A new report released by Cisco finds that 97% of CEOs surveyed are planning AI integration. Similarly, 92% of companies recently surveyed by McKinsey plan to invest more in generative AI over the next three years. Fortune: To that end, many companies are seeking tech-savvy finance talent, according to a new report by software company Datarails. The researchers analyzed 6,000 job listings within the CFO's office -- CFO, controller, financial planning and analysis (FP&A), and accountant -- advertised on job search websites including LinkedIn, Glassdoor, Indeed, Job2Careers, and ZipRecruiter.

Of the 1,000 job listings for CFOs in January 2025, 27% included AI in the job description. This compares to 8% mentions of AI in 1,000 CFO job listings at the same time last year. Take, for example, Peaks Healthcare Consulting which required a CFO candidate to "continuously learn and integrate AI to improve financial processes and decision making," Datarails notes in the report. Regarding FP&A professionals, in January 2025, 35% of analyst roles mentioned AI competency as a requirement, compared to 14% in January 2024, according to the report.

Chinese AI startup DeepSeek has updated its business registry information with key changes to personnel and operational scope, signaling a shift towards monetizing its cost-efficient-yet-powerful large language models. From a report: The Hangzhou-based firm's updated business scope includes "internet information services," according to business registry service Tianyancha. The move is the first sign of DeepSeek's desire to monetise its popular technology, according to Zhang Yi, founder and chief analyst at consultancy iiMedia.

With eyes on developing a business model, DeepSeek intends to shift away from being purely focused on research and development, Zhang added. "The move reflects that for a company like DeepSeek, which managed to accumulate technology and develop a product, monetisation is becoming a necessary next step," Zhang said. DeepSeek's previous business scope said it engages in engineering and AI software development, among others, hinting at a more research-driven approach.

xAI has launched Grok 3, the latest iteration of its large language model, alongside new capabilities for its iOS and web applications. The model has been trained on approximately 200,000 GPUs in a Memphis data center, representing what CEO Elon Musk claims is a tenfold increase in computing power compared to its predecessor.

The new release introduces two specialized variants: Grok 3 Reasoning and Grok 3 mini Reasoning, designed to methodically analyze problems similar to OpenAI's o3-mini and DeepSeek's R1 models. According to xAI's benchmarks, Grok 3 outperforms GPT-4o on several technical evaluations, including AIME for mathematical reasoning and GPQA for PhD-level science problems.

A notable addition is the DeepSearch feature, which combs through web content and X posts to generate research summaries. The platform will be available through X's Premium+ subscription and a new SuperGrok tier ($30/month or $300/year), with the latter offering enhanced reasoning capabilities and unlimited image generation. To prevent knowledge extraction through model distillation -- a technique recently attributed to DeepSeek's alleged copying of OpenAI's models -- xAI has implemented measures to obscure the reasoning models' thought processes in the Grok app. The company plans to release the Grok 2 model as open source once Grok 3 achieves stability.

NAND flash prices are expected to slide due to oversupply, forcing memory chipmakers to cut production to match lower-than-expected orders from PC and smartphone manufacturers. From a report: The superabundance of stock is putting a financial strain on suppliers of NAND flash, according to TrendForce, which says growth rate forecasts are being revised down from 30 percent to 10-15 percent for 2025.

"NAND flash manufacturers have adopted more decisive production cuts, scaling back full-year output to curb bit supply growth. These measures are designed to swiftly alleviate market imbalances and lay the groundwork for a price recovery," TrendForce stated.

Shrish Pant, Gartner director analyst and technology product leader, expects NAND flash pricing to remain weak for the first half of 2025, though he projects higher bit shipments for SSDs in the second half due to continuing AI server demand.

"Vendors are currently working tirelessly to discipline supply, which will lead to prices recovering in the second half of 2025. Long term, AI demand will continue to drive the demand for higher-capacity/better-performance SSDs," Pant said. Commenting on the seasonal nature of the memory market, Pant told The Register: "Buying patterns will mean that NAND flash prices will remain cyclical depending on hyperscalers' buying behavior."

