Last Week in AI #200: A Review of AI in 2022
From text-to-everything to ChatGPT, here's a look at the most important AI news in 2022
Happy New Year!
We began publishing our weekly newsletter in 2018 to provide accessible and informed coverage of the latest AI news and trends. As grad students in AI, it was clear to us that the field was rapidly advancing and would significantly impact the rest of the world, but media coverage often focused on hype rather than facts. We wanted to make a newsletter that made it possible to find the signal amid the noise and understand what is happening in the world of AI, even if you are not a technical person. Much has changed since 2018, and the pace of AI progress and deployment has grown enormously, making us more confident than ever this sort of newsletter needs to exist.
Here's to the next 200!
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2022 A Year in Review
The world of AI began the year relatively quietly. We saw continued deployments of AI-powered solutions in real applications, like self-driving tractors, nurse-assistant robots, and early Covid detection systems. AI-powered fighter jets saw new tests and inch closer to becoming a reality.
February saw the use of Deep Reinforcement Learning to control plasma shape in nuclear fusion reactor. This is a very impressive and impactful real-world application of Deep RL, which often gets criticized for its lack of real-world uses. Meanwhile, several stories highlighted that facial recognition remains one of the most concerning trends related to AI. Fortunately, progress on AI regulation around the world continued to accelerate.
Russia began its invasion of Ukraine in late February, and AI-powered technology has played a part in this war. Among other things, we saw perhaps for the first time a malicious wartime use of deepfake technology that showed the Ukrainian president giving a surrender speech. This would and will not be the last time AI is used in war.
This year for AI really kicked off with OpenAI’s release of DALL-E 2, the first really good text-to-image AI model. For many in the general public, DALL-E 2 would be their first contact with really powerful generative AI, but more impressive models turned out to be just around the corner. At the same time, Google’s massive Pathways Language Model demonstrated that text-generating AI will keep making huge progress alongside their text-to-image peers.
DeepMind’s Gato model convinced many that transformers may be “the only thing that you need” in deep learning - this one model, trained on text, images, and audio. Hugging Face, the “GitHub of machine learning”, reached enormous evaluations that once again demonstrated the growth of AI’s importance in industrial applications, and AI’s growing footprint on the economy in general.
The field of Embodied AI - AI that must act within an environment to complete temporally extended tasks - also picked up steam this year with many research groups using Minecraft as a diverse, open-world, and multi-task AI testbed. This VPT work is only one such example. Also this month, one of the biggest AI stories of the year emerged: the claim that Google’s LaMDA model is sentient. As we wrote, this was not the case, but the story spread like wildfire regardless.
Editorial: LaMDA’s Sentience is Nonsense - Here’s Why
Perhaps somewhat quietly, and amid all the tech layoffs and downsizing, the industry of robo-taxis is slowly maturing and gaining ground. Cruise was the first company to widely deploy its robo-taxi service in San Francisco, with Waymo soon following soon. These self-driving cars are not perfect and are susceptible to mistakes like causing the traffic jam above. However, it’s undeniable that these services “work”, and robo-taxis may actually be soon upon us.
If there’s been one trend that defined AI progress in 2022, it was the advancements in text-conditioned generative models. Only a few months after OpenAI’s DALL-E 2, Google, Facebook, and others developed their own incredible image generation models, with some progress also being made in creating entire videos as well. The key driver to all this progress is scale — using larger models, more data, and more computational power to train the same simple models.
September proved to be a fairly quiet month, with a mix of news about new exciting research, business moves, growth in the AI ecosystem, progress on regulations, and AI’s growing impact on the media we consume.
Ever the hype man, Elon Musk promised a lot when it came to the reveal of the TeslaBot on “AI Day 2”. Many robotics researchers were skeptical, but the general consensus after the event was that Tesla had achieved a lot with their prototype in just one year. Still, it is clearly far from a finished product. Aside from that notable highlights, many stories this month familiar themes: text-to-X generation, self driving cars, regulation, and new exciting applications of AI.
Editorial: Tesla's AI Day Was a Success
November was the month that many artists made their disdain for AI-generated art known, and many platforms had to figure out their policies regarding AI-generated images. Text generation tools also grew in notoriety, with more and more op-eds being written about their impact on schoolwork; how do you assign students to write essays, when an AI can write a convincing unique essay on just about any topic? Meanwhile, the AI community went through some drama as Meta released an AI model that purported to be capable of writing papers, but was soon demonstrated to often output silly things.
Editorial: Robots That Write Their Own Code
Editorial: Robots That Write Their Own Code
The year ended with ChatGPT capturing the spotlight and making it more widely known than ever how advanced text-generating AI has become. At the same time, the AI-powered portrait generation app Lensa went viral, making it known how advanced image-generating AI has become. As we had to 2023, one thing is for certain: the scale of AI models, the public’s awareness of AI, and the number of AI research advancements will all continue to grow.
Editorial: AI's Year of Text-to-Everything
Throughout the last few years, Last Week in AI has been a side project of three people —- Andrey, Jacky, and Daniel. We are continuously encouraged to find the time and energy for it because of all of you, the readers! So thank you. Have a great 2023!
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