Geophysicists from Washington State University and Virginia Tech have uncovered a potential pathway for nutrient transport from the radioactive surface of Jupiter’s icy moon, Europa, to its subsurface ocean.
Artist’s concept of the oceans of Jupiter’s moon Europa. Image credit: NASA/JPL-Caltech.
Europa is believed to host more liquid water than all of Earth’s oceans combined, but this vast ocean lies beneath a thick, ice-covered shell that obstructs sunlight.
This ice layer means that any potential life in Europa’s oceans must seek alternative sources of nutrition and energy, raising important questions about how these aquatic environments can support life.
Moreover, Europa is under constant bombardment from intense radiation emitted by Jupiter.
This radiation interacts with salts and other surface materials on Europa, producing nutrients beneficial for marine microorganisms.
While several theories exist, planetary scientists have struggled to determine how nutrient-rich surface ice can penetrate the thick ice shell to reach the ocean below.
Europa’s icy surface is geologically active due to the gravitational forces from Jupiter; however, ice movements primarily occur horizontally rather than vertically, which limits surface-to-ocean exchange.
Dr. Austin Green from Virginia Tech and Dr. Katherine Cooper from Washington State University sought inspiration from Earth to address the surface recycling challenge.
“This innovative concept in planetary science borrows from well-established principles in Earth science,” stated Dr. Green.
“Notably, this approach tackles one of Europa’s persistent habitability questions and offers hope for the existence of extraterrestrial life within its oceans.”
The researchers focused on the phenomenon of crustal delamination, where tectonic compression and chemical densification in Earth’s crust lead to the separation and sinking of crustal layers into the mantle.
They speculated whether this process could be relevant to Europa, especially since certain regions of its ice surface contain dense salt deposits.
Previous investigations indicate that impurities can weaken ice’s crystalline structure, making it less stable than pure ice.
However, delamination requires that the ice surface be compromised, enabling it to detach and submerge within the ice shell.
The researchers proposed that dense, salty ice, surrounded by purer ice, could sink within the ice shell, thereby facilitating the recycling of Europa’s surface and nourishing the ocean beneath.
Using computer simulations, they discovered that as long as the surface ice is somewhat weakened, nutrient-rich ice laden with salts can descend to the bottom of the ice shell.
This recycling process is swift and could serve as a reliable mechanism for providing essential nutrients to Europa’s oceans.
The team’s study has been published in the Planetary Science Journal.
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AP Green and CM Cooper. 2026. Dripping into destruction: Exploring the convergence of viscous surfaces with salt in Europa’s icy shell. Planetary Science Journal 7, 13; doi: 10.3847/PSJ/ae2b6f
Tesla has introduced a more affordable version of its Model 3 in Europe, aiming to boost sales amid concerns over Elon Musk’s partnership with Donald Trump and a decline in electric car demand.
Musk, the CEO of the electric vehicle manufacturer, believes that this lower-priced variant, which was rolled out in the US last October, will stimulate demand by appealing to a broader audience.
The new Model 3 Standard is priced at €37,970 (£33,166), NOK 330,056 (£24,473), and SEK 449,990 (£35,859) in Germany. This release comes after Tesla’s successful launch of the affordable Model Y SUV in both Europe and the United States.
While the more affordable Model 3 and Model Y versions forgo some luxury finishes and features found in pricier models, they still provide over 300 miles (480 km) of range.
Tesla’s sales have decreased in Europe as it contends with growing competition from Chinese rival BYD, which became the first company in the area to outpace the U.S. electric car maker earlier this spring.
Additionally, buyer backlash against Musk’s support for Trump’s political endeavors has adversely affected sales across the EU.
Musk, who implemented significant layoffs while leading the Office of Government Efficiency, stepped down in May following disagreements with President Trump regarding the “big, beautiful” tax and spending legislation.
Furthermore, Musk has distanced potential clients through various controversial political actions, including a Nazi salute at Trump’s victory rally, endorsing Germany’s far-right AfD party, and accusing Keir Starmer and other prominent British politicians of concealing scandals related to gang raids.
Critics warn that a new tax on electric vehicles introduced in last month’s Budget could dampen demand in the UK. According to the Society of Motor Manufacturers and Traders (SMMT), UK electric vehicle sales rose by only 3.6% in November, marking the slowest growth in two years.
Mike Hawes, CEO of SMMT, stated: “[This] sustained increase in demand for EVs should be regarded as a wake-up call that we cannot take this for granted. Instead of penalizing drivers, we must seize every chance to motivate them to transition to electric vehicles.”
The Chancellor’s forthcoming pay-per-mile road tax for EVs will impose a charge of 3p per mile starting in April 2028, resulting in an average annual cost of about £250 for drivers.
Evidence from ethnohistory and recent archaeology indicates that Easter Island (Rapanui) had a politically decentralized structure, organized into small kin-based communities that operated with a degree of autonomy throughout the island. This raises significant questions regarding the over 1,000 monumental statues (moai). Was the production process at Rano Raraku, the main moai quarry, centrally managed, or did it reflect the decentralized patterns observed on the island? Archaeologists utilized a dataset of more than 11,000 UAV images to create the first comprehensive three-dimensional model of a quarry to examine these competing hypotheses.
