Meta released the paper Toolformer: Language Models Can Teach Themselves to Use Tools which presents an LLM specially trained in using APIs to call and incorporate returned results. This allows the model to get relevant and accurate information to generate better output.
Author: Maximilian Kannen (Page 5 of 5)
A Book Review of “A World Without Work” by Daniel Susskind
The Book “A World Without Work” by Daniel Susskind from 2020 is a thought-provoking book that explores the technological changes happening in today’s workforce and the potential impacts on society. Dr. Daniel Susskind, a Research Professor in Economics at King’s College London and a Senior Research Associate at the Institute for Ethics in AI at Oxford University, examines how automation and artificial intelligence are affecting jobs and the future of work. He argues that we need to rethink our economic and social systems to adapt to the coming technological changes.
I greatly enjoyed reading the book and even though I was familiar with many of the topics, I still learned a lot, particularly about economics. The book is divided into three parts. The first part “The Context”, describes the history of Automation and shows the parallels and differences between Industrialization and today’s development. For example, the author portrays the Luddites and their fight against textile machinery which helps readers to understand the recent fight against Generative AI.
The second part “The Threat”, explains in great detail the different reasons for technological unemployment and why the negative effects of automation outweigh the positive ones. It also explains how the current development leads to ever-greater inequality. Although many of the numbers were not new to me, the author manages to connect all the dots and paints a coherent picture of the problem.
The last part “The Response”, discusses solutions on how to build a working society in a world without work. The author addresses big tech companies and their political power, and how states have to fight back and tax in a way that allows everyone to receive an appropriate part of the economic pie. In the end, he addresses the problem of meaning and how humans can cope with too much free time. My biggest problem with this part is the missing description of how to transition from today’s system to the proposed solution, which in my opinion, is the hardest part. The book ends with an overly optimistic view, placing a lot of trust in humans and governments to build a working system in the future. Unfortunately, I do not share this trust in humanity. I also wish that the author had addressed the influence of other aspects of scientific progress on the economy, such as longevity or space exploration. However, I understand that this would have been outside the scope of the book.
I highly recommend this book to everyone who is working or will be in the next decade. Regardless of your occupation, this book is relevant to you. I also wish that political leaders would read it and act as proposed, to prevent a dystopia where rampant unemployment makes many societies fall apart.
Overall, “A World Without Work” is a thought-provoking and important book that raises important questions about the future of work and the economy. The author provides a clear and concise overview of the current state of the job market and the potential consequences of technological changes. He also does a good job of providing a balanced and nuanced view of the potential impacts of these changes, highlighting both the potential benefits and drawbacks.
The author’s proposal for a universal basic income is well-argued.
The book offers a clear and viable solution for addressing the issues of unemployment and inequality that may arise as a result of these changes. However, it is important to note that the solutions proposed in the book are not easy to implement, and it will require a collective effort from society, governments, and big tech companies to overcome the challenges that come with technological advancements.
2022 was an eventful year with lots of ups and downs. While the global economy is struggling, and problems like climate change and social instability continue to grow, there have also been some significant technological and scientific breakthroughs.
The most prominent developments probably happened in deep learning with the appearance of generative models that are able to generate human-level music, art, dialog, and code. In this context, I want to talk about two specific papers that shaped the field this year and most likely next year. The paper “Denoising Diffusion Probabilistic Models” which is the basis for Dall-E 2, Stable diffusion, and many other generative models, and the chinchilla paper from Deepmind, which demonstrated the importance of high-quality training data over model size. This will likely shape the design and cost of future models, including the anticipated release of OpenAI’s GPT-4 in 2023, which is expected to outperform humans in many text-based tasks. The improvements are not only driven by Moore’s law and architectural improvements but also the money spent to train and develop these systems increases. This is expected as the potential is more and more recognized and the value these systems provide is ever-increasing.
But not just GPT-4. 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.
Fusion, the process of combining atomic nuclei to release a large amount of energy, has made significant strides in recent years. This is largely due to the incorporation of machine learning and advancements in various fields such as materials science and engineering. Recently, the U.S. Department of Energy announced that they were able to achieve a positive net outcome from a fusion reaction, which is a major milestone in the pursuit of unlimited clean energy. 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. However, the upcoming start of the Iter project, an international collaboration to build a fusion reactor, may refuel interest and drive further developments in this promising area.
