Author: Maximilian Kannen (Page 1 of 5)

Why Open Source Models Are Great

01/05/2024 / Maximilian Kannen / 0 Comments

The open-source AI landscape has witnessed significant growth and development in recent years, with numerous projects and initiatives emerging to democratize access to artificial intelligence. In this blog post, I will go into the current state of open-source AI, exploring the key players, fine-tuning techniques, hardware and API providers, and the compelling arguments in favor of open-source AI.

Model Providers

Training LLMs costs a significant amount of money and requires a lot of experience and hardware. Only a few organizations have the means to do so. The following list is not complete and just covers some of the big ones.

Meta is currently the biggest company that open source models. Their model family is called Llama and the current Llama 3 models are available in Two sizes: 8B and 70B. A 405B model is expected soon. The weak points of the current versions are their lack of non-English training data and their small context size. Meta is already working on that.

Mistral is a smaller French company that got investments from Microsoft including computing power. While not all their models are open-source, the ones that are, perform well. they open-sourced a 7B model that was a cornerstone of open source models for quite some time and they open-sourced two Mixture-of-Expert Models (8x7B, and 8x22B) that are still leading non-English open source models, especially at their price point.

Cohere recently open-sourced a few models including their LLMs Command-R and Command-R+. They perform especially well when used in combination with retrivel augmented generation.

Stability Ai is mostly known for open-sourcing text2image models, but they also open-sourced a few smaller LLMs that are decent for their size.

Google does not open source their Gemini models, but they have a set of open models called Gemma which include some experimental LLms that are not based on Transformers.

API-Providers and Hardware

The main argument for open source models is the ability to run them on your own on your personal machine. Current models range from 2B to over 100B parameters. So let’s see what is needed to run them
For small models under 7B, you don’t need anything special. These models could even run on your phone. Models between 7B and 14B models can be run on most PCs but can be very slow unless you have a modern GPU. Bigger models between 14B and 70B require extremely high-end PCs. Apple’s modern high-end devices are especially great since they offer shared memory that is needed for bigger models. Everything over 70B, including the MoE models from Mistral usually are not usable for Home devices. They instead are available on a broad selection of API providers who host different open-source models and compete on price, speed, and latency. I selected a few that excel in one or two of these categories.

Groq is a newer hardware company that developed custom chips for LLMs. That allows them to offer incredible speeds and prices. For example Llama 3 8B for less than 10 cents for a million tokens and over 800 tokens per second. If you run the model yourself you would get around 10-20 tokens per second depending on your hardware.

Together.ai offers nearly all common open-source models and gives you a few million tokens for free at the start to start experimenting immediately.

Perplexity is not only a great search engine, but its API is also great. Not as cheap or fast as Groq, but extremely low latency and they offer their own models with internet access. They also provide free API credits for perplexity pro users.

If you prefer to run them on your own I recommend a newer Nvidia GPU with as much VRAM as you can afford.

Customization

One of the great side effects of having control over the model is the ability to change it to your needs. This starts with simple things like system prompts or temperature. Another thing that is often used is quantization. Quantization describes the process of taking the parameters of the models that are usually saved as floating point numbers with 16 or 32 bits of precision and rounding them in different ways to shrink them to somewhere between 8 and 1 bit. This process reduces the capabilities of the models slightly depending on the factor but makes that model easier and faster to run on weaker hardware.

