After weeks of “less exciting” news in the AI space since the release of Llama 2 by Meta on July 18, 2023, there were a bunch of announcements in the last few days by major players in the AI space:
1st-level generative AI as applications that are directly based on X-to-Y models (foundation models that build a kind of operating system for downstream tasks) where X and Y can be text/code, image, segmented image, thermal image, speech/sound/music/song, avatar, depth, 3D, video, 4D (3D video, NeRF), IMU (Inertial Measurement Unit), amino acid sequences (AAS), 3D-protein structure, sentiment, emotions, gestures, etc., e.g.
X = 3D-protein, Y = AAS: ProteinMPNN (from Baker Lab)
X = 3D structure, Y = AAS: RFdiffusion (from Baker Lab)
and 2nd-level generative AI that builds some kind of middleware and allows to implement agents by simplifying the combination of LLM-based 1st-level generative AI with other tools via actions (like web search, semantic search [based on embeddings and vector databases like Pinecone, Chroma, Milvus, Faiss], source code generation [REPL], calls to math tools like Wolfram Alpha, etc.), by using special prompting techniques (like templates, Chain-of-Thought [COT], Self-Consistency, Self-Ask, Tree Of Thoughts, ReAct [Reason + Act], Graph of Thoughts) within action chains, e.g.
we currently (April/May/June 2023) see a 3rd-level of generative AI that implements agents that can solve complex tasks by the interaction of different LLMs in complex chains, e.g.
However, older publications like Cicero may also fall into this category of complex applications. Typically, these agent implementations are (currently) not built on top of the 2nd-level generative AI frameworks. But this is going to change.
Other, simpler applications that just allow semantic search over private documents with a locally hosted LLM and embedding generation, such as e.g. PrivateGPT which is based on LangChain and Llama (functionality similar to OpenAI’s ChatGPT-Retrieval plugin), may also be of interest in this context. And also applications that concentrate on the code generation ability of LLMs like GPT-Code-UI and OpenInterpreter, both open-source implementations of OpenAI’s ChatGPT Code Interpreter/AdvancedDataAnalysis (similar to Bard’s implicit code execution; an alternative to Code Interpreter is plugin Noteable), or smol-ai developer (that generates the complete source code from a markup description) should be noticed. There is a nice overview of LLM Powered Autonomous Agents on GitHub.
The next level may then be governed by embodied LLMs and agents (like PaLM-E with E for Embodied).
On the same day as OpenAI released GPT-4 (March 14, 2023), Google also announced the availability of the PaLM API for developers on Google Cloud [video]. They said that they are now providing access to foundation models on Google Cloud’s Vertex AI platform, initially for generating text and images, and over time also for audio and video. In addition, with the Generative AI App Builder, they introduced the possibility of quickly building AI-powered chat interfaces and digital assistants.
Finally, Google also made for a limited set of trusted test users generative AI features available within Google Workspace (Gmail and Google Docs).
By using the stable diffusion model v1.5 without any modifications, just fine-tuned on images of spectrograms paired with text, the software RIFFUSION (RIFF + diffusion) generates incredibly interesting music from text input. By interpolating in latent space it is possible to transition from one text prompt to the next. You can try out the model here.
The authors provide source code on GitHub for an interactive web app and an inference server. A model checkpoint is available on Hugging Face.
There is a nice video about RIFFUSION by Alan Thompson on youtube.
Even more shocking than using diffusion on spectrograms and getting great results may be a paper by Google Research published on Dec 15, 2022. They use text as an image and train their model with contrastive loss alone, thus calling their model CLIP-Pixels Only (CLIPPO). It’s a joint model for processing images and text with a single ViT (Vison Transformer) approach and astonishing performance.
Google presents DreamBooth, a technique to synthesize a subject (defined by 3-5 images) in new contexts defined by text input.
The method is based on Google’s pre-trained text-to-image model Imagen which is not publicly available. However, source code based on Stable Diffusion already exists on GitHub.
Google researchers present Imagic, a method to edit images by text input.
The method is based on Google’s pre-trained image generator Imagen which is not publicly available. However, source code based on Stable Diffusion already exists on GitHub.
A recent publication by University of California, Berkeley [InstructPix2Pix] goes into a similar direction and shows even more impressive results.