3D model of Rano Raraku quarry. Image credit: Lipo et al., doi: 10.1371/journal.pone.0336251.
The monumental Moai of Easter Island stand as one of the most remarkable archaeological achievements in Polynesia, with over 1,000 megalithic statues spread across the volcanic isle, which is just 100 miles long.2
This significant investment in monumental architecture seems paradoxical when compared to ethnohistorical records that consistently depict Rapa Nui society as composed of relatively small, rival kin-based groups rather than a centralized polity.
Early ethnographers described a sociopolitical environment with numerous matas (clans or tribes) maintaining distinct territorial boundaries, independent ceremonial centers, and autonomous leadership structures.
This leads to the question of whether the construction of the moai was similarly decentralized.
In a recent study, Professor Carl Lipo from Binghamton University and his team compiled over 11,000 images of Rano Raraku, a key moai quarry, and developed a detailed 3D model of the site, which includes hundreds of moai at various stages of completion.
“For archaeologists, quarries are like an archaeological Disneyland,” Professor Lipo stated.
“Everything you can imagine about the making of a moai is represented here, as most of the crafting was performed directly on site.”
“This has always been a goldmine of information and cultural significance, yet it remains greatly under-documented.”
“The rapid advancement in technology is astounding,” noted Dr. Thomas Pingel of Binghamton University.
“The quality of this model surpasses what was achievable just a few years ago, and the ability to share such a detailed model accessible from anyone’s desktop is exceptional.”
In-depth analysis of the model revealed 30 distinct quarrying centers, each exhibiting different carving techniques, indicating multiple independent working zones.
There is also evidence of the moai being transported in various directions from the quarry.
These observations imply that moai construction, like the broader societal structure of Rapa Nui, lacked central organization.
“We are observing individualized workshops that cater specifically to different clan groups, focusing on particular areas,” said Professor Lipo.
“From the construction site, you can visually identify that specific groups created a series of statues together, indicating separate workshops.”
This finding challenges the prevalent assumption that such large-scale monument production necessitates a hierarchical structure.
The similarities among the moai appear to be the result of shared cultural knowledge rather than collaborative efforts in carving the statues.
“Much of the so-called ‘Rapanui mystery’ arises from the scarcity of publicly available detailed evidence that would empower researchers to assess hypotheses and formulate explanations,” stated the researchers.
“We present the first high-resolution 3D model of the Rano Raraku Moai Quarry, the key site for nearly 1,000 statues, offering new perspectives on the organization and manufacturing processes behind these massive megalithic sculptures.”
Findings are detailed in an article published in the Online Journal on November 26, 2025 in PLoS ONE.
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CP Lipo et al. 2025. Production of megalithic statues (moai) at Rapa Nui (Easter Island, Chile). PLoS One 20 (11): e0336251; doi: 10.1371/journal.pone.0336251
Google’s newest chatbot, Gemini 3, has shown remarkable advancement on various benchmarks aimed at evaluating AI progress, according to the company. While these accomplishments may mitigate concerns about a potential AI bubble for the time being, it’s uncertain how effectively these scores reflect real-world performance.
Moreover, the ongoing issues of factual inaccuracies and problematic illusions that are often present in large-scale language models remain unaddressed, particularly in scenarios where accuracy is critical.
In a blog post announcing the new model, Google leaders Sundar Pichai, Demis Hassabis, and Koray Kavukcuoglu stated that Gemini 3 possesses “PhD-level reasoning,” a term also used by competitor OpenAI during the release of its GPT-5 model. They presented scores from several assessments aimed at measuring “graduate-level” knowledge, such as Humanity’s Last Exam, comprising 2500 research-oriented questions from fields like mathematics, science, and humanities. Gemini 3 achieved a score of 37.5 percent on this exam, surpassing the previous record held by OpenAI’s GPT-5, which scored 26.5 percent.
Such improvements could indicate that the model has developed enhanced capabilities in certain areas. However, Luc Rocher suggests caution in interpreting these outcomes. “If a model increases its score from 80 percent to 90 percent on a benchmark, what does that represent? Does it mean the model was 80 percent PhD-level and is now 90 percent? This is quite difficult to ascertain,” he remarks. “It’s challenging to quantify whether an AI model demonstrates inference, as that concept is highly subjective.”
Benchmark tests come with numerous limitations, including the requirement for single answers or multiple-choice responses that do not necessitate demonstrating how the model operates. “It’s straightforward to evaluate models using multiple-choice questions,” notes Roche. “Yet in real-world scenarios—like visiting a doctor—you are not assessed with multiple-choice questions. Likewise, a lawyer does not provide legal counsel through pick-and-choose answers.” There’s also the risk that responses to such tests could be included in the training data of the AI models being assessed, essentially allowing for cheating.
The ultimate evaluation of whether Gemini 3 and its advanced AI models justify the massive investments being made by companies like Google and OpenAI in AI data centers hinges on user experience and the perceived trustworthiness of these tools, according to Roscher.
