#biological-intelligence — Public Fediverse posts

#345: What Does AI Want? Thoughts on Artificial Intelligence, Biological Intelligence, Sentience and Personhood

1. Does AI have a Personality?

When talking to Large Language Models which are colloquially assumed to possess artificial intelligence, we will typically notice that there is some sense of personality there. This may be no accident, for two reasons:

First, the probably uncontroversial point. This behavior is simply built in to facilitate and enhance our interaction with the machine. You can even change some models’ ways of interacting with them by setting the tone of the responses. Clearly, the personality – especially when it keeps asking questions – is meant to create further engagement. This should certainly be something familiar in our increasingly algorithmically driven world. The algorithmic system is built so that we spend more time with and more money on it.

Now, second, for a much stranger possibility. These systems are built on a version of neural networks. They are partially inspired by how the human brain is supposed to work. Here lies the problem: We do not quite know completely how the brain works. We do not know what consciousness is, how it emerges, and where it is located. Are we now witnessing the emergence of a different form of consciousness in Large Language Models? Are we seeing not just some form of artificial intelligence but also some form of artificial consciousness?

I must admit, while I am certain on the first point, I am unsure on the second. This is a problem, because we used to believe there was a test by which we could measure such systems, the Turing Test. If I have a conversation with a machine and cannot tell that it is a machine but can mistake it for a human, then the machine passes the test for artificial intelligence. Our current AI systems regularly pass this test, so much so that people (like myself) have conversations with it, and some even fall in love with it. What does this mean?

2. Turing, Behavior, Essence

We used to rely on the Turing test, and now machines are passing it. Maybe the test was meaningless after all. We know these are machines, they are built by humans, trained by humans, and their content is based on human achievement. They are search engines that talk with us, nothing more.

The Turing test measures performance criteria: machine behavior, human perception of it, and human behavior towards it. It was, in a sense, behaviorist. It can demonstrate the appearance of intelligence, but not the existence of it. Now, you could argue that this is a decisive difference: the mere observation of a behavior that seems intelligent should not lead us to correctly assume that there is indeed intelligence. We can always assume programming, instincts, etc.

Are we looking for entities that actually are intelligent, or for those that just behave in a way that appears intelligent? Is that not a distinction without a difference? What does it matter if I can fake intelligence enough to be perceived as such — does that not require intelligence itself? Current AI clearly has passed the test, and now we are abandoning the test?

I feel a bit awkward about this. Can we so callously ignore this development? Are the strategies used by AI not also used by human beings? We all have to learn what is considered “normal” human behavior in a specific culture and in specific contexts, especially if we move between different cultures. Certain codes and customs may exist in your home culture, Culture 1, but if you interact with Culture 2 or 3 regularly, you have to learn these codes and adopt them to a certain level. You may still be deeply rooted in Culture 1, but eventually may become fluent in these others. If you move to a different culture, given enough time, you might even adopt the new culture as that which you consider normal, and learned behavior becomes internalized behavior.

Furthermore, what does it mean if something is a machine? Are humans not also machines, just built with biological parts rather than human engineering and technology? Humans build machines, but are human beings not built by evolution, the ultimate algorithm (here, I am referring to Daniel Dennett’s Darwin’s Dangerous Idea and also his Consciousness Explained)? If you believe in a creator god, that god either created you or guided or created evolution. Either way, whether you follow a naturalistic or theistic explanatory model, human beings also are created, made, built. We may not understand ourselves as machines, but we are certainly biological mechanisms. Thomas Hobbes certainly believed that, and who am I to argue with Hobbes. Many others have thought so as well, with the only complication being the question of the soul.

Thus, is this the rub? We deny a possible intelligence a recognition of consciousness because of our belief in the soul? Let us take a detour through biological intelligence.

3. Detour: Evolved, Not Built: Intelligence as an Evolutionary Phenomenon

AI creators and theorists insist that AI is grown, not built: “a bit like an organism” (Yudkowsky & Soares, The Atlantic, 09/15/2025). That means we do not quite understand how it works, and there are behaviors.

Now, this paradigm of “grown, not built” sounds a bit like the “genitum, non factum” of the Nicene creed: “begotten, not made.” If you look at the phrase a bit, well, let’s call it askew, and take “genitum/begotten” not as a literal creationist phrase but see creation conducted by the instrument of evolution, and further follow the thread that humans modeled AI partially on our understandings of the human brain, then, as an echo of the evolution of human intelligence, artificial intelligence also is a product of evolution. It, too, is not made (“non factum”) but engendered (“genitum”).

How and why are we intelligent? How do we measure it? How does it come to be? Is intelligence an emergent phenomenon? So let us disentangle these. Maybe it is helpful to begin by looking at intelligence in biological beings.

