It has been a few years since AI began successfully tackling the challenge of predicting the three-dimensional structure of proteins, complex molecules that are essential for all life. Next-generation tools are now available, and the Nobel Prizes have been handed out . But people not involved in biology can be forgiven for asking whether any of it can actually make a difference.

A nice example of how the tools can be put to use is being released in Nature on Wednesday. A team that includes the University of Washington's David Baker, who picked up his Nobel in Stockholm last month, used software tools to design completely new proteins that are able to inhibit some of the toxins in snake venom. While not entirely successful, the work shows how the new software tools can let researchers tackle challenges that would otherwise be difficult or impossible.

Blocking venom

Snake venom includes a complicated mix of toxins, most of them proteins, that engage in a multi-front assault on anything unfortunate enough to get bitten. Right now, the primary treatment is to use a mix of antibodies that bind to these toxins, produced by injecting sub-lethal amounts of venom proteins into animals. But antivenon treatments tend to require refrigeration, and even then, they have a short shelf life. Ensuring a steady supply also means regularly injecting new animals and purifying more antibodies from them.

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It has been a few years since AI began successfully tackling the challenge of predicting the three-dimensional structure of proteins, complex molecules that are essential for all life. Next-generation tools are now available, and the Nobel Prizes have been handed out . But people not involved in biology can be forgiven for asking whether any of it can actually make a difference.

A nice example of how the tools can be put to use is being released in Nature on Wednesday. A team that includes the University of Washington's David Baker, who picked up his Nobel in Stockholm last month, used software tools to design completely new proteins that are able to inhibit some of the toxins in snake venom. While not entirely successful, the work shows how the new software tools can let researchers tackle challenges that would otherwise be difficult or impossible.

Blocking venom

Snake venom includes a complicated mix of toxins, most of them proteins, that engage in a multi-front assault on anything unfortunate enough to get bitten. Right now, the primary treatment is to use a mix of antibodies that bind to these toxins, produced by injecting sub-lethal amounts of venom proteins into animals. But antivenon treatments tend to require refrigeration, and even then, they have a short shelf life. Ensuring a steady supply also means regularly injecting new animals and purifying more antibodies from them.

Read full article

Comments

It has been a few years since AI began successfully tackling the challenge of predicting the three-dimensional structure of proteins, complex molecules that are essential for all life. Next-generation tools are now available, and the Nobel Prizes have been handed out . But people not involved in biology can be forgiven for asking whether any of it can actually make a difference.

A nice example of how the tools can be put to use is being released in Nature on Wednesday. A team that includes the University of Washington's David Baker, who picked up his Nobel in Stockholm last month, used software tools to design completely new proteins that are able to inhibit some of the toxins in snake venom. While not entirely successful, the work shows how the new software tools can let researchers tackle challenges that would otherwise be difficult or impossible.

Blocking venom

Snake venom includes a complicated mix of toxins, most of them proteins, that engage in a multi-front assault on anything unfortunate enough to get bitten. Right now, the primary treatment is to use a mix of antibodies that bind to these toxins, produced by injecting sub-lethal amounts of venom proteins into animals. But antivenon treatments tend to require refrigeration, and even then, they have a short shelf life. Ensuring a steady supply also means regularly injecting new animals and purifying more antibodies from them.

Read full article

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In 2023, AI researchers at Meta interviewed 34 native Spanish and Mandarin speakers who lived in the US but didn’t speak English. The goal was to find out what people who constantly rely on translation in their day-to-day activities expect from an AI translation tool. What those participants wanted was basically a Star Trek universal translator or the Babel Fish from the Hitchhiker’s Guide to the Galaxy : an AI that could not only translate speech to speech in real time across multiple languages, but also preserve their voice, tone, mannerisms, and emotions. So, Meta assembled a team of over 50 people and got busy building it.

What this team came up with was a next-gen translation system called Seamless. The first building block of this system is described in Wednesday’s issue of Nature; it can translate speech among 36 different languages.

Language data problems

AI translation systems today are mostly focused on text, because huge amounts of text are available in a wide range of languages thanks to digitization and the Internet. Institutions like the United Nations or European Parliament routinely translate all their proceedings into the languages of all their member states, which means there are enormous databases comprising aligned documents prepared by professional human translators. You just needed to feed those huge, aligned text corpora into neural nets (or hidden Markov models before neural nets became all the rage) and you ended up with a reasonably good machine translation system. But there were two problems with that.