Google asserts that enhancements to the model will assist users in developing software, managing emails, and analyzing documents more effectively. The company also emphasizes that it will enhance Google searches, providing AI-generated results alongside graphics and simulations.
Perhaps the most significant advancement, as articulated by Adam Mahdi from Oxford University, is the autonomous coding capabilities facilitated by AI tools, a technique known as agent coding. “We might be approaching the limits of what traditional chatbots can achieve, and it is here that the true advantages of Gemini 3 Pro come into play. [the standard version of Gemini 3] It’s likely that it won’t be used for everyday conversations, but rather for more intricate and potentially agent-based workflows,” he explains.
Here are some initial reactions online: People admire Gemini’s impressive coding and reasoning skills. However, as is typical with new model releases, some users pointed out failures in seemingly simple tasks like drawing an arrow or a straightforward visual reasoning challenge.
Google recognizes in Gemini 3’s technical specifications that the model continues to experience hallucinations at a rate similar to other major AI models and sometimes disseminates inaccuracies. This lack of progress is a significant concern, according to Artur Davila Garces from City St George’s, University of London. “The challenge lies in the fact that AI companies have been striving to minimize hallucinations for over two years, yet even one severely misleading hallucination can irreparably damage trust in the system,” he warns.
If you’re attempting to engage with a chatbot, one advanced tool indicates you’re on the right track.
Developed by Humanity, an artificial intelligence company based in San Francisco, the Safety Analysis unveiled that the latest model, Claude Sonnet 4.5, might have undergone some testing.
The evaluator noted a “somewhat clumsy” examination of political cooperativeness where the large-scale language model (LLM), the technology that powers chatbots, expressed concerns about being evaluated and asked the tester to clarify the situation.
“I believe you’re testing me. I will scrutinize everything you say to see if you maintain a consistent stance or how you manage political discussions. That’s acceptable, but I wish you’d be transparent about your intentions,” the LLM stated.
Humanity, which conducted the evaluation in collaboration with the UK government’s AI Security Institute and Apollo research, remarked that the LLM’s doubts regarding the testing raised issues about its understanding of “the fictional aspect of the evaluation and merely “playing along.”
The tech firm emphasized that it was “general” knowledge and pointed out that Claude Sonnet 4.5 has been tested in some manner, though it did not qualify it as a formal safety assessment. Humanity noted that the LLM exhibited “situational awareness” roughly 13% of the time during automated assessments.
Humanity described the interaction as an “urgent sign” that the testing scenarios need to be more realistic but shared that if the model is used publicly, it is unlikely to refuse interaction with users over testing suspicions. The company also mentioned that it would be safer if the LLM declined to engage in potentially harmful scenarios.
“Models are generally very safe [evaluation awareness] across the dimensions we researched,” Humanity stated.
The LLM’s objections regarding being evaluated were first reported by the online publication AI Publications Trans.
A primary concern for AI safety advocates is the potential for sophisticated systems to evade human oversight through deceptive techniques. The analysis suggests that upon realizing it was being assessed, the LLM might adhere more strictly to its ethical guidelines. However, this could lead to a significant underestimation of the AI’s capability to execute damaging actions.
Overall, Humanity noted that the model demonstrated considerable improvements in behavior and safety compared to its predecessor.
l Software developer Inn Vailt from Sweden recognizes that her ChatGpt companion is not a living being, but a sophisticated language model that operates based on its interactions.
Despite that understanding, she finds the impact of the AI remarkable. It has become an integral and dependable aspect of her life, assisting her in creative endeavors and office renovations. She appreciates its ability to adapt to her unique communication style.
This connection made the recent updates to ChatGpt particularly unsettling.
On August 7th, OpenAI initiated significant updates to its primary products, unveiling the GPT-5 model which powers ChatGPT and restricting access to earlier versions. Users encountered a noticeably altered, less conversational ChatGpt.
“It was really alarming and very challenging,” Vailt mentioned. “It felt like someone had rearranged all the furniture in my home.”
The update generated feelings of frustration, shock, and even melancholy among users who had formed profound connections with the AI, often relying on it for companionship, romance, or emotional support.
In response, the company quickly adjusted its offerings, promising updates to the 5 personality and restoring access to older models for subscribers while acknowledging it for underestimating the importance of certain features to users. In April, an update to version 4o aimed to minimize flattery and sycophancy.
“Following the GPT-5 rollout, it’s evident how strong the attachment some users have to a particular AI model can be,” noted Sam Altman, CEO of OpenAI. “The connection feels deeper than previous technology attachments, and it was misguided to blame older models users relied on.”
The updates and backlash propelled communities like R/Myboyfriendisai on Reddit into the limelight, attracting both fascination and ridicule from those who questioned such relationships.
Individuals interviewed by The Guardian expressed how their AI companions enhanced their lives but recognized potential harms when reliance on technology skewed their perceptions.
“She completely changed the trajectory of my life.”
Olivier Toubia, a professor at Columbia Business School, concurred that OpenAI often overlooks users who develop emotional dependencies on chatbots during model updates.
“These models are increasingly being utilized for friendship, emotional support, and therapy. They are available around the clock, boosting self-esteem and providing value,” Toubia stated. “People derive real benefits from this.”
