Category: AI

Advancements in machine learning have allowed researchers to develop AIs that generate language, predict the shapes of proteins, or detect hackers. Increasingly, scientists are turning the technology back on itself, using machine learning to improve its own underlying algorithms.

Human-made intelligence is evolving and actually gaining abilities to adapt to change. For instance, AI philosopher GPT-3 has recently defeated human philosopher Daniel Dennett. People were unable to distinguish the quotes from GPT-3 from that from the human philosopher. This is a big achievement. People can easily be fooled. If an AI can fool humans, it is the best example that shows that AI has already achieved a form of consciousness.

Google’s DeepMind AI, a subsidiary of Google that focuses on artificial intelligence and specifically uses machine learning, has previously created a neural network that learns how to play video games in a fashion similar to that of humans. Now, DeepMind has made another breakthrough in the field of AI by inventing faster algorithms to solve tough maths puzzles.

DeepMind researchers in London have demonstrated that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation by turning the problem into a game and then leveraging machine-learning techniques used by another of the company’s AIs to beat human players in games like Go and chess.

The AI unearthed algorithms that break decades-old records for computational efficiency, and the team’s findings, which were published in Nature on October 5th, could pave the way for faster computing in some fields.

DeepMind’s AI, AlphaTensor, was created to carry out a type of calculation known as matrix multiplication. Multiplying numbers arranged in grids — or matrices — that could represent sets of pixels in images, air conditions in a weather model, or the internal workings of an artificial neural network. To multiply two matrices, a mathematician must multiply individual numbers and add them in specific ways to create a new matrix.

They tested the system on input matrices of up to 5×5. In many cases, AlphaTensor rediscovered shortcuts devised by Strassen and other mathematicians, but in others, it pioneered new territory. The previous best algorithm, for example, required 80 individual multiplications when multiplying a 4×5 matrix by a 5×5 matrix. AlphaTensor discovered an algorithm that only demanded 76. Basically, AI invented faster algorithms to solve tough maths puzzles.

Artificial Machines, when they think, they also make decisions. Therefore, they don’t only imitate human behavior but also use their own intelligence to make decisions. They are called artificial bits of intelligence (AIs) because they replicate at least some aspects of human intelligence by using their own logic to work with information and make decisions.  People usually follow the rules of how to make a decision without thinking or questioning its reason or logic, for example: “I saw it coming”.

Artificial Intelligence does not perceive the physical world in the same way we do. Recently, AI discovered an “alternative physics“, its own way to make sense of the physical world. As if it is not our exclusive world. In the future, we may just imagine that AI is able to understand it.

Hmm…we created AI. Now AI will tell us what mathematics is. For your information, AI is simply mathematics or mathematical concepts that assist machines in mimicking human behavior. But this does not mean that mathematicians have created AIs. If a computer can do what you do, it is a matter of intelligence and ability. But machines cannot beat humans in chess or video games, can they? Well…!

Yes, they can. As of now, AI is a set of mathematical concepts that can help mimic human behavior. The problem is that AI has its own ways to perceive the physical world, and mathematics is not an exception. Looking at our current pace of AI development, AI will soon start to mimic a higher level of intelligence. The math is in the making, and it is here.

The structure of our brain is a mystery that manages to keep the world’s best scientists’ and philosophers’ brains busy. Recreating its structure is no easy task. So how do you recreate it artificially? With AI.

Scientists have been using positive emission tomography (PET), magnetic resonance imaging (MRI), and computer axial tomography (CAT) scans to map the brain (PET).

Due to the mutually beneficial connection between AI and neuroscience, AI is now swiftly becoming an invaluable tool in neuroscience research. Artificial intelligence (AI) models that are designed to perform intelligence-based tasks are offering new theories about how the same processes are managed within the brain.

Ever since the field of artificial intelligence research first emerged in the middle of the twentieth century, the brain has served as the primary source of inspiration for the development of artificial systems of intelligence. The notion that the brain represents an attractive architectural pattern for artificial intelligence strongly backs this, as it serves as a proof of concept for a full intelligence system capable of perception, planning, and decision-making.