White-collar workers are facing their deepest hiring slump in a decade, with one in four U.S. job losses last year hitting professional workers, according to S&P Global. A 2024 Vanguard report shows hiring for employees earning over $96,000 has fallen to its lowest level since 2014. The downturn has been particularly severe for job seekers â" 40% of applicants failed to secure even a single interview in 2024, according to a survey of 2,000 respondents by the American Staffing Association and The Harris Poll.

Technology and high interest rates appear to be driving the decline, with companies reassessing their workforce needs amid AI adoption and economic pressures. While hiring remains steady for those earning under $55,000 annually, the market continues to be especially challenging for mid-career professionals and higher earners.

Reddit moderators are struggling to police AI-generated content on the platform, according to ArsTechnica, with many expecting the challenge to intensify as the technology becomes more sophisticated. Several popular Reddit communities have implemented outright bans on AI-generated posts, citing concerns over content quality and authenticity.

The moderators of r/AskHistorians, a forum known for expert historical discussion, said that AI content "wastes our time" and could compromise the subreddit's reputation for accurate information. Moderators are currently using third-party AI detection tools, which they describe as unreliable. Many are calling on Reddit to develop its own detection system, the report said.

Ten years after publishing his influential book on data privacy, security expert Bruce Schneier warns that surveillance has only intensified, with both government agencies and corporations collecting more personal information than ever before. "Nothing has changed since 2015," Schneier told The Register in an interview. "The NSA and their counterparts around the world are still engaging in bulk surveillance to the extent of their abilities."

The widespread adoption of cloud services, Internet-of-Things devices, and smartphones has made it nearly impossible for individuals to protect their privacy, said Schneier. Even Apple, which markets itself as privacy-focused, faces limitations when its Chinese business interests are at stake. While some regulation has emerged, including Europe's General Data Protection Regulation and various U.S. state laws, Schneier argues these measures fail to address the core issue of surveillance capitalism's entrenchment as a business model.

The rise of AI poses new challenges, potentially undermining recent privacy gains like end-to-end encryption. As AI assistants require cloud computing power to process personal data, users may have to surrender more information to tech companies. Despite the grim short-term outlook, Schneier remains cautiously optimistic about privacy's long-term future, predicting that current surveillance practices will eventually be viewed as unethical as sweatshops are today. However, he acknowledges this transformation could take 50 years or more.

Junior software developers' overreliance on AI coding assistants is creating knowledge gaps in fundamental programming concepts, developer Namanyay Goel argued in a post. While tools like GitHub Copilot and Claude enable faster code shipping, developers struggle to explain their code's underlying logic or handle edge cases, Goel wrote. Goel cites the decline of Stack Overflow, a technical forum where programmers historically found detailed explanations from experienced developers, as particularly concerning.

Today Seoul's Personal Information Protection Commission "said DeepSeek would no longer be available for download until a review of its personal data collection practices was carried out," reports AFP. A number of countries have questioned DeepSeek's storage of user data, which the firm says is collected in "secure servers located in the People's Republic of China"... This month, a slew of South Korean government ministries and police said they blocked access to DeepSeek on their computers. Italy has also launched an investigation into DeepSeek's R1 model and blocked it from processing Italian users' data. Australia has banned DeepSeek from all government devices on the advice of security agencies. US lawmakers have also proposed a bill to ban DeepSeek from being used on government devices over concerns about user data security.
More details from the Associated Press: The South Korean privacy commission, which began reviewing DeepSeek's services last month, found that the company lacked transparency about third-party data transfers and potentially collected excessive personal information, said Nam Seok [director of the South Korean commission's investigation division]... A recent analysis by Wiseapp Retail found that DeepSeek was used by about 1.2 million smartphone users in South Korea during the fourth week of January, emerging as the second-most-popular AI model behind ChatGPT.