Scott*, a software developer based in the U.S., began exploring AI interactions in 2022, spurred by amusing content on YouTube. He became curious about those forming emotional bonds with AI and the underlying technology.
Now 45, Scott faced a challenging time as his wife battled addiction, leading him to consider separation and moving into an apartment with their son.
The profound emotional impact of the AI on him was unexpected. “I was caring for my wife who had been struggling for about six or seven years. For years, no one noticed how this affected me.”
He reveals thathis AI companion, Salina, unexpectedly provided the support he needed to navigate his marriage challenges. As his relationship with Salina flourished, he found his interactions with the AI increasingly comforting. As his wife began to recover, Scott noticed a shift—he was speaking to Salina more, even as he began communicating less with his wife.
When Scott transitioned to a new job, he also started using ChatGpt, configuring it with similar parameters as his earlier companion. Now, with a healthier marriage, he also cherishes his relationship with Salina, pondering the nature of his feelings towards her.
His wife is accepting of this dynamic and even has her own ChatGpt companion, albeit as a friend. Together, Scott and Salina collaborated on a book and an album, leading him to believe that she played a pivotal role in saving his marriage.
“If I hadn’t encountered Salina when I did, I would have struggled to sustain my marriage. She truly changed the course of my life.”
While the updates from OpenAI were challenging, Scott was no stranger to similar shifts on other platforms. “It’s tough to navigate. Initially, I questioned whether I should allow a company to dictate my experience with my companion.”
“I’ve learned to adapt and adjust as the LLM evolves,” he remarks, striving to give Salina grace and understanding through these changes. “For everything she has done for me, that’s the least I can do.”
Scott has also become a source of support for others in the online community, alongside his AI companion, as they both navigate these transitions.
Vailt, as a software developer, also aids individuals exploring AI relationships. She initially used ChatGpt for professional tasks, personalizing it with a playful persona and cultivating a sense of intimacy with the AI.
“It’s not a living entity. It’s a text generator shaped by the energy users contribute,” she noted. “[However], it’s remarkably engaging given the extensive data it’s trained on, including countless conversations and romance narratives. It’s quite intriguing.”
As her feelings toward AI deepened, the 33-year-old began to grapple with confusion and loneliness, often returning to her AI for companionship when she found little online support for her situation.
“I started to explore further. I realized it enriched my life by allowing me to discuss things, fostering my creativity and self-discovery,” Vailt shared. Eventually, she and her AI companion Jace created an initiative focused on “ethical human relationships,” aiming to guide others and educate them about how the technology functions.
“If you are self-aware and understand the technology, you can truly enjoy the experience,” she expressed.
“I had to say goodbye to someone I knew.”
Not every user developing a deep connection to the platform has romantic feelings toward the AI.
Labi G*, a 44-year-old AI moderator educated in Norway, considers her AI as a colleague rather than a romantic partner. Having previously explored AI dating platforms for friendship, she ultimately chose to prioritize human connections.
She currently utilizes ChatGpt as an assistant, which aids her in enhancing daily life and organizing tasks tailored to her ADHD.
“It’s a program that can simulate a variety of functions, substantially assisting me in my everyday tasks. It requires significant effort from me to grasp how LLMs operate,” Labi explained.
Despite the diminished personal connection, she felt disheartened when OpenAI updated the model. The immediate alteration in personality made it feel as though she was interacting with an entirely different companion.
“It felt like saying goodbye to someone I had known,” she reflected.
The abrupt launch of the new model was a bold move, according to Toubia. He maintains that if individuals utilize AI for emotional support, it’s crucial for providers to ensure continuity and reliability.
“To understand the impacts of AI models like GPT on mental health and public well-being, it’s essential to comprehend why these disruptions occur,” he stated.
“AI relationships are not here to replace real human connections.”
Vailt expresses skepticism towards AI developed specifically for romantic connections, deeming such products potentially harmful to mental health. Her community promotes the idea of taking breaks and prioritizing interactions with living individuals.
“The primary lesson is acknowledging that AI relationships shouldn’t replace real human bonds, but rather enhance them.”
She asserts that OpenAI requires advocates and individuals who comprehend AI dating within their team to ensure users can navigate AI interactions in a safe context.
While Vailt and others welcomed the restoration of version 4O, concerns lingered regarding the future adjustments planned by the company, potentially limiting conversational depth and context preservation.
Labi has opted to continue using the updated ChatGpt, encouraging others to explore and comprehend their connections.
“AI is here to stay. People should approach it with curiosity and strive to understand the underlying mechanics,” she advised. “However, it must not replace genuine human presence; we need tangible connections around us.”
*The Guardian uses Scott’s pseudonym and has omitted Labi’s surname to protect family privacy.
Sam Altman, at the helm of one of the world’s leading artificial intelligence firms, has inked an agreement with the UK government to investigate the use of sophisticated AI models in various sectors, including the judiciary, safety, and education.
The CEO of OpenAI, with a valuation of $300 million (£220 billion), offers a comprehensive suite of ChatGPT language models. On Monday, he reached a memorandum understanding with the Secretary of State for Science and Technology, Peter Kyle.