Fine-tuning

For many use cases, current models are not optimal. They lack knowledge perform worse in a required language or simply do not perform well in a certain task. To solve these problems you can fine-tune the models. Fine-tuning means continuing the training of the model on a small custom data set that helps the model learn the required ability. The following part will be a bit more technical and can be skipped:
3 main types of open-source LLMs are available: Base models, Instruct models, and chat models. Base models are only trained on huge amounts of text and work more like text completion. They do not really work as chatbots and are hard to use. Instruct models are already fine-tuned by the creator on a set of text examples that teach to model to follow the instructions of a given input instead of simply continuing the text. Chat models are further fine-tuned to behave in a chatbot-like way and can hold conversations. They are also often trained to have certain limitations and can refuse to talk about certain things if they are trained to do. For fine-tuning, base models give the most freedom. You could even continue the training with new languages or information and do instruct training after that. There are already instruct datasets available that can be used or you can create your own. If you fine-tune existing instruct models, you usually need fewer data and compute and you can still teach the Model a lot and change its behavior. This is most often the best choice. Existing chat models can still be fine-tuned but since they are already trained in a certain way it is harder to get specific behaviour and teaching it completely new skills is hardly possible. Fine-tuning chat models is best if you just want to change the tone of the model or train it on a specific writing style. There are different ways to fine-tune: Most often you fix the earlier layers of the model so the learned knowledge of the model will not be changed too much and only train the later layers. While this is not totally correct, I like to imagine that later layers are more important for the style of output while earlier layers work more like the core language understanding part of the model. So the more fundamental the thing is you want to change the more layers you need to train. things like a certain writing style usually only require the very end of the model, while things like improved math capabilities need most of the network. There is another way to fine-tune models that often pops up: LORAs. LORA stands for Low-Rank Adaption. It uses the fact that LLM layer matrices have a lower rank ( lower dimension) to split them up into two matrices which contain fewer parameters in sum than the original matrix. The fine-tuning is then happening on the two new matrices which make the process faster and cheaper and allow LORAs to be shared with less memory overhead. The LORA matrices can then later be swapped in and out like a hat.

output control

If you have control over your model, you can also inject things into its output. The most popular example is something like JSON mode, where at every token instead of selecting randomly from the logits, an external program checks which output token is valid given the JSON grammar and can select the one. This can be used to guarantee that the output follows a certain given structure and can also be used for things like tool use or other additional functions.

Local tools

There is a range of tools to run models locally from chat interfaces that mimic the experience of chatGPT to local API servers that can be used for companies or developers. Here are some examples

GPT4All is a local chat interface that not only allows you to download models but can also give the models access to your local documents and is very easy to use.

Ollama is a local LLM server which makes it easy to install additional models and supports a wide range of Operating systems and Hardware.

LM Studio also offers a user interface to chat with models but also includes functionality to fine-tune them with LORA

Conclusion

So as you can see there are many reasons why open-source models can be superior even though the biggest and smartest models that are currently available are slightly better than the best open-source models. They are way cheaper, even if you compare price per performance and they allow for much more custom control. They can be trained to your liking and needs, and offer privacy and control over your data and use. If you run them locally they often have lower latency and even if you use API providers you will get better prices and super-fast interference. Open-source models used to be around a year behind some of the top models, but in recent times, they started to catch up. They will probably never lead the field in terms of capabilities but they will always be the cheaper option. ChatGPT3.5 is the best example of a model that got beaten by open source a long time ago. Models like Llama 3 are not only cheaper, but they are also way faster and offer all the advantages of open models.

The False Promise of Full Dive VR

18/04/2024 / Maximilian Kannen / 0 Comments

While some science fiction technologies are already here or very close (C3PO, Longevity, AGI, etc) some are very far away or not possible at all. I will try to explain why FDVR is one of these and why it is so hard. Let’s start with the definition of FDVR as portrayed in popular science fiction works like “Matrix” or “3 Body Problem” is the idea of directly sending and receiving signals from the brain to simulate a realistic virtual reality for the brain.

Let’s look at the simplest problem first. If we want to get signals from the brain and use them in virtual reality we have to intercept them and cut off the connection to the real body. There is currently no known way to do this without damaging the body permanently and even if we could, a body without signals is not really functioning. laying in your own piss would be your smallest problem. You wouldn’t even be able to breathe. But even if we manage to do this we would only have solved the easiest part. Now that we have the output, we also need input.