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In 2023, AI researchers at Meta interviewed 34 native Spanish and Mandarin speakers who lived in the US but didn’t speak English. The goal was to find out what people who constantly rely on translation in their day-to-day activities expect from an AI translation tool. What those participants wanted was basically a Star Trek universal translator or the Babel Fish from the Hitchhiker’s Guide to the Galaxy : an AI that could not only translate speech to speech in real time across multiple languages, but also preserve their voice, tone, mannerisms, and emotions. So, Meta assembled a team of over 50 people and got busy building it.

What this team came up with was a next-gen translation system called Seamless. The first building block of this system is described in Wednesday’s issue of Nature; it can translate speech among 36 different languages.

Language data problems

AI translation systems today are mostly focused on text, because huge amounts of text are available in a wide range of languages thanks to digitization and the Internet. Institutions like the United Nations or European Parliament routinely translate all their proceedings into the languages of all their member states, which means there are enormous databases comprising aligned documents prepared by professional human translators. You just needed to feed those huge, aligned text corpora into neural nets (or hidden Markov models before neural nets became all the rage) and you ended up with a reasonably good machine translation system. But there were two problems with that.

Read full article

Comments

In 2023, AI researchers at Meta interviewed 34 native Spanish and Mandarin speakers who lived in the US but didn’t speak English. The goal was to find out what people who constantly rely on translation in their day-to-day activities expect from an AI translation tool. What those participants wanted was basically a Star Trek universal translator or the Babel Fish from the Hitchhiker’s Guide to the Galaxy : an AI that could not only translate speech to speech in real time across multiple languages, but also preserve their voice, tone, mannerisms, and emotions. So, Meta assembled a team of over 50 people and got busy building it.

What this team came up with was a next-gen translation system called Seamless. The first building block of this system is described in Wednesday’s issue of Nature; it can translate speech among 36 different languages.

Language data problems

AI translation systems today are mostly focused on text, because huge amounts of text are available in a wide range of languages thanks to digitization and the Internet. Institutions like the United Nations or European Parliament routinely translate all their proceedings into the languages of all their member states, which means there are enormous databases comprising aligned documents prepared by professional human translators. You just needed to feed those huge, aligned text corpora into neural nets (or hidden Markov models before neural nets became all the rage) and you ended up with a reasonably good machine translation system. But there were two problems with that.

Read full article

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News stories about the likely existence of extraterrestrial life, and our chances of detecting it, tend to be positive. We are often told that we might discover it any time now. Finding life beyond Earth is “only a matter of time,” we were told in September 2023. “ We are close ” was a headline from September 2024.

It’s easy to see why. Headlines such as “We’re probably not close” or “Nobody knows” aren’t very clickable. But what does the relevant community of experts actually think when considered as a whole? Are optimistic predictions common or rare? Is there even a consensus? In our new paper, published in Nature Astronomy , we’ve found out.

During February to June 2024, we carried out four surveys regarding the likely existence of basic, complex, and intelligent extraterrestrial life. We sent emails to astrobiologists (scientists who study extraterrestrial life), as well as to scientists in other areas, including biologists and physicists.

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News stories about the likely existence of extraterrestrial life, and our chances of detecting it, tend to be positive. We are often told that we might discover it any time now. Finding life beyond Earth is “only a matter of time,” we were told in September 2023. “ We are close ” was a headline from September 2024.

It’s easy to see why. Headlines such as “We’re probably not close” or “Nobody knows” aren’t very clickable. But what does the relevant community of experts actually think when considered as a whole? Are optimistic predictions common or rare? Is there even a consensus? In our new paper, published in Nature Astronomy , we’ve found out.

During February to June 2024, we carried out four surveys regarding the likely existence of basic, complex, and intelligent extraterrestrial life. We sent emails to astrobiologists (scientists who study extraterrestrial life), as well as to scientists in other areas, including biologists and physicists.

Read full article

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News stories about the likely existence of extraterrestrial life, and our chances of detecting it, tend to be positive. We are often told that we might discover it any time now. Finding life beyond Earth is “only a matter of time,” we were told in September 2023. “ We are close ” was a headline from September 2024.

It’s easy to see why. Headlines such as “We’re probably not close” or “Nobody knows” aren’t very clickable. But what does the relevant community of experts actually think when considered as a whole? Are optimistic predictions common or rare? Is there even a consensus? In our new paper, published in Nature Astronomy , we’ve found out.

During February to June 2024, we carried out four surveys regarding the likely existence of basic, complex, and intelligent extraterrestrial life. We sent emails to astrobiologists (scientists who study extraterrestrial life), as well as to scientists in other areas, including biologists and physicists.

Read full article

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