The James Webb Space Telescope (JWST) is an important milestone in the field of astronomy because it is designed to be the most powerful and advanced space telescope ever built. It started to operate this year. It is a collaboration between NASA, the European Space Agency (ESA), and the Canadian Space Agency (CSA). One of the main goals of the JWST is to study the early universe and the formation and evolution of galaxies. It will be able to observe some of the most distant objects in the universe, including the first stars and galaxies that formed after the Big Bang. In addition to studying the early universe, the JWST will also be able to observe exoplanets (planets outside of our solar system) and potentially search for signs of life on these planets. It will have the ability to study the atmospheres of exoplanets and look for biomarkers, such as oxygen and methane, which could indicate the presence of life. The JWST is also expected to make important contributions to our understanding of planetary science, by studying the atmospheres and surfaces of planets in our own solar system and beyond.
The hardware industry has faced challenges this year due to manufacturing bottlenecks. Despite the continuation of Moore’s law and the development of new alternatives to silicon, it has been difficult to obtain chips at this time. The industry is restructuring in order to better handle future demand for hardware. Specialized hardware, such as AI processors and quantum computers, are seeing rapid development. According to IBM’s roadmap, 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. Ensuring a stable and efficient supply chain will be crucial in meeting the increasing demand for these specialized hardware components.
Virtual Reality (VR) technology has experienced a difficult period in recent years due to overhyping of its potential. While some people may have expected VR to revolutionize the way we interact with and experience the world, it has yet to reach the level of ubiquity and practicality that was promised by Meta. 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
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. Depending on the availability and price of the chips and other components I expect glasses from many different companies with even more capabilities than Oppo air Glass 2.
One of the most exciting developments in computer interfaces is the emergence of brain-computer interfaces (BCIs). These allow for direct communication between the brain and a computer, enabling the possibility of controlling devices with thought alone. While companies like Neuralink are claiming to begin human trials next year, non-invasive BCIs present a much lower barrier to entry and are being actively developed by startups such as Synchron, which has received significant funding. AI will also help the field by decoding brain signals. It is likely that we will see at least one viral video showcasing the capabilities of these non-invasive BCIs, similar to the viral video of a monkey playing pong using a BCI that was released last year. The potential applications for BCIs are vast and diverse, ranging from medical and therapeutic uses to gaming and everyday tasks. As these technologies continue to evolve, it is exciting to consider the possibilities for the future of human-computer interaction.
Researchers from biotech and other fields were able to develop an mRNA vaccine for COVID-19 in less than a year. The same technology was also used to create a universal flu vaccine and a vaccine for malaria. The combination of biology and AI has yielded promising results in the development of treatments for various viruses and illnesses. For example, a team led by Chris Jones of the Institute of Cancer Research used AI tools to identify a new drug combination to fight diffuse intrinsic pontine glioma, a type of incurable childhood brain cancer. The proposed combination extended survival in mice by 14% and has been tested in a small group of children. Additionally, Dr. Luis A. Diaz Jr. of Memorial Sloan Kettering Cancer Center published a paper in the New England Journal of Medicine describing a treatment that resulted in complete remission in all 18 rectal cancer patients who took the drug. Overall, the progress in the field is accelerating thanks to advancements in AI, such as Alphafold 2, which are designed to find and develop treatments for various diseases. If this continues we will be able to beat cancer in the next few years, which leads to the next field.
I predict that every person under 60 has the potential to live forever, as I mentioned in my post about longevity escape velocity. The field of aging research has made significant progress in recent years and is more confident than ever in its understanding of the aging process and life itself. For example, researchers at the Weizmann Institute of Science in Israel were able to create fully synthetic mouse embryos in a bioreactor using stem cells cultured in a Petri dish, without the use of an egg or sperm. These embryos developed normally, starting to elongate on day three and developing a beating heart by day eight. This marked a major advancement in the study of how stem cells form different organs and how mutations can cause developmental diseases. This is a promising step toward the end goal: Achieving complete control over all biological processes in the body.
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.
The Future of Personal AI: Opportunities and Challenges
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.
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.
AI Art Generation: A Prime Example for Exponential Growth
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.
The Metaverse part 2: AR Hardware
After we discussed Virtual Reality (VR) and its implications let’s take a closer look at Augmented Reality (AR). While AR is currently not as present in the news or as developed as VR, it has the potential to be the more disruptive technology. Let us start with the current state of AR, its problems, and challenges, and after that, we take a closer look at its potential in the next few years.
We have to differentiate between devices that have AR capabilities like most recent VR headsets, and AR devices made for everyday use like glasses or contact lenses. While AR functionality in VR devices is important and opens up a lot of useful functionalities they are not the main topic of this post. The goal is a device that is stylish and comfortable enough to be worn all day and that provides a basic set of functionalities.