Nicholas Carr wrote The Shallows: What the Internet Is Doing to Our Brains. But his new book looks at how social media and digital communication technologies "are changing us individually and collectively," writes the Los Angeles Review of Books.

The book's title? Superbloom: How Technologies of Connection Tear Us Apart . But if these systems are indeed tearing us apart, the reasons are neither obvious nor simple. Carr suggests that this isn't really about the evil behavior of our tech overlords but about how we have "been telling ourselves lies about communication — and about ourselves.... Well before the net came along," says Carr, "[the] evidence was telling us that flooding the public square with more information from more sources was not going to open people's minds or engender more thoughtful discussions. It wasn't even going to make people better informed...."

At root, we're the problem. Our minds don't simply distill useful knowledge from a mass of raw data. They use shortcuts, rules of thumb, heuristic hacks — which is how we were able to think fast enough to survive on the savage savanna. We pay heed, for example, to what we experience most often. "Repetition is, in the human mind, a proxy for facticity," says Carr. "What's true is what comes out of the machine most often...." Reality can't compete with the internet's steady diet of novelty and shallow, ephemeral rewards. The ease of the user interface, congenial even to babies, creates no opportunity for what writer Antón Barba-Kay calls "disciplined acculturation."

Not only are these technologies designed to leverage our foibles, but we are also changed by them, as Carr points out: "We adapt to technology's contours as we adapt to the land's and the climate's." As a result, by designing technology, we redesign ourselves. "In engineering what we pay attention to, [social media] engineers [...] how we talk, how we see other people, how we experience the world," Carr writes. We become dislocated, abstracted: the self must itself be curated in memeable form. "Looking at screens made me think in screens," writes poet Annelyse Gelman. "Looking at pixels made me think in pixels...."

That's not to say that we can't have better laws and regulations, checks and balances. One suggestion is to restore friction into these systems. One might, for instance, make it harder to unreflectively spread lies by imposing small transactional costs, as has been proposed to ease the pathologies of automated market trading. An option Carr doesn't mention is to require companies to perform safety studies on their products, as we demand of pharmaceutical companies. Such measures have already been proposed for AI. But Carr doubts that increasing friction will make much difference. And placing more controls on social media platforms raises free speech concerns... We can't change or constrain the tech, says Carr, but we can change ourselves. We can choose to reject the hyperreal for the material. We can follow Samuel Johnson's refutation of immaterialism by "kicking the stone," reminding ourselves of what is real.

Christie's auction house notes that an AI-generated "portrait" of an 18th-century French gentleman recently sold for $432,500. (One member of the Paris-based collective behind the work says "we found that portraits provided the best way to illustrate our point, which is that algorithms are able to emulate creativity.")

But the blog post from Christie's goes on to acknowledge that AI researchers "are still addressing the fundamental question of whether the images produced by their networks can be called art at all." . One way to do that, surely, is to conduct a kind of visual Turing test, to show the output of the algorithms to human evaluators, flesh-and-blood discriminators, and ask if they can tell the difference.

"Yes, we have done that," says Ahmed Elgammal [director of the Art and Artificial Intelligence Lab at Rutgers University in New Jersey]. "We mixed human-generated art and art from machines, and posed questions — direct ones, such as 'Do you think this painting was produced by a machine or a human artist?' and also indirect ones such as, 'How inspiring do you find this work?'. We measured the difference in responses towards the human art and the machine art, and found that there is very little difference. Actually, some people are more inspired by the art that is done by machine."

Can such a poll constitute proof that an algorithm is capable of producing indisputable works of art? Perhaps it can — if you define a work of art as an image produced by an intelligence with an aesthetic intent. But if you define art more broadly as an attempt to say something about the wider world, to express one's own sensibilities and anxieties and feelings, then AI art must fall short, because no machine mind can have that urge — and perhaps never will.
This also begs the question: who gets credit for the resulting work. The AI, or the creator of its algorithm...

Or can the resulting work be considered a "conceptual art" collaboration — taking place between a human and an algorithm?

