Could AI and technological advances usher in a new era of evolution? Facing the existential threat associated with the growing fusion of the biosphere and technosphere – Developpez.com

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Mark C. Taylor, professor of religion at Columbia University, explores the possibility of a new era of evolution arising from the growing symbiosis between humans and technology, particularly artificial intelligence (AI). He rejects the idea that humanity represents the final stage of evolution, instead envisioning a symbiotic relationship between humans and technology. In a paper published on October 12, the researcher examines neuroprosthetics, biobots, synthetic biology and organic-relational AI as key pathways in this development. Neuroprostheses are portrayed as technological extensions of human intelligence, while biobots developed using nanotechnology could have significant medical applications.

Synthetic biology explores the creation of new functional life forms, such as xenobots, resulting from a complex interaction between genes and proteins. Finally, organic-relational AI, based on the evolutionary understanding of neural networks, is presented as a promising alternative to traditional AI approaches. Taylor emphasizes the importance of this symbiotic development in light of today’s existential challenges while calling for a non-anthropocentric perspective on AI for meaningful transformation.

Could AI and technological advances usher in a new era

Quote Sent by Mark C Taylor

Our study of quantum phenomena, conscious bodies, relational ecology, and plant and animal cognition has revealed that we are surrounded by and entangled with all kinds of alternative intelligences. AI is another form of alternative intelligence. Critics will say that what makes AI special is that it was consciously created by humans. However, all organisms shape and are shaped by their expanding bodies and minds.

Rather than obsessing over the prospect of creating machines whose functioning is indistinguishable from human cognition, it is more important to examine how AI differs from human intelligence. The question shouldn’t be, “Can AI do what humans can do?” Can AI do what humans can?”, but rather, “What can AI do that humans can’t?” What can AI do that humans can’t?

What is needed is a non-anthropocentric form of “artificial” intelligence. If humanity is to survive, AI must become smarter than the humans who created it. Why should we care about reconciling superintelligence with human values ​​when human values ​​are destroying the Earth, without which humans and many other life forms cannot survive?

Given the increasing interconnectedness of the biosphere and technosphere, further symbiogenesis would be the only way to address the very real existential threat we face. But it’s too easy to be optimistic about the technology’s life-saving benefits without being specific. Here I would like to propose four development paths that are becoming increasingly important for the symbiotic relationship between humans and machines: neuroprosthetics, biobots, synthetic biology and organic-relational AI.

“What comes after humans will be neither simply organic nor machine-like, but the result of the increasingly symbiotic relationship between humans and technology,” says Mark C. Taylor.

Neuroprostheses

We live in a time when dystopian fear has been weaponized to create a paralyzing despair that leaves many people – especially young people – without hope. Without underestimating the actual and potential harmful effects of rapidly changing technologies, it is important that these gloomy views do not overshadow the remarkable benefits that many of these technologies bring.

The Internet of Things connects smart devices to global networks that increase intelligence by expanding minds. While critics and regulators of recent innovations attempt to distinguish acceptable technologies for therapy from unacceptable technologies for enhancement, the line between these alternative uses is blurred, to say the least. What starts as a treatment inevitably becomes an improvement.

Neither neuroprosthetics nor cognitive augmentation are new. After all, writing is a memory technique that strengthens the mind. Today, personal devices allow us to archive and access our memories. More recently, technological innovations have taken cognitive enhancement to another level: brain implants, for example, have been around since at least 2006, and entrepreneurs like Elon Musk (who founded Neuralink to “…create a symbiosis with ‘artificial intelligence’”) ) ) aim to establish embodied human-machine interfaces.

The growing possibilities of symbiotic relationships between computers and brains will lead to alternative forms of intelligence that are neither human nor machine, but something in between. So-called “artificial” intelligence is the latest addition to the emerging process by which life takes on ever more diverse and complex forms, Mark C. Taylor.

Biobots

In recent years, robotics has experienced a true revolution thanks to advances in nanotechnology and the improvement of important language models such as ChatGPT. Individual or swarm nanorobots could one day be implanted into the body and used for diagnostic and therapeutic purposes, perhaps to deliver drugs and repair tissues. Instead of acting on the entire body, nanorobots could target the exact location where a drug is needed and regulate its delivery.

The most notable use of nanotechnology to date is its use in vaccines, including Covid vaccines. As a group of microbiology and pharmacology experts wrote in a paper published in 2021, nanotechnologies played an important role in the success of these vaccines; “The emergency use authorization, which enabled the rapid development and testing of this technology, is an important milestone and underscores the immense potential of nanotechnology for vaccine delivery and combating future pandemics.”

Research and development in the field of nanotechnology is still in its infancy, but is growing rapidly. As bodies advance, not only do they become more alert, but it also becomes increasingly difficult to distinguish the natural from the artificial.