These devices are difficult to build, which is the reason why we haven’t seen them until now. You need sensors to embed the virtual elements into the real world and displays or lasers that allow you to present them without blocking the field of view and you need a lot of computational power and energy to make that possible. The displays work by either projecting the light directly into the eye or by projecting it on the glasses. the latter has the disadvantage of making it visible to other people around you which should be avoided due to privacy concerns. Some companies tried to build glasses like that. Hololens 2 from Microsoft is a good example of a device like that.
This is a good example of a product that has some good functionality but is not built for everyday use and it is not designed or priced for the consumer market. Some “smart” glasses provide audio but are not powerful enough to be called AR devices. Contact lenses are smaller and that makes the problem of fitting everything in even harder which is the reason why we have not seen a smart contact lens until now. So how do we get all the needed technology small enough to fit into a stylish pair of glasses? The answer is we do not. The solution is in our pockets. Companies like Apple spent years putting powerful computers in our pockets. While many argue that today’s smartphones are already more than powerful enough, their capabilities are barely at the point we need for the next step. When we connect our glasses to our phone we can outsource most of the computational power to our phone and can thus focus on sensors and displays that allow us to make the devices smaller. The key idea is a PAN (personal area network) with our phone as the main router and controller.
Apple is fighting since 2021 to get more bandwidth for Bluetooth to enable such functionality. Let us assume we bring enough power to our smartphones and we get a technology that allows high data rates in our PAN. We still have to fit sensors, displays, antennas, and batteries inside a small form factor. Some companies made incredible steps in this direction like the Mojo lens from Mojo vision, which managed to put everything needed in a contact lens and is confident to start selling them to the consumer market in 1-2 years.
But I think we will most likely see glasses from companies like Apple or Samsung in the next 10-20 months. Especially, Apple is a good candidate for the first AR device since they have all the needed functionality. Powerful chips in their phones and with ARKit already a software framework for this Hardware. The adoption rate will depend on the initial price. If they decide to lower the price as meta did with their meta quest, the glasses could be mainstream in two years. But if they push for the best possible hardware and sell them as a premium product, we will have to wait for the competition to release a cheaper option.
One of the best possible capabilities of AR devices will be what I call synchronized reality. If two people with AR devices meet, it will be important to have the possibility to make things that you see visible to the other person. This feature is important because things only appear real to us if others can see and interact with them too. An early example of something like this would be the “pokemon center” in the popular AR game Pokemon Go. The location of this virtual place is the same for every player, which is a core element of the game. Without this consistency, AR will be limited to the functionality that a modern smartwatch can provide. I am confident that a company like Apple is capable of implementing something like that for their devices. My biggest fear is that virtual objects will stay inside a system and the integration between different systems will not be possible. Considering the current state of message integration between iOS and Android, this scenario is most likely.
My guess is that useful AR technology will be available sometime in 2023 and will not be mainstream until 2025. At this point, some enthusiasts will experiment with the commercial use of Brain-computer interfaces which will then enhance AR devices and later replace them. I do not think that most people will adopt BCIs since the barrier of entry is way higher compared to AR devices and the gain will be marginal for a long time.
The Metaverse part 1: VR Hardware
I decided to split the metaverse blog post into a mini-series since the topic is so broad, that when I tried to put everything into one post I simply failed.
We start with the currently most relevant part: VR Hardware.
VR is one of the two technologies that will be the platforms for the metaverse soon. Arguably not the most important one, but the one that will be available first.
2023 will be a big year for VR. We will see some new VR devices from Meta, Apple, Pico, and others. Some of these new devices will tackle the most important problems for VR hardware.
The problem with existing VR devices, like the meta quest, is that you cannot use them for extended periods, and it is not a pleasant experience at all. They are too heavy, and they cause eye strain. The movement in VR leads to nausea and the ways to interact with VR are limited. On top of that, the viewing itself is far from reality.
Some of these problems will be fixed this year. Each new headset is lighter than the last one and Apple’s VR headset is supposed to have a way higher resolution than most currently available headsets thanks to apple silicon. Eye tracking is coming in meta’s next headset and in many others, which will help with performance and resolution, and will give us new ways to interact.
Some other problems like contrast, adaptive depth, distortion, and field of view are harder to fix and will take some time, but mark Zuckerberg recently showed some prototypes that tackle some of these problems too.
Most of these solutions require huge amounts of computation power, especially higher resolutions. Standalone Headsets will not be able to perform fast enough, at least not for the next year. I think apple is most likely to be able to bring a good visual experience to a standalone headset thanks to apple silicon, but their first model which is expected to launch in January 2023 will not be able to fix all the existing visual problems. Even PC-VR is still limited by data rates of cables and wireless transmission. We need at least Wi-Fi 6 to reach a point where wireless transmission is viable for realistic-looking VR experiences.