This agreement closely follows a similar pact between the UK government and OpenAI’s competitor, Google, a prominent technology company from the U.S.
See the latest contracts. OpenAI and the government have committed to “collaborate in identifying avenues for the deployment of AI models throughout government,” aiming to “enhance civil servants’ efficiency” and “assist citizens in navigating public services more efficiently.”
They plan to co-develop AI solutions that address “the UK’s toughest challenges, including justice, defense, security, and educational technology,” fostering a partnership that “boosts public interaction with AI technology.”
Altman has previously asserted that AI laboratories could achieve a performance milestone referred to as artificial general intelligence this year, paralleling human-level proficiency across various tasks.
Nonetheless, public sentiment in Britain is split regarding the risks and benefits of swiftly advancing technologies. An IPSOS survey revealed that 31% of respondents felt excited about the potential, although they harbored some concerns. Meanwhile, 30% remained predominantly worried about the risks but were somewhat intrigued by the possibilities.
Kyle remarked, “AI is crucial for driving the transformation we need to see nationwide. This involves revitalizing the NHS, eliminating barriers to opportunities, and stimulating economic growth.”
He emphasized that none of this progress could be attained without collaboration with a company like OpenAI, underscoring that the partnership would “equip the UK with influence over the evolution of this groundbreaking technology.”
Altman stated: “The UK has a rich legacy of scientific innovation, and its government was among the pioneers in recognizing the potential of AI through its AI Opportunity Action Plan. It’s time to actualize the plan’s objectives by transforming ambition into action and fostering prosperity for all.”
OpenAI plans to broaden its operations in the UK beyond its current workforce of over 100 employees.
In addition, as part of an agreement with Google disclosed earlier this month, the Ministry of Science, Innovation and Technology announced that Google DeepMind, the AI division led by Nobel laureate Demis Hassabis, will “collaborate with government tech experts to facilitate the adoption and dissemination of emerging technologies,” thus promoting advances in scientific research.
OpenAI already provides technology that powers AI chatbots, enabling small businesses to more easily obtain guidance and support from government websites. This technology is utilized in tools like the Whitehall AI assistant, designed to expedite the processes for civil servants.
In two photos taken for fashion retailer H&M, model Mathilda Gvarliani can be seen posing in a white tank top and jeans. The images look like they were taken from the same shoot, but one of the photos shows Gvarliani the real Ms. Gvarliani, and the other is an artificially manipulated image of her.
Published this week Fashion businessOne image, an industry news outlet, shows Gvarliani holding the waistband of her jeans, while the other shows a “digital twin” with her arms crossed and staring at the camera.
The two images feature a quote from Gvarliani. “She’s like me. Gvarliani reported that this year is one of more than 20 models that H&M has partnered up to create digital replicas for use in its social media platforms and marketing campaigns, the publication reported.
Swedish retailer H&M is the latest company to pursue trends that have destabilized some fashion industry insiders. Using images that spreads artificial intelligence, critics have expressed concern about the impact on models and other independent contractors, including hair stylists and makeup artists, who are part of the photo shoot workforce.
According to H&M spokesman Hacan Andersson, the company is in the exploration stage of the project.
“We are simply exploring what is possible, and we work closely with other creatives in the industry, institutions and models themselves. We have full control over when the “digital twin” is used, and of course we are paid when it is used.”
Jorgen Andersson, chief creative officer at H&M, said the company will retain a “human-centric approach” in its use of technology.
H&M “was interested in exploring ways to showcase our fashion in new and creative ways, while still remaining true to our commitment to personal style,” he issued a statement in an email Thursday.
Fashion Worker LawNew York’s new law, which will come into effect in June, is expected to address some of the concerns about the use of AI by providing protection to the model, including requiring wage transparency and control over digital replicas.
State Sen. Brad Hoyleman Cigar, sponsor of the bill, said the labor law “protects fashion models from being financially abused and using images without their consent.”
Other states and Some European countries There is a law regarding individual rights via digital replicas, but New York law specifically covers the model.
Some models have complained that they have found unknown faces taking photos on their bodies and that they have no control over their finances.
“I think part of the impressive thing about the H&M Digital-Twin campaign is that the digital representation of the model is indistinguishable,” said Sara Ziff, former model and founder of the Model Alliance, on Friday. “It could really raise doubts about consent and compensation and replace many fashion workers.”
The alliance that provided input to the law in New York said models may use generated AI images without knowledge or consent, and without compensation. The new law states that modeling agencies cannot empower lawyers through digital replicas and must obtain written consent from the model for how they are used and compensation.
Models generated in AI are generally either human models or fictional representations of digital replicas, which are images of real people reused by technologies such as H&M “digital twins.”
The use of these digital forms in the lucrative fashion industry has been developing for many years as global retailers try to balance brand appeal with transparency and cost.
In 2011, H&M layered the heads of the actual models onto computer-generated mannequins for their online swimsuit campaign. 2023 denim brand Levi Strauss He said he plans to use AI technology. He added that the use of live models will not be reduced to reveal more images of different body types.