As nanorobots are implanted into the body and act on a molecular level, other robots are becoming more autonomous and able to think and act more like humans. Kevin Roose reported in the New York Times that Google’s newest robot, the RT-2, can interpret images and analyze the world around it.

It does this by translating the robot’s movements into a series of numbers – a process called “tokenizing” – and embedding these tokens in the same training data as the language model. Just as ChatGPT or Bard learns to guess the following words in a poem or historical essay, RT-2 can learn to guess how a robot’s arm should move to pick up a ball or throw an empty soda can in the trash .

RT-2 is based on VLMs that take one or more images as input and generate a sequence of tokens that, by convention, represent natural language text. Such VLMs have been successfully trained on web-scale data to perform tasks such as visual question answering, image captioning, or object recognition. To control a robot, you need to teach it to perform actions. Google wants to address this challenge by representing actions as tokens in the model output – similar to speech tokens – and describing actions as strings that can be processed by standard natural language tokenizers, as shown here:1700488737 356 Could AI and technological advances usher in a new era

Representation of a chain of effects used in RT-2 training. An example of such a string could be a sequence of robot action token numbers, for example 1 128 91 241 5 101 127 217. The chain begins with a tag indicating whether the current episode should continue or end without subsequent commands are executed and continues with the position change and rotation commands for the end effector as well as the desired lengthening of the robot gripper. So instead of programming a robot to perform a specific task, it is possible to give it instructions for the task to be performed and let it discover how to perform it.

Building on these recent advances, Hod Lipson, director of the Creative Machines Lab at Columbia University, is taking robotics research to the next level by building robots that are creative and creative. His research is inspired by biology and he seeks new biological concepts for engineering and new technical perspectives for biology.

Jürgen Schmidhuber vs. Geoffrey Hinton: Conflicting views on AI

In an interview with The New York Times (NYT), Hod Lipson, a mechanical engineer who runs the Creative Machines Lab at Columbia University, said he has been working on it for about 20 years. He noted that the topic used to be taboo, but now more and more people are willing to discuss it publicly. This topic was taboo. We were almost forbidden to talk about it – “Don’t talk about the C-word, you won’t get a permanent position” – so at first I had to disguise it as if it were something else, he says. By using the “C” word, Professor Lipson is undoubtedly referring to the word “consciousness.”

Lipson’s ultimate goal is to create robots that can not only reason, but are also conscious and self-aware. He defines consciousness as the ability to imagine the future and confidently predicts that these machines will eventually understand what they are and what they are thinking. As the cognitive abilities enabled by generative AI become more sophisticated, physical movements and activities become more “natural.” With these new capabilities, robots could gain the agility to navigate their environment as effectively as humans.

In a media release on the development and risks of artificial intelligence, Jürgen Schmidhuber, considered the father of AI, said that the rise of AI is inevitable and should not be feared as it aims to improve human life. This sets him apart from some of his colleagues, such as Geoffrey Hinton, who left Google to denounce the AI ​​arms race and its potential dangers.

In response to comments from Geoffrey Hinton, an AI pioneer who left Google to warn that AI could soon surpass humans in intelligence and learn to destroy humanity itself, Meredith Whittaker, an artificial intelligence researcher, responded (AI), which also left Google in 2019 after it exposed the company’s military-grade AI practices and ethics, suggesting that Geoffrey Hinton’s scaremongering diverted attention from more pressing threats.

Whittaker criticizes Hinton’s alarmist view as a distraction from more pressing threats such as corporate control of AI technologies and the social and political consequences of their use. She calls for more solidarity and action from technicians to counteract the damage caused by AI.

Taylor examines in depth the growing interaction between humans and technology and highlights the possibility of an evolution toward greater symbiosis or even a new era of evolution. The idea that AI and technological advances can be seen as a natural extension of the evolutionary process is fascinating. However, some of the ideas presented raise questions and provoke thought.

The argument that artificial intelligence is a new form of alternative intelligence that is different from human intelligence is an interesting one. The proposal to develop a form of non-anthropocentric artificial intelligence that goes beyond human values ​​opens a reflection on the ethical and philosophical implications of creating an autonomous intelligence.

The use of concrete examples such as neuroprosthetics, biobots and synthetic biology strengthens the credibility of the proposals presented. Discussions about xnobots and their ability to self-organize, self-assemble, and self-repair provide a fascinating perspective on the potential of biomechanical advances. However, the text could be criticized for its optimistic view of the impact of these technological advances. It highlights the need for increased symbiogenesis without fully addressing potential risks, such as those related to security, privacy and over-reliance on technology.

Furthermore, the discussion about organic-rational AI, while fascinating, can also seem speculative and raise questions about the actual feasibility of creating artificial intelligence based on complex biological models.

Sources: Berggruen Institute, Google

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