The problem with nausea will become less bad with improved visuals but as long as we use a controller to move the problem persists. I do not think omnidirectional treadmills are the way to go. they are too expensive, and most people do not want to waste that much space, money, and energy in their free time. Some applications use teleporting or walking in place to move, and many other solutions are currently being tested. While treadmills are not likely going to be a standard accessory, full-body tracking will be. The difference in emersion with full body tracking is huge and it gives VR another important input tool. Cheap full-body tracking solutions like slimeVR will become better and better and will give us realistic bodies in VR. The already mentioned eye tracking is another step of emersion that will be important for social VR. Being able to look someone in the eyes and read their mimic is a core element of human interaction and we are sensitive to strange facial movements. But eye tracking can do even more. It improves performance by limiting the resolution in areas that we are not looking at and it serves as an input device for VR. We can look at objects and control elements and the software will be able to extrapolate what we want to touch or click, which will remove frustrating moments like not being able to click the right button because of unprecise hand tracking. This brings me to my last point: hand tracking. It is arguably part of full-body tracking, but it is so important since hands are our primary way to interact with VR. Realistic and precise hand tracking is one of the most important aspects of emersion.
Perfect Virtual Hands – But At A Cost! 👐
Near-Perfect Virtual Hands For Virtual Reality! 👐
This AI Creates Virtual Fingers! 🤝
These videos show some of the key papers for hand tracking, published in the last two years. These papers are the foundation of meta’s hand tracking and will most likely continue to improve in the coming year.
If we look at the current development of headsets in the market it looks pretty good.
And the number of Headsets that are used every month for gaming is a good indicator for this upcoming billion-dollar entertainment industry.
I think we will see an even greater wave of people getting into VR in the next 2 years. Not just for gaming, but with apple joining the market, we will also see increases in areas like education and industry.
In the end, I want to take a short look into the far future of VR and virtual reality. I am talking about 5-10 years, probably after a technological singularity. The final goal of VR is full dive. The ability to simulate all 5 senses directly within the brain and to intercept all outputs from our brain to paralyze our body and redirect all movement into virtual reality. I will not talk about the implications for society that is a topic for another time, but from a pure hardware perspective, this is extremely challenging. While reading the output of the brain is an area where we are currently making a lot of progress, intercepting the signal to prevent our body from moving is not possible right now without a lot of medical expertise and long-lasting effects. Sending signals for all senses directly into the brain is even harder since every brain is different. I do not think we will be able to do this without an AGI, but if in the far future a machine overlord decides to put us all in our own matrix it will hopefully be heaven and not hell.
I was going to write about the Metaverse next, but the recent acceleration of technological progress convinced me to write about the singularity immediately before it is too late. The technological singularity is the event or the process when machine intelligence surpasses human intelligence, and the speed of progress becomes so fast that no human can keep up. This might be a slow process, some argue we are already in the singularity, or it might be a sudden event, where people live their normal life and from one day to another, the earth gets transformed into a giant CPU by a swarm of self-replicating nanomachines. I cannot predict what it will be like and nobody can predict what will happen after, but I will try to predict the events on the way. My predictions are obviously subjective and will most likely not be precise, they should act as a wake-up call though, to show how fast it might happen. All my predictions neglect the high probability that humanity will destroy itself or will be destroyed by climate change, Sun storms, viruses, war, or something else. Most people without a deeper understanding of Moores’s law look back on the last 10 or 100 years and think we will just continue to develop. Some people who work in fields like machine learning or biology look at their progress at the moment and base their predictions on that. Very few people can to grasp exponential growth, but I tried to always keep it in mind when I make my predictions based on everything I know and believe and every source I can find.
Hardware
Fusion reactor (2023-2026): Fusion is one of the core technologies that we need to fight climate change and solve the energy crises. With fusion reactors like Iter and advancements in artificial intelligence we are on a good way to solving fusion. Breakthroughs like this one are the reason why I am so confident that we will see an energy net plus from a fusion reactor in the next few years. I hope commercial use will be possible shortly after. Fusion technology is a perfect example where people thought it would take way longer because they only looked at the engineering side and ignored progress in areas like math and computing.
Quantum computing(now – 2025): quantum computers are already available and will be an essential part of the supercomputing landscape in the coming years. They will not be used in every household, instead, we will use them for cloud computing and solving big problems like machine learning or traffic control. The double exponential growth in quantum computing will blow their ability up in the next 3 years. I think quantum computers are one of the most overlooked technologies because it is so useless right now. But it is one of the fastest developing technologies at the moment and when they are ready they will unlock a lot of things at the same time.