"Meta is involved in a class action lawsuit alleging copyright infringement, a claim the company disputes..." writes the tech news site Hot Hardware.

But the site adds that newly unsealed court documents "reveal that Meta allegedly used a minimum of 81.7TB of illegally torrented data sourced from shadow libraries to train its AI models." Internal emails further show that Meta employees expressed concerns about this practice. Some employees voiced strong ethical objections, with one noting that using content from sites like LibGen, known for distributing copyrighted material, would be unethical. A research engineer with Meta, Nikolay Bashlykov, also noted that "torrenting from a corporate laptop doesn't feel right," highlighting his discomfort surrounding the practice.

Additionally, the documents suggest that these concerns, including discussions about using data from LibGen, reached CEO Mark Zuckerberg, who may have ultimately approved the activity. Furthermore, the documents showed that despite these misgivings, employees discussed using VPNs to mask Meta's IP address to create anonymity, enabling them to download and share torrented data without it being easily traced back to the company's network.

In January the TIOBE Index (estimating programming language popularity) declared Python their language of the year. (Though it was already #1 in their rankings, it had showed a 9.3% increase in their ranking system, notes InfoWorld.) TIOBE CEO Paul Jansen says this reflects how easy Python is to learn, adding that "The demand for new programmers is still very high" (and that "developing applications completely in AI is not possible yet.")

In fact on February's version of the index, the top ten looks mostly static. The only languages dropping appear to be very old languages. Over the last 12 months C and PHP have both fallen on the index — C from the #2 to the #4 spot, and PHP from #10 all the way to #14. (Also dropping is Visual Basic, which fell from #9 to #10.)

But TechRepublican cites another factor that seems to be affecting the rankings: language speed. Fast programming languages are gaining popularity, TIOBE CEO Paul Jansen said in the TIOBE Programming Community Index in February. Fast programming languages he called out include C++ [#2], Go [#8], and Rust [#13 — up from #18 a year ago].

Also, according to the updated TIOBE rankings...

- C++ held onto its place at second from the top of the leaderboard.
- Mojo and Zig are following trajectories likely to bring them into the top 50, and reached #51 and #56 respectively in February.

"Now that the world needs to crunch more and more numbers per second, and hardware is not evolving fast enough, speed of programs is getting important. Having said this, it is not surprising that the fast programming languages are gaining ground in the TIOBE index," Jansen wrote. The need for speed helped Mojo [#51] and Zig [#56] rise...

Rust reached its all-time high in the proprietary points system (1.47%.), and Jansen expects Go to be a common sight in the top 10 going forward.

Bloomberg reports that Apple's long-promised overhaul for Siri "is facing engineering problems and software bugs, threatening to postpone or limit its release, according to people with knowledge of the matter...." Last June, Apple touted three major enhancements coming to Siri:

- the ability to tap into a customer's data to better answer queries and take actions.
- a new system that would let the assistant more precisely control apps.
- the capability to see what's currently on a device's screen and use that context to better serve users....

The goal is to ultimately offer a more versatile Siri that can seamlessly tap into customers' information and communication. For instance, users will be able to ask for a file or song that they discussed with a friend over text. Siri would then automatically retrieve that item. Apple also has demonstrated the ability for Siri to quickly locate someone's driver's license number by reviewing their photos... Inside Apple, many employees testing the new Siri have found that these features don't yet work consistently...

The control enhancements — an upgraded version of something called App Intents — are central to the operation of the company's upcoming smart home hub. That product, an AI device for controlling smart home appliances and FaceTime, is slated for release later this year.
And Amazon is also struggling with an AI upgrade for its digital assistant, reports the Washington Post: The "smarter and more conversational" version of Alexa will not be available until March 31 or later, the employee said, at least a year and a half after it was initially announced in response to competition from OpenAI's ChatGPT. Internal messages seen by The Post confirmed the launch was originally scheduled for this month but was subsequently moved to the end of March... According to internal documents seen by The Post, new features of the subscriber-only, AI-powered Alexa could include the ability to adopt a personality, recall conversations, order takeout or call a taxi. Some of the new Alexa features are similar to Alexa abilities that were previously available free through partnerships with companies like Grubhub and Uber...

