The Future is Now
In dieser Episode reden Nico und Ich über die neuen Ankündigungen von Nvidia und Microsoft und geben eine ausführlichere Erklürung wie ChatGPT denkt und lernt.
Mehr informationen auf dem Discord server
https://discord.gg/3YzyeGJHth
oder auf https://mkannen.tech/
In dieser Episode reden Florian und Ich über den EU AI Act die US Senats Anhörungen und wie Copyright und Kunst im Zeitalter von KI funktionieren kann. Wenn ihr mehr von Florians musik hören wollt: https://open.spotify.com/artist/4CKbYO3CbkhsJe0CvexCAx?si=s2pEu5PATcSngnvVoIMKIQ
Mehr informationen auf dem Discord server
https://discord.gg/3YzyeGJHth
oder auf https://mkannen.tech/
In dieser Episode reden Florian und Ich über mögliche Formen künstlichen Bewusstseins, den Zweck von Emotionen und warum es schwierig ist eine KI mit unseren Interessen in Einklang zu bringen.
Mehr informationen auf dem Discord server
https://discord.gg/3YzyeGJHth
oder auf https://mkannen.tech/
Intel just announced a new supercomputer named Aurora. It is expected to offer more than 2 exaflops of peak double-precision compute performance and is based on their new GPU series which outperforms even the new H100 cards from NVIDIA.
They are going to use Aurora to train their own LLMs up to a trillion parameters. This would likely be the first 1T model.
I am excited to see even bigger models and more diverse hardware and software options in the field.
Today the US Senate held an AI testimony to discuss the risks and chances of AI and possible ways to regulate the sector nationally and globally.
Witnesses testifying include Sam Altman, CEO of OpenAI; Gary Marcus, professor emeritus at New York University, and Christina Montgomery, vice president and chief privacy and trust officer at IBM.
I think the discussion was quite good and is relevant for everyone. One thing that stands out is the companies’ wish to be controlled and guided by the government. The EU AI Act was a topic and the need for a global solution was a main talking point. A critical idea was for an agency to give out licenses to companies for developing LLMs, which Sam Altman proposed.
I hope Governments find a way to make sure AI is deployed in a way where everyone profits and the development of the technology is not slowed down or limited to a few people or profits.
Google IO happened yesterday and the keynote focused heavily on AI. Some of the things that I found most important are:
PaLM 2 is their new LLM. It comes in different sizes from small enough for pixel phones, to big enough to beat ChatGPT-3.5. It is used in Bard and many of their productivity tools.
Gamini is a multimodal model and the product of the Google DeepMind fusion. It is getting trained right now and could be a contender for the strongest AI when it comes out. I am quite excited about this release since DeepMind is my personal favorite for AGI.
Moreover, they showcased their seamless integration of PaLM and other advanced generative AI tools throughout their product suite as a direct response to Microsoft’s Copilot. They applied the same innovative approach to their search functionality, incorporating PaLM to deliver a search experience reminiscent of Bing GPT. This development fills me with hope, considering their search results outperform those of Bing. It’s likely that their decision to keep PaLM smaller was driven by cost considerations, allowing for more economical operation in the realm of search.
Anthropic, the OpenAI competitor just announced a new version of their LLM Claude. This new Version has a context length of 100K tokens, which corresponds to around 75K words. It is not clear from the announcement how they implemented that and how the full context gets fed into the attention layers.
OpenAI is planning to release a 32K context version of GPT-4 soon.
Longer context means you can feed long-form content like books, reports, or entire code bases into the model and work with the entirety of the data.
One of the main problems of LLMs is that they are black boxes and how they produce an output is not understandable for humans. Understanding what different neurons are representing and how they influence the model is important to make sure they are reliable and do not contain dangerous trends.
OpenAI applied GPT-4 to find out the different meanings of neurons in GPT-2. The methodology involves using GPT-4 to generate explanations of neuron behavior in GPT-2, simulate what a neuron that fired for the explanation would do, and then compare these simulated activations with the real activations to score the explanation’s accuracy. This process helps in understanding and could potentially help improve the model’s performance.
The tools and datasets used for this process are being open-sourced to encourage further research and development of better explanation generation techniques. This is part of the recent efforts in AI alignment before even more powerful models are trained. Read more about the process here and the paper here. You can also view the neurons of GPT-2 here. I recommend clicking through the network and admiring the artificial brain.
In dieser Episode rede ich mit Florian über die verschiedenen Anwendungsmöglichkeiten von KI in der Bildung. Die erwähnten Beispiele für text-to-speech:
https://mkannen.tech/text-to-speech-is-reaching-a-critical-point/
https://github.com/suno-ai/bark
Für weitere Informationen besuche https://mkannen.tech/
Shap-E can generate 3D assets from text or images. Unlike their earlier model Point-E, this one can directly generate the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. It is also faster to run and open-source! Read the paper here.