Room temperature superconductors(2025-never): If and only if a room temperature superconductor exists, we will find it in the next 3 years. Material science will have the support of quantum computing and A.I. to find every possible material. This would be the single most important discovery of all time since it not only solves all energy problems but also allows for cheap transport like the hyperloop and many other applications. Examples like multilayered graphene show that there is still from for discovery but we have to wait and see if this dream is achievable.
AR glasses and contact lenses (2023-2025): In the next few years people will spend most of their time looking at or through a display. Both smart glasses and lenses are right around the corner and will change the way we interact with the internet forever. It is the technology that has the most impact on our everyday lives. the biggest obstacle for AR technology will be the bandwidth of our wireless technology. Since the computation of these devices will happen in the cloud or in our “smartphones” we need to send a lot of high-resolution video streams to a lot of people. current wifi and xG technology will not be enough and we have to wait for wifi 7 and 6G to achieve mass adoption.
VR (now-2025): Virtual reality is already part of modern gaming and will be part of the workspace in the coming years. The Hardware will be there in the next 2 years and will be affordable and good enough for all use cases at the end of 2025. I will talk about VR more when I write about the metaverse.
Brain-Computer-Interface (now-2030) BCIs are already in a test stage for medical applications. With companies like Neuralink, we will most likely see BCI in use for non-medical applications within the next 5 years. I do not believe they will be popular if they are not needed for a medical condition since the risk of putting a chip in your head is too high for most people. The only way I can imagine BCIs becoming mainstream in the next 10 years is through advancements in nanorobotics. With small nanorobots in our bloodstream, we can not only supervise our body but we can also use them as reading devices from inside our brain. The risks won’t be as high and the barrier of entry will be lower. I wrote more about that topic in my post about Human-Machine-Merging.
Robotics (now-2026): I think most physical tasks are already manageable by machines, but most of the time humans are still cheaper. With progress in robotics and third-world countries, machines will replace more and more physical jobs. The global economy and our society will have to change drastically. One of the biggest challenges will be to ensure that everyone profits from a world with an abundance of workforce, so we do not end up with an unemployed underclass.
Space Travel (2025-2030): I am not a fan of space travel. At least not now. It wastes money and time and brain power to get us to the moon or mars just so we can say we were there. The truth is that Mars and Moon are extremely unhabitable and survival is impossible for extended periods thanks to radiation, gravity, temperature, resources, and so on. While humanity will most likely spread out someday, if we survive that long, the idea should be to terraform Mars over a century with technology that will not be available for the next 15 years and let machines do it for us. Sending humans to Mars now is too early and just a waste. Sending machines on the other hand can be quite useful. Space is full of resources, and energy that we can harvest. And we also reached a point where looking out for potential threats to humanity can be useful since we achieved a level where we are able to prevent some of them.
Software
The main reason why I couldn’t wait any longer with this post is the progress in A.I. While breakthroughs in machine learning models used to be a yearly event (GPT 1-3 for example) they started to appear monthly beginning with Aplhafold and nowadays they appear weekly with Models like dall2-2, Gato, Imagen, and other impressive results. Even compared to other exponential metrics like humanity’s energy consumption the growth in machine intelligence is sudden. While the first computer is not even 100 years old we already reached the point where the top supercomputer rival the human brain using the positive feedback loop of hardware and software improvements. If the exponential growth continues like this, machines will surpass the entirety of humanity around 2045. Newer studies suggest that quantum computers improve with double exponential speed, which would mean we reach this point even faster.
Let’s take a look at some of the recent achievements. When Dalle-2 came out in January 2021 people started to dream of an A.I. that could produce Videos out of prompts like Dalle did with pictures and they thought it could happen in the next 5 years. Just one year later we have CogVideo which produces short videos. People think we continue as we did in the last few years, but that is not how exponential growth works. Models like Gato, that can perform 600 different tasks are already impressive, but Gato is more like a proof of concept and is relatively small. Deepmind announced that they are in the process of training a bigger version, while other companies are already working on the next step. Not long until they appear daily and when the hardware can keep up, we will likely see the singularity within the next 5-10 years. It is impossible to say what will happen after that. It depends on factors like; Will the models develop consciousness or not? Will they help humanity or kill us? I think we are already at a point where machines outperform a single human in every single task depending on the metric. In the coming year or two, this will become increasingly obvious to the public when models like GPT-4 or Gato 2 get released. Maybe we find the missing idea for consciousness or maybe it will just appear when they become bigger and more capable but, in the end, it does not matter. They will outperform us and help to speed up the progress in every single area to a point where no human can ever follow. This brings me to the final and most important prediction: When will we achieve AGI (Artificial General Intelligence ) and ASI (Artificial SuperIntelligence)? I predict that we will have some form of AGI around 2025. ASI will greatly depend on the limits humans apply to a potential AGI. If we keep it disconnected from the internet and limit its input and output we can delay an ASI for a few more years, but If we give an AGI access to the internet, its own code, and enough hardware, it could be a matter of minutes.