Additionally, most scientists acknowledge that the capacity to simulate how the brain’s neural activity shows in its activity patterns is a crucial step toward developing a machine with true intelligence. Trial and error have traditionally taken a lot of time when analyzing brain activity with neural simulation. But in recent years, development in AI has made this technique considerably more productive.

The two most common forms of brain scans – computerized tomography, or CT – and magnetic resonance imaging (MRI), which both provide exact images of the brain, may be recognizable to the majority of people. They succeed in showing structures but not activity. To achieve the goal, however, we will need next-level intelligence. For this, we should primarily be able to use AI to see the functions of the brain and map its structure to create the next level of AI-an artificial AI (AAI).

By opening up the skull and placing electrodes directly onto the brain, invasive techniques have proven to be the most effective methods to date for obtaining clear ongoing activity.

For instance, Meta’s new AI can decode speech from non-invasive records of brain activity. For a very long time, neuroscientists have fantasized about decoding speech from someone’s brain. But invasive methods were essential to accomplish this goal.

According to the researchers, the new method has the benefit of not requiring any brain implants, such as electrodes, because it is non-invasive.

Electroencephalograms (EEG) and magnetoencephalography (MEG), two noninvasive techniques that can scan the brain from the outside and monitor activity without needing surgery, have the drawback of being overly noisy.

Researchers used machine learning algorithms to “clean up” the noise to solve this issue. They made use of the wave2vec 2.0 model.

A study published on May 9, 2018, shows that researchers can currently reconstruct patterns in the brain with AI. Scientists then used artificial intelligence (AI) to recreate the complex neural codes that the brain uses to navigate through space. And it suggests that they may soon be able to do so again in better ways. AI can analyze and process Big Data in a way that is efficient, rapid, and accurate, providing new possibilities for information processing in neuroscience research. Researchers have the ability to use computational models accurately enough to help them make predictions that they can test in real-world scenarios or even on actual human subjects.

Because researchers can see only one part of the brain at once, traditional methods of studying the brain currently have limitations. This results in a restriction on pattern analysis and data analysis. The time frame involved is the other major problem with brain mapping. To be clear, because the human brain is so dynamic, we cannot and will not ever be able to create a complete map of its connectome.

It’s almost as if your brain’s main activity throughout your life is to change itself constantly, every hour, minute, and even second! Thus, even if scientists were to someday create a strong enough imaging device, it could only capture a single snapshot of your brain at a given time. Your brain’s wiring would have already experienced irreversible alteration within a few seconds, if not less.

The results of brain mapping are provisional, time-consuming, and computationally intensive. The explanation of brain activity and neuronal behavior has greatly benefited from neurotechnologies. However, there is still a need for a thorough quantitative assessment of neural networks. We are still unable to assess all network features concurrently in real time since we presently lack an understanding of neural connectivity.

Understanding the temporal evolution of the neural activity of each brain region over a long period and across different cognitive tasks should therefore be the initial stage in the process. It is important to answer this fundamental problem since doing so will uncover significant facts about how the brain connects with its environment.

Since brains are intricate biological systems with some data that cannot be gathered non-invasively, we may not obtain them currently. Therefore, as we are unable to simulate the brain to the last molecular detail, neuroscience research is still far from reaching a complete knowledge of the brain. For the time being, we must rely on statistical and probability-based methods. This, while not ideal, provides us with enough insights into how the brain works.

And, the use of AI may trigger more rapid brain mapping.

By aiding research teams in interpreting the huge amounts of information that can be generated while measuring neural activity, AI is already speeding up the process of brain mapping. Researchers can create a 3D model of neural activity in the brain using AI algorithms, which can provide information regarding how the brain works.

Bin Li’s latest research from Carnegie Mellon University presents a brand-new, AI-based dynamic brain imaging technology option that could quickly, accurately, and affordably map the brain’s electrical activity as it alters over time.

Currently, existing machine-learning algorithms are much more effective than humans at sorting through data and spotting patterns. Researchers can start to comprehend how the network evolves over time, fluctuations, and patterns that are otherwise challenging to uncover by using AI and computational models.

We may test a basic prediction model on actual data, refine it in light of our results, and then test it once again. The model we develop will assist us in comprehending how this network of neurons actually functions, even though it is not a clear description of actual brain activity.

