


Li Feifei takes stock of the top ten AI highlights of the year: nuclear fusion, ChatGPT, and AlphaFold are on the list
The explosion of artificial intelligence is distorting our sense of time.
Can you believe that Stable Diffusion is only 4 months old and ChatGPT has been around for less than a month?
To use a vivid metaphor, as long as you blink, you will miss a brand new industry.
In the AI field in 2022, large-scale generative models have sprung up like mushrooms after a rain, changing the landscape of the entire AI industry.
Moreover, these models are rapidly moving out of the laboratory and being applied in reality.
For example, LLM technology has inspired two emerging fields-decision-making agents (games, robots, etc.) and AI4Science.
Jim Fan, a disciple of Li Feifei, summarized the top ten AI highlight moments in 2022 for us. Let’s turn back the clock and see what amazing AI breakthroughs there will be in 2022.
1. Text-image generation
DALLE-2 is the first to generate realistic high-resolution images from any title Large-scale diffusion models for images.
It launched an artistic revolution in AI, spawning many new applications, startups, and ways of thinking.
But DALLE-2 is protected behind the walls of OpenAI and is not open source.
After OpenAI, LMU's StabilityAI and runwayml took a heroic step and trained their own Internet-scale text2image model based on the "potential diffusion" algorithm. They call the model "stable diffusion" and open source the code and weights.
Facts have proved that the openness of Stable Diffusion has brought great changes to the game.
Now, many startups and research labs are creating new applications based on Stable Diffusion, and Stable Diffusion itself is continuously improved by the open source community.
Recently, Stable Diffusion has reached v2.1 and can run on a single GPU.
In addition, there are two image2text models from GoogleAI this year. GoogleAI has neither released the model nor the API, but from the paper, we can still see many interesting insights.
Imagen
https://imagen.research.google
Parti
https://parti.research.google. It is a Transformer model without diffusion.
2. Text-text generation
As everyone knows, I am talking about ChatGPT!
This is the only app in history to gain 1 million users in 5 days.
ChatGPT has also greatly inspired our human creativity.
In this list, you can see all useful and imaginative ideas about ChatGPT: https://github.com/f/awesome-chat
Both ChatGPT and GPT-3.5 use a new technology called RLHF ("Reinforcement Learning from Human Feedback").
This also means that the prompt project may disappear soon.
The popularity of ChatGPT has spawned a wave of new startups and competitors, such as Jasper Chat, YouChat, Replit’s Ghostwriter chat, and perplexity_ai.
These competitors provide such intuitive search methods that even Google executives are starting to sweat!
3. Text-Robot Model
How to give GPT arms and legs so they can clean your messy kitchen?
Unlike NLP, robot models need to interact with the physical world.
This year, the large pre-trained Transformer finally began to solve the most difficult problems in the field of robotics!
VIMA
In October, my colleagues and I Created a "robot GPT" - a transformer named VIMA.
It can receive any mixed text, images and videos as prompts and output the control of the robot arm.
Our model is called VIMA ("VisuoMotor Attention") and is completely open source.
Now, a single agent can solve visual targets, one-time imitation of videos, new concept foundations, visual constraints, etc., with strong scalability of model capacity and data.
RT-1
Following a similar path to VIMA, researchers from GoogleAI released RT-1, a Robot transformer trained on 700 tasks and 130K human demonstrations.
This data was collected over 17 months by 13 robots, a literal army of steel!
4. Text-Video
Essentially, a video is a series of images tied together over time, giving us Creates the illusion of movement.
If we can do text2image, why not add a timeline to it for some extra fun?
Currently, there are three major works in the text-to-video field, but none of them are open source.
Make-A-Video
The first is Meta AI’s Make-A-Video: No need for paired text-video data, you can get text-video of generation.
You can sign up for trial access here: https://makeavevideo.studio
Paper link: https://arxiv.org/abs /2209.14792
Phenaki
Phenaki from Google AI: Generating variable-length videos from open-domain text descriptions. Demo: https://phenaki.videoDreamFusion
The first to appear is DreamFusion jointly developed by the Google AI research team and UC Berkeley.Magic3D
The second result is two projects of the NVIDIA AI team, named GET3D and Magic3D.
Point-E
After the DALL-E 2 launched at the beginning of the year surprised everyone with its genius brush, OpenAI released its latest image generation model "POINT-E" on Tuesday , which can generate 3D models directly from text.So, can AI use its imagination as much as humans can?
Jim Fan and colleagues collaborated to develop the first AI to play "Minecraft", "MineDojo", which can solve many tasks under natural language prompts.
Paper link: https://arxiv.org/pdf/2206.08853.pdf
Fan’s ultimate goal is to build an “embodied ChatGPT” . Currently, the MineDojo platform is completely open source.
At the same time, Jeff Clune’s team announced a model called Video Pre-Training (VPT), which can directly output keyboard and mouse movements.
Paper link: https://arxiv.org/pdf/2206.11795.pdf
VPT has a broader perspective, But it is not restricted by language conditions. At this point, MineDojo and VPT complement each other.
References:
https://twitter.com/drjimfan/status/1607746957753057280?s=46&t=OVM_4zdRW2rQwqLohMdPpw
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