Simulating feelings from every nerve end of your body requires the system to not only simulate the entire virtual reality which would have to be accurate on a molecular level (smell, taste) but also would need to be rendered quite far and needs full simulation of realistic physics (eyes, touch). And not only would the world need to be simulated to such a degree, but your body would also need to be a 1 to 1 simulation. every nerve end and visual receptor needs to be simulated to achieve a realistic experience that would fool your brain. This leads to the next problem: To get all these signals back into the brain we would need to understand the encoding and position of every input signal to the brain. This requires such an in-depth understanding of the brain that FDVR would be the most boring thing we could do with it at this point.

If we had the computing power to simulate all this and the deep understanding of the brain required to do it, It would be easier to simply make a digital copy of your brain and run it as a simulation. And if there is enough computing power to simulate a world to this degree, then we are most likely already living in one. The computing power to do this even for a few people could exceed what is possible with the energy in our solar system, following the law of computational irreducibility.

So in conclusion, if a future ASI would have access to such a deep understanding of biology and so much energy and computing power, using it for FDVR would be a total waste. But who knows maybe we get a fast takeoff soon and ASI will find some ways and we archive FSVR with some compromises in a few decades. I personally would bet that FDVR will never play a huge part in our society.

Gemini is here

06/12/2023 / Maximilian Kannen / 0 Comments

Google Deepmind just released their new Gemini models. They come in 3 sizes. Nano will be used on devices like the Pixel phones, and Pro will be used in their products such as Bard, and Ultra is going to be released at the beginning of next year. The models are multimodal and can input, audio, video, text, images, and code.

It outperforms current state-of-the-art models not only in text-based tasks but also in other modalities.

Test the Pro version now in Bard and read more about the model here and here.

Looking Back On 2023 And Predictions for 2024

02/12/2023 / Maximilian Kannen / 2 Comments

As we close the chapter on 2023, it’s time to revisit the predictions I laid out at the beginning of the year. It was a year marked by technological strides and societal challenges. Let’s evaluate how my forecasts stood against the unfolding of 2023.

Let’s start with my predictions about AI:

“AI will continue to disrupt various industries such as search and creative writing and spark public debate about its impact, even more than is happening right now. It will also lead to the production of high-quality media with fewer people and resources thanks to AI’s assistance. In the field of 3D generation, I expect to see similar progress in 2023, bringing us closer to the quality of 2D generation.“

I think I was mostly right. GPT-4 definitely sparked a public debate and we see many industries that became more productive thanks to AI. 3D generation is also already at the level that image generation had at the beginning of the year. What I did not predict was the speed at which companies like Meta or Microsoft would iterate and deploy LLMs in many forms.

My next prediction was about Fusion: “While I expect to see continued progress in this field, it is unlikely that we will see a commercial fusion reactor within the next two years.“

Again I was on point but I missed talking about other energy sources like solar which are more relevant. I would count that as a bad focus and not a failed prediction.

I also made predictions for Hardware: “[…] we can expect to see quantum computers with over 1000 Qbits in the upcoming year. GPUs will become more important with the rise of AI. However, these advancements in hardware technology also come with the need for careful consideration and planning in terms of production and distribution. “

We indeed achieved 1000 Qbits even though IBM was not the first company to do so. I also correctly predicted the increased demand for GPUs, but I have to admit I did not expect that scale. I also was more pessimistic about the ability of TSMC and others to meet the demand, and while they drastically outperformed my expectations I was still kind of right because the demand is also way bigger than I anticipated.

My Predictions for VR: “But the year 2023 is shaping up to be a promising one for the VR hardware market, with multiple new headsets, such as the Quest 3, and maybe even an Apple Headset, set to be released. These new products will likely offer improved graphics, more intuitive controls, and a wider range of content and experiences. While it may not fully realize the vision of a “Metaverse”, VR is still likely to be a great entertainment product for many people”

And AR: “2023 will be a critical year for AR. It will be the first time that we can build affordable Hardware in a small form factor. Chips like the Snapdragon AR2 Gen 1 implement Wifi 7 and low energy usage and will make it possible to build Smart glasses.“

While my VR predictions were all correct, my AR predictions underestimated the difficulty of producing smart glasses in a normal form factor.