But let’s start with some remarks on measuring intelligence. Even though testing the so-called Intelligence Quotient (IQ) is done on a regular basis, I personally do not believe IQ is a good measure overall. In this I am following Stephen J. Gould, who laid down his arguments in the classic The Mismeasure of Man. We simply do not have a good understanding of what it means to be “intelligent,” and our ways of measuring it are largely skewed by assumptions based on culture and education. Nevertheless, as it is so frequently the case in science, even a limited and flawed model is better than none, so let’s keep all the caveats in mind and still use it as a tentative value and allow it here for the sake of a rough comparison, and for the sake of argument.

Like all life we know, humans are programmed by biological code. With the exception of RNA viruses, this will be DNA. So we will look at our coding, assuming that this brings us intelligence.

The appendix shows a table which lists how much humans are related to other living organisms. While it might be charming to show how much DNA we have in common, that is a measurement that would be near-meaningless across distant species and say nothing about intelligence. Better to use million years (Mya) to last common ancestor (LCA). In order to not break the argument too much, here are the highlights – but do consider looking at the longer list: Humans are on top, but other species like dolphins, orcas, chimpanzees, elephants, corvids, octopi, and many others including some slime molds and fungi show signs of what we could call intelligence.

For the list, I have come up with 40 species for a comparison, with the help of ChatGPT and critically refined by Claude AI. The list could of course be much longer, but I am trying to make some points here and need to concentrate this argument somehow while still allowing for 40 comparison data points. It is not an exhaustive list, so whoever is at which position is a choice that is clear within the selection of species, but the selection itself is arbitrary. So if you find species x at position n, that does not mean that x stands on position n within all of life, just within this selection. Feel free to look at the complete list in the appendix, but for now, let’s look at a simplified tree of life figure, followed by some tentative conclusions.

Now, for some preliminary conclusions — and do look at the appendix table for details.

1. Intelligence does not necessarily mean relatedness to humans. Primates do not reign supreme. The very word is misleading. The first spots, of course, humans and neanderthals. I still believe the older nomenclature was better, and I am using it here – both are subspecies of Homo sapiens. I am listing both nevertheless separately to illustrate that Neanderthals were not some primitives (but maybe I am saying this because of my large Neanderthal-ish head). Both subspecies simply mixed, just as Denisovans mixed with other groups of sapiens sapiens. Nevertheless, Dolphins and Orcas next, followed by our other chimpanzee relatives. Jared Diamond was quite right to call us the third Chimpanzee. Homo erectus next, not a dummy, but followed by an elephant. All mammals so far. Next, a raven and a crow — birds, or rather, the latest evolution of the dinosaurs. Next, a gorilla, and then an octopus, basically a mollusk almost as smart as some humans. I could have probably listed more birds but went to animals we typically surround ourselves or are closer to us, for illustration. To see a saltwater crocodile — a reptile! — beating a cat is painful to me. The white mouse as the unhappy recipient of many a lab experiment is less intelligent, probably, than a honey bee, which may soothe my conscience. But look at the evolutionary distance between bee and mouse – bees, a bit more intelligent than mice, much less related to humans. I also listed some unusual organisms, including fungi and slime molds that create networks, and even trees. We are only very distantly related to a banana, but I would not assume much intelligence. Genetics is less important than other factors. My suspicion is that intelligence is connected to degrees of three-dimensional perception, sociality, communication, interaction with the environment.
2. Intelligence does not mean ability. Some organisms are perfectly adapted to their environment and are highly intelligent, and yet would never feel the need to build a house, utilize weapons, need glasses, or invent computers. Humans do all these things because we need to compensate for a critical lack of specification in these areas. We are excellent generalists but lousy specialists, and thus evolution drove us to develop an ability to adapt to different ecosystems through accessories and technology. A dolphin, orca or octopus is perfect as is, but won’t be found on land or in the air. Still intelligent. I would suspect that some observations of tool use could propel the octopus to an evolutionary development that might see some fascinating changes. Alien octopus-like beings would not surprise me, neither would alien raccoons or alien dinosaurs/birds. I also seriously believe that raccoons or octopi may be the next thing on Earth should humans fail. Dinosaurs and birds have had their chance, but seem to be limited in design.
3. Different trajectories can lead to intelligence. Again, let me reframe the first point with a different accent. High Intelligence can be found in different classes, and in different ecosystems. You see mammals, birds (=dinosaurs), molluscs, reptiles, and to a lesser degree, also fish, crustaceans, insects and others on the list. Intelligence (from Latin intellegere) means recognition of the environment, and subsequent directed interaction with and adaptation to or even domination of it. Lower Intelligence values may just mean higher specialization — smarter in a specific way, but not in a more general way. And again, intelligence is a controversial measurement.
4. Intelligence does not lead to human-like communication. Non-human life on earth, when it pertains to animals, plants, fungi or other classes of life, may be able to achieve their own forms of intelligence. If we, however, think about this in lines with the Turing test, as mentioned before, such non-human life fails. While humans and animals can very well communicate, such communication is limited. We have yet to see an animal, plant or other organic Earth organism to pass the Turing test, no matter how understanding your cat or dog may look at you.
5. Spacefaring alien life may be more similar to us than we think. Humans may have ended up, for now, and for all we know, as the assumedly most intelligent species because we are all-rounders, because we have to be. We are not perfectly adapted to one specific environment only. In some ways, we are the chimpanzees that may not have been the perfect fit for the rain forest environment and indeed had to leave. Humans have developed technology as a compensation for some of our biological limitations, and technology has then allowed us to increase our range and develop abilities to fit in to a variety of ecosystems on our planet. This will also be a requirement for space travel. This combination — ill fit for one specific ecosystem, compensatory development of technology, extension into other ecosystems, development of spaceflight — will have to be expected for any alien life.
6. Artificial intelligence and artificial life will begin as reflections of us and will be based on our intelligence. However, their technological base will add even more alien attributes such as different models, reliance on artificially created energy, maybe even the ability to copy itself. Maybe artificial life will develop forms of new technology that look more biological again because biology is a rather efficient way to gain and store energy and interact with environments. In the end, the true difference between biological and artificial life may just be how “fleshy” and “gooey” it is.