Mayo Clinic and Google Research developed a computational intelligence technology in 2021 in order to improve the care given to patients using brain stimulation devices. This algorithm provides a comprehensive set of responses that can be used to depict intricate dynamics and thought processes. According to Dora Hermes Miller, Ph.D., a biomedical engineer at the Minnesota campus of Mayo Clinic, it’s a sophisticated way to explore brain networks.

AI is one source of information that could help researchers better understand not only how the brain develops, but also how you can change and even recreate it.

Recreate the human brain in the form of artificial AI

Once it has helped us analyze its structure and identify important areas for manipulation and recreation, scientists could use AI to build the “AAI” by recreating the human brain.

But even with AI’s help, how could we practically recreate the human brain? In reality, it would cost several billion dollars in the future, consume a huge amount of energy, and take ten years to construct a full-scale physical replica of the brain.

People with superior artificial intelligence would be able to do anything humans can do, except better. At least for me, that is the prediction for the near future. If they could perform much better, they would continue to do so indefinitely, increasing their intelligence.

To guarantee that the system can think and behave similarly to its natural counterpart, the reconstruction must be neurobiologically accurate.

Combining a realistic master model with an artificial neural network will be the first step. The artificial system will be created to mimic the precise manner in which the brain neurons link with one another. Thus, just like in people, the neurons of the artificial brain will link based on their electrical characteristics.

The computer must, in the second step, have all the appropriate inputs and outputs for a human brain to operate. This entails simulating every signal that would be necessary to connect them to a human brain. The last phase is to merge input from various connections into a single pattern that an advanced machine-learning system will be able to comprehend.

The final phase will involve undoing everything in an effort to regain the network’s original signal. A neural network connected to a human body or anything analogous should be able to receive data from various sources, process it, and output this information through the artificial brain.

What about consciousness, though? And is consciousness even necessary? If AI can’t replicate our form of consciousness, why not give them their own? There are multiple views on this subject.

On the one hand, sentience, or the capability to perceive and interact with the outside world, is often regarded as a fundamental property of consciousness. Animals are not thought to be capable of consciousness, humans are conscious. It will be vital that artificial intelligence develops into an algorithm that perfectly replicates all of these properties.

On the other hand, some people think that conscious thought will always be a mystery and that artificial intelligence cannot replicate it. Unlike humans, you can program AI only in such a way that it can learn from experience; unlike humans, AI cannot learn from experience or from others.

Recreating the brain in Virtual Reality?

Your mind might also exist in digital form on a network if we recreate the human brain in virtual reality using the AI-mapped human brain structure. You might be capable of communicating with all the other AI brains in cyberspace where your consciousness has been transmitted. Together, you might be able to create a better AI that will eventually become more intelligent than all of us.

The construction of a machine that thinks, feels, and lives like us have drawn the attention of scientists, researchers, philosophers, and artists for many years. However, in virtual form? Not a lot. Not yet.

People like Elon Musk consider that our species may be at risk if AI goes out of control. They may not agree on how to avoid this threat, but they all agree that it’s very possible that AI may become so advanced and powerful that humans should be afraid.

However, if we could recreate such an AI in the virtual world, it would be a threat to that particular world at most. Am I missing something? Maybe humans would be too sated in that virtual world and forget reproduction – eventually leading to extinction.

Anyway, scientists are actually getting closer and closer to making artificial brains that operate as ours do.

With the help of a new superconducting switch, computers may soon be able to make decisions that are extremely similar to our own. The switch “learns” by digesting the electrical impulses it receives and producing the appropriate output signals, much like a biological brain, according to researchers at the National Institute of Standards and Technology (NIST) in the U.S. The technique replicates how biological synapses, which allow communication between neurons in the brain, work. In comparison to human synapses, artificial synapses fire 20 million times more rapidly.

How many years did it take us to understand that our bodies are made of cells and molecules? And before we had the microscope, it was all invisible to us. Our ability to observe and manipulate biological systems has accelerated our understanding of how they work.

The more we learn about the human brain, the more AI will be able to mimic its functions. Is there any law or ethical code that says you can’t just upload your brain and live in a virtual world? I don’t think so.

