm, a superficial definition of the diagnostic task, difficulties in comparing similar algorithms, insufficient characterization of safety and performance elements, and a lack of resources to assess performance at each installed site. Similar to the pharma industry and the multi-step clinical trial process, the authors proposed four phases of development and evaluation.
Facebook is collaborating
with CMU on the Open Catalyst 2020
, the largest dataset of quantum mechanical simulations for the purposes of finding better electrocatalysts for renewable energy storage. The company is making use of its computing infrastructure and expertise in graph neural networks.
Nature featured a fun historical foray into the science of mapping neuron connections
in the brain, and how advances in high-content microscopy, superresolution, and machine learning are accelerating a once painstakingly manual workflow. An excerpt: “Reconstructing these circuits with then-standard techniques — moving from slice to slice, manually tracing each nerve cell — would have taken hundreds of thousands of hours, Helmstaedter estimates. So, his team combined automated image-processing algorithms with machine learning approaches and focused human effort on marking neuron branches while letting computers handle the volumetric reconstruction. This cut the workload to 20,000 hours — still the equivalent of 10 people working full-time for a year. Further AI improvements sped up the process still more, by training computers to evaluate the machine-assembled reconstructions and requesting human help only when needed.”
One latest FDA 510(k) clearance approval for medical imaging went to Ezra, which develops a prostate segmentation
AI on MRIs. This solves for measuring the volume of the prostate, the size of a lesion, or segmenting the lesion or gland. In the US, there are almost 200k new cases of prostate cancer in 2020.
Google Health announced
a collaboration with the Mayo Clinic, focused on using computer vision technology for segmenting head and neck cancers before radiotherapy.
🌎 The (geo)politics of AI
In our State of AI Report 2020, we highlighted the proliferation of facial recognition systems around the world. Only 3 countries have partial bans on the technology. While some companies (e.g. Apple, MSFT) are taking more thoughtful approaches and new legal precedents are set in the UK, an investigation
by Amnesty International revealed that three companies based in France, Sweden, and the Netherlands sold facial recognition technology to key players of the Chinese mass surveillance apparatus. This finding highlights the tension between calls to strengthen export rules to include strong human rights safeguards and the corporate goals of making profits. Meanwhile, there are home-grown facial recognition systems that are popping up, for example in Africa
that examines the funding sources of tenure-track researchers faculty in computer science departments at MIT, University of Toronto, Stanford, and Berkeley finds that 52% of faculty with known funding sources have been directly funded by Big Tech (GAFAMI). Of note, the percentage increases to 58% when the analysis is limited to faculty who have published at least one ethics or fairness paper since 2015. The study warns that Big Tech can have an (in)direct influence on the output of fairness research through these funding mechanisms.
Interesting talent stat: Between 2009-18, Germany saw 90% growth
in the number of university-trained computer scientists, but in the same period of time, the number of faculty only grew 15%…
🚗 Autonomous everything
Tesla has begun shipping its full self-driving software update to beta testers, some of whom created reaction videos
to its use. The verdict so far is that progress is impressive but it goes without saying that human supervision at all times is needed
Waymo will relaunch
and expand its autonomous ride-hailing service in Phoenix. Waymo plans to open access to all customers in a 50-square mile area. The company has also moved into trucking
, which it believes is a natural extension of its technology.
Cruise joins Waymo, Nuro, and AutoX as the fourth company to receive a permit
from the California DMV to remove the human backup driver from their self-driving cars. They will be sending unmanned cars out onto the streets of SF before 2020 is out.
Here’s a fun consumer review of Comma.ai OpenPilot
, the $1,199 device that adds driverless capabilities to your car.
The big evolving story is NVIDIA’s pending acquisition of Arm for $40B from its current owners, SoftBank. The deal has vocal proponents on both sides. In our State of AI Report, we predict that the transaction does not end up being completed. Even though NVIDIA is adding sweeteners to the deal, e.g. building
a £40M supercomputer for health AI research in Cambridge (UK), it appears that Chinese technology companies are pushing the state
to block the deal unless access to Arm’s designs remains unhindered. Given Arm’s hundreds of licensees who depend on its RISC technology, there is concern that its ownership by NVIDIA could jeopardize unhindered technology access (not factoring the issues of geopolitics). What’s more, there is talk that large enterprises are exploring the main alternative to RISC, which is RISC-V, championed by inventors SiFive as a means of long-term hedging. Indeed, SiFive just announced
their Intelligence VIU7 Series, a vector processor designed for AI and graphics workloads. Recall that China did put up a drawn-out fight against NVIDIA’s acquisition of Mellanox. If you’re looking for a primer on the history of semiconductors and their role in geopolitics, I encourage you to read this piece
Arm announced a new addition
to their micro-neural processing unit accelerator with more power and low-power consumption.
In other big-chip news, AMD announced a $35B all-stock deal to acquire Xilinx
, the makers of FPGAs in a bid to compete with Intel in the data center.
their iPhone 12 Pro that comes packed with a LiDAR sensor. The company demonstrated its use in augmented reality and 6x faster low-light auto-focusing for photos and videos.
👩💻 Enterprise software and big tech
Earlier this year, DocuSign acquired
Seal Software, an AI-based contract analysis company for $188M in cash. Now, DocuSign announced
the availability of this technology to its customers. The feature uses machine learning to identify clauses and conduct a risk assessment based on an organization’s own legal and business standards. The goal is to improve the thoroughness of review in less time.
Google is adding further improvements
to language-understanding in Search. After pushing BERT-based search into production, the company pushed a new 680M parameter model that powers the “did you mean” feature, i.e. understanding what you meant even if your query was full of typos. Meanwhile, Apple is moving into search
Developer tools for machine learning (both open and closed source) are on fire right now. There are new startups and projects popping up every month that attack various points of the developer workflow, from version control, pipelining, experiment tracking, serving, monitoring, and more. However, there are several issues with this product segment and this post
does a great job of itemizing them. Of note (and I have noticed this first hand over the years) is that there is still no dominant design pattern for machine learning. There are specific tools, there are end-to-end products, and everything in between. The ground has still not firmed up and enterprises of all shapes and sizes have their own particularities. It’s the wild west ;-) a16z had a nice piece on emerging architectures
for modern data infrastructure.
Adobe (finally) released
ML-powered image and video editing features, in addition to tools that help creators prove that their images are real (and not generated).
NVIDIA creates lots of buzz around its replacement of video codecs
with a neural network, which resulted in orders of magnitude lower bandwidth. They call it NVIDIA Maxine, a cloud-native video streaming AI SDK. Magic Pony’s vision is still alive :-)