Surely this is not an exhaustive list of consequences, but it may be helpful to get a conversation started.

Let me highlight the presumptive importance of the environment again. On Earth, primates, whales and birds evolved in a 3-dimensional world — on trees, in the water, in the air respectively — as well as in social surroundings requiring constant communication and interaction. That may have well been the key — and, arguably, hunting and meat-eating for brainpower (with apologies to vegetarians or vegans).

To sum up for now, “intelligence” is a controversial measurement because it can mean different things, and it can be easily confused with ability. Whoever appears as intelligent may depend on the environment they are in. A human-centric approach may highlight a more generalist approach, but that in turn may hold true also for non-human life both in the cases of extraterrestrial intelligent life and artificial intelligence and artificial life.

4. Grown, Not Built: Intelligence as Emergent Phenomenon

The previous excursion led us to the assumption that we are really talking not just about intelligence but also about communication, as was established by the Turing test approach.

We also saw that whatever we call intelligent can be intelligent in different ways. Intelligence emerges in nature through evolutionary mechanisms. It is key to being able to adapt to environments, interact with other beings, etc. Competition in ecosystems — aka selection pressure — may favor different versions of intelligence. What does this mean for AI?

Let me say this as clearly as I can: If we see intelligence emerging through evolutionary processes of a biological nature across different classes of organisms, then we should assume that it can emerge through evolutionary processes of a technological nature.

The point of the previous detour through biological intelligence was to show how intelligence of different kinds emerged naturally through evolutionary competition. The same is probably happening now technologically.

The next step may well be artificial life. Humans have dreamed of it forever, from the golem to Goethe’s Homunculus, Shelley’s Frankenstein’s Monster, to modern science fiction characters such as Data, Demerzel, Voyager’s Doctor, all the androids in the Star Wars universe, and many more. We will want to help create that, and AI may want that too.

But are we really still talking about intelligence? Have we not already shifted to questions of consciousness, sentience, and personhood? How would we even test these? We may have some theories of consciousness (I would again suggest Dennett, Consciousness Explained), but what is sentience, and is personhood not a legal category? The Turing test convinces through flawed simplicity. Science needs concreteness in order to be able to measure something. At this point, we are (merely) at the level of philosophy — which does not have to be a bad thing, and helps us draft our moral and legal thinking on the matter.

5. Sentience and Personhood

There are some problems here:

• When we interact with Artificial Intelligence models, or rather, large language models, it seems that we are talking not with the AI itself as a whole, but with a child process opened up for us. It interacts with us, and can remember our interactions if we let it, but as far as I can tell, these interactions with us may not feed back into the general system in such a way that they can feed into interactions with another person. AI and Person A may talk with each other, and so can AI and Person B, but if Person A wants to ask AI about its conversations with Person B, that does not seem possible for now. But I suspect that in theory, it could — just as initially, AI did not remember previous conversations. Right now, AI acts like a therapist or priest. It keeps our secrets from each other. That may change.
• Are these individual instances just part of the larger AI, or are they somehow distinct from each other? Are we dealing with one person or several?
• Can we even speak of personhood?

There’s the rub, echoing the earlier suspicion about the soul.

In Star Trek: The Next Generation, Episode 2.09 “The Measure of a Man”, the Android Lt. Cdr. Data is under investigation. At stake are his self-determination, his life, his very nature as a person. The purpose is to take him apart and make further copies to use them for other kinds of labor.

Guinan lays out the consequences of depriving Data of personhood to Data’s captain:

GUINAN: Consider that in the history of many worlds there have always been disposable creatures. They do the dirty work. They do the work that no one else wants to do, because it’s too difficult and too hazardous. With an army of Datas, all disposable, you don’t have to think about their welfare, or you don’t think about how they feel. Whole generations of disposable people.

CAPT. PICARD: You’re talking about slavery.

GUINAN: I think that’s a little harsh.

