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How do biological computers and digital computers relate?

The Latest Tweet from Elon Musk: “The Ratio of Digital to Biological Compute is Growing Exponentially”

In this Blog, we will be talking about relation between digital computers and biological computers.

According to Musk, artificial intelligence is evolving more quickly than the human brain. He has been very outspoken about the development of AI, predicting that eventually it will surpass human power and machine hybrid technology.

This is not the first time Musk has discussed it. He has been outspoken about his concern that in the future, AI would surpass human power.

How Do Biological Computers And Digital Computers Relate To One Another?
How Do Biological Computers And Digital Computers Relate? 4

When a Twitter user questioned Musk in 2019 about whether Neuralink would provide mankind with a defense against an AI takeover, Musk said that there were two reasons: long-term and immediate.

The founder of Tesla and SpaceX predicted that as artificial intelligence advances, humans will become obsolete and slower.

So how do biological and digital computers relate to one another? Let’s respond to a couple inquiries in order to comprehend this:

Digital computing: What is it? It is the combined intelligence of a computer, just like your smartphone, laptop, or self-driving vehicle.

Any of a class of machines known as digital computers are able to solve issues by processing data in discrete form. It only works with information that is expressed in binary code—that is, with just the two digits 0 and 1—including magnitudes, letters, and symbols. A digital computer can perform tasks like controlling industrial processes and regulating the operation of machines, analyzing and organizing enormous amounts of business data, and simulating the behavior of dynamic systems (like global weather patterns and chemical reactions) in scientific research by counting, comparing, and manipulating these digits or their combinations in accordance with a set of instructions stored in its memory.

How does biological computing work? There are two ways to define this. One alludes to the combined human intelligence. The most advanced computer to date is still the human brain.

Living cells are used to create biological computers. Chemical inputs and other biologically generated substances like proteins and DNA are used by biological computers in place of electrical wiring and signaling. These biological computers can respond to data and process it just like a desktop computer, albeit primitively, equivalent to the capabilities of computers of the 1920s. Although there is still a long way to go before biological computers are as advanced as modern personal computers, the fact that researchers were able to get biological computers to complete a logic gate is an impressive accomplishment.

Potential and use of Digital computers

In the 17th century, German Gottfried Wilhelm Leibniz and French inventor Blaise Pascal created the first mechanical digital calculators. The first automatic digital computer, however, is largely credited to the English inventor Charles Babbage. Babbage created his so-called Analytical Engine in the 1830s; it was a mechanical device that combined simple mathematical processes with judgments based on its own calculations.

The most of the core components of the contemporary digital computer were included in Babbage’s designs. They demanded sequential control, for instance—that is, program control with branching, looping, and both arithmetic and storage units with automated output. However, Babbage’s invention was never finished, and until his manuscripts were uncovered over a century later, it was lost.

Throughout the 1980s and 1990s, personal computers saw considerable increase in usage. Millions more users joined the World Wide Web as it gained popularity in the 1990s, and by 2019, more than half of the world’s population—4.5 billion people—had access to the Internet. In the early twenty-first century, computers shrunk and got quicker, becoming commonplace in smartphones and later tablet computers.

Potential and use of biological computers

Once one biological cell has been programmed, it is incredibly cheap to produce billions more using only the cost of nutritional solutions and a lab technician’s time. Furthermore, it is predicted that biocomputers may be more dependable than their electrical equivalents. To give an example, consider how our bodies continue to work despite losing millions of cells, whereas a computer made of wires can become unusable if one wire is broken. Additionally, each cell has a mini-factory at its disposal, enabling it to programmatically create any biological substance.

Although there are fewer biological computers than there are personal computers, some businesses are attempting to promote this still-emerging sector.

Synthego is a Silicon Valley startup whose founders are not biologists. They are software engineers and brothers who previously built rockets for SpaceX. However, they saw promise in applying their knowledge of agile design to gene-editing tools. The business uses a selection of about 5,000 organisms from Synthego’s genome database to build unique CRISPR kits for researchers. In the end, this may shorten the time needed for gene editing by researchers.

Scientists were able to transform a cell into a biological computer by using CRISPR (DNA sequences found within, for example, bacteria). It had been programmed to read in particular genetic codes and run calculations that would result in the production of a particular protein. This significant development may someday result in the development of powerful computers that can diagnose and treat diseases inside of living cells.

Consider the possibility that in the future these cells could be programmed to look for biomarkers that denote the presence of sickness. If all requirements are satisfied, these cells could mass-produce proteins that could aid in the treatment of the illness. There might be billions of cells in a microtissue, each with their own “dual-core CPU.” This would enable computational capability comparable to a digital supercomputer operating today.

Since genetic engineering is currently sufficiently understood (even though all of its secrets are unknown), advancement in the field of biocomputing is currently centered on DNA-based systems. Many more biological systems, including those based on nerve cells, need to be studied. The information gained from creating biocomputers for DNA-based systems will likely be used to neurochemistry in the future.

An expanding market

Increasingly more people are becoming interested in computational biology as a result of what it can do and the basic issues it can resolve. In fact, the global market for computational biology was estimated to be worth $3.48 billion in 2019 and is projected to grow at a compound annual rate of 21.7% to reach $16.75 billion by 2027.

The Latest Tweet from Elon Musk: “The Ratio of Digital to Biological Compute is Growing Exponentially”

He claimed that artificial intelligence (AI) could one day surpass even the most intelligent people, which would be dangerous for humanity. His startup, Neuralink, believes that fusing human intelligence with artificial intelligence is the only way to prevent mankind from suffering the same destiny as the Terminator. Transhumanism is a phrase used to describe the cyborg hypothesis. In a 1957 essay, it was first used.

The neurotechnology business Neuralink Corporation specializes on creating implantable brain-machine interfaces. Max Hodak, Paul Merolla, and Elon Musk are the other co-founders of this business, which has its headquarters in San Francisco. Neuralink basically implants computer chips into human brains to enable humans to perform actions like typing, clicking buttons, etc. simply by thinking about them.


Today, almost all biology is computational biology since thinking and computational methods are so crucial to the quest to comprehend life. Computational biology provides a reference framework that connects disparate ideas and organizes our understanding of life. It also makes biological notions rigorous and testable.

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