#Biocomputers

[wmptyy]

i don't like the scientists giving the homunculi mental illnesses.. it makes me feel less special :(((

[jcmarchi]

Living Cellular Computers: A New Frontier in AI and Computation Beyond Silicon

New Post has been published on https://thedigitalinsider.com/living-cellular-computers-a-new-frontier-in-ai-and-computation-beyond-silicon/

Living Cellular Computers: A New Frontier in AI and Computation Beyond Silicon

Biological systems have fascinated computer scientists for decades with their remarkable ability to process complex information, adapt, learn, and make sophisticated decisions in real time. These natural systems have inspired the development of powerful models like neural networks and evolutionary algorithms, which have transformed fields such as medicine, finance, artificial intelligence and robotics. However, despite these impressive advancements, replicating the efficiency, scalability, and robustness of biological systems on silicon-based machines remains a significant challenge.

But what if, instead of merely imitating these natural systems, we could use their power directly? Imagine a computing system where living cells — the building block of biological systems — are programmed to perform complex computations, from Boolean logic to distributed computations. This concept has led to a new era of computation: cellular computers. Researchers are investigating how we can program living cells to handle complex calculations. By employing the natural capabilities of biological cells, we may overcome some of the limitations of traditional computing. This article explores the emerging paradigm of cellular computers, examining their potential for artificial intelligence, and the challenges they present.

The Genesis of Living Cellular Computers

The concept of living cellular computers is rooted in the interdisciplinary field of synthetic biology, which combines principles from biology, engineering, and computer science. At its core, this innovative approach uses the inherent capabilities of living cells to perform computational tasks. Unlike traditional computers that rely on silicon chips and binary code, living cellular computers utilize biochemical processes within cells to process information.

One of the pioneering efforts in this domain is the genetic engineering of bacteria. By manipulating the genetic circuits within these microorganisms, scientists can program them to execute specific computational functions. For instance, researchers have successfully engineered bacteria to solve complex mathematical problems, such as the Hamiltonian path problem, by exploiting their natural behaviors and interactions.

Decoding Components of Living Cellular Computers

To understand the potential of cellular computers, it’s useful to explore the core principles that make them work. Imagine DNA as the software of this biological computing system. Just like traditional computers use binary code, cellular computers utilize the genetic code found in DNA. By modifying this genetic code, scientists can instruct cells to perform specific tasks. Proteins, in this analogy, serve as the hardware. They are engineered to respond to various inputs and produce outputs, much like the components of a traditional computer. The complex web of cellular signaling pathways acts as the information processing system, allowing for massively parallel computations within the cell. Additionally, unlike silicon-based computers that need external power sources, cellular computers use the cell’s own metabolic processes to generate energy. This combination of DNA programming, protein functionality, signaling pathways, and self-sustained energy creates a unique computing system that leverages the natural abilities of living cells.

How Living Cellular Computers Work

To understand how living cellular computers work, it’s helpful to think of them like a special kind of computer, where DNA is the “tape” that holds information. Instead of using silicon chips like regular computers, these systems use the natural processes in cells to perform tasks.

In this analogy, DNA has four “symbols”—A, C, G, and T—that store instructions. Enzymes, which are like tiny machines in the cell, read and modify this DNA just as a computer reads and writes data. But unlike regular computers, these enzymes can move freely within the cell, doing their work and then reattaching to the DNA to continue.

For example, one enzyme, called a polymerase, reads DNA and makes RNA, a kind of temporary copy of the instructions. Another enzyme, helicase, helps to copy the DNA itself. Special proteins called transcription factors can turn genes on or off, acting like switches.

What makes living cellular computers exciting is that we can program them. We can change the DNA “tape” and control how these enzymes behave, allowing for complex tasks that regular computers can’t easily do.

Advantages of Living Cellular Computers

Living cellular computers offer several compelling advantages over traditional silicon-based systems. They excel at massive parallel processing, meaning they can handle multiple computations simultaneously. This capability has the potential to greatly enhance both speed and efficiency of the computations. Additionally, biological systems are naturally energy-efficient, operating with minimal energy compared to silicon-based machines, which could make cellular computing more sustainable.