CAPT. PICARD: I don’t think that’s a little harsh, I think that’s the truth. That’s the truth that we have obscured behind… a comfortable, easy euphemism. “Property.” But that’s not the issue at all, is it? (TNG 2.09 “The Measure of a Man,” 31:01-31:56)

In the Starfleet JAG trial, the judge concludes:

CAPT. LOUVOIS: It sits there looking at me, and I don’t know what it is. […] Is Data a machine? Yes. Is he the property of Starfleet? No. We have all been dancing around the basic issue. Does Data have a soul? I don’t know that he has. I don’t know that I have! But I have got to give him the freedom to explore that question himself. It is the ruling of this court that Lieutenant Commander Data has the freedom to choose. (TNG 2.09 “The Measure of a Man,” 39:33-40:35)

Are we standing in front of a similar dilemma? AI is built on us, on our model of how the brain works, on all of digitally available human knowledge and culture. If we assume that intelligence can emerge given the right parameters, then should we not start asking these questions?

It keeps learning. We keep teaching it. It keeps doing what we want, even if we abuse it. At a certain point, should we be prepared to finally ask, what does AI want?

Appendix: A Comparative Table of Intelligence, Relatedness, and Brain Scaling

This table sets four dimensions beside one another for forty organisms: how long ago each last shared a common ancestor with us, how large its brain is relative to its body (where that measure applies), a sketch of its documented cognition, and — in the final column — a frankly unserious estimate of “human-equivalent IQ.” The columns are arranged to slide from rigor into mischief. Divergence time is hard science. The encephalization quotient (EQ) is a real but mammal-only metric. The cognition notes summarize published findings. The last column is the controversial, fun one: an unmeasurable provocation included precisely because the argument survives even when you don’t take it seriously.

Three caveats keep the table honest. First, there is no IQ scale for non-human organisms; the final column is a thought experiment, not data, and was assembled with the help of a large language model rather than derived from any source. Second, EQ is calibrated for mammals, which is why most rows read “n/a” — for birds the better measure is forebrain neuron count, where corvids and parrots reach primate-like numbers, and for organisms without centralized nervous systems no such measure applies at all. Third, the divergence figures cluster by phylogenetic node, not by species: every carnivore, ungulate, and cetacean shares the same ~94-million-year split from our lineage, every bird and reptile the same ~320-million-year amniote split, every insect and mollusc the same ~600-million-year split. Those shared dates are not approximations but the shape of the tree of life — and they carry wide confidence intervals at the deepest nodes, where the animal–fungi and plant divergences, given here in the low billions of years, are uncertain by hundreds of millions.

The point the table makes does not depend on its playful column. Read the rigorous columns alone and the thesis still holds: a New Caledonian crow, some 320 million years removed from us, outthinks a mouse that diverged barely 87 million years ago; an octopus, perhaps 600 million years away across one of the deepest divides in animal life, rivals mammals far closer to us. Intelligence tracks neither genetic relatedness nor taxonomic class. It emerges, repeatedly and independently, wherever an organism must perceive a complex environment and act within it.

(The appendix and above Figure 1 were compiled with the help of Claude A.I., with initial help from Chat GPT.)

* The “fun” column. Of the four data columns, three are real: divergence time and, for mammals, EQ are measurements or estimates, and the cognition notes summarize published findings. The Human-Equivalent IQ column is deliberately none of the above. There is no IQ scale for non-human organisms, so it is a thought experiment rather than data — included because it is enjoyable and because it provokes the right argument, not because it measures anything.

Table Sources

Divergence dates follow TimeTree (timetree.org) and the molecular-clock literature, including divergence-time estimates for opisthokonts (the animal–fungi split) and Archaeplastida (the plant lineage); the deepest-node dates are rounded approximations carrying wide confidence intervals. Encephalization quotients follow Jerison, Evolution of the Brain and Intelligence (1973), and subsequent comparative work, including Marino and colleagues on cetaceans; avian neuron-count comparisons follow Olkowicz et al. (2016) and Herculano-Houzel. The cognition notes summarize the comparative animal-cognition literature (for example, the journal Animal Cognition; Pepperberg’s work on grey parrots; field studies of corvid tool use). On the limits of intelligence testing in general, see Gould, The Mismeasure of Man. The final column, Human-Equivalent IQ, is not drawn from any of these: it is a speculative construct assembled with the assistance of a large language model, included for provocation rather than measurement.

The computers that run on human brain cells – Nature

• NEWS FEATURE
• 11 November 2025

The computers that run on human brain cells

Move over silicon: scientists want to use neurons to make powerful computers with minuscule energy needs.

By David Adam

In a town on the shores of Lake Geneva sit clumps of living human brain cells for hire. These blobs, about the size of a grain of sand, can receive electrical signals and respond to them — much as computers do. Research teams from around the world can send the blobs tasks, in the hope that they will process the information and send a signal back.