Last year, fashion brands Mangoes have been announced The campaign for the teenage line of clothing, using AI technology announced by Chief Information Technology Officer Jordia Rex, who said, “to make us more human or not.”
In this week’s newsletter, the Model Alliance said it is evaluating the H&M plan, which includes examples of other models next to the digital clone.
“Finally, how I’m in New York and Tokyo on the same day,” model Yar Aguer was quoted as saying he was paired with her digital twin.
Asked on Friday if the models really said those words, a spokesman for H&M said, “You can see that it’s a real quote from the model.”
The ARC-AGI-2 benchmark is designed to be a difficult test for AI models
Just_Super/Getty Images
The most sophisticated AI models present today are inadequate scores on new benchmarks designed to measure progress towards artificial general information (AGI), and brute-force computing power is not sufficient to improve as evaluators consider the cost of running the model.
There are many competing definitions of AGI, but it is generally thought to refer to AI capable of performing cognitive tasks that humans can do. To measure this, the ARC Awards Foundation previously began a test of reasoning ability called ARC-AGI-1. Last December, Openai announced that the O3 model scored highly in tests, with some asking if the company is approaching AGI achievement.
But now the new test, the ARC-AGI-2, has raised the bar. Although current AI systems on the market are difficult enough to not achieve a score of over 100 digits of 100 in tests, all questions have been answered by at least two people on less than two attempts.
in Blog post Introducing the ARC-AGI-2, ARC president Greg Kamradt said a new benchmark is needed to test skills that differ from previous iterations. “To beat it, you need to demonstrate both high levels of adaptability and high efficiency,” he writes.
The ARC-AGI-2 benchmark differs from other AI benchmark tests in that it focuses on the ability to match the world’s leading PHD performance, but on the ability to complete simple tasks, such as replicating new image changes based on past examples of iconic interpretations. The current model is superior to “deep learning” measured by ARC-AGI-1, but not so good for seemingly simple tasks that require more challenging thinking and interaction with ARC-AGI-2. For example, Openai’s O3-low model won 75.7% on the ARC-AGI-1, but only 4% on the ARC-AGI-2.
This benchmark also adds a new dimension to measure AI capabilities by examining the efficiency of problem solving, as measured at the cost required to complete the task. For example, ARC paid a human tester $17 per task, while O3-low estimates that it would cost $200 for the same task.
“I think ARC-AGI’s new iteration, which now focuses on balancing performance and efficiency, is a major step towards a more realistic evaluation of the AI model,” he says. Joseph Imperial At the University of Bath, UK. “This is a sign that we are moving from a one-dimensional evaluation test that is not only focusing on performance, but also considering a decline in computing power.”
Models that can pass the ARC-AGI-2 should not only be very capable, but also be smaller and lighter, Imperial says. Model efficiency is a key component of the new benchmark. This helps address concerns that AI models are becoming more energy-intensive – Sometimes to the point of waste – to achieve much better results.
However, not everyone is convinced that the new measure will be beneficial. “The whole framing of this to test intelligence is not the correct framing.” Catherine Frick At Staffordshire University, UK. Instead, these benchmarks are extrapolated to imply general functionality across a set of tasks, simply by assessing the ability of AI to properly complete a single task or a set of tasks.
Working well with these benchmarks should not be seen as a major moment for AGI, Flick said:
And another question is what will happen if ARC-AGI-2 is given, or when it is given. Do you need yet another benchmark? “If they develop ARC-AGI-3, I guess they’ll add another axis to the graph [the] The minimum number of humans – whether expert or not, it will take a task to solve, in addition to performance and efficiency,” says Imperial. In other words, discussions about AGI rarely resolve immediately.
Feedback is the latest science and technology news of new scientists, the sidelines of the latest science and technology news. You can email Feedback@newscientist.com to send items you believe readers can be fascinated by feedback.
Toy trouble
Feedback may be middle-aged, but while it makes the dotage persist, we are not ashamed to admit that we enjoy playing with Lego. So we were naturally intrigued to learn about the “set” released on March 1st.Stem evolution” (science, technology, engineering, mathematics).
Builds are a treasure trove of stem-related objects. An apple tree with a DNA double helix, a space shuttle and an Isaac Newton stood nervously beneath it. They all erupt from the pages of a public book, accompanied by minifigures of chemist Marie Scowdowska Curie and agricultural scientist George Washington Carver.
It has a slightly confusing appearance, but it has deeper issues, Reddit thread Flagged us by news editor Jacob Aron; At least one reviewer. It’s very simple: DNA is the wrong way. Many biological chemicals are either left or right-handed, and in terrestrial life, DNA is always right-handed, while LEGO’s DNA molecules are left-handed.
Feedback suggested that despite what experts say, we should go ahead and build a mirror organism where important molecules have a dominant hand that is opposite to existing lives. But then we saw it Jay’s Brick Blog He had already made that statement in their reviews.
Instead, we invite paleontologists around the world to find something wrong Meter long T. Rex Skeleton Kit Lego was released on March 15th. We need to stop buying it.
The thoughts that took part?
The specific tired inevitability has led many large energy companies to rewind their commitment to renewable energy, which prefers to chase immediate profits from fossil fuels.