Conclusion
Our governments were left behind when the internet emerged, and they never caught up. In the last five years, we left behind most of the general population, and in the coming five years not even the experts are going to keep up. We are going to experience the most eventful decade in human history, and there are few things we can do. I find the reactions of people who find out about the singularity quite interesting. Some lose all hope and motivation and become scared of the future and others cheer up and are looking forward to the moment the machine takes over. Many ask how they should prepare and it is hard to answer since nobody knows what will happen. I think it is clear that money will be irrelevant after the singularity, but I would never recommend anyone to waste all their money in the 5 years. It is quite the opposite. Having money could be highly important in the years before the singularity for things like Human-Machine-Merging. Other than that there is not much an individual can do besides hoping for a good outcome.
Dying is overrated, so let’s talk about longevity escape velocity. LEV is the moment when the speed with which the life expectancy grows becomes greater than one year per year. This means that your anticipated remaining life span stays the same at every point in time, and you theoretically achieved immortality. I will give a brief introduction to why I believe we will achieve this, when we will achieve this and what are the consequences.
As with nearly everything that has to do with technological singularity we must look at an exponential graph first.
As you can see human life expectancy exploded in the last few years thanks to modern medicine and a safer environment. This graph is generous but you get the idea. Even if you take other data you get the same exponential growth everywhere. Most of it is thanks to medical breakthroughs like antibiotics, vaccines, and other core drugs. These drugs help against many diseases and viruses, but they do not stop aging itself. So why I am so confident that we will get there? I don’t want to take the success away from the biologist, but computer science plays a bigger part than ever before. Most symptoms of aging, like cancer, dying cells, and neurological diseases are related to the smallest building blocks of the human body: proteins. Proteins are complex molecular structures with complicated geometry and they have many different tasks in a cell. Finding out what they do and how they work was nearly impossible for the longest time. Until Deepmind came and presented Alphafold 2, an AI system that predicts a protein’s 3D structure from its amino acid sequence. Alphafold 2 came out a year ago and we are slowly getting the first results from it. Alphafold 2 also helps to solve the mystery of aging itself. Deepmind is planning to use Alphafold 2, to build an entire simulated cell. This could not only reveal the last secrets of aging but also help to find a way to stop it. Deepmind is not the only Company, there are many more. This leads to my next point, why I think we will achieve immortality: Money. We have the technology, and we are getting the knowledge, but we also have the funding. The amount of money that is invested in this kind of research is stunning and not surprising at the same time. Every rich person not knowing what to do with their money uses it to try to live longer. We have Jeff Bezos, a google co-founder, and peter Thiel who founded Altos Lab with approx. 6 billion dollars, then we have the Saudis with a billion, and at the end Mark Zuckerberg with 3 billion in his foundation. These big names are just the tip of the iceberg. I would guess the overall amount is around 60-70 billion dollars, just to cure aging. The money that is spent in all the related areas like cancer or Alzheimer’s treatment is several times higher. And it works. There are already impressive results and tests that treat or stop symptoms of aging or even reverse the process. A famous example from last year was the result of David Sinclair at Harvard University who reversed the age of mice by turning cells back to their original state as stem cells.
These kinds of experiments are exciting, but need rigorous testing and finetuning to be applied to humans. The result, to stop aging will most likely not be a single pill, but several drugs and procedures combined. Another technology that is rapidly advancing, is organ printing. Instead of using the heart or liver of another person, it will be possible to print your own using your own stem cells. This will enable organ transplantation not only as a last resort but as a legitimate way to renew parts of your body. And then there is one last strategy that is used by the desperate to extend their lives if they are already dying: cryonics. Freezing a body shortly before or after their death to preserve the brain and/or body for a longer period until all needed technology is available. I think that this is mostly a scam since there is no known way to freeze a body in a way that preserves it for more than a few weeks. I would probably do it if I had that much money left when I die. You have nothing to lose, and it is probably the best thing that you could try at this point, but the chances that someone will think about you in 100 years and will spend all this time and effort into resurrecting you in case the company that was responsible for your freezing did not fuck up are low. So, I would prefer to stay alive until there are better ways to extend my life.