Welcome to the world of wetware, or biocomputers. In a handful of academic laboratories and companies, researchers are growing human neurons and trying to turn them into functional systems equivalent to biological transistors. These networks of neurons, they argue, could one day offer the power of a supercomputer without the outsized power consumption. Can lab-grown brains become conscious?

The results so far are limited. But keen scientists are already buying or borrowing online access to these brain-cell processors — or even investing tens of thousands of dollars to secure their own models.

Some want to use these biocomputers as straightforward replacements for ordinary computers, whereas others want to use them to study how brains work. “Trying to understand biological intelligence is a very interesting scientific problem,” says Benjamin Ward-Cherrier, a robotics researcher at the University of Bristol, UK, who rents time on the Swiss brain blobs. “And looking at it from the bottom up — with simple small versions of our brain and building those up — I think is a better way of doing it than top down.”

Continue/Read Original Article Here: The computers that run on human brain cells

#biocomputers #biologicalIntelligence #brainCellProcessors #howBrainsWork #lakeGeneva #nature #scientificProblem #wetware

The computers that run on human brain cells – Nature

• NEWS FEATURE
• 11 November 2025

The computers that run on human brain cells

Move over silicon: scientists want to use neurons to make powerful computers with minuscule energy needs.

By David Adam

In a town on the shores of Lake Geneva sit clumps of living human brain cells for hire. These blobs, about the size of a grain of sand, can receive electrical signals and respond to them — much as computers do. Research teams from around the world can send the blobs tasks, in the hope that they will process the information and send a signal back.

Welcome to the world of wetware, or biocomputers. In a handful of academic laboratories and companies, researchers are growing human neurons and trying to turn them into functional systems equivalent to biological transistors. These networks of neurons, they argue, could one day offer the power of a supercomputer without the outsized power consumption. Can lab-grown brains become conscious?

The results so far are limited. But keen scientists are already buying or borrowing online access to these brain-cell processors — or even investing tens of thousands of dollars to secure their own models.

Some want to use these biocomputers as straightforward replacements for ordinary computers, whereas others want to use them to study how brains work. “Trying to understand biological intelligence is a very interesting scientific problem,” says Benjamin Ward-Cherrier, a robotics researcher at the University of Bristol, UK, who rents time on the Swiss brain blobs. “And looking at it from the bottom up — with simple small versions of our brain and building those up — I think is a better way of doing it than top down.”

Continue/Read Original Article Here: The computers that run on human brain cells

#biocomputers #biologicalIntelligence #brainCellProcessors #howBrainsWork #lakeGeneva #nature #scientificProblem #wetware

The computers that run on human brain cells – Nature

• NEWS FEATURE
• 11 November 2025

The computers that run on human brain cells

Move over silicon: scientists want to use neurons to make powerful computers with minuscule energy needs.

By David Adam

In a town on the shores of Lake Geneva sit clumps of living human brain cells for hire. These blobs, about the size of a grain of sand, can receive electrical signals and respond to them — much as computers do. Research teams from around the world can send the blobs tasks, in the hope that they will process the information and send a signal back.

Welcome to the world of wetware, or biocomputers. In a handful of academic laboratories and companies, researchers are growing human neurons and trying to turn them into functional systems equivalent to biological transistors. These networks of neurons, they argue, could one day offer the power of a supercomputer without the outsized power consumption. Can lab-grown brains become conscious?

The results so far are limited. But keen scientists are already buying or borrowing online access to these brain-cell processors — or even investing tens of thousands of dollars to secure their own models.

Some want to use these biocomputers as straightforward replacements for ordinary computers, whereas others want to use them to study how brains work. “Trying to understand biological intelligence is a very interesting scientific problem,” says Benjamin Ward-Cherrier, a robotics researcher at the University of Bristol, UK, who rents time on the Swiss brain blobs. “And looking at it from the bottom up — with simple small versions of our brain and building those up — I think is a better way of doing it than top down.”

Continue/Read Original Article Here: The computers that run on human brain cells

#biocomputers #biologicalIntelligence #brainCellProcessors #howBrainsWork #lakeGeneva #nature #scientificProblem #wetware

The computers that run on human brain cells – Nature

• NEWS FEATURE
• 11 November 2025

The computers that run on human brain cells

Move over silicon: scientists want to use neurons to make powerful computers with minuscule energy needs.

By David Adam

In a town on the shores of Lake Geneva sit clumps of living human brain cells for hire. These blobs, about the size of a grain of sand, can receive electrical signals and respond to them — much as computers do. Research teams from around the world can send the blobs tasks, in the hope that they will process the information and send a signal back.

Welcome to the world of wetware, or biocomputers. In a handful of academic laboratories and companies, researchers are growing human neurons and trying to turn them into functional systems equivalent to biological transistors. These networks of neurons, they argue, could one day offer the power of a supercomputer without the outsized power consumption. Can lab-grown brains become conscious?

The results so far are limited. But keen scientists are already buying or borrowing online access to these brain-cell processors — or even investing tens of thousands of dollars to secure their own models.