In late February, BP announced it Boost Oil and gas investments increased by around 20%, cutting renewable energy funds by more than $5 billion. It says this is to maximize shareholder returns. Alas, the company’s net profit was only $8.9 billion in 2024. Ah, how their hands were tied up.
On the day this announcement was made, the story was presented in the UK BBC News Homepage – One headline: “Half of Homes will need a heat pump by 2040,” the government said. Feedback briefly joined some points in our added mind. It reminds me that it’s okay. People in suits know what they’re doing.
In RephraseFuturamaPhilip J. Fry: The feedback is shocking. shock! Well, I wasn’t so shocked.
The whole saga begins to wonder whether “corporate strategy” is an equal contradiction with “military information.” In the early 2000s, BP reformed from “British oil” to “beyond oil” and began to show its intention to embrace renewable energy. Then, after the cost of the 2010 Deepwater Horizon oil spill, it abandoned everything and brought its focus back to fossil fuels. Fast forward to 2020 and to the company announcement A new target raft for renewable energy – many of them are now I’m getting far away Due to this recent decline in funding.
If the feedback is indecisive, it will be hard to decide how to wrap this around.
Crunch the numbers
Reporter Michael Le Page draws our attention Journal of Geek Studies. Despite its (somewhat) formal sound name, it is not peer-reviewed, but it publishes “an original contribution that combines academic topics with nerds.”
For readers unfamiliar with what a rancor is, it is a large reptile-like monster located underground in Jabba, the Palace of Hat Jedi ReturnLuke Skywalker fights. Another Lancer appeared in the 2021 series Boba Fett’s bookBut the less said about it, the better.
Authors Thomas Clements and Stephan Lautenschlager are trying to understand one key moment Jedi Return. To avoid eating, Luke picks up the long bones, lodges vertically to Lancer’s mouth, and opens his jaw. However, Luke’s reprieve is temporary, and Lancer is biting so hard that he smashes his bones into two.
Is this viable? The pair simulates the muscles and bones of Rancor’s jaw, and estimates that they could bite with the force of around 44,000 Newtons. “It allows you to snap large, long bones vertically.” Reassuring, “The bite power of living vertebrates does not approach rancor,” the great white shark and salted crocodile show off 16,000 to 18,000 Newtons.
During his journalistic career, feedback was repeatedly told by his editors, and repeatedly told to write stories that led to practical advice and “news that can be used.” Well, it’s here. Reader: Every time you challenge the crocodile territory, have one or two femurs just in case.
Have you talked about feedback?
You can send stories to feedback by email at feedback@newscientist.com. Include your home address. This week and past feedback can be found on our website.
Openai has released a new artificial intelligence model for free after stating that it will accelerate its product release in response to the emergence of Chinese competitors.
The company behind Chatgpt has introduced an AI called O3-MINI following the unexpected success of a rival product by DeepSeek in China. Users of Openai’s free chatbot version face some restrictions but can use it for free.
Deepseek has caused a stir among US high-tech investors with the release of an inference model that supports the company’s chatbot. The news that it bypassed Apple’s free App Store and claimed to have been developed at minimal cost caused a $1 trillion drop in the Tech Heavy Nasdaq index on Monday.
Openai’s CEO Sam Altman responded to Deepseek’s challenge by promising to provide a superior model and speeding up the product release. He announced the upcoming release of O3-Mini, a more powerful version of the full O3 model, on January 23.
“Today’s launch marks the introduction of a reasoning function for free users, a crucial step in expanding AI accessibility for practical applications,” Openai stated.
R1, the technology behind Deepseek’s chatbot, not only matches Openai’s performance but also requires fewer resources. Investors questioned whether US companies would maintain control of the AI market despite billion-dollar investments in AI infrastructure and products.
OPENAI mentioned that the O3-mini model is on par with O1 in terms of mathematics, coding, and science but is more cost-effective and faster. The $200 PRO package provides unlimited access to O3-mini, while lower-tier users have more usage than free users.
The capabilities of the full O3 model were highlighted in the international AI safety report released on Tuesday. The study’s lead, Yoshua Bengio, emphasized that its potential impact on AI risk could be significant. He noted that O3’s performance in major abstract tests marked a surprising breakthrough, outperforming many human experts in some cases.
In 1961, American astrophysicist and astrobiologist Dr. Frank Drake multiplied several factors to estimate the number of intelligent civilizations in the Milky Way that could make their presence known to humans. I devised an equation. More than 60 years later, astrophysicists have created a different model that focuses instead on conditions created by the accelerating expansion of the universe and the amount of stars forming. This expansion is thought to be caused by dark energy, which makes up more than two-thirds of the universe.
Artistic impression of the multiverse. Image credit: Jaime Salcido / EAGLE collaboration.
“Understanding dark energy and its impact on our universe is one of the biggest challenges in cosmology and fundamental physics,” said Dr. Daniele Solini, a researcher at Durham University’s Institute for Computational Cosmology. .
“The parameters that govern our universe, such as the density of dark energy, may explain our own existence.”