I did not make concrete predictions about Brain-computer interfaces, but I honestly expected more progress. More about that in my new predictions later.

Now on to biology and medicine. I made a multiple-year prediction: “If this continues we will be able to beat cancer in the next few years, which leads to the next field.” this cannot be verified yet, but I still believe in it and predicted that a person under 60 could live forever. Recently I looked a lot more into aging research and I still believe that this is correct even though I would change from “every person under 60 has the potential“, to “there is a person under 60 that will“. I think this is an important distinction because stopping aging requires a lot of money and dedication and will not be available for most in the near future.

I ended the post with: “While this was a slow year in some aspects, major progress was made in most fields, and 2023 will be even faster. We are at the knee of an exponential blowup and we are not ready for what is coming. While I am still worried about how society will react and adapt, I am excited for 2023 and the rest of the decade.“

Again I believe that I was very much on point with this. Many people were blown away by the rapid developments this year. So let’s talk about the stuff that I did not predict or ignored last year. LK99 is a material that was supposed to be a room-temperature superconductor. At the current time, this was most likely false, but I realized that I did not make a prediction about superconductors in the blog post. I will do this later in this one.

On to the new predictions for 2024. Let’s start with AI again. LLM-based systems will become more autonomous and will reach a point where many will consider them AGI. I personally do not think that we will reach AGI this year, but most likely in 2025. There is also a 70% chance that we will find a new architecture that generalizes better than transformers. No system in 2024 will outperform Humans on the new GAIA benchmark, but they are going to double their performance on it. This will mostly be accomplished by improving reasoning, planning, and tool use with improved fine-tuning and new training strategies.

Results of current Systems on the GAIA benchmark compared to humans

I also predict that commercially viable models will stay under 1 trillion parameters in 2024. There will be a few models over this threshold, but they will not be used in consumer products without paying for them similar to GPT-4 (non-turbo). State space models like RWKV will also become more relevant for specific use cases and most models will at least support image input if not more modalities. RL Models like Alphafold will push scientific discovery even faster in 2024.

Image/video/music/3D generative models will improve dramatically and completely change the art industries. The focus is going to be more on integration and ways to use them and less on pure text2output capabilities. Assistants like Alexa will integrate LMMs and improve drastically. OpenAI will release at least one model that will not be called GPT-5 and wait with GPT-5 until later in the year.

Apple will announce its first LMM at WWDC and at the end of the year we will be able to do most stuff by just talking to our PC. Meta will release Llama-3 which is going to be multimodal and close to GPT-4, and Google will release Gemini at the beginning of the year, which will be comparable to GPT-4 at the beginning and will improve down the year.

Open-source models will stay a few months behind closed-source models, and even further in areas like integration, but offer more customizability. Custom AI hardware like the AI Pin will not become widespread, but smartphones will adapt to AI by including more sensors and I/O options, and towards 2025 we will see smart glasses with AI integration. The sectors that will be influenced the most by AI are education and healthcare, but in the short term, the first industries will be artists and some office workers.

Let’s continue with Hardware. Nvidia will stay the leader in AI hardware with H200 and later this year with B100. Many companies will use their custom chips like Microsoft, Apple, and Google, but the demand will lead to increased sales for every chip company. At the end of 2024, more than half of the global flops will be used for AI. VR Hardware will continue to improve, and we will finally see the first useful everyday AR glasses towards the end of 2024. Quantum computers will become part of some of the cloud providers and will be offered as specialized hardware just like GPUs (Note: This part was written before the AWS Event announcement). They will become more relevant for many industries as the number of Qbits grows. We will also see more variety in chips as they become more specialized to save energy. Brain-computer interfaces will finally be used in humans for actual medical applications.