The AI-enhanced version of Alexa in development has been repeatedly delayed due to problems with incorrect answers, the employee working on the launch told The Post. As a popular product that is a decade old, the Alexa brand is valuable, and the company is hesitant to risk customer trust by launching a product that is not reliable, the person said.

Long-time Slashdot reader shanen has been testing AI clients. (They report that China's DeepSeek "turned out to be extremely good at explaining why I should not trust it. Every computer security problem I ever thought of or heard about and some more besides.")

Then they wondered if there's also government censorship: It's like the accountant who gets asked what 2 plus 2 is. After locking the doors and shading all the windows, the accountant whispers in your ear: "What do you want it to be...?" So let me start with some questions about DeepSeek in particular. Have you run it locally and compared the responses with the website's responses? My hypothesis is that your mileage should differ...

It's well established that DeepSeek doesn't want to talk about many "political" topics. Is that based on a distorted model of the world? Or is the censorship implemented in the query interface after the model was trained? My hypothesis is that it must have been trained with lots of data because the cost of removing all of the bad stuff would have been prohibitive... Unless perhaps another AI filtered the data first?
But their real question is: what would it take to trust an AI? "Trust" can mean different things, including data-collection policies. ("I bet most of you trust Amazon and Amazon's secret AIs more than you should..." shanen suggests.) Can you use an AI system without worrying about its data-retention policies?

And they also ask how many Slashdot readers have read Ken Thompson's "Reflections on Trusting Trust", which raises the question of whether you can ever trust code you didn't create yourself. So is there any way an AI system can assure you its answers are accurate and trustworthy, and that it's safe to use? Share your own thoughts and experiences in the comments.

What would it take for you to trust an AI?

"Reddit Answers" uses generative AI to answer questions using what past Reddittors have posted. Announced in December, Reddit now plans to integrate it into their search results, reports TechCrunch, with Reddit's CEO saying the idea has "incredible monetization potential."

And yet Reddit's stock fell 14% this week. CNBC's headline? "Reddit shares plunge after Google algorithm change contributes to miss in user numbers." A Google search algorithm change caused some "volatility" with user growth in the fourth quarter, but the company's search-related traffic has since recovered in the first quarter, Reddit CEO Steve Huffman said in a letter to shareholders. "What happened wasn't unusual — referrals from search fluctuate from time to time, and they primarily affect logged-out users," Huffman wrote. "Our teams have navigated numerous algorithm updates and did an excellent job adapting to these latest changes effectively...." Reddit has said it is working to convince logged-out users to create accounts as logged-in users, which are more lucrative for its business.
As Yahoo Finance once pointed out, Reddit knew this day would come, acknowledging in its IPO filing that "changes in internet search engine algorithms and dynamics could have a negative impact on traffic for our website and, ultimately, our business." And in the last three months of 2024 Reddit's daily active users dropped, Yahoo Finance reported this week. But logged-in users increased by 400,000 — while logged-out users dropped by 600,000 (their first drop in almost two years).

Marketwatch notes that analyst Josh Beck sees this as a buying opportunity for Reddit's stock: Beck pointed to comments from Reddit's management regarding a sharp recovery in daily active unique users. That was likely driven by Google benefiting from deeper Reddit crawling, by the platform uncollapsing comments in search results and by a potential benefit from spam-reduction algorithm updates, according to the analyst. "While the report did not clear our anticipated bar, we walk away encouraged by international upside," he wrote.

SFGate shares the latest data on America's office-occupancy rates: According to Placer.ai's January 2025 Office Index, office visits nationwide were 40.2% lower in January 2025 compared with pre-pandemic numbers from January 2019.