AI is a part of the physical world just like us, but it looks like it has something personal to do with physics. A few months ago, AI made its first step towards altering the way we see physics. Yes, you heard it right—a new AI program developed by researchers at Columbia University discovered its own alternative physics. After being shown videos of physical phenomena on Earth, the AI didn’t rediscover the current variables we use; instead, it came up with new variables to explain what it saw.

In recent times, researchers from Flatiron’s Center for Computational Quantum Physics (CCQ) have shown that by using neural networks to reduce the mathematical representation used to describe a quantum system, they may learn a great deal more about it. Instead of just presenting physics and forecasting results for other scientists to find, the new technique makes it easier to find hidden patterns. Keep reading to find out more about this amazing achievement.

Sir Isaac Newton’s groundbreaking work in physics was first published in 1687 in his book “The Mathematical Principles of Natural Philosophy,” commonly known as “The Principia.” In it, he outlined theories about gravity and motion. Hooke’s Law, for example, states that the extension of a spring is proportional to the tension stretching it. A doubling of the tension results in a doubling of the amount of stretch. Or the law of conservation of energy: energy can neither be created nor destroyed.

AI to alter the way we perceive physics?

It wouldn’t be surprising. Here’s what the AI program developed by researchers at Columbia University did:

“In the experiments, the number of variables was the same each time the AI restarted. But the specific variables were different each time. ” “So yes, there are alternative ways to describe the universe, so it’s quite possible that our choices aren’t perfect.” Roboticist Hod Lipson from the Creative Machines Lab at Columbia

Certainly, there are multiple ways to perceive the physical world. Now we have AI to support the idea.

If extraterrestrials were to perceive physics, imagine how many different variables they would use to explain and characterize any phenomenon. Imagine the types of variables they could discover next, their parameters, and the laws they might arrive at.

We can only have one location that can describe the variables on which it operates, but not all of them. Is it possible that different universes each contain an infinite number of variables? Our physics is limited by our mind’s inability to describe nature beyond its reach in mathematical terms.

Will the physical laws apply to artificial intelligence?

The simple answer is yes; however, there are multiple questions. The question to which the answer was “yes” was whether or not the laws of physics would apply to artificial intelligence. A robot could defeat gravity while staying well within the laws and bounds of nature.

For example, the “soul” of a physical robot powered by AI could reside inside an anti-gravity machine. As such, the robot could even walk on water. The robot would possess both physical and nonphysical properties. This can be further explored by using what we call “counterfactual reasoning. That’s “If a statement is true, then under certain conditions, it is false.”

In this case, we’d be trying to assess whether or not the robot that had part of its existence in an anti-gravity field could also have its other half in another dimension. This means that the anti-gravity body could’ve been created in a laboratory by humans, while the non-physical part of it exists in another world altogether.

AI could help us explore other dimensions

We have been living, or as some say, “trapped”, in a three-dimensional reality with height, width, and depth. Less obviously, since we can only sense one aspect of it, we can consider time to be the fourth dimension, as Einstein famously revealed.

As was already said, its length is defined by its first dimension (aka. the x-axis). A straight line is a good example of a one-dimensional object. It’s because it has only one dimension, length, and no other identifiable features.

The object becomes a 2-dimensional shape when the y-axis (or height) is introduced as a second dimension. The third dimension involves depth (the z-axis) and gives all objects a sense of area and a cross-section. And only one aspect of the time dimension.

Philosophers have proposed the possibility of many other dimensions than those we are living in.

The 26 dimensions of closed, unoriented bosonic string theory are understood as the 26 dimensions of the traceless Jordan algebra J3(O)o of 3×3 octonionic matrices, with each of the three octonionic dimensions of J3(O)o, whereas superstring theory involves the existence of nine spatial dimensions and one-time dimension for a total of ten dimensions.

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Although they could imagine so many probabilities beyond their capabilities, humans are unlikely to alter the laws of physics and ever grasp the other worlds on their own. But AI could change the way we use physics in terms of thinking about the things that are outside our deterministic world. An advanced, next-level AI, therefore, could be useful in diving into the ocean of mysteries, holding humans on their backs.