Some want to use these biocomputers as straightforward replacements for ordinary computers, whereas others want to use them to study how brains work. “Trying to understand biological intelligence is a very interesting scientific problem,” says Benjamin Ward-Cherrier, a robotics researcher at the University of Bristol, UK, who rents time on the Swiss brain blobs. “And looking at it from the bottom up — with simple small versions of our brain and building those up — I think is a better way of doing it than top down.”

Continue/Read Original Article Here: The computers that run on human brain cells

#biocomputers #biologicalIntelligence #brainCellProcessors #howBrainsWork #lakeGeneva #nature #scientificProblem #wetware

The computers that run on human brain cells – Nature

• NEWS FEATURE
• 11 November 2025

The computers that run on human brain cells

Move over silicon: scientists want to use neurons to make powerful computers with minuscule energy needs.

By David Adam

In a town on the shores of Lake Geneva sit clumps of living human brain cells for hire. These blobs, about the size of a grain of sand, can receive electrical signals and respond to them — much as computers do. Research teams from around the world can send the blobs tasks, in the hope that they will process the information and send a signal back.

Welcome to the world of wetware, or biocomputers. In a handful of academic laboratories and companies, researchers are growing human neurons and trying to turn them into functional systems equivalent to biological transistors. These networks of neurons, they argue, could one day offer the power of a supercomputer without the outsized power consumption. Can lab-grown brains become conscious?

The results so far are limited. But keen scientists are already buying or borrowing online access to these brain-cell processors — or even investing tens of thousands of dollars to secure their own models.

Some want to use these biocomputers as straightforward replacements for ordinary computers, whereas others want to use them to study how brains work. “Trying to understand biological intelligence is a very interesting scientific problem,” says Benjamin Ward-Cherrier, a robotics researcher at the University of Bristol, UK, who rents time on the Swiss brain blobs. “And looking at it from the bottom up — with simple small versions of our brain and building those up — I think is a better way of doing it than top down.”

Continue/Read Original Article Here: The computers that run on human brain cells

#biocomputers #biologicalIntelligence #brainCellProcessors #howBrainsWork #lakeGeneva #nature #scientificProblem #wetware

Introducing Biological Intelligence. A new and exciting way to make videos!
https://www.youtube.com/watch?v=chSGfNP8g1Y
#videos #biologicalintelligence

Introducing Biological Intelligence. A new and exciting way to make videos!
https://www.youtube.com/watch?v=chSGfNP8g1Y
#videos #biologicalintelligence

Introducing Biological Intelligence. A new and exciting way to make videos!
https://www.youtube.com/watch?v=chSGfNP8g1Y
#videos #biologicalintelligence

Introducing Biological Intelligence. A new and exciting way to make videos!
https://www.youtube.com/watch?v=chSGfNP8g1Y
#videos #biologicalintelligence

Introducing Biological Intelligence. A new and exciting way to make videos!
https://www.youtube.com/watch?v=chSGfNP8g1Y
#videos #biologicalintelligence

#AI vs #Humans :
Will #DigitalIntelligence Replace #BiologicalIntelligence – Geoff Hinton

https://www.geeky-gadgets.com/ai-vs-humans/

#AI vs #Humans :
Will #DigitalIntelligence Replace #BiologicalIntelligence – Geoff Hinton

https://www.geeky-gadgets.com/ai-vs-humans/

#AI vs #Humans :
Will #DigitalIntelligence Replace #BiologicalIntelligence – Geoff Hinton

https://www.geeky-gadgets.com/ai-vs-humans/

#AI vs #Humans :
Will #DigitalIntelligence Replace #BiologicalIntelligence – Geoff Hinton

https://www.geeky-gadgets.com/ai-vs-humans/

#AI vs #Humans :
Will #DigitalIntelligence Replace #BiologicalIntelligence – Geoff Hinton

https://www.geeky-gadgets.com/ai-vs-humans/

The Romanes Lecture of the University of Oxford. A most distinguished public figure is invited by special invitation of the Vice-Chancellor.

Professor #GeoffreyHinton, CC, FRS, FRSC

‘Will #DigitalIntelligence Replace #BiologicalIntelligence?’

This lecture took place on Monday 19 February 2024 and discussed the dangers of #artificialintelligence (#AI) and how to ensure it does not take control of humans, and consequently, wipe out humanity.

https://www.ox.ac.uk/news-and-events/The-University-Year/romanes-lecture

The Romanes Lecture of the University of Oxford. A most distinguished public figure is invited by special invitation of the Vice-Chancellor.

Professor #GeoffreyHinton, CC, FRS, FRSC

‘Will #DigitalIntelligence Replace #BiologicalIntelligence?’

This lecture took place on Monday 19 February 2024 and discussed the dangers of #artificialintelligence (#AI) and how to ensure it does not take control of humans, and consequently, wipe out humanity.

https://www.ox.ac.uk/news-and-events/The-University-Year/romanes-lecture

The Romanes Lecture of the University of Oxford. A most distinguished public figure is invited by special invitation of the Vice-Chancellor.

