Facebook, as the company was known before changing its name, has always been seen as a leader in AI. The popular open-source framework PyTorch was Facebook’s creation and earlier this year Meta became a founding member of a foundation aiming to drive the adoption of AI.

In its pursuit to become a leader in the metaverse, changing its very company name to reflect, many people – including shareholders – have been concerned that it will reduce its focus on other important areas.

Brad Gerstner, the founder of Meta shareholder Altimeter Capital, penned a letter in which he urged Meta to reduce its metaverse investments and “solidify the company’s position” as one of the world’s leaders in AI.

“Meta’s investment in AI will lead to exciting and important new products that can be cross-sold to billions of customers. From Grand Teton to Universal Speech Translator to Make-A-Video, we are witnessing a Cambrian moment in AI, and Meta is no doubt well positioned to help invent and monetize that future,” wrote Gerstner.

“Perhaps it was the re-naming of the company to Meta that caused the world to conclude that you were spending 100% of your time on Reality Labs instead of AI or the core business. Whatever the reason, that is certainly the perception.”

Meta’s announcement this week of its protein-folding AI could help to alleviate such concerns.

The company has released the ESM Metagenomic Atlas – which features over 600 million proteins and predictions for almost the entire MGnify90 database – in addition to the model used to create the database and an API that allows researchers to use it for scientific discovery.

Meta says that it found using a language model of protein sequences accelerated structure prediction by up to 60x.

“ESMFold shows how AI can give us new tools to understand the natural world, much like the microscope, which enabled us to see into the world at an infinitesimal scale and opened up a whole new understanding of life,” explained Meta. 

“Much of AI research has focused on helping computers understand the world in a way similar to how humans do. The language of proteins is one that is beyond human comprehension and has eluded even the most powerful computational tools. AI has the potential to open up this language to our understanding.”

ESM code and models can be found on GitHub here.

(Photo by Kelly Sikkema on Unsplash)

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Last year, the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP) recognised AlphaFold as a solution to the grand challenge of figuring out what shapes proteins fold into.

Professor John Moult, Co-founder and Chair of CASP, University of Maryland, said:

“We have been stuck on this one problem – how do proteins fold up – for nearly 50 years.

To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.”

AlphaFold is a major scientific advance that will play a crucial role in helping scientists to solve important problems such as the protein misfolding associated with diseases such as Alzheimer’s, Parkinson’s, cystic fibrosis and Huntington’s disease.

Arthur D. Levinson, Founder and CEO of Calico, explained:

“AlphaFold is a once-in-a-generation advance, predicting protein structures with incredible speed and precision.

This leap forward demonstrates how computational methods are poised to transform research in biology and hold much promise for accelerating the drug discovery process.”

Using AI, AlphaFold has successfully predicted the structure of nearly all 20,000 proteins expressed by humans. An independent benchmark proved the system was capable of predicting the shape of a protein to a decent standard around 95 percent of the time.

DeepMind is now releasing its database of every single protein in the human body, as well as for the proteins of 20 additional organisms that scientists rely on for their research, for free, for any researchers to use for the betterment of humankind.

“This will be one of the most important datasets since the mapping of the Human Genome,” said Ewan Birney, Deputy Director-General of EMBL and Director of EMBL-EBI.

Thanks to the “astonishingly accurate” models produced by AlphaFold, Professor Andrei Lupas, Director of the Max Planck Institute for Developmental Biology, claims they were able to solve a protein structure they were stuck on for close to a decade.

The full AlphaFold protein structure database can be accessed for free online here.

(Photo by Carolina Garcia Tavizon on Unsplash)

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‘If You’re Under 50, You’ll Live Forever: Hello Transhumanism’ was the name of the session and featured Alex Rodriguez Vitello of the World Economic Forum and Stephen Dunne of Telefonica-owned innovation facility Alpha.

Transhumanism is the idea that humans can evolve beyond their current physical and mental limitations using technological advancements. In some ways, this is already happening.

Medical advancements have extended our lifespans and AI is helping to make further breakthroughs in areas such as cancer treatment.

Vitello notes how Dr Aubrey de Grey from the SENS Research Foundation has been able to extend the lifespan of mice threefold (Fun fact: Grey was an AI reseearcher before switching fields to biology.)

“That’s about 300 years in human years. And these mice are super happy, they’re like having sex and everything is great,” jokes Vitello.

