How Time Travel Boostrapping Accelerates AGI Development

“Time Travel Boostrapping” — Generated by MidJourney

For over four billion years, the global biosphere has been performing massive parallel computations that have ultimately led to the emergence of humans. This incredible feat of natural computation is a testament to the complexity and resilience of life on Earth. However, it also serves as a reminder of the limits of human technology. Despite the rapid progress of artificial intelligence, we are still far from being able to create truly autonomous machines that can rival the complexity and adaptability of the biosphere. Instead, we must rely on a reverse bootstrapping process to gradually improve the capabilities of our AI systems. AI is rapidly progressing with what I describe here as a reverse bootstrap process!

In the world of biology, innovation is a natural byproduct of the mindless process of evolution. This process, which operates without any conscious direction or intention, has given rise to the incredible diversity of life on Earth. But how does this process actually work? To understand this, we must first understand the concept of the bootstrap.

In simple terms, a bootstrap is a process by which something is able to create itself or improve itself through its own actions. In the case of evolution, the process of natural selection acts as a kind of bootstrap. By favoring those individuals who are best adapted to their environment, natural selection gradually drives the evolution of new species and new capabilities.

But this process is not just about individual organisms. In fact, one of the most fascinating aspects of evolution is the way that different species and different abilities can combine and interact to create new innovations. This is what happened with eukaryotic cells, which are the cells that make up the majority of life on Earth.

Eukaryotic cells are a synergy of two different kinds of cells. These cells, known as prokaryotic cells, were the first cells to evolve on Earth. They were simple, single-celled organisms that were able to survive and reproduce in a wide range of environments. Over time, these bacteria and archaea cells evolved different capabilities, such as the ability to photosynthesize or to break down complex molecules.

But then something remarkable happened. Through a process of symbiogenesis, some prokaryotic cells began to merge and form larger, more complex cells known as eukaryotic cells. These cells were able to combine the capabilities of the smaller cells and create new capabilities of their own. This was a major innovation in the history of life on Earth, and it paved the way for the evolution of plants, animals, and ultimately humans.

In short, the evolution of eukaryotic cells was a consequence of a mindless bootstrap process. It was an accidental synergy of capabilities that had been invented elsewhere in the biosphere. And it illustrates the incredible power of evolution to drive innovation and create new forms of life.

As we have seen, the evolution of eukaryotic cells was a consequence of a mindless bootstrap process. This process, driven by natural selection, allowed for the accidental synergies of different cellular capabilities, leading to the emergence of more complex and versatile cells. But this was only the beginning.

The complexity of the eukaryotic genome, which contains the instructions for building and maintaining these cells, is a testament to the power of this bootstrapping process. Over the course of billions of years, this genome has undergone countless rearrangements and modifications, giving rise to the incredible diversity of life on Earth.

But this complexity is not just a product of random chance. It is the result of millions of years of evolution, as different species and different genetic variations have competed and coexisted in a never-ending process of adaptation and innovation. And it is this process, mindlessly creative and endlessly inventive, that has led to the complex multicellular creatures of today, from single cells to blue whales. In short, biology is a testament to the power of mindless processes to drive innovation and creativity. The Eukaryotic genome, with its massive number of accidental synergies, is a prime example of this.

While biology is a testament to the power of mindless processes to drive innovation and creativity, humanity has also invented mathematics and computers in a mindful, collaborative manner. This systematic way of thinking, known as the scientific method, has allowed us to unlock the secrets of the natural world and create new technologies and innovations.

The scientific method, which involves making observations, forming hypotheses, and testing these hypotheses through experimentation, has been incredibly successful in driving the advancement of human knowledge and technology. Over the past 400 years, this method has led to an explosion of creativity and innovation, as scientists and engineers have used it to explore and understand the world around us.