Another key benefit is the self-replication and repair abilities of living cells. This feature could lead to computer systems that are capable of self-healing, a significant leap from current technology. Cellular computers also have a high degree of adaptability, allowing them to adjust to changing environments and inputs with ease—something traditional systems struggle with. Finally, their compatibility with biological systems makes them particularly well-suited for applications in fields like medicine and environmental sensing, where a natural interface is beneficial.

The Potential of Living Cellular Computers for Artificial Intelligence

Living cellular computers hold intriguing potential for overcoming some of the major hurdles faced by today’s artificial intelligence (AI) systems. Although the current AI relies on biologically inspired neural networks, executing these models on silicon-based hardware presents challenges. Silicon processors, designed for centralized tasks, are less effective at parallel processing—a problem partially addressed by using multiple computational units like graphic processing units (GPUs). Training neural networks on large datasets is also resource-intensive, driving up costs and increasing the environmental impact due to high energy consumption.

In contrast, living cellular computers excel in parallel processing, making them potentially more efficient for complex tasks, with the promise of faster and more scalable solutions. They also use energy more efficiently than traditional systems, which could make them a greener alternative.

Additionally, the self-repair and replication abilities of living cells could lead to more resilient AI systems, capable of self-healing and adapting with minimal intervention. This adaptability might enhance AI’s performance in dynamic environments.

Recognizing these advantages, researchers are trying to implement perceptron and neural networks using cellular computers.  While there’s been progress with theoretical models, practical applications are still in the works.

Challenges and Ethical Considerations

While the potential of living cellular computers is immense, several challenges and ethical considerations must be addressed. One of the primary technical challenges is the complexity of designing and controlling genetic circuits. Unlike traditional computer programs, which can be precisely coded and debugged, genetic circuits operate within the dynamic and often unpredictable environment of living cells. Ensuring the reliability and stability of these circuits is a significant hurdle that researchers must overcome.

Another critical challenge is the scalability of cellular computation. While proof-of-concept experiments have demonstrated the feasibility of living cellular computers, scaling up these systems for practical applications remains a daunting task. Researchers must develop robust methods for mass-producing and maintaining engineered cells, as well as integrating them with existing technologies.

Ethical considerations also play a crucial role in the development and deployment of living cellular computers. The manipulation of genetic material raises concerns about unintended consequences and potential risks to human health and the environment. It is essential to establish stringent regulatory frameworks and ethical guidelines to ensure the safe and responsible use of this technology.

The Bottom Line

Living cellular computers are setting the stage for a new era in computation, employing the natural abilities of biological cells to tackle tasks that silicon-based systems handle today. By using DNA as the basis for programming and proteins as the functional components, these systems promise remarkable benefits in terms of parallel processing, energy efficiency, and adaptability. They could offer significant improvements for AI, enhancing speed and scalability while reducing power consumption. Despite the potential, there are still hurdles to overcome, such as designing reliable genetic circuits, scaling up for practical use, and addressing ethical concerns related to genetic manipulation. As this field evolves, finding solutions to these challenges will be key to unlocking the true potential of cellular computing.

[techninja]

How Wetware Computers Are Being Used in Advanced Diagnostics

Wetware Computers: Pioneering the Next Era of Computing

As technology continues to evolve at a rapid pace, wetware computers stand out as a revolutionary innovation that blends biological elements with traditional computing. These cutting-edge systems promise to transform the landscape of computing, offering unparalleled efficiency and capabilities. This article delves deep into the realm of wetware computers, exploring their principles, current advancements, and future implications.

What Are Wetware Computers?

Wetware computers, also referred to as biocomputers or organic computers, incorporate biological materials with conventional hardware. Unlike traditional computers that depend on silicon-based semiconductors, wetware computers use living cells and tissues to execute computational tasks. This synergy of biology and technology unlocks new potential, leveraging the innate complexity and efficiency of biological systems.

Core Components of Wetware Computers

Wetware computers feature several distinct components that set them apart from conventional systems:

Living Cells: The foundation of wetware computers consists of living cells, such as neurons or engineered bacteria, which process information via biochemical reactions.

Biological Circuits: These circuits mimic the functions of electronic circuits, utilizing biological materials to transmit signals and perform logical operations.

Interface Technologies: Advanced interfaces are developed to facilitate communication between biological components and electronic hardware, ensuring smooth integration.

