Dear friends,
Since the pandemic started, several friends and teammates have shared with me privately that they were not doing well emotionally. I’m grateful to each person who trusted me enough to tell me this. How about you — are you doing okay? Many people outwardly look like they’re doing well, but inside they’re lonely, anxious, or uncertain about the future. If you’re feeling fine, that’s great! But if you’re among the millions who feel that something is off-balance, I sympathize, and I want you to know that I care about you and appreciate you.
Love, Andrew
NewsShots in the DarkA crime-fighting AI company altered evidence to please police, a new investigation claims — the latest in a rising chorus of criticism. What’s new: ShotSpotter, which makes a widely used system of the same name that detects the sound of gunshots and triangulates their location, modified the system’s findings in some cases, Vice reported. Altered output: ShotSpotter’s output and its in-house analysts’ testimony have been used as evidence in 190 criminal cases. But recent court documents reveal that analysts reclassified as gunshots sounds the system had attributed to other causes and changed the location where the system determined that gunshots had occurred.
The response: In a statement, ShotSpotter called the Vice report “false and misleading.” The company didn’t deny that the system’s output had been altered manually but said the reporter had confused two different services: automated, real-time gunshot detection and analysis after the fact by company personnel. “Forensic analysis may uncover additional information relative to a real-time alert such as more rounds fired or an updated timing or location upon more thorough investigation,” the company said, adding that It didn’t change its system’s findings to help police. Behind the news: Beyond allegations that ShotSpotter has manually altered automated output, researchers, judges, and police departments have challenged the technology itself.
Why it matters: ShotSpotter’ technology is deployed in over 100 U.S. cities and counties. The people who live in those places need to be able to trust criminal justice authorities, which means they must be able to trust the AI systems those authorities rely on. The incidents described in legal documents could undermine that trust — and potentially trust in other automated systems. We’re thinking: There are good reasons for humans to analyze the output of AI systems and occasionally modify or override their conclusions. Many systems keep humans in the loop for this very reason. It’s crucial, though, that such systems be transparent and subject to ongoing, independent audits to ensure that any modifications have a sound technical basis. Biomedical Treasure ChestDeepMind opened access to AlphaFold, a model that finds the shapes of proteins, and to its output so far — a potential cornucopia for biomedical research.
Behind the news: Until recently, scientists had to rely on time-consuming and expensive experiments to figure out protein shapes. Those methods have yielded about 180,000 protein structures. AlphaFold debuted in 2018, when it won an annual contest for predicting protein structures. A revised version of the model won again in 2020 with an average error comparable to the width of an atom.
A MESSAGE FROM DEEPLEARNING.AIAI is undergoing a shift from model-centric to data-centric development. How can you implement a data-centric approach? Register to hear experts discuss this and other topics on August 11, 2021, at 10 A.M., Pacific time at “Data-centric AI: Real-World Approaches.” Olympic AIComputer vision is keeping a close eye on athletes at the Summer Olympic Games in Tokyo. What’s new: Omega Timing, a Swiss watchmaker and the Olympic Games’ official timekeeper, is providing systems that go far beyond measuring milliseconds. The company’s technology is tracking gameplay, analyzing players’ motions, and pinpointing key moments, Wired reported. How it works: Omega Timing’s systems track a variety of Olympic sports including volleyball, swimming, and trampoline. Their output is intended primarily for coaches and athletes to review and improve performance, but it’s also available to officials and broadcasters.
Behind the news: Omega Timing has measured Olympic performance since 1932. It introduced photo-finish cameras at the 1948 Olympiad in London. Its systems are certified by the Swiss Federal Institute of Metrology. Why it matters: Technology that helps athletes examine their performance in minute detail could give them a major edge in competition. It offers the rest of us a finer appreciation of their accomplishments. We’re thinking: For this year’s games, the International Olympic Committee added to the schedule competitive skateboarding, surfing, and climbing. Next time, how about a data-centric AI competition? Revenge of the PerceptronsWhy use a complex model when a simple one will do? New work shows that the simplest multilayer neural network, with a small twist, can perform some tasks as well as today’s most sophisticated architectures. What’s new: Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, and a team at Google Brain revisited multilayer perceptrons (MLPs, also known as vanilla neural networks). They built MLP-Mixer, a no-frills model that approaches state-of-the-art performance in ImageNet classification. Key insight: Convolutional neural networks excel at processing images because they’re designed to discern spatial relationships, and pixels that are nearby one another in an image tend to be more related than pixels that are far apart. MLPs have no such bias, so they tend to learn interpixel relationships that exist in the training set and don’t hold in real life. By modifying MLPs to process and compare images across patches rather than individual pixels, MLP-Mixer enables this basic architecture to learn useful image features. How it works: The authors pretrained MLP-Mixer for image classification using ImageNet-21k, which contains 21,000 classes, and fine-tuned it on the 1,000-class ImageNet.
Results: An MLP-Mixer with 16 mixer layers classified ImageNet with 84.15 percent accuracy. That’s comparable to the state-of-the-art 85.8 percent accuracy achieved by a 50-layer HaloNet, a ResNet-like architecture with self-attention. Yes, but: MLP-Mixer matched state-of-the-art performance only when pretrained on a sufficiently large dataset. Pretrained on 10 percent of JFT300M and fine-tuned on ImageNet, it achieved 54 percent accuracy on ImageNet, while a ResNet-based BiT trained the same way achieved 67 percent accuracy. Why it matters: MLPs are the simplest building blocks of deep learning, yet this work shows they can match the best-performing architectures for image classification. We’re thinking: If simple neural nets work as well as more complex ones for computer vision, maybe it’s time to rethink architectural approaches in other areas, too.
Work With Andrew Ng
Software Development Engineer: Landing AI is looking for software development engineers to develop scalable AI applications and deliver optimized inference software. A strong background in Docker, Kubernetes, infrastructure, network security, or cloud-based development is preferred. Apply in North America or Latin America.
Machine Learning Engineer (Customer Facing): Landing AI is looking for a machine learning engineer to work with internal and external engineers on novel models for our customers. Solid machine learning and deep learning background with the proven ability to implement, debug, and deploy machine learning models is a must. Apply here
Data Engineer (Latin America): Factored is looking for top data engineers with experience in data structures and algorithms, operating systems, computer networks, and object-oriented programming. You must have experience in Python and excellent skill in English. Apply here
Head of Engineering: LandingInsight is looking for a head of engineering to manage and grow our world-class team. LandingInsight is developing a variety of products to assist ecommerce companies. Apply here
Learning Technologist: DeepLearning.AI seeks a technologist to guide and support learners across the platform. We’re looking for someone with a passion for online learning, teaching, and improving the learner experience. Apply here
Head of Engineering: Workera is looking for a head of engineering to manage and grow its world-class engineering team. You will lead engineering execution and delivery to make Workera a rewarding place to work and participate in company oversight. Apply here
Enterprise Lead Product Manager: Workera is looking for a lead product manager to develop its enterprise-grade precision upskilling platform and integrate it within the human-resources and learning-tech ecosystem. We believe that talented product managers can adapt to any context. Apply here
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