Prosthetics, meanwhile, are enabling people to overcome their disabilities. Today, you can even be turned into a human compass with an implant that vibrates every time you face north.

CRISPR gene editing will one day help to eliminate disorders prior to birth. “You can eliminate cancer, muscular dystrophy, multiple sclerosis… all these things,” comments Vitello.

Artificial limbs will go beyond matching the abilities of natural body parts and provide things such as enhanced vision or superhuman strength beyond what even Arnie achieved in his prime.

These are exciting possibilities, but some transhumanist concepts are many years from becoming available. Even when they are, most enhancements will remain unaffordable for quite some time.

Cryogenics, the idea of being frozen to be revived years in the future, is one such example of something that’s possible today but unaffordable to most. One of the biggest companies in the field is Alcor if you’re willing to part with $200,000.

In answer to whether he agreed with the panel’s title, Dunne responded that a better question to ask is whether the first person is alive that will live forever. On that basis, he believes they might be.

“If you’re [Amazon CEO] Jeff Bezos, maybe,” commented Dunne. “If you put all your resources towards that.”

One concept is that we’ll be able to live forever virtually through storing a digital copy of our brains. American inventor and futurist Ray Kurzweil wants his brain to be downloaded and uploaded elsewhere when he dies.

“What’s more, he [Ray] has all these recordings of his father and he wants to take all of this information and put it on a computer brain to see if he can reproduce the essence of his father,” says Vitello.

This kind of thing requires the ability to emulate the brain. While huge strides in computing power are being made, we’re some way off from that level of processing power.

“I met Ray recently and he thinks of it as a computer scientist, that if we have enough computing power we can simulate the brain,” comments Dunne. “I think we’re so far off understanding how the brain works this is just wrong at the moment.”

Even what conciousness is still eludes researchers. Only last year was a whole new neuron was discovered which goes to show how little we know about the brain at this point.

“The company I used to work for [Neurolectrics] has a project on measuring consciousness, but just the level of it,” Dunne continues. “We just don’t know how this stuff works at a very fundamental level.”

When asked how far along ‘the loading bar’ we are towards brain emulation, Dunne said he’d put it at somewhere around one percent. However, things such as stimulating the brain to improve memory retention or boost certain abilities he believes is a lot closer.

That isn’t without its own challenges. Dunne explains how it’s almost impossible for someone able-sighted to learn braille as not enough brain power is dedicated to the task.

“If you enhance one feature, you kind of have to take that processing power from somewhere else,” he says. “To learn braille you need to be blind as otherwise you’re using your visual cortex and there’s not enough computing power for the task.”

Dunne then goes on to note how AI could help to speed up breakthroughs that are difficult for us to comprehend today: “If we do invent artificial general intelligence, it might figure out all we need to know about the brain to do this within the next 30 years.”

AI is keeping the dream alive, but it seems unlikely that many – if any – under 50 will be living forever. At least we can look forward to some transhumanist enhancements in the coming years.

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Fully understanding the ‘internal GPS’ used by humans and other mammals to navigate from point A to B has eluded neuroscientists for decades. By analysing a new AI, which developed ‘grid cells’ similar to our brains, researchers believe we could be closer than ever.

The new AI was designed by a team from Google DeepMind and University College London to navigate a virtual environment from one point to another in the most efficient way possible.

In findings posted to science journal Nature, the AI developed grid cells similar to mammals. Grid cells were first discovered in 2005 by Norwegian neuroscientists May-Britt and Edvard Moser, earning them a share of the 2014 medicine Nobel Prize.

The neuroscientists made their discovery after observing rats navigating and finding grid cells in their brains firing at points which formed a hexagonal pattern.

Animation provided by DeepMind

Grid cells work in combination with other brain cells. This includes ‘place cells’ which activate when a mammal is in a specific location, and ‘head direction cells’ which fire when the head is pointed in a specific direction.

How all of these cells work together is less well known, but the researchers are hoping to find some answers by observing the AI. They expect this will be just the start in using AI to gain a greater understanding of biology.

You can find the formatted Nature paper here, but note that it’s behind a controversial paywall. Alternatively, the unformatted full paper is available free here (PDF).

What are your thoughts on the use of AI to gain a deeper understanding of biology? Let us know in the comments.

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