While human technology has advanced rapidly in recent centuries, it pales in comparison to the technologies found in biology. In many ways, the capabilities of living organisms are so advanced that they might as well be considered magical. From the ability of plants to convert sunlight into energy, to the incredible feats of endurance and adaptation displayed by many animals, the natural world is full of technologies that we can only dream of replicating.

However, the technologies that we have invented are often incompatible with the biosphere. Many of our technologies, such as fossil fuels and synthetic chemicals, are polluting and degrading the natural world. As a result, we are incrementally destroying the very environment that has sustained us for so long.

In this sense, civilization can be seen as mindlessly demanding that the biosphere press a reset button. We are using up the Earth’s resources and destroying its ecosystems without thought for the long-term consequences. If we want to continue to thrive and prosper, we must learn to live in harmony with the natural world and its technologies, rather than trying to destroy them.

As we have seen, biology is a technology so advanced that it might as well be considered magic. The abilities of living organisms, from their ability to self-repair to their incredible adaptability, are far beyond anything we have been able to invent. But the stark reality is that if we want to survive and thrive on this planet, we must be able to invent technologies that are as sustainable and resilient as biology.

The human mind is simply too limited to invent something as complex as biology from scratch. We can only build upon the foundations that have been laid by millions of years of evolution. And if we fail to do this, if we continue to destroy the natural world and its technologies, then we are bound towards a future where the only inhabitants of Earth are dead machines, not living organisms.

In short, if we want to avoid a world occupied by dead machines, we must learn from biology and strive to create technologies that are sustainable and compatible with the biosphere. Only then can we hope to build a future where humans and other living beings can thrive together.

As researchers sought to reverse engineer intelligence, they stumbled upon something very curious: tools that expanded our ability to create complex systems. These tools are not like the ones we have used in the past; they are “living-like” in their ability to adapt and evolve. This unexpected discovery has the potential to revolutionize the field of artificial intelligence, and could lead to the creation of machines that are truly autonomous and adaptable.

Deep learning is a powerful tool for building artificial intelligence, but it is certainly not “human-like” in its capabilities. Instead, deep learning is a way of training artificial neural networks to recognize patterns in data. This process involves feeding the network large amounts of data and adjusting the connections between its neurons to improve its performance.

While this process is certainly very different from the way the human brain works, there are some elements of it that are similar to gardening. Just as a gardener cultivates and tends to their plants, a deep learning researcher must carefully nurture and train their neural network. This involves providing the network with the right data and adjusting its parameters to optimize its performance.

In this sense, developing a deep-learning network has some elements of gardening. Both involve carefully nurturing and tending to a complex system, in order to help it grow and thrive. And just as a well-tended garden can yield beautiful and useful plants, a well-trained deep-learning network can provide powerful insights and predictions.

Deep learning AI is a consequence of humanity’s ability to harness the power of digital computers and calculus. By using these tools, we have been able to create predictive and generative machines that can perform a wide range of tasks.

Calculus, the mathematical study of change and motion, is a crucial tool for building AI systems. It allows us to model and analyze complex systems, and to make predictions about how they will behave over time. By using calculus, we can design algorithms that can learn from data and make accurate predictions about the world around us.

Digital computers, on the other hand, provide the massive computational power that is needed to run these algorithms. Modern computers can perform billions of calculations per second, making it possible to process vast amounts of data and train complex machine learning models.

Together, calculus and digital computers have enabled the development of deep learning AI. These systems, such as GPT-3 and Dall-E, are able to learn from data and generate new outputs that are similar to human-generated content. This has opened up new possibilities for artificial intelligence, and has the potential to revolutionize many different fields.

There is something that everyone seems to be overlooking about the deep learning development process, and I want to point it out. I call this the time-travel bootstrap.

A reverse bootstrap is when you accelerate evolution by finding a capability invented in the future and applying it to the present. This is what we are doing with deep learning. Instead of just studying the brain and trying to mimic its development, we are using deep learning to create new capabilities that the brain has never had before. In other words, we are using deep learning to create technologies that a biological brain could never have invented on its own.