The Mechanisms of Wetware Computing

Biological Processing Units (BPUs)

At the core of wetware computing are biological processing units (BPUs), akin to central processing units (CPUs) in traditional computers. BPUs exploit the natural processing abilities of biological cells to perform complex computations. For instance, neurons can form intricate networks that process information simultaneously, offering significant advantages in speed and efficiency over traditional silicon-based processors.

Biochemical Logic Gates

Biochemical logic gates are crucial elements of wetware computers, operating similarly to electronic logic gates. These gates employ biochemical reactions to execute logical operations such as AND, OR, and NOT. By harnessing these reactions, wetware computers achieve highly efficient and parallel processing capabilities.

Synthetic Biology and Genetic Modification

Progress in synthetic biology and genetic modification has been instrumental in advancing wetware computers. Scientists can now engineer cells to exhibit specific behaviors and responses, tailoring them for particular computational tasks. This customization is essential for creating dependable and scalable wetware systems.

Potential Applications of Wetware Computers

Wetware computers have immense potential across a variety of fields, including:

Medical Research and Healthcare

In medical research, wetware computers can simulate complex biological processes, providing insights into disease mechanisms and potential treatments. In healthcare, these systems could lead to the development of advanced diagnostic tools and personalized medicine, where treatments are tailored to the individual’s unique biological profile.

Environmental Monitoring

Wetware computers can be deployed for environmental monitoring, using genetically engineered organisms to detect and respond to pollutants. These biocomputers can offer real-time data on environmental conditions, aiding in pollution management and mitigation.

Neuroscience and Brain-Computer Interfaces

The fusion of biological components with computing paves the way for significant advancements in neuroscience and brain-computer interfaces (BCIs). Wetware computers can help develop sophisticated BCIs, enabling direct communication between the human brain and external devices. This technology holds great promise for medical rehabilitation, enhancing the quality of life for individuals with neurological conditions.

Current Progress and Challenges

Advancements in Wetware Computing

Recent advancements in wetware computing have shown the feasibility of integrating biological components with electronic systems. Researchers have successfully created basic biocomputers capable of performing fundamental logical operations and processing information. These milestones highlight the potential of wetware computers to complement and eventually surpass traditional computing technologies.

Challenges and Obstacles

Despite promising progress, wetware computing faces several challenges:

Stability and Reliability: Biological systems are inherently complex and can be unstable. Ensuring the stability and reliability of biocomputers remains a significant challenge.

Scalability: Scaling wetware computing systems to handle more complex and large-scale computations is a critical hurdle.

Ethical Considerations: The use of living organisms in computing raises ethical questions regarding the manipulation of life forms for technological purposes.

The Future Prospects of Wetware Computers

The future of wetware computers is promising, with ongoing research and development aimed at overcoming current limitations and unlocking their full potential. As technology advances, we anticipate several key trends:

Hybrid Computing Models

Wetware computers are likely to complement traditional computing systems, creating hybrid models that leverage the strengths of both. This integration could lead to more efficient and powerful computing solutions, addressing complex problems that are currently beyond our reach.

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Advancements in Synthetic Biology

Continued advancements in synthetic biology will enable the creation of more sophisticated biological components for wetware computers. Improved genetic engineering techniques will allow for greater precision and control, enhancing the performance and reliability of these systems.

Ethical and Regulatory Frameworks

As wetware computing technology advances, the development of robust ethical and regulatory frameworks will be essential. These frameworks will ensure that the use of biological components in computing is conducted responsibly and ethically, addressing concerns related to the manipulation of life forms.

Conclusion

Wetware computers represent a transformative leap in the field of computing, merging the biological and technological worlds in unprecedented ways. The potential applications of this technology are vast, from medical research and healthcare to environmental monitoring and neuroscience. While challenges remain, the continued progress in this area promises to revolutionize the way we approach computation, offering new possibilities and efficiencies.

[shuga-hill]

“Computing and artificial intelligence have been driving the technology revolution but they are reaching a ceiling,” said Hartung, senior study author, in a statement. “Biocomputing is an enormous effort of compacting computational power and increasing its efficiency to push past our current technological limits.”

[mindblowingscience]

There is no computer even remotely as powerful and complex as the human brain. The lumps of tissue ensconced in our skulls can process information at quantities and speeds that computing technology can barely touch. Key to the brain's success is the neuron's efficiency in serving as both a processor and memory device, in contrast to the physically separated units in most modern computing devices. There have been many attempts to make computing more brain-like, but a new effort takes it all a step further – by integrating real, actual, human brain tissue with electronics.