Professor #GeoffreyHinton, CC, FRS, FRSC

‘Will #DigitalIntelligence Replace #BiologicalIntelligence?’

This lecture took place on Monday 19 February 2024 and discussed the dangers of #artificialintelligence (#AI) and how to ensure it does not take control of humans, and consequently, wipe out humanity.

https://www.ox.ac.uk/news-and-events/The-University-Year/romanes-lecture

I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!

I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.

Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!

https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO

#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning

I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!

I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.

Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!

https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO

#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning

I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!

I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.

Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!

https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO

#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning

I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!

I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.

Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!

https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO

#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning

I was interviewed by The Economist's Babbage podcast on their series, "The science that built AI" last month. My hour long conversation was edited to about six minutes!

I am glad they edited/fit my conversation as taking the perspective that this big data, big compute driven deep-net approach is orthogonal to human/biological vision. And that, without incorporating biological principles (in this case, vision), autonomous visual navigation systems (i.e., self-driving cars) are unlikely and/or limited.

Unfortunately, the podcast requires a subscription to The Economist (I too had to access it from my university account!). But if you do have access, let me know what you think!

https://open.spotify.com/episode/4adN2gVRkQctA55Q0xswiO

#Neuroscience #History #AI #Deepnets #BiologicalIntelligence #BiologicalVision #HumanVision #MachineVision #TheEconomist #Babbage #MachineLearning

5 days left to apply for our Emerging Scientist Talk Series! You’re a postdoc in neuroscience, ecology or any field related to our research? Submit your application today! The series will take place in our institute and all travel and accommodation charges will be covered. Find out more: https://www.bi.mpg.de/2599696/postdoc-seminars

#mpiforbi #maxplanck #maxplanckinstitute #biologicalintelligence #emergingscientists #postdoc #neuroscience #ecology #research #science

5 days left to apply for our Emerging Scientist Talk Series! You’re a postdoc in neuroscience, ecology or any field related to our research? Submit your application today! The series will take place in our institute and all travel and accommodation charges will be covered. Find out more: https://www.bi.mpg.de/2599696/postdoc-seminars

#mpiforbi #maxplanck #maxplanckinstitute #biologicalintelligence #emergingscientists #postdoc #neuroscience #ecology #research #science

5 days left to apply for our Emerging Scientist Talk Series! You’re a postdoc in neuroscience, ecology or any field related to our research? Submit your application today! The series will take place in our institute and all travel and accommodation charges will be covered. Find out more: https://www.bi.mpg.de/2599696/postdoc-seminars

#mpiforbi #maxplanck #maxplanckinstitute #biologicalintelligence #emergingscientists #postdoc #neuroscience #ecology #research #science

5 days left to apply for our Emerging Scientist Talk Series! You’re a postdoc in neuroscience, ecology or any field related to our research? Submit your application today! The series will take place in our institute and all travel and accommodation charges will be covered. Find out more: https://www.bi.mpg.de/2599696/postdoc-seminars

#mpiforbi #maxplanck #maxplanckinstitute #biologicalintelligence #emergingscientists #postdoc #neuroscience #ecology #research #science

5 days left to apply for our Emerging Scientist Talk Series! You’re a postdoc in neuroscience, ecology or any field related to our research? Submit your application today! The series will take place in our institute and all travel and accommodation charges will be covered. Find out more: https://www.bi.mpg.de/2599696/postdoc-seminars

#mpiforbi #maxplanck #maxplanckinstitute #biologicalintelligence #emergingscientists #postdoc #neuroscience #ecology #research #science

The early bird catches the worm? Extensive field research on blue tits finds that higher age and earlier morning activity of males correlate independently with more mating success outside their existing partnership.

🐣

https://doi.org/10.1016/j.anbehav.2023.01.016

#ornithology #biologicalintelligence #research #science #scicomm #wisskomm #maxplanck #birdresearch

📷 Axel Griesch

The early bird catches the worm? Extensive field research on blue tits finds that higher age and earlier morning activity of males correlate independently with more mating success outside their existing partnership.

🐣

https://doi.org/10.1016/j.anbehav.2023.01.016

#ornithology #biologicalintelligence #research #science #scicomm #wisskomm #maxplanck #birdresearch

📷 Axel Griesch

The early bird catches the worm? Extensive field research on blue tits finds that higher age and earlier morning activity of males correlate independently with more mating success outside their existing partnership.

🐣

https://doi.org/10.1016/j.anbehav.2023.01.016

#ornithology #biologicalintelligence #research #science #scicomm #wisskomm #maxplanck #birdresearch

📷 Axel Griesch

The early bird catches the worm? Extensive field research on blue tits finds that higher age and earlier morning activity of males correlate independently with more mating success outside their existing partnership.