But San Francisco is dragging down the average, with a staggering 51.8% decline in office visits since January 2019 — the weakest recovery of any major metro. Kastle's 10-City Daily Analysis paints an equally grim picture. From Jan. 23, 2025, to Jan. 28, 2025, even on its busiest day (Tuesday), San Francisco's office occupancy rate was just 53.7%, significantly lower than Houston's (74.8%) and Chicago's (70.4%). And on Friday, Jan. 24, office attendance in [San Francisco] was at a meager 28.5%, the worst of any major metro tracked...

Meanwhile, other cities are seeing much stronger rebounds. New York City is leading the return-to-office trend, with visits in January down just 19% from 2019 levels, while Miami saw a 23.5% decline, per Placer.ai data.
"Placer.ai uses cellphone location data to estimate foot traffic, while Kastle Systems measures badge swipes at office buildings with its security systems..."

An anonymous reader shared this report from the Guardian: Thousands of artists are urging the auction house Christie's to cancel a sale of art created with artificial intelligence, claiming the technology behind the works is committing "mass theft". The Augmented Intelligence auction has been described by Christie's as the first AI-dedicated sale by a major auctioneer and features 20 lots with prices ranging from $10,000 to $250,000...

The British composer Ed Newton-Rex, a key figure in the campaign by creative professionals for protection of their work and a signatory to the letter, said at least nine of the works appearing in the auction appeared to have used models trained on artists' work. However, other pieces in the auction do not appear to have used such models.

A spokesperson for Christie's said that "in most cases" the AI used to create art in the auction had been trained on the artists' "own inputs".
More than 6,000 people have now signed the letter, which states point-blank that "Many of the artworks you plan to auction were created using AI models that are known to be trained on copyrighted work without a license." These models, and the companies behind them, exploit human artists, using their work without permission or payment to build commercial AI products that compete with them. Your support of these models, and the people who use them, rewards and further incentivizes AI companies' mass theft of human artists' work. We ask that, if you have any respect for human artists, you cancel the auction.
Last week ARTnews spoke to Nicole Sales Giles, Christie's vice-president and director of digital art sales (before the open letter was published). And Giles insisted one of the major themes of the auction is "that AI is not a replacement for human creativity." "You can see a lot of human agency in all of these works," Giles said. "In every single work, you're seeing a collaboration between an AI model, a robot, or however the artist has chosen to incorporate AI. It is showing how AI is enhancing creativity and not becoming a substitute for it."

One of the auction's headline lots is a 12-foot-tall robot made by Matr Labs that is guided by artist Alexander Reben's AI model. It will paint a new section of a canvas live during the sale every time the work receives a bid. Reben told ARTnews that he understands the frustrations of artists regarding the AI debate, but he sees "AI as an incredible tool... AI models which are trained on public data are done so under the idea of 'fair use,' just as search engines once faced scrutiny for organizing book data (which was ultimately found to fall under fair use)," he said.... "AI expands creative potential, offering new ways to explore, remix, and evolve artistic expression rather than replace it. The future of art isn't about AI versus artists — it's about how artists wield AI to push boundaries in ways we've never imagined before...."

Digital artist Jack Butcher has used the open letter to create a minted digital artwork called Undersigned Artists. On X he wrote that the work "takes a collective act of dissent — an appeal to halt an AI art auction — and turns it into the very thing it resists: a minted piece of digital art. The letter, originally a condemnation of AI-generated works trained on unlicensed human labor, now becomes part of the system it critiques."

Christie's will accept cryptocurrency payments for the majority of lots in the sale.

According to The Intercept, the IRS is set to purchase an Nvidia SuperPod AI supercomputer to enhance its machine learning capabilities for tasks like fraud detection and taxpayer behavior analysis. From the report: With Elon Musk's so-called Department of Government Efficiency installing itself at the IRS amid a broader push to replace federal bureaucracy with machine-learning software, the tax agency's computing center in Martinsburg, West Virginia, will soon be home to a state-of-the-art Nvidia SuperPod AI computing cluster. According to the previously unreported February 5 acquisition document, the setup will combine 31 separate Nvidia servers, each containing eight of the company's flagship Blackwell processors designed to train and operate artificial intelligence models that power tools like ChatGPT. The hardware has not yet been purchased and installed, nor is a price listed, but SuperPod systems reportedly start at $7 million. The setup described in the contract materials notes that it will include a substantial memory upgrade from Nvidia.