Continue Reading.

[maxdanposts]

Introduction time I guess

hello tumblr my beloathed!! I am MaximumDanger, aka Max, aka Jim, an ex-cohoster after the site has been set to explode soon. I personally am not very thrilled by the idea of having to use this site but you gotta do what you gotta do i suppose.

Bit of quick info:

Crimean, minor, speaks RU/ENG + learning german(but horrendous at it), artist person of any/all pronouns. my art is here btw

May do stupid stuff sometimes but not intentionally, if i upset you poke me & i'll try 2 fix my behaviour.

if yer a terf bigot racist or any other variety of such yucky people then get off my 🅱age!!!!! im also a body horror & bugs & gore & etc enjoyer and these will only be tagged in original posts so beware!!

(also additional note i usually dont follow people who post very often(dont wanna flood my feed too much sorry) but if i like yer posts ill just check your blog separately from time to time c:)

Longer version(my interests n shit + other socials):

Media:

The Stanley Parable, The Beginner's Guide & Dr. Langeskov

corru.observer

Knuckle Sandwich(game)

Blame! & Biomega

17776 and its sequel(s)(hoping for the 20021 release)

O Sarilho webcomic(its very good, it has good art character death war crimes gay people trans people nice aliens body horror torture.. generally p cool you can read it here)

VALLONO & ARRILLUM(its an absolute banger the art is absolutely gorgeous please go read it i beg of you)

Bigtop Burger

Half-life

Yuppie psycho(i havent interacted with this thang for a long time but it still has a warm place in my heart c:)

Just stuff in general:

Speculative biology

Biopunk

Biocomputing!!!!!!!!!!!!!!

Teratomas. in my circles im known as the "biocomputing and hairy tumors person".

Microbial cellulose fashion(< this interest is very new and i barely know anything abt it but i like the concept a big lot)

Worldbuilding

Dieselpunk

Robots & other humanoid creachures(usually not androids tho. not a very big fan of androids. unless theyre fucked up then theyre cool)

Artificial Intelligence(not the boring kind) & artificial life(& as such, aliens)(i love foreign conciousness!!!)

Brutalism

Body horror

Dada(its very hard to figure out what dada is about but i read a bit and the ideas seem interesting)

Middle ages & renaissance(i think the aesthetic fucks immensely)(trying to research it a bit too sometimes but very hard 4 me to do)

Bugs!!!!! Fungus!!!! Birdies!!!!! Creachures!!!!!!

Some hobbies i guess:

i do birdwatching sometimes!! enjoyer of looking at living things in general

i . try to do a bit of coding sometimes. im not very good at it. i did however make a lil thingy to add discord emojis to your posts on cohost tho so theres that

i draw, of course

does exercise count as a hobby. idk its something im interested in and that brings me Nice Feelings. very hard to do consistently tho

i like video games. not playing video games. important distinction. i am terrible at video games. i do enjoy analyzing them sometimes to the best of my ability.

You may find me on:

Cohost(rip my beloved💔): https://cohost.org/MaxDanArts

Discord: @maximumdanger

Steam(i do not check it very often poke me in dms if you did something there): https://s.team/dr_furins

art sideblog, once again @maxdanarts

[rosewind2007]

Are these guys serious?

Final Spark?

Imagine a future where the boundaries between biology and technology dissolve, and the power of intelligence and computing transcends traditional silicon-based devices. Welcome to the next evolutionary leap – the era of biocomputing

Can’t think of anything that might be an issue there, luckily

Just re-reading Murderbot:

When constructs were first developed, they were originally supposed to have a pre-sentient level of intelligence, like the dumber variety of bot. But you can’t put something as dumb as a hauler bot in charge of security for anything without spending even more money for expensive company-employed human supervisors. So they made us smarter.

The anxiety and depression were side effects.