This is a truly revolutionary development. By using deep learning to create new capabilities, we are effectively traveling back in time and giving deep learning networks access to technologies that they could never have evolved on their own.

When I refer to a capability being “invented in the future,” I mean that it should not exist in the normal course of the biosphere’s computation. In other words, this is a capability that would not have arisen through natural evolution.

As a crude example, consider the human mind’s need to invent calculus. This is a complex mathematical tool that allows us to model and analyze change and motion. It is a crucial tool for building AI systems, and it has opened up new possibilities for human creativity and innovation.

But what if knowledge of calculus had driven evolution instead? What if the ability to understand calculus was an innate part of the human brain, rather than something that we had to invent? This would be a fundamentally different world, one where our understanding of the natural world would be vastly different from what it is today.

This is what I mean by a capability being invented in the future. It is a capability that would not have arisen through natural evolution, but that has the potential to revolutionize the way we think and the way we live. By using deep learning to create new capabilities, we are effectively traveling back in time and giving our brains access to technologies that they could never have invented on their own. This is the time-travel bootstrap, and it has the potential to unlock new levels of creativity and innovation.

The time-travel bootstrap is not just a general concept, but a tactical method that makes it possible to create wildly capable generative machines. This method involves using deep learning to create new capabilities that would not have arisen through natural evolution. By doing this, we are effectively traveling back in time and giving our brains access to technologies that they could never have invented on their own.

One example of this time-travel bootstrap in action is diffusion models, which are used to generate artistic images and videos. These models use deep learning to create new visual styles and patterns that are not found in the natural world. This is only possible because we are using deep learning to create new capabilities that the brain could never have invented on its own.

In short, the time-travel bootstrap is a tactical method that makes it possible to create wildly capable generative machines. By using deep learning to create new capabilities, we are effectively traveling back in time and giving our deep learning networks access to skills that they could never have evolved into on their own. This has the potential to unlock new levels of creativity and innovation, and to revolutionize the way we think and the way we live.

It is crucial to realize that the primary reason why humans can control the generation of images using diffusion models is that we previously invented a technology known as transformers. Transformers are a type of artificial neural network that are used to process language and generate text. They are the driving force behind language models like GPT-3, and they are essential for allowing us to control the generation of images using diffusion models.

Without transformers and language models, we would not have a driving signal for diffusion models. Diffusion models rely on being able to understand and generate text, and without this ability they would not be able to produce the complex and varied images that they do. In other words, without language models, diffusion models would not be possible.

As we have seen, the time-travel bootstrap is a tactical method that makes it possible to create wildly capable generative machines. This involves using deep learning to create new capabilities that would not have arisen through natural evolution, effectively traveling back in time and giving our brains access to technologies that they could never have invented on their own.

One example of this time-travel bootstrap in action is transformer models, which are used to process language and generate text. These models were invented to solve a problem that does not exist in the normal path of biological evolution. Predicting how to generate dramatically correct text is not something that biology attempts to solve, as it is not a problem that arises in the natural world.

Therefore, it is reasonable to ask whether transformer models are also a consequence of the time-travel bootstrap. It seems likely that they are, as they are a technology that could not have arisen through natural evolution, but that has the potential to revolutionize the way we think and the way we live. By using deep learning to create new capabilities, we are effectively traveling back in time and giving our brains access to technologies that they could never have invented on their own.

A key aspect of the time-travel bootstrap is the idea that human technologies are used to build other technologies. This is evident in the case of language models like GPT-3, which are able to generate text that is similar to human-generated content. The capabilities of these models result from feeding them massive text databases, but where do these databases come from?

The answer is that these databases are created using other human technologies. For example, text databases can be created by using computer programs to scan books, articles, and other written materials. This process relies on the previous invention of other technologies, such as computers and scanning software.

In short, the capabilities of language models like GPT-3 result from the previous invention of other human technologies. This illustrates the idea that technology is used to build other technologies, and that the time-travel bootstrap is a key part of this process. By using deep learning to create new capabilities, we are effectively traveling back in time and giving our artificial brains access to technologies thus accelerating its development.