The core question is who will live long enough to live forever. I think that every person under 60 with a healthy body could be in reach of this goal. There are obviously a lot of conditions. First, we assume that this person has access to all the technology and knowledge that is required and has enough power and money to apply them without being stopped by a government or any other entity that wants to prevent immortality. Unsurprisingly, the people who spend a lot of money, come to mind. I will not discuss the ethical questions of immortality in this post, but I want to shortly address the problem of overpopulation and the imbalance in the world if some people can live forever and some do not. If we look at immortality isolated it looks really scary, but in the context of the technological singularity, it is not that much of a problem. The number of births is dropping dramatically in every country with enough education. More convenient and accessible ways to prevent high birth rates will reduce the number of newborn babies even more. According to the World Population Prospect report, the global population is growing at its slowest rate since 1950 and we will peak at around 10 billion people in a few decades. And just because people can live forever, does not mean they do. Some people just do not want to, some will just die due to accidents, and some will get killed. Most people will probably not even have access to this technology, because they lack the money or whatever form of power is used in the future. I would guess that the average lifetime of a human will be around 200 years even if immortality would already be possible. The limited access to immortality sounds unfair and it is, I wish I could say that we will solve all conflicts and find a way to give every human access to whatever he wants, but I am not that optimistic. I believe that A.G.I. and other breakthroughs will solve many problems and the overall quality of life will improve, but at our core, we are all still apes throwing shit at each other and fighting for every piece of food even if there is an unlimited supply. I hope I am wrong, but as with all things that grow with exponential speed, our governments are not well prepared. When the first drugs for life extension are hitting the market in a few years, I bet the price will be high and the rules will be unclear and different in every part of the world. If some people live significantly longer than others, wealth and power will move towards longer-living people since they are able to invest time and money into larger projects that pay out after decades. The only hope is to replace humans in leading positions with A.I. systems to prevent them from gathering power over decades.
The last form and ultimate form of immortality is digital immortality. I add this at the end since it is not part of the LEV discussion, but I want to mention it. If you read my post about human-machine merging, you will know that parts of our brain will likely be digital in a few years. At this point, it could become possible to completely transfer your conciseness into the machine and live without a biological body. This idea is inspired by the Ship of Theseus paradox. The alternative would be to make a digital copy of your brain, which brings a lot of practical and ethical problems. The way of slowly merging with the machine circumvents this problem and gives us a way to become immortal in the best possible way. As digital entities, we would have the possibility to live as many lives as we want, in whatever form we want, and in whatever world we want. Most people will likely lose all interest in the real world that we could only experience through an artificial body, while our digital world allows us to experience our surroundings in whatever way we want. It is close to whatever people imagine when they talk about heaven, and I would not be surprised if biological people would try to talk to the digital ancestors like they pray today, but this time they would get an answer. It is hard to predict how exactly this will look like since this is probably already behind the event horizon of the technological singularity and at this point, everything I say is just guessed, but I like to dream about a future where there is no limit to what we can be and what we can experience.
I want to take a look into the relationship between the human body and technology.
I am a huge fan of the symbiotic relationship between us and machines, but whenever I talk to my mother or someone who is not into technology, they get alienated by the idea of letting electronics into their body.
This is understandable, but I want to believe that there is something great that we can achieve if we leave our fear behind and solve the problems and risks that come with the fusion of humans and machines. There are many more steps to take before we become cyborgs as we know them from movies or games. We are taking them and we are already beyond the point of no return.
Maybe you heard people calling each other out for behaving like zombies when using their smartphones and the smartphone is the most prominent indicator of this kind of change, but there is more.
I will go from apparent things to some that you maybe never thought about.
Let’s start with “wearables”, small devices like watches, trackers, headphones, and glasses.
I think we can agree that they all are part of our path to merge with machines even if some people try to argue that they just use them as a tool and
can live without them. The truth is our brain is so adaptive that once we use a tool often enough and it is always available our brain will just accept it as part of our body. This may sound strange, but experiments and studies show that this happens quickly. One experiment that I want to bring up as an example was done by the University of Pittsburgh, which used a brain-computer interface to connect a monkey to a robotic arm.
It didn’t take long for the ape to use the robotic arm like his own.
He performed tasks like eating intuitively with his extra arm and became visibly confused after they removed the brain-computer interface again. Of course, a BCI is a much more extreme example than a smartwatch, but don’t make the mistake to think there is a huge difference between
a brain-computer interface and normal tools, just because the connection is more direct.
If you use your thump only to type messages and for nothing else, then the part of your brain that controls your thump is reprogrammed just like the brain of the ape. I will talk about the brain-computer interface later when we come to the next steps in human-machine merging,
but first, let us go back to the more subtle things that influence us and merge with us: Drugs. Ok this may sound strange but drugs are not as far away from machines then you might think and the road ahead of us is quite clear. Modern medicine and medical tools like CRISPR become more and more complex.