🐣

https://doi.org/10.1016/j.anbehav.2023.01.016

#ornithology #biologicalintelligence #research #science #scicomm #wisskomm #maxplanck #birdresearch

📷 Axel Griesch

The early bird catches the worm? Extensive field research on blue tits finds that higher age and earlier morning activity of males correlate independently with more mating success outside their existing partnership.

🐣

https://doi.org/10.1016/j.anbehav.2023.01.016

#ornithology #biologicalintelligence #research #science #scicomm #wisskomm #maxplanck #birdresearch

📷 Axel Griesch

When hummingbirds evolved hovering flight, they lost the muscle enzyme fructose-bisphosphatase 2 through an inactivating mutation. This novel metabolic adaptation fuels hummingbirds’ extremely energy-intensive flight style.

Read more in the Science paper from Katya Osipova and Michael Hiller, with contributions from the Baldwin group: https://www.science.org/doi/10.1126/science.abn7050

Photo: Maude Baldwin
#science #sciencemastodon #mpiforbi #maxplanckinstitute #biologicalintelligence #ornithology #hummingbird

When hummingbirds evolved hovering flight, they lost the muscle enzyme fructose-bisphosphatase 2 through an inactivating mutation. This novel metabolic adaptation fuels hummingbirds’ extremely energy-intensive flight style.

Read more in the Science paper from Katya Osipova and Michael Hiller, with contributions from the Baldwin group: https://www.science.org/doi/10.1126/science.abn7050

Photo: Maude Baldwin
#science #sciencemastodon #mpiforbi #maxplanckinstitute #biologicalintelligence #ornithology #hummingbird

When hummingbirds evolved hovering flight, they lost the muscle enzyme fructose-bisphosphatase 2 through an inactivating mutation. This novel metabolic adaptation fuels hummingbirds’ extremely energy-intensive flight style.

Read more in the Science paper from Katya Osipova and Michael Hiller, with contributions from the Baldwin group: https://www.science.org/doi/10.1126/science.abn7050

Photo: Maude Baldwin
#science #sciencemastodon #mpiforbi #maxplanckinstitute #biologicalintelligence #ornithology #hummingbird

When hummingbirds evolved hovering flight, they lost the muscle enzyme fructose-bisphosphatase 2 through an inactivating mutation. This novel metabolic adaptation fuels hummingbirds’ extremely energy-intensive flight style.

Read more in the Science paper from Katya Osipova and Michael Hiller, with contributions from the Baldwin group: https://www.science.org/doi/10.1126/science.abn7050

Photo: Maude Baldwin
#science #sciencemastodon #mpiforbi #maxplanckinstitute #biologicalintelligence #ornithology #hummingbird

When hummingbirds evolved hovering flight, they lost the muscle enzyme fructose-bisphosphatase 2 through an inactivating mutation. This novel metabolic adaptation fuels hummingbirds’ extremely energy-intensive flight style.

Read more in the Science paper from Katya Osipova and Michael Hiller, with contributions from the Baldwin group: https://www.science.org/doi/10.1126/science.abn7050

Photo: Maude Baldwin
#science #sciencemastodon #mpiforbi #maxplanckinstitute #biologicalintelligence #ornithology #hummingbird

Happy 2023! We are now officially the Max Planck Institute for Biological Intelligence, no longer in foundation! The two institutes of Neurobiology and Ornithology now officially merged to become our new institute. We wish you all such a wonderful start and happy new year! 🎉✨

#mpibi #mpiforbi #maxplanckinstitute #maxplanck #happynewyear #newyear #biologicalintelligence

Happy 2023! We are now officially the Max Planck Institute for Biological Intelligence, no longer in foundation! The two institutes of Neurobiology and Ornithology now officially merged to become our new institute. We wish you all such a wonderful start and happy new year! 🎉✨

#mpibi #mpiforbi #maxplanckinstitute #maxplanck #happynewyear #newyear #biologicalintelligence

Happy 2023! We are now officially the Max Planck Institute for Biological Intelligence, no longer in foundation! The two institutes of Neurobiology and Ornithology now officially merged to become our new institute. We wish you all such a wonderful start and happy new year! 🎉✨

#mpibi #mpiforbi #maxplanckinstitute #maxplanck #happynewyear #newyear #biologicalintelligence

Happy 2023! We are now officially the Max Planck Institute for Biological Intelligence, no longer in foundation! The two institutes of Neurobiology and Ornithology now officially merged to become our new institute. We wish you all such a wonderful start and happy new year! 🎉✨

#mpibi #mpiforbi #maxplanckinstitute #maxplanck #happynewyear #newyear #biologicalintelligence

Happy 2023! We are now officially the Max Planck Institute for Biological Intelligence, no longer in foundation! The two institutes of Neurobiology and Ornithology now officially merged to become our new institute. We wish you all such a wonderful start and happy new year! 🎉✨

#mpibi #mpiforbi #maxplanckinstitute #maxplanck #happynewyear #newyear #biologicalintelligence