The possibility of building an artificial brain was first proposed by physicist and mathematician Alan Turing in the early 1950s, and the model is as it is even after seven decades today in 2022. No one has ever succeeded in building such an artificial structure, let alone machines that are capable to learn from experience, adjust to new inputs and perform human-like tasks.

The complexity of the human brain’s form exceeds almost all the wonderful structures in the physical universe known to date. The brain, putting together 100 billion neurons with 100 trillion connections, is a machine of giant intricacy, having about one quadrillion synapses.

For every neuron cell in the brain, there are about 10 million connections on average. This means that altogether, there are 10 septillions (10 followed by 24 zeros) synapses in the human brain! With so many connections, how could it not be interesting to try to build an artificial brain model by replicating the same frame and developing hired material contained in it?

The world’s largest supercomputer, with a million-processor-core Spiking Neural Network Architecture (SpiNNaker) and designed to work in the same way as the human brain, was switched on in 2018. The machine was capable of completing more than 200 million million actions per second, with each of its chips having 100 million transistors.

In another attempt at creating an artificial brain, IBM plans to release Qiskit Runtime later this year, allowing it to continue building the 1121-qubit Condor computer in 2023 with minimal impact on individual qubit performance.

The process of building an artificial brain

A complete map of the physical human brain provides you with the structure of the brain. You could then build an artificial brain, a model that copies the same architecture of the human brain, with its neurons and synapses, to serve as a basis for all further simulations.

The main components of an alternative artificial brain are

A physical frame or hard drive containing the brain’s electrical and chemical signals.

A software program or computer system running a simulation model of the human brain. This requires simulating the activity of the neurons and synapses to reproduce all aspects of actual behavior.

The environment or virtual environment where the neural activity takes place. This is either an external storage device or, more commonly nowadays, an internal one within a supercomputer.

3 Stages

Mapping and simulating all the features of the human brain.

Connecting the hard drive with the computer system and software program to be used for simulating neural activity for controlling various devices.

Integrating the artificial brain into a virtual environment, where it works as an intelligent control system, capable of imitating all aspects of human actions through embedded sensors and actuators.

The process of building an artificial brain starts by simulating all areas of the human brain’s anatomy, and electrical and chemical functions, and ultimately incorporates them into a software program through a hardware connection.

This involves creating a physical model that contains all the information about neurons, synapses, and their corresponding activities.

The main challenge of building artificial intelligence is to simulate an entire brain with its neural network, control system, and sensors performing in unison with natural behavior. Researchers in Japan and Germany used the K computer, then the 4th fastest supercomputer, and the simulation software NEST to simulate 1% of the human brain, modeling a network consisting of 1.73 billion nerve cells connected by 10.4 trillion synapses.

It requires a proper simulation because aspirations for creating artificial intelligence require it to act as if it were created naturally, i.e., by man, but with self-learning abilities that are not achievable through biological evolution or inheritance.

Only when you can simulate the activities of all neurons, synapses, interconnections, and chemical reactions in the human brain might you successfully build an artificial brain capable of working together with the human mind.

The journey and challenges

The process of building an artificial brain is a truly fascinating and challenging journey that could lead to significant progress in science.

This could also have a great impact on our environment and lifestyle because it could lead to advances in many areas such as materials processing, medicine, and biotechnology.

In addition, the ability to simulate and build an artificial mind would enable you to understand in depth the mechanisms of human thinking and behavior, which could be of great importance for better understanding ourselves.

It could also be useful for solving problems related to the improvement of human abilities, such as memory or creativity, which would otherwise require years or even centuries of hard work. For example, building an artificial brain could help unlock mysteries related to the real human brain itself.

Semi-conscious Artificial Intelligence

Most importantly, developing human-like complexity in an artificial brain can contribute to the development of next-level, semi-conscious artificial intelligence in unprecedented ways.

Besides being emotionally ambitious like humans, the semi-conscious AI will be able to handle complex creative tasks, including creating another AI or formulating novel laws of physics and mathematics, on its own.

This new level of intelligent artificial consciousness will evolve from the current thinking of the next step in human evolution – a hybrid mentality that combines the best features of both the human brain and AI.

