Dear friends,
Here’s a quiz for you. Which company said this?
Generative AI is very exciting! Nonetheless, today’s models are far from AGI. Here’s a reasonable definition of from Wikipedia:
“Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that human beings or other animals can.” The latest LLMs exhibit some superhuman abilities, just as a calculator exhibits superhuman abilities in arithmetic. At the same time, there are many things that humans can learn that AI agents today are far from being able to learn.
If you want to chart a course toward AGI, I think the baby steps we’re making are very exciting. Even though LLMs are famous for shallow reasoning and making things up, researchers have improved their reasoning ability by prompting them through a chain of thought (draw one conclusion, use it to draw a more sophisticated conclusion, and so on).
To be clear, though, in the past year, I think we’ve made one year of wildly exciting progress in what might be a 50- or 100-year journey. Benchmarking against humans and animals doesn’t seem to be the most useful question to focus on at the moment, given that AI is simultaneously far from reaching this goal and also surpasses it in valuable ways. I’d rather focus on the exciting task of putting these technologies to work to solve important applications, while also addressing realistic risks of harm.
Keep learning! Andrew
NewsMicrosoft Cuts Ethics SquadMicrosoft laid off an AI ethics team while charging ahead on products powered by OpenAI. How it works: Ethics & Society was charged with ensuring that AI products and services were deployed according to Microsoft’s stated principles. At its 2020 peak, it included around 30 employees including engineers, designers, and philosophers. Some former members spoke with Platformer anonymously.
Behind the news: Microsoft isn’t the only major AI player to have shifted its approach to AI governance.
Why it matters: Responsible AI remains as important as ever. The current generative AI gold rush is boosting companies’ motivation to profit from the latest developments or, at least, stave off potential disruption. It also incentivizes AI developers to fast-track generative models into production.
All the News That’s Fit to LearnWhat does an entrepreneur do after co-founding one of the world’s top social networks? Apply the lessons learned to distributing hard news. What’s new: Kevin Systerom and Mike Krieger, who co-founded Instagram, launched Artifact, an app that uses reinforcement learning to recommend news articles according to users’ shifting interests. How it works: The founders were inspired to launch a news app after witnessing TikTok’s success at designing a recommendation algorithm that learned from users’ habits, Systrom told The Verge. The app starts by classifying each user as a persona that has a standardized constellation of interests, the founders explained to the tech analysis site Stratechery. Then a transformer-based model selects news articles; its choices are continually fine-tuned via reinforcement learning, TechCrunch reported.
Behind the news: Artifact joins a crowded field of personalized news feeds from Google, Apple, Japan-based SmartNews and China-based Toutiao (owned by TikTok’s parent ByteDance). NewsBreak of California focuses on local news. Yes, but: Delivering news is a tough business. Never mind the precipitous decline of traditional newspapers. SmartNews announced it was laying off 40 percent of its staff.
A MESSAGE FROM DEEPLEARNING.AIAre you interested in hands-on learning for natural language processing and machine learning for production? Join us on March 23, 2023, at 10:00 a.m. Pacific Time for a workshop in “Building Machine Learning Apps with Hugging Face: LLMs to Diffusion Modeling.” RSVP
How AI Kingpins Lost the Chatbot WarAmazon, Apple, and Google have been building chatbots for years. So how did they let the alliance between Microsoft and OpenAI integrate the first smash-hit bot into Microsoft products? Amazon: Alexa hit the market in 2014. It garnered great enthusiasm as Amazon integrated it into a range of hardware like alarm clocks and kitchen appliances.
Apple: Siri became a fixture in iPhones in 2011. It drove a spike in sales for a few years, but the novelty wore off as it became mired in technical complexity.
Google: Google debuted Assistant in 2016. It touted Assistant’s ability to answer questions by querying its search engine. Meanwhile, it pioneered the transformer architecture and built a series of ever more-capable language models.
Why it matters: The top AI companies devoted a great deal of time and money to developing mass-market conversational technology, yet Microsoft got a jump on them by providing cutting-edge language models — however flawed or worrisome— to the public. We’re thinking: Microsoft’s chatbot success appears to be a classic case of disruptive innovation: An upstart, OpenAI, delivered a product that, although rivals considered it substandard, exceeded their products in important respects. But the race to deliver an ideal language model isn’t over. Expect more surprise upsets to come!
Real-World Training on the DoubleRoboticists often train their machines in simulation, where the controller model can learn from millions of hours of experience. A new method trained robots in the real world in 20 minutes. What's new: Laura Smith, Ilya Kostrikov, and Sergey Levine at UC Berkeley introduced a process to rapidly train a quadruped robot to walk in a variety of real-world terrains and settings. Key insight: One way to train a model on less data is to train it repeatedly on the same examples (in this case, the robot's orientation, velocity, and joint angles at specific points in time). However, this may lead the model to overfit (for instance, the robot may learn to walk effectively only on the terrain used in training). Regularization or normalization enables a model to train multiple times on the same examples without overfitting. How it works: The authors trained a motion-planning model to move a Unitree A1 robot forward on a given terrain using an actor-critic algorithm, a reinforcement-learning method in which an actor function learns to take actions that maximize the total return (roughly the sum of all rewards) estimated by a critic function. The actor was a vanilla neural network and the critic was an ensemble of such networks.
Results: The authors trained the model to walk the robot on each of five surfaces (starting from scratch for each surface): flat ground, mulch, lawn, a hiking trail, and a memory foam mattress. The robot learned to walk on each in about 20 minutes, which is roughly equivalent to 20,000 examples. Competing methods use either simulation or more time in the real world. For example, the authors of DayDreamer: World Models for Physical Robot Learning trained the same type of robot to walk on an indoor surface without a simulation, but it took one hour and 3.6 times more examples. Why it matters: Training on simple features (those with a small number of dimensions, such as robot orientation and velocity) rather than complex features (such as images) reduces the number of examples required to learn a task, and regularizing the model prevents overfitting. This is a simple, general setup to train reinforcement learning models in the real world. We're thinking: Reinforcement learning algorithms are famously data-hungry, which is why much of the progress in the past decade was made in simulated environments. A recipe for training a quadruped rapidly in the real world is a great step forward!
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