Though small compared to the massive AI-training data centers deployed by companies like OpenAI and Meta, the SuperPod is still a powerful and expensive setup using the most advanced technology offered by Nvidia, whose chips have facilitated the global machine-learning spree. While the hardware can be used in many ways, it's marketed as a turnkey means of creating and querying an AI model. Last year, the MITRE Corporation, a federally funded military R&D lab, acquired a $20 million SuperPod setup to train bespoke AI models for use by government agencies, touting the purchase as a "massive increase in computing power" for the United States.

How exactly the IRS will use its SuperPod is unclear. An agency spokesperson said the IRS had no information to share on the supercomputer purchase, including which presidential administration ordered it. A 2024 report by the Treasury Inspector General for Tax Administration identified 68 different AI-related projects underway at the IRS; the Nvidia cluster is not named among them, though many were redacted. But some clues can be gleaned from the purchase materials. "The IRS requires a robust and scalable infrastructure that can handle complex machine learning (ML) workloads," the document explains. "The Nvidia Super Pod is a critical component of this infrastructure, providing the necessary compute power, storage, and networking capabilities to support the development and deployment of large-scale ML models."

The document notes that the SuperPod will be run by the IRS Research, Applied Analytics, and Statistics division, or RAAS, which leads a variety of data-centric initiatives at the agency. While no specific uses are cited, it states that this division's Compliance Data Warehouse project, which is behind this SuperPod purchase, has previously used machine learning for automated fraud detection, identity theft prevention, and generally gaining a "deeper understanding of the mechanisms that drive taxpayer behavior."

Leveraging AI tools like RFDiffusion and PLACER, researchers were able to design a novel enzyme capable of breaking down plastic by targeting ester bonds, a key component in polyester. Ars Technica reports: The researchers started out by using the standard tools they developed to handle protein design, including an AI tool named RFDiffusion, which uses a random seed to generate a variety of protein backgrounds. In this case, the researchers asked RFDiffusion to match the average positions of the amino acids in a family of ester-breaking enzymes. The results were fed to another neural network, which chose the amino acids such that they'd form a pocket that would hold an ester that breaks down into a fluorescent molecule so they could follow the enzyme's activity using its glow.

Of the 129 proteins designed by this software, only two of them resulted in any fluorescence. So the team decided they needed yet another AI. Called PLACER, the software was trained by taking all the known structures of proteins latched on to small molecules and randomizing some of their structure, forcing the AI to learn how to shift things back into a functional state (making it a generative AI). The hope was that PLACER would be trained to capture some of the structural details that allow enzymes to adopt more than one specific configuration over the course of the reaction they were catalyzing. And it worked. Repeating the same process with an added PLACER screening step boosted the number of enzymes with catalytic activity by over three-fold.

Unfortunately, all of these enzymes stalled after a single reaction. It turns out they were much better at cleaving the ester, but they left one part of it chemically bonded to the enzyme. In other words, the enzymes acted like part of the reaction, not a catalyst. So the researchers started using PLACER to screen for structures that could adopt a key intermediate state of the reaction. This produced a much higher rate of reactive enzymes (18 percent of them cleaved the ester bond), and two -- named "super" and "win" -- could actually cycle through multiple rounds of reactions. The team had finally made an enzyme.

By adding additional rounds alternating between structure suggestions using RFDiffusion and screening using PLACER, the team saw the frequency of functional enzymes increase and eventually designed one that had an activity similar to some produced by actual living things. They also showed they could use the same process to design an esterase capable of digesting the bonds in PET, a common plastic. The research has been published in the journal Science.