Just like video generation, the quality is still behind image generation. I expect this to change by the end of this year.
Microsoft announced, that not only Bing Chat is now available for everyone but also that Bing Chat will get new features such as image search, and more ways to present visual information. They also add the ability to summarise PDFs and other types of content.
But the biggest news is that they bring plugins to Bing Chat, which will work similarly to the ChatGPT plugins. I recommend reading the entire announcement yourself. This is the first step to their promise of a copilot for the web and I think they are doing a good job. This also puts pressure on their partner OpenAI which work on their own improvements to ChatGPT and now have to fight against their Investor Microsoft.
Researchers have made a breakthrough in the field of artificial intelligence, successfully extending the context length of BERT, a Transformer-based natural language processing model, to two million tokens. The team achieved this feat by incorporating a recurrent memory into BERT using the Recurrent Memory Transformer (RMT) architecture.
The researchers’ method increases the model’s effective context length and maintains high memory retrieval accuracy. This allows the model to store and process both local and global information, improving the flow of information between different segments of an input sequence.
The study’s experiments demonstrated the effectiveness of the RMT-augmented BERT model, which can now tackle tasks on sequences up to seven times its originally designed input length (512 tokens). This breakthrough has the potential to significantly enhance long-term dependency handling in natural language understanding and generation tasks, as well as enable large-scale context processing for memory-intensive applications.
Google and DeepMind just announced that they will unite Google Brain and Deepmind into Google DeepMind. This is a good step for both sites since Deepmind really needs the computing power of Google to make further progress on AGI and Google needs the Manpower and knowledge of the Deepmind team to quickly catch up to OpenAi and Microsoft. This partnership could lead to a real rival on the way to AGI for OpenAI. I personally always liked that DeepMind had a different approach to AGI and I hope they will continue to push different ideas other than language models.
Stability-AI finally released their own open-source language model. It is trained from scratch and can be used commercially. The first two models are 3B and 7B parameters in size, which is comparable to many other open-source models.
What I am more excited about are their planned 65B and 175B parameter models which are bigger than most other recent open-source models. These models will show how close open-source models can actually get to chatGPT and if local AI assistants have a future.
NVIDIA’s newest model, VideoLDM can generate videos with resolutions up to 1280 x 2048. They archive that by training a diffusion model in a compressed latent space, introducing a temporal dimension to the latent space, and fine-tuning on encoded image sequences while temporally aligning diffusion model upsamplers.
It is visibly better than previous models and it looks like my prediction for this year is coming true and we get video models as capable as the image models from the end of the last year. Read the paper here.
Today, Microsoft published a paper called “NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers“. In this paper, they show a new text-to-speech model which is not only able to copy human speech, but also singing. The model uses a latent diffusion model and neural audio codec to synthesize high-quality, expressive voices with strong zero-shot ability by generating quantized latent vectors conditioned on text input.
With this model, we are reaching a critical point. text-to-speech is now good enough to fool people and replace many jobs and positions that require speech. It also allows for better speech interfaces to language models which makes the interaction more natural from now on. As we are approaching a future where people have personal Ai assistants, natural speech is a core technology. And even though NaturalSpeech 2 is not perfect it is good enough to start this future.
MiniGPT-4, is an open-source multimodal model similar to the version of GPT-4 that was shown during OpenAI’s presentation. It combines a Visual encoder with an LLM. They used Vicuna which is a fine-tuned version of LLaMA.
In the future, I hope more teams try to add new ideas to their models instead of creating more and more small language models.
OpenAssistent is an open-source project to build a personal assistant. They just released their first model. you can try it out here.
While the progress on smaller models by the open-source community is impressive there are a few things I want to mention. Many advertise these models as local alternatives to chatGPT or even compare them to GPT-4. This is sadly not true. it is not possible to replicate the capabilities of a model like GPT-4 on a local machine; at least not yet. This does not mean that they are not good. many of them are able to generate good answers or even use APIs like chatGPT.
Neural Radiance Fields (NeRFs), which are used for synthesizing high-quality images of 3D scenes are a class of generative models that learn to represent scenes as continuous volumetric functions, mapping 3D spatial coordinates to RGB colors and volumetric density. Grid-based representations of NeRFs use a discretized grid to approximate this continuous function, which allows for efficient training and rendering. However, these grid-based approaches often suffer from aliasing artifacts, such as jaggies or missing scene content, due to the lack of explicit understanding of scale.
This new paper proposes a novel technique called Zip-NeRF that combines ideas from rendering and signal processing to address the aliasing issue in grid-based NeRFs. This allows for anti-aliasing in grid-based NeRFs, resulting in significantly lower error rates compared to previous techniques. Moreover, Zip-NeRF achieves faster training times, being 22 times faster than current approaches.
This makes them applicable for VR and AR applications and allows for high-quality 3d scenes. Next year when the Hardware improves we will see some very high-quality VR experiences.
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