In the deployment center, when I was standing there while Dr. Mensah explained why she didn’t want to rent me as part of the bond guarantee agreement, she had called the increase in intelligence a

“hellish compromise”

[sw5w]

Aurra Sing Watches the Racers Pass

STAR WARS EPISODE I: The Phantom Menace 01:01:11

[cbirt]

Scientists from Johns Hopkins University lay out the possibility of energy-efficient biocomputing using brain organoids (3D brain cell cultures) that surpass in-silico computing capabilities. The recent advances in how human stem cell-derived brain organoids replicate crucial cellular, and molecular aspects of learning and memory have led to the coining of the term Organoid Intelligence (OI). The development in brain organoid research promises to address in-vitro cognition or, as the authors say, intelligence-in-a-dish. The authors anticipate OI-based systems to yield faster decision-making capabilities, uninterrupted learning during tasks, and greater data and energy efficiency as compared to Artificial Intelligence-based systems. The research was recently published in Frontiers in Science.

Biological learning or Machine learning: which is more efficient?

Biological as well as machine learning involves building internal representations of the world to enhance task performance. But the implementation mechanisms involved in the two learning paradigms result in drastic efficiency differences.

Continue Reading

[just-somehuman]

someone explain why that last sentence absolutely broke me

[dawnoboo]

On da computah seeking the truth.

[touchastar]

the next time someone inquires about (mars and daisy story setting) ill probably end up linking them this clip and letting them figure it out from there.

[mehmetyildizmelbourne-blog]

Creating an Innovation Storm from Mini-Brains in a Teacup: The Simplified Science of Organoids 

A simplified version of my conceptual and intuitive exploration of the mysteries behind organoid intelligence for a potential discovery merging artificial intelligence with biocomputing.  Combinatorial Innovation in Science and Technology I have been fascinated by various kinds of intelligence for combinatorial innovation, exploring ideas on how the human brain works—how it learns, remembers,…

[hennethgalad]

"now genetically engineered bacteria are doing the same. Such observations raise new questions about the meaning of “intelligence” and offer some insight on the biochemical nature and the origin of intelligence"

🤬

fourteen cells. fourteen individual cells and they’re better at maths than me. gah

[jcmarchi]

MIT named No. 2 university by U.S. News for 2024-25

New Post has been published on https://thedigitalinsider.com/mit-named-no-2-university-by-u-s-news-for-2024-25/

MIT named No. 2 university by U.S. News for 2024-25

MIT has placed second in U.S. News and World Report’s annual rankings of the nation’s best colleges and universities, announced today. 

As in past years, MIT’s engineering program continues to lead the list of undergraduate engineering programs at a doctoral institution. The Institute also placed first in six out of nine engineering disciplines.

U.S. News placed MIT second in its evaluation of undergraduate computer science programs, along with Carnegie Mellon University and the University of California at Berkeley. The Institute placed first in four out of 10 computer science disciplines.

MIT remains the No. 2 undergraduate business program, a ranking it shares with UC Berkeley. Among business subfields, MIT is ranked first in three out of 10 specialties.

Within the magazine’s rankings of “academic programs to look for,” MIT topped the list in the category of undergraduate research and creative projects. The Institute also ranks as the third most innovative national university and the third best value, according to the U.S. News peer assessment survey of top academics.

MIT placed first in six engineering specialties: aerospace/aeronautical/astronautical engineering; chemical engineering; computer engineering; electrical/electronic/communication engineering; materials engineering; and mechanical engineering. It placed within the top five in two other engineering areas: biomedical engineering and civil engineering.

Other schools in the top five overall for undergraduate engineering programs are Stanford University, UC Berkeley, Georgia Tech, Caltech, the University of Illinois at Urbana-Champaign, and the University of Michigan at Ann Arbor.

In computer science, MIT placed first in four specialties: biocomputing/bioinformatics/biotechnology; computer systems; programming languages; and theory. It placed in the top five of five other disciplines: artificial intelligence; cybersecurity; data analytics/science; mobile/web applications; and software engineering.

The No. 1-ranked undergraduate computer science program overall is at Stanford. Other schools in the top five overall for undergraduate computer science programs are Carnegie Mellon, Stanford, UC Berkeley, Princeton University, and the University of Illinois at Urbana-Champaign.

Among undergraduate business specialties, the MIT Sloan School of Management leads in analytics; production/operations management; and quantitative analysis. It also placed within the top five in three other categories: entrepreneurship; management information systems; and supply chain management/logistics.

The No. 1-ranked undergraduate business program overall is at the University of Pennsylvania; other schools ranking in the top five include UC Berkeley, the University of Michigan at Ann Arbor, and New York University.

[palsaa]

Wetware Computers: A New Era in Technology