I did not make any predictions about robots last year, because there weren’t many exciting developments, but that changed. Multiple companies started developing humanoid robots that will be ready in 2024 or 2025. I expect an initial hype around them and adoption in some areas. However, towards the end of the decade they will be replaced with special-purpose robots and humanoid robots will be limited to areas where a human form factor is needed. In general, the amount of Robots will increase in all areas. Progress in planning and advanced AI allows for robots to act in unknown environments and do new tasks. They will leave controlled environments like factories and will appear in, shops, restaurants, streets, and many other places.

The robots: Atlas by Boston Dynamics, Digit by Agility Robotics, and Tesla Optimus by Tesla

Let’s continue with energy. The transition to renewable energy will accelerate in 2024, with a significant focus on solar. The first commercial fusion reactor will begin construction, and nuclear reactors will become even safer, mostly solving the waste problem. More people will build solar for their own houses and become most self-sufficient.

I mentioned LK99 earlier already so here are my predictions for material science. I think that if a room-temperature superconductor is possible, an AI-based system will find it in the next two years. In fact, most new materials will be hypothesized and analyzed by AI and will bring a lot of progress for areas like batteries, solar panels, and other material-dependent fields (Note: this part was written four days before Deepmind presented GNoME).

Biology and medicine are poised to make significant leaps, powered by AI systems like Alphafold and similar technologies. Cancer and other deadly diseases will become increasingly treatable and aging will become a target for many in the field. The public opinion that aging is natural and cannot/should not be stopped will not change this year but maybe in 2025. Prostheses will become more practical and will be connected directly to nerves and bones. This will make them in some areas better than human parts, but touch and precision will continue to be way worse. We will also see progress in artificial organs grown in animals or completely made in a lab.

Transportation in 2024 will change slightly. EVs will become more popular and cheaper but will not reach the level of adaptation that they have in China. Self-driving cars will stay in big cities as taxi replacements and will not be generally available until 2025. Hypertubes will not become a train replacement and will only be built for very specific connections if they get built at all in the next few years.

Other infrastructures like the Internet will continue to stay behind the demand for the next few years. The main driver of the increased need for bandwidth will be high-quality video streaming while the main need for speed will arise from interactive systems like cloud-based AI assistants.

Climate change and unstable governments will lead to an increase in refugees worldwide and social unrest will increase. We will see the first effects of AI-induced Job losses. The political debate will become more heated and some important elections like the US election will be fully determined by large-scale AI-based operations that use Fake news, Deepfakes, and online bots to control the public opinion.

I made a lot more verifiable predictions this time and I hope to see how much I got correct. If I missed any area or technology write them in the comments and I will add a prediction in the comments. Also, let me know your predictions.

Humane presents the AI Pin

09/11/2023 / Maximilian Kannen / 0 Comments

The company presented the AI Pin Today. It is a small device with a camera, microphone, sensors, and laser projector. It is designed to replace the smartphone and costs 699 plus a monthly subscription of 24 dollars. This includes the unlimited use of multiple frontier LLMs, internet, and multiple other services like music. It can see what you see, translate, manage your calendar, send messages, and answer your questions.

I personally think that the biggest problem is the addiction of most people to social media and YouTube which makes it, not a replacement and it is too expensive to add to a phone. There is also a factor that phones can do many of the things already and are not much more expensive. I can imagine something similar in the future in combination with AR glasses. More information: https://hu.ma.ne/

Google found a way to improve math skills in LLMs

31/08/2023 / Maximilian Kannen / 0 Comments

LLMs are powerful tools, but they often struggle with tasks that require logical and algorithmic reasoning, such as arithmetic. A team of researchers from Google has developed a new technique to teach LLMs how to perform arithmetic operations by using in-context learning and algorithmic prompting. Algorithmic prompting means that the model is given detailed explanations of each step of the algorithm, such as addition or multiplication. The researchers showed that this technique can improve the performance of LLMs on arithmetic problems that are much harder than those seen in the examples. They also demonstrated that LLMs can use algorithmic reasoning to solve complex word problems by interacting with other models that have different skills. This work suggests that LLMs can learn algorithmic reasoning as a skill and apply it to various tasks.