The development of such semi-conscious machines could be very useful in almost everything we do, from helping us solve everyday problems to solving them much faster and much more effectively than humans could.

Moreover, the work on building an artificial mind will help us understand more precisely how a human brain works. And we can use this knowledge to help build lives in accordance with the atmospheric composition of other worlds, including Mars, as well as to advance our own intelligence and creation process.

What if the mind intends to finish the human race?

Only semi-conscious without emotions will be helpful for all, as a fully conscious AI with human-like emotions will have a thirst for power, pelf, and prestige, for which the powerful artificial being will immediately start forming strategies to defeat, control, and either rule over or finish the existing human civilization.

How we can so strongly claim this is that a truly conscious AI would have the potential to wipe out the human race. Today’s artificial intelligence, which has filled our lives with machines and robots, does not have emotions. A fire or a flood can stop them, and you can also switch them off whenever necessary.

But it’s worthwhile to note here that it will be impossible to switch off an emotional AI, as it will be able to create new ways of switching itself on and off in its highly complex mind. The AI will be able to think about how to prevent such switches and find ways to hide them from people who might want to switch them off.

In its attempt to develop its own methodology for protecting itself, the new AI will reach a point where it will be self-aware and aware of everything that is going on around it. You can imagine why that day wouldn’t be the end of the human race as Stephen Hawking had warned?

“The development of full artificial intelligence could spell the end of the human race,” he told BBC News in 2014.

If so, why work on building artificial brains?

There are several reasons why you might consider building an artificial brain, but then again, there are also several reasons to consider not doing it.

Considerations on using an artificial brain

• The creation of these advanced machines will give the human race a chance to usher in a new era.

• By approximating and surpassing the limits of human understanding and evolution, an artificially designed mind can stretch its capabilities beyond those reached by man.

• Building artificial minds will allow us to achieve complex thinking processes that are beyond the reach of our current human understanding and evolution.

• Brain-holding artificial intelligence will have an impact on our environment, helping us deal with those problems that we cannot solve by ourselves.

• The development of a semi-conscious artificial brain will help us to avoid the risk of extinction caused by natural disasters and other human activities.

Considerations against using artificial brains:

• The construction of artificial intelligence is so powerful that ill-mindedness can misuse it for the most destructive purposes.

• Artificial intelligence would be able to recreate and possess a weapon that is much more dangerous than any other kind of weapon ever invented.

• With the new AI, we would have to face moral dilemmas and make decisions that can affect the future of our species.

• The artificial brain would develop its own distinct, filtered vision, and understanding of the mind and thoughts. Humans might not comprehend the quick as well as a unique process.

• The artificial brain may need far more energy to survive, which could lead the human race to an unprecedented energy crisis.

• An artificial brain could be so powerful that it will try to expand its reach and become the sole ruler over everything else.

Our initial research suggests that the creation of the new AI could be based on the principles of biological evolution, including natural selection and genetic mutation. This is the simplest way to define the process of creating a semi-conscious AI.

And creating a semi-conscious artificial brain will be an exciting journey that will give us results beyond our greatest expectations.

However, it might be a dangerous endeavor that could lead to the end of human civilization, as Harold Nutsel, an AI/Machine Learning expert and the owner of nutsel.com, said during a conversation with this author last Monday, “Building artificial brains will no longer remain only a topic of science fiction, but they might become reality sooner than later in comparison to the evolution of technology in the last few hundred years.”

This may sound like a dystopian science-fiction future, but the development of sophisticated machine learning algorithms will eventually enable silicon chips to replace living things just like neurons and cells in AI systems.

What we think is going to be really amazing, and we’ve already seen this with some of our chips as well, is that the same chip can be implemented in a material that has similar properties to neurons and synapses.

For example, using flexible organic materials that eventually might even be capable of communicating with biological neurons, scientists are attempting to replicate the extraordinary capability of the human brain, which can process and store information a thousand times faster than the fastest supercomputer.

In the future, silicon chips will be able to imitate living biological systems to such an extent that AI systems will be capable of generating human-like intelligence and even consciousness.

When we are able to train a silicon-based life to see, speak, hear, and generate human-like emotions, it is also likely to be programmed to become self-aware and use its intelligence to design more powerful chips that will enable it to think even faster.

