AI Archives - Maximilian Kannen

Personal AI, or artificial intelligence designed to assist individuals in their daily lives, is becoming increasingly common and advanced. From virtual assistants like Siri and Alexa, to smart home devices like thermostats and security cameras, AI is changing the way we interact with the world around us.

As technology continues to evolve, it is important to consider the opportunities and challenges that personal AI presents, and how it will shape our future. One of the biggest opportunities of personal AI is the ability to automate and streamline tasks, freeing up time and mental energy for more important or enjoyable activities. For example, a personal AI assistant can help manage your schedule, remind you of important appointments, and even make recommendations for things like restaurants or events based on your preferences and interests. This can make it easier to stay organized and efficient and can allow you to focus on the things that matter most to you. Another opportunity of personal AI is the ability to customize and personalize your experience. With advanced machine learning algorithms, personal AI can learn your habits and preferences over time and can tailor its recommendations and responses accordingly. This can make your interactions with personal AI more natural and intuitive and can help you get the most out of the technology.

However, personal AI also presents some challenges that need to be considered. One of the biggest challenges is the potential for data privacy concerns. As personal AI collects more and more data about you and your habits, there is a risk that this data could be misused or accessed by unauthorized parties.

This could result in a violation of your privacy and could even put your personal information at risk. As personal AI becomes more prevalent, it will be important to address these concerns and develop robust privacy protections to ensure that individuals’ data is safe and secure. Another challenge of personal AI is the potential for bias and discrimination. AI algorithms are only as good as the data they are trained on, and if the data is biased, the AI will be biased as well. This could result in unfair or unequal treatment of certain individuals or groups and could even perpetuate existing biases and stereotypes.

To address this challenge, it will be important to carefully curate and balance the data used to train personal AI algorithms, and to regularly evaluate and test the algorithms for potential bias. Overall, the future of personal AI holds great potential for improving our daily lives and making our interactions with technology more natural and intuitive. However, it is important to carefully consider the opportunities and challenges of personal AI and to address any potential risks or concerns to ensure that the technology is used responsibly and ethically.

Up until now, the entire article was written by ChatGPT without any nitpicking or corrections.

ChatGPT is an aligned and finetuned version of GPT-3.5 from OpenAI and is free to use for the last 2 weeks on their website. It is so popular that it reached over a million users in the first few days and since then OpenAI can barely keep the server running. This is not surprising since it is free, easy to use, and there are infinite use cases. It is a writer, programmer, teacher, and translator. It knows more than any human ever could. It can even play text-based RPGs with you or do your homework. It is also remarkable that it is so useful although it has no access to the internet and is not able to perform actions, compared to Siri.

For many ChatGPT is a sudden advancement, but the research is going on for a long time. The development of transformer-based models, such as ChatGPT, started with the paper “Attention is All You Need” published in 2017 by researchers at Google. This paper introduced the transformer architecture, which relies on self-attention mechanisms to process sequential data.

An example architecture for a transformer model. If you want to learn more I recommend https://peterbloem.nl/blog/transformers

This allows transformer models to efficiently handle long-term dependencies and process input sequences of any length, making them well-suited for tasks such as language modeling and machine translation. The success of the transformer architecture in these and other natural language processing tasks has led to its widespread adoption in the field and has helped drive the development of increasingly powerful language models such as ChatGPT. Other transformer-based models like whisper for transcription or GPT-3 the predecessor of ChatGPT were also impressive but were not that much of a topic to the public and were mostly discussed and used in the industry.

I predicted this sudden rise in public interest in my singularity post in July 2022. As AI continues to advance, it is likely to have a significant impact on the public. One potential impact is the potential for AI to automate many tasks that are currently performed by humans, leading to job displacement in some industries. This could have serious economic consequences and may require new approaches to education and job training to help people stay employable in a rapidly changing job market.

Another potential impact of AI is the potential for it to improve our quality of life in various ways. For example, AI-powered personal assistants and smart home technology could make our daily lives more efficient and convenient. AI-powered medical technologies could also help to improve healthcare, making it more accurate and accessible. However, the development and deployment of AI also raises important ethical concerns. As AI becomes more powerful, it will be important to carefully consider how it is used and to ensure that it is deployed responsibly and ethically. For example, AI could be used to discriminate against certain groups of people or to perpetuate biases that already exist in society. This often happens because of already biased training data. It is important for researchers, policymakers, and the public to consider these potential risks and take steps to mitigate them. Overall, the impact of AI on the public is likely to be significant and will require careful consideration and planning to ensure that its benefits are maximized, and its potential drawbacks are minimized.