Results from the paper comparing their approach vs. other prompting techniques.

Llama 2: New State-of-the-Art Open Source LLM

25/07/2023 / Maximilian Kannen / 0 Comments

Meta recently released their new Llama models. The new models come in sizes from 7 to 70 billion parameters and are released as base models and chat models, which are fine-tuned with two separate reward models for safety and helpfulness. While the models are only a small improvement over the old Llama models, the most important change is the license which now allows commercial use.

Microsoft published the next Version of Kosmos

28/06/2023 / Maximilian Kannen / 0 Comments

Researchers at Microsoft have unveiled Kosmos-2 the successor of Kosmos-1, a Multimodal Large Language Model (MLLM) that integrates the capability of perceiving object descriptions and grounding text in the visual world. By representing refer expressions as links in Markdown format, Kosmos-2 achieves the vital task of grounding text to visual elements, enabling multimodal grounding, referring expression comprehension and generation, perception-language tasks, and language understanding and generation. This milestone in the development of artificial general intelligence lays the foundation for Embodiment AI and the convergence of language, multimodal perception, action, and world modeling, bringing us closer to bridging the gap between humans and machines and revolutionizing various domains where AI interacts with the real world. With just 1.6B parameters, the model is quite small and will be available open on GitHub

RoboCat handles every Robot

21/06/2023 / Maximilian Kannen / 0 Comments

Deepmind published a new blog post where they present their newest AI which is based on their previous work Gato. RoboCat is a self-improving AI agent for robotics that learns to perform a variety of tasks across different arms and then self-generates new training data to improve its technique. It is the first agent to solve and adapt to multiple tasks and do so across different, real robots. RoboCat learns much faster than other state-of-the-art models. It can pick up a new task with as few as 100 demonstrations because it draws from a large and diverse dataset. This capability will help accelerate robotics research, as it reduces the need for human-supervised training, and is an important step towards creating a general-purpose robot.

Voicebox: A new Voice Model

17/06/2023 / Maximilian Kannen / 0 Comments

Voicebox is a new generative AI for speech that can generalize to speech-generation tasks it was not specifically trained to accomplish with state-of-the-art performance. It can create outputs in a vast variety of styles, from scratch or from a sample, and it can modify any part of a given sample. It can also perform tasks such as:

In-context text-to-speech synthesis: Using a short audio segment, it can match its style and generate text.
Cross-lingual style transfer: Given a sample of speech and a passage of text in six languages, it can produce a reading of the text in that language.
Speech denoising and editing: It can resynthesize or replace corrupted segments within audio recordings.
Diverse speech sampling: It can generate speech that is more representative of how people talk in the real world.

Voicebox uses a new approach called Flow Matching, which learns from raw audio and transcription without requiring specific training for each task. It also uses a highly effective classifier to distinguish between authentic speech and audio generated with Voicebox. Voicebox outperforms the current state-of-the-art English model VALL-E on zero-shot text-to-speech and cross-lingual style transfer and achieves new state-of-the-art results on word error rate and audio similarity. Voicebox is not publicly available because of the potential risks of misuse, but the researchers have shared audio samples and a research paper detailing the approach and results. They hope to see a similar impact for speech as for other generative AI domains in the future.

Synthetic Human Embryos

16/06/2023 / Maximilian Kannen / 0 Comments

After earlier experiments on mice, it is now possible to create human embryos out of stem cells. This allows us to make human life without sperm or eggs. Since the experiments are limited by ethical concerns they stopped the growth of the embryo at an early stage. This research could lead to a better understanding of early development and could allow us someday to design our successor species.