The time-travel bootstrap is not just a general abstraction of technological evolution, but an extremely powerful method that has the potential to circumvent Moravec’s paradox. Moravec’s paradox is the observation that high-level cognitive tasks, such as language and problem-solving, are easy for humans but difficult for computers, while low-level tasks, such as perception and motor control, are easy for computers but difficult for humans.

In this sense, the time-travel bootstrap is like a frictionless flywheel that acts as a catalyst to accelerate the bootstrap process. By using deep learning to create new capabilities, we are able to bypass the limitations of natural evolution and unlock new levels of innovation and creativity. This is an incredibly powerful method, and it has the potential to change the way we think about technology and its role in our lives.

As a matter of fact, humankind uses the time-travel bootstrap process all the time. We are who we are because we come into this world with adults who teach us how to function. We don’t have to recreate the world in its entirety when we are born, because we are born into a world that has already been created by previous generations.

In this sense, the time-travel bootstrap process is similar to the way that adults teach children how to function in the world. When a child is born, they are not able to survive on their own. They need to be taught how to eat, how to walk, and how to communicate with others. In other words, they need to be taught how to use the technologies that have already been invented by previous generations.

Just as children learn from adults, we can use deep learning to learn from the technologies that have already been invented. By using deep learning to create new capabilities that would not have arisen through natural evolution.

The ultimate kind of “time-travel reversal” was invented by C.S. Peirce over 100 years ago. Peirce was a philosopher and logician who is best known for his work on the foundations of mathematics and the philosophy of science. In his writings, Peirce sketched out the framework for any scientific discovery, which he called the Architectonic.

The Architectonic is a framework for understanding how scientific knowledge is created and organized. It was developed by the philosopher and logician C.S. Peirce over 100 years ago, and it consists of three main components: semiosis, inference, and rhetoric.

Semiosis is the process of creating and interpreting signs and symbols. This is the foundation of the Architectonic, and it involves the use of language, logic, and other symbolic systems to represent and communicate ideas.

Inference is the process of making logical deductions and inferences from given information. This is the second component of the Architectonic, and it involves the use of logic and reasoning to draw conclusions from data and evidence.

Rhetoric is the process of using language and symbols to persuade and influence others. This is the third component of the Architectonic, and it involves the use of language, logic, and other symbolic systems to persuade and convince others of the validity of one’s arguments.

Together, these three components form the basis of the Architectonic, and they provide a framework for understanding how scientific knowledge is created and organized. By using the tools and methods of semiosis, inference, and rhetoric, we can build on the knowledge of previous generations and create new technologies and innovations.

What this means is that we do not need to reinvent systematic knowledge discovery. It exists for us to bootstrap. By using the tools and methods of science, we can build on the knowledge of previous generations and create new technologies and innovations. This is the time-travel bootstrap, and it has the potential to unlock new levels of creativity and innovation, and to revolutionize the way we think and the way we live.

One way that the time-travel bootstrap is being used to accelerate AI is through the use of student networks that are trained from much larger teacher networks. By using this method, student networks are able to learn from the knowledge and capabilities of much larger networks, and they are able to become more capable as they become smaller. This is a powerful example of the time-travel bootstrap in action, and it shows the incredible potential of deep learning to unlock new levels of creativity and innovation.

We are finally very close to harnessing the exponential power of biological creativity. The time-travel bootstrap is a powerful method that has the potential to accelerate the development of artificial intelligence, and it holds the key to unlocking new levels of creativity and innovation. Hold on to your seats, because the future of AI is going to be wild!

Disclaimer: This text was created in collaboration with ChatGPT and its driven by the ideas of this tweetstorm: https://twitter.com/IntuitMachine/status/1587745945172721668

Note: You can listen to this generated text by clicking the listen link near the title. I hope you enjoy it!