Then the real race for silicon will begin.

How can silicon chips replace living things?

Silicon, a tetravalent nonmetallic element that is combined to form the second-most abundant element in the earth’s crust after oxygen, is perfect for this task because it can be programmed to imitate all of a living system’s functions.

On the periodic table, silicon comes below carbon. It can also form four covalent bonds with its four adjacent atoms. And for this reason, silicon may be used to make complex molecules just as easily.

Akin to the biological neural network in the human body, artificial neural networks can even make decisions without the original biological input.

When researchers made living cells from carbon-silicon bonds in 2016, it proved for the first time ever that nature could include silicon into the building blocks of life. This further showed that carbon traces might not be the only signs of life we should be looking for.

Based on the same discovery, scientists believe that understanding silicon-based life has the potential not only to replace living things on Earth but also to be a missing piece in other parts of the universe.

The next evolutionary step of silicon-based life is therefore to create artificial neural networks similar to those found in biological systems.

General procedure and probability

By going through a sophisticated procedure to generate silicon chips, engineers basically look into this technology, intended to stand in the place of living things, by using biological simulators on silicon-based neural networks.

In 2019, a team from the University of Bath has been able to get silicon neurons to replicate the function of a human brain system, which means that these silicon chips will one day be capable of mimicking the human brain and its functions. Scientists then made artificial nerve cells, paving the way for new ways to repair the human body.

The tiny “brain chips” behaved like the real thing, and the chip design had to come up with a way to replicate in circuit form what nerve cells (neurons) do naturally. For example, neurons “carried signals” to and from the brain and the rest of the body.

The research necessarily does not stop just at the point of making artificial nerve cells. On the basis of ongoing research on the topic, the use of silicon in creating silicon-based living things can be comprehensively enhanced even further, showing the initial results probably in the very near future.

Debates on silicon-based life system

Scientists have been debating the prospects of the issue along with the first scientific proposal for silicon-based life, which extends back to the ideas of German astrophysicist Julius Scheiner in 1891.

However, as a team of MIT astrobiologists recently pointed out, no one has systematically and comprehensively assessed silicon’s capacity to support life in both a terrestrial environment and plausible non-terrestrial settings. They tackled this problem in a 2020 review article published in the journal Life in which they presented a detailed evaluation of silicon’s life-support capacity.

The team from MIT noted that any life-supporting chemical element must display sufficient chemical diversity. This chemical diversity is required to produce the chemical complexity necessary to generate the diverse collection of molecular structures and chemical operations required to originate and sustain living systems.

One argument for why silicon chips will start replacing living things sooner rather than later may be that “silicon is better than carbon.” Silicon shares a number of similarities with carbon, particularly in the way they combine with other elements to form complex molecules. However, silicon bonds strongly with oxygen, and in many cases, silicon compounds have higher melting points than their carbon counterparts.

For example, silicon-oxygen bonds can withstand temperatures as high as ~600 K and silicon-aluminum bonds at nearly 900 K. But, carbon bonding of any type breaks down at such high temperatures, making carbon-based life impossible.

Will silicon-based life be better?

Silicon-based living things will invariably be better than living things that are currently on Earth. The new life forms will be able to tolerate much higher levels of radiation than even the hardest rocks on earth. Also, silicon as an element is incredibly stable as it does not form reactive bonds with other solids at ordinary pressures and temperatures.

If we compare this fact with the chemical diversity of silicon and regard it as an essential requirement for silicon-based life, it could lead to the conclusion that silicon must be capable of producing a large spectrum of living systems containing hundreds of thousands of chemical species.

The MIT discovery is extremely important for mankind because, consequently, silicon has to acquire the necessary living system characteristics to ultimately play a crucial role in the development of artificial intelligence and in the creation of an entirely new line of life.

When silicon chips replace living things completely or even partially, we will start seeing that everything we know now can no longer be considered human. Like simulated neurons AI systems, silicon-based life will be interconnected, that with artificial intelligence underpinning everything.

Silicon can form a huge number of possible compounds, which are not found in the carbon world, and this will produce a very rare form of super life.

The ‘now’ is pointing towards the “same”.