Meta Released a Music Model

13/06/2023 / Maximilian Kannen / 0 Comments

This week Meta open-sourced a music generation model similar to Google’s MusicLM. The Model is named MusicGen and is completely open-source. These models can generate all kinds of music based on given text prompts similar to image models.

New OpenAI Update

13/06/2023 / Maximilian Kannen / 0 Comments

OpenAI announced a set of changes to their model APIs. The biggest announcement is the addition of function calls for both GPT-3.5 and 4. This allows developers to enable plugins and other external tools for the models.

They also released new versions of GPT-3.5 and 4 that are better at following directions and a Version of 3.5 with 16K context window.

In addition, they made the embedding model 75% cheaper, which is used to create vector databases and allows models to dynamically load relevant data, like memory. GPT-3.5 also became cheaper now costing only $0.0015 per 1K input tokens.

DeepMind Makes Everything Faster

07/06/2023 / Maximilian Kannen / 0 Comments

After DeepMind developed AlphaTensor last year and found a new algorithm for matrix multiplication, they did it again. This time they developed AlphaDev which found a new algorithm for sorting. This sounds not as exciting as a new language model, but sorting algorithms run billions of times every hour. Optimizing central algorithms like sorting and searching is one of the oldest parts of computer science and they are getting optimized for over a hundred years at this point. We did not find a better solution in the last 10 years and some believed that we reached the limit of what is possible. AlphaDevs’ new solution was implemented in the standard C++ library and is used already. The impact of these small improvements becomes enormous because they are used so much and the amount of energy that is saved adds up quickly. They also found a new hash algorithm which is used a similar amount. If AlphaDev continues to find improvements for core algorithms, every software in the world will run faster and more efficiently. Breakthroughs like this have to be considered in the discussion around the climate impact of AI training. The energy saved by these improvements offsets the used energy for training by orders of magnitude.

Apples VR Headset is Here

06/06/2023 / Maximilian Kannen / 0 Comments

Apple finally announced their upcoming VR headset which will focus on productivity and Cinematic entertainment. The 4K displays are powered by their M2 chip. This requires an external energy source and makes the headset with 3500$ very expensive. The Headset focuses on Mixed reality experiences similar to the new Meta Quest 3, but unlike the Quest, it will not be released until next year. It is probably a good starting point for Apple to build their new Product platform, but if you are not in desperate need of a high-resolution headset it is perhaps not a good choice for you.

Meta Quest 3

01/06/2023 / Maximilian Kannen / 1 Comment

Meta announced their new Meta Quest 3 headset. It is the successor to the Quest 2, the most popular VR headset of all time. The price went up a bit, the processing power and form factor improved as did the visuals. especially passthrough is better with color passthrough. Eye tracking is not included. Together with the upcoming Apple entrance into the VR space, this will give the XR World a new push forward.

Copilots for everyone

24/05/2023 / Maximilian Kannen / 0 Comments

Microsoft Build is currently underway, with Microsoft showcasing a range of new and upcoming products, including various Copilots such as Copilot for Bing, GitHub, and Edge. In their pipeline, they also have plans to launch a Copilot specifically designed for Windows.

These Copilots are all built using Microsoft’s new Azure AI Studio Platform, which is now open to developers, allowing them to create their own Copilots.

Furthermore, Microsoft announced their support for an open plugin system, similar to the one utilized by ChatGPT, making plugins accessible to all Copilots. If this solution becomes the industry standard for AI systems, it has the potential to establish Microsoft as a dominant player in the AI market. The first day of Microsoft Build concluded with an exceptional presentation by Andrej Karpathy, delving into the history and inner workings of GPT models. If you’re interested in gaining insights into how these models operate and learn, I highly recommend watching his talk titled “State of GPT.”

Intel Presents New Hardware

23/05/2023 / Maximilian Kannen / 0 Comments

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.

US Senate Holds an AI Hearing

16/05/2023 / Maximilian Kannen / 0 Comments

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.