From simple molecules that stop pain or help our stomach, we moved to complex molecular structures that perform actions like cutting DNA and killing viruses. The idea of nanomachines that perform sophisticated actions in our body is not science-fiction but is already in development. 2022 made one thing clear,
people are afraid of medicine they don’t understand. So the question is, will people accept machines in their drugs? I think yes. Not because I have a lot of trust in humanity, but because I have a lot of trust in the greed and envy of people.
If you combine the greed of the pharma industry with the desire of people to stay healthy and some good marketing, you get a big market for the next generation of medical devices and drugs. Now that we have already looked at the future, let me explain what I imagine will happen in the next 10 years.
I will differentiate between people who grew up with a smartphone (2001 and younger) and those who are older ( 2000-1960 ). I am sorry but for everyone older than that the following topics are not that interesting (I will probably write another blog post about Longevity escape velocity, but that’s another topic).
The next step after the smartphone is AR glasses. I am not talking about full VR,(Metaverse is another topic I will write about at some point) but light AR glasses that are indistinguishable from normal glasses. They will be an important step since they will merge our virtual life with the real world.
Many people in the younger group spend more time awake on the internet than in the real world.
This may sound extreme for some people but having 6-8hr on a smartphone and pc combined is not that rare nowadays. Ar-glasses will increase these numbers and every person using one will be online whenever they are awake. This means our brain will interact even more often and directly with the machine.
The technology will be available in the next few years. As usual, the younger group will adapt first and the older generation will take 1-2 years more. For people who want to completely dive into the virtual world, contact lenses will be available just one or 2 years after glasses.
These are not just wild guesses of mine, the technology already exists, even the contact lenses, and just must become cheaper to produce. These non-invasive technologies will be adapted way faster than real brain-computer interfaces like Neuralink, where the barrier to entry is way higher and the risks are even greater.
Though in the end, the ultimate step will be such a device. It is hard to imagine how productive a human with a computer-enhanced mind can be.
I would guess that the overhead of bringing your thoughts and ideas into a useful form by using a keyboard, mouse, and some randomly designed computer interfaces is around 80%. Human productivity will skyrocket even with just a few million brain-computer interfaces.
There are a lot of risks though. While the first brain-computer interfaces will be unidirectional and only take inputs, at some point we will use the other direction too, to increase our input of information and to keep up with our own thoughts.
This step is incredibly dangerous and it is not hard to see how much could go wrong when a potentially dangerous device can manipulate our thoughts. Some might say these devices should be banned but as Fridrich Dürenmatt wrote in his book “the physicist”
Everything, that is thinkable, will be thought.
What Solomon found can also be found by someone else.
Fridrich Dürenmatt
we can’t stop the machine from being built and we also can’t stop anyone from using them, just like we failed with drugs, nuclear weapons, and many other things.
Body-machine merging
While mind-enhancing machines are the most powerful and life-changing devices, enhancing our bodies will also be possible.
If you imagine a cyborg you think about metal limps and build-in weapons, but we already have prostheses that surpass human legs in some aspects.
You may remember the debate about whether Pistorius and other disabled athletes had an advantage at the Olympics. But you probably never heard about someone cutting off their legs to get artificial ones.
I don’t believe it will become a trend to exchange limbs if it is not necessary from a medical standpoint, even if they become better than human limbs in every aspect.
Way more popular and already used are small implants with a variety of abilities.
From small NFC chips in our hands to replace keys to small devices under the skin that measure the blood sugar level and inject insulin automatically.
Another implant that is already used by some enthusiasts is a magnet on the tip of their finger.
It allows feeling electromagnetic waves like wires in the wall or the microwave in the room next door.
It sounds like a gimmick at first glance, but if you ask a group of aliens without ears if they want to attach something to their heads to feel pressure changes in the air, they will probably think the same.
I would argue that the number of senses we have is an entirely random outcome of evolution and increasing this number is the best way to enhance our worldview.
Every time we use technology to leverage our senses we will have a similar experience to a person who sees colors for the first time. We can live without it,
but it is just nicer to have it. Enhancing our senses is the second-best thing that we can do after enhancing our mind with machines. The last and least popular step will be increasing the motoric functions of our body. We could add arms to our body or have a small robot that we control with our minds, but at this point, we are already back at Brain-computer interfaces. I will end the post here to have a readable length. I hope the topic sparked your interest and I promise I will go into greater detail the next time I write about the merging of flesh and silicon.