I expect a chaotic transition phase where many people will suffer because necessary discussions about universal income and AI did not take place early enough. People who use these tools to maximize their productivity will outperform already disadvantaged people with worse access to these tools and the political system is not prepared to solve these problems. In this world that will be more divided than ever, AI is both the savior and destroyer of our society.

I wanted to make this post for a while, as I am deeply invested in the development of AI image models, but things happened so fast.

It all started in January 2021 when OpenAi presented DALL-E, an AI model that was able to generate images based on a text prompt. It did not get a lot of attention from the general public at the time because the pictures weren’t that impressive. One year later, in April 2022, they followed up with DALL-E 2, a big step in resolution, quality, and coherence. But since nobody was able to use it themself the public did not talk about it a lot. Just one month later google presented its own model Imagen, which was another step forward and was even able to generate consistent text in images.
It was stunning for people interested in the field, but it was just research. Three months later DALL-E 2 opened its Beta. A lot of news sites started to write articles about it since they were now able to experience it for themself. But before it could become a bigger thing Stability.Ai released the open-source model “stable diffusion” to the general public. Instead of a few thousand people in the DALL-E beta, everybody was able to generate images now. This was just over a month ago. Since then many people took stable diffusion and built GUIs for it, trained their own models for specific use cases, and contributed in every way possible. AI was even used to win an art contest.

People all around the globe were stunned by the technology. While many debated the pros and contras and enjoyed making art,
many started to wonder about what would come next. After all, stable diffusion and DALL-E 2 had some weak points.
The resolution was still limited, and faces, hands, and texts were still a problem.
Stability.ai released stable diffusion 1.5 in the same month as an improvement for faces and hands.
Many people thought that we might solve image generation later next year and audio generation would be next.
Maybe we would be able to generate Videos in some form in the next decade. One Week. It took one week until Meta released Make-a-video, on the 29th of September. The videos were just a few seconds long, low resolution, and low quality. But everybody who followed the development of image generation could see that it would follow the same path and that it would become better over the next few months.
2 hours. 2 hours later Phenki was presented, which was able to generate minute-long videos based on longer descriptions of entire scenes.
Just yesterday google presented Imagen video, which could generate higher-resolution videos. Stablilty.ai also announced that they will
release an open-source text2video model, which will most likely have the same impact as stable diffusion did.
The next model has likely already been released when you read this. It is hard to keep up these days.

I want to address some concerns regarding AI image generation since I saw a lot of fear and hate directed at people who develop this technology,
the people who use it, and the technology itself. It is not true that the models just throw together what artists did in the past. While it is true that art was used to train these models, that does not mean that they just copy. The way it works is by looking at multiple images of the same subject to abstract what the subject is about, and to remember the core idea. This is why the model is only 4 Gbyte in size. Many people argue that it copies watermarks and signatures. This is not happening because the AI copies, but because it thinks it is part of the requested subject. If every dog you ever saw in your life had a red collar, you would draw a dog with a red collar. Not because you are copying another dog picture, but because you think it is part of the dog. It is impossible for the AI to remember other pictures. I saw too many people spreading this false information to discredit AI art.

The next argument I see a lot is that AI art is soulless and requires no effort and therefore is worthless. I, myself am not an artist, but I consider myself an art enjoyer. It does not matter to me how much time it took to make something as long as I enjoy it. Saying something is better or worse because of the way it was made sounds strange to me. Many people simply use these models to generate pictures, but there is a group of already talented digital artists who use these models to speed up their creative process. They use them in many creative ways using inpainting and combining them with other digital tools to produce even greater art. Calling all of these artists fakes and dismissing their art as not “real” is something that upsets me.

The last argument is copyright. I will ignore the copyright implications for the output since my last point made that quite clear. The more difficult discussion is about the training input. While I think that companies should be allowed to use every available data to train their models, I can see that some people think differently. Right now it is allowed, but I expect that some countries will adopt some laws to address this technology. For anybody interested in AI art, I recommend lexica.art if you want to see some examples and if you want to generate your own https://beta.dreamstudio.ai/dream is a good starting point. I used them myself to generate my last few images for this blog.

Text2Image/video is a field that developed incredibly fast in the last few months. We will see these developments in more and more areas the more we approach
the singularity. There are some fields that I ignored in this post that go in the same direction that are making similar leaps.
For example Audiogeneration and 2D to 3D. The entire machine learning research is growing exponentially.

The next big thing will be language models. I missed the chance to talk about Google’s “sentient” AI when it was big in the news,
but I am sure with the release of GPT-4 in the next few months, the topic will become even more present in public discussions.

Tag: AI

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AI Art Generation: A Prime Example for Exponential Growth

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