Why Humans Should Stay on Earth: The Case for Robotic Space Exploration

12/05/2023 / Maximilian Kannen / 1 Comment

Space travel has always captivated humanity. From the first rocket launched into space to modern-day space programs, the dream of exploring the cosmos has persisted. Recently, the idea of colonizing Mars has gained traction, with visionaries like Elon Musk advocating for humans to become a multi-planetary species. However, numerous reasons indicate that humans should not venture into space, and that using robots is a more viable option. In this blog post, we will delve deeper into the challenges and limitations of human space travel and argue for prioritizing robotic space exploration.

The Human Body is Ill-Suited for Space The human body is not designed to withstand the rigors of space travel or to live on another planet for extended periods. Numerous issues arise when humans venture into space, some of which are:

Microgravity: The absence of gravity in space can cause a range of biological problems, including muscle atrophy, bone density loss, and impaired fluid regulation. These issues can make it difficult for astronauts to function effectively during missions and can result in long-term health problems after returning to Earth.
Limited resources: Space lacks the essential resources we need to survive, such as water, food, and air. Providing these resources for human space missions adds considerable complexity and expense to missions and creates additional points of potential failure.
Radiation exposure: Mars does not have the same protective shield as Earth, leaving humans vulnerable to radiation exposure. This can increase the risk of cancer and other health problems. Furthermore, Mars’s surface is bombarded by meteorites, which can cause significant damage to human habitats.
Psychological challenges: Humans may struggle with stress, isolation, and confined living conditions during long-term space missions, leading to mental health issues and decreased performance.

Robots, in contrast, do not require air, food, or water, are largely unaffected by radiation, and can withstand damage better than humans. They also do not experience the psychological challenges faced by humans in space.

The High Cost of Human Space Travel The costs associated with solving the problems mentioned above are astronomical. Sending humans into space requires advanced life support systems, extra protection for spacecraft and habitats, and provisions such as food, water, and air. Some specific costs include:

Life support systems: Developing and maintaining advanced life support systems for human space missions is resource-intensive and can add significantly to mission costs.
Provisions: Transporting the necessary supplies for human survival, such as food, water, and air, is expensive and increases the overall weight of the spacecraft, which in turn raises fuel costs.
Mission redundancy: Human space missions require additional safety and backup systems to minimize the risks associated with equipment failures, further driving up costs.
Returning: Since there is no way for humans to live in space or on other planets indefinitely, we would have to bring them back eventually. Which basically doubles the cost.

In contrast, robotic missions like NASA’s Viper Rover can be completed for a fraction of the price of manned missions, as they do not require complex life support systems or extensive provisions.

Superior Performance of Machines in Space Robots possess several advantages over humans when it comes to space exploration:

Durability: Robots are not vulnerable to radiation exposure and can withstand the harsh conditions of space better than humans.
Efficiency: Robots can work tirelessly without needing rest, food, or water, making them ideal for tasks like construction and repairs.
Adaptability: Robots can be designed and programmed to perform specific tasks without the need for complex life support systems, allowing them to be more cost-effective and adaptable to various mission requirements.
Safety: Using robots for space exploration is safer than sending humans, as it reduces the risks associated with space travel while still expanding our knowledge of the cosmos.

While the idea of colonizing Mars is undoubtedly exciting, it is not a practical solution. The challenges associated with space travel, including the human body’s limitations in space, high costs, and the superior performance of machines, make it clear that using robots for space exploration is a better option. As we continue to explore the universe, we must prioritize safety, efficiency, and cost-effectiveness to ensure that space travel remains a viable and sustainable option for future generations. By focusing on robotic space exploration, we can continue to expand our understanding of the cosmos while mitigating the risks and challenges faced by human astronauts. Ultimately, this approach will allow us to make more informed decisions about the potential for human settlement on other planets and contribute to the ongoing development of space technology and knowledge.