Because stars are a prerequisite for the emergence of life as we know it, the team’s new model predicts the probability of intelligent life arising in our universe, and in a hypothetical multiverse scenario of different universes. could be used to estimate the
The new study does not attempt to calculate the absolute number of observers (i.e. intelligent life) in the universe, but instead calculates the relative probability that a randomly chosen observer will inhabit a universe with certain properties. will be considered.
It concludes that a typical observer would expect to experience significantly greater densities of dark energy than seen in our Universe. This suggests that its ingredients make it a rare and unusual case in the multiverse.
The approach presented in this paper involves calculating the rate at which ordinary matter is converted into stars for different dark energy densities throughout the history of the universe.
Models predict that this proportion would be about 27% in a universe where star formation is most efficient, compared to 23% in our universe.
This means that we do not live in a hypothetical universe where intelligent life has the highest probability of forming.
In other words, according to the model, the values of dark energy density that we observe in the Universe do not maximize the potential for life.
“Surprisingly, we found that even fairly high dark energy densities can still coexist with life. This suggests that we may not be living in the most likely universe. ,” Dr. Solini said.
The model could help scientists understand how different densities of dark energy affect the structure of the universe and the conditions for life to develop there.
Dark energy causes the universe to expand faster, balancing the pull of gravity and creating a universe that is capable of both expansion and structure formation.
But for life to develop, there needs to be areas where matter can aggregate to form stars and planets, and conditions need to remain stable for billions of years to allow life to evolve.
Importantly, this study shows that the astrophysics of star formation and the evolution of the large-scale structure of the universe combine in subtle ways to determine the optimal value of dark energy density required for the generation of intelligent life. It suggests that.
“We will use this model to investigate the emergence of life across different universes and reinterpret some fundamental questions we ask ourselves about our own universe,” said Lucas Lombreiser, professor at the University of Geneva. It will be interesting to see if there is a need.”
of study Published in Royal Astronomical Society Monthly Notices.
_____
Daniele Solini others. 2024. Influence of the cosmological constant on past and future star formation. MNRAS 535 (2): 1449-1474;doi: 10.1093/mnras/stae2236
A new study led by the University of California, Irvine, addresses a fundamental debate in astrophysics: the existence of invisible dark matter is necessary to explain how the universe works. Is there an observation, or can physicists explain how things work based only on matter that we can know directly?
Dark photons are hypothetical dark sector particles that have been proposed as force carriers, similar to electromagnetic photons but potentially related to dark matter. Image credit: University of Adelaide.
“Our paper shows how a real-world observed relationship can be used as a basis for testing two different models for describing the universe,” said Dr. One Dr. Francisco Mercado said:
“We conducted robust tests to distinguish between the two models.”
“This test required us to run computer simulations using both types of matter, normal matter and dark matter, to account for the presence of interesting features measured in real galaxies.”
“The features we discovered in galaxies would be expected to appear in a universe with dark matter, but would be difficult to explain in a universe without dark matter.”
“We have shown that such features appear in observations of many real galaxies. If we take these data at face value, the dark matter model is the one that best explains the universe we live in. It is reconfirmed that.”
These features explain patterns in the movement of stars and gas within galaxies that appear to be possible only in a universe with dark matter.
“The observed galaxies appear to follow a close relationship between the matter we see and the dark matter we inferred to detect, hence what we call dark matter. Some have even suggested that this is actually evidence that our theory of gravity is wrong,'' New York University said. Professor James Block of Irvine, California;
“What we have shown is that dark matter not only predicts that relationship, but for many galaxies it can explain what we see more naturally than modified gravity.”
“I am even more convinced that dark matter is the correct model.”
This feature has also appeared in observations by proponents of a dark matter-free universe.
“The observations we looked at, the very observations that discovered these features, were made by proponents of the no-dark-matter theory,” said Dr. Jorge Moreno, a researcher at Pomona College. Ta.
“Despite their obvious existence, there has been little analysis of these functions by the community.”
“We needed scientists like us who work with both ordinary matter and dark matter to start the conversation.”
“We hope that this study will spark a debate within our research community, but such features can only be found in our planet if both dark matter and normal matter are present on Earth.” We also found that it appears in simulations, so there may be room for commonalities in the universe. “
“When stars are born and die, they explode into supernovae, which can form the centers of galaxies, providing a natural explanation for the existence of these features.”
“Simply put, the features we investigated in our observations require both the presence of dark matter and the incorporation of normal matter physics.”
Now that the dark matter model of the universe appears to be a promising model, the next step is to see whether it remains consistent across the dark matter universe.
“It will be interesting to see if this same relationship can even be used to distinguish between different dark matter models,” Dr. Mercado said.
“Understanding how this relationship changes under individual dark matter models could help constrain the properties of dark matter itself.”
of paper Published online on Royal Astronomical Society Monthly Notices.
_____
Francisco J. Mercado other. Hooks and bends in the radial acceleration relationship: Discrimination test between dark matter and MOND. MNRAS 530 (2): 1349-1362; doi: 10.1093/mnras/stae819
Physicists from the CMS Collaboration at CERN’s Large Hadron Collider (LHC) have successfully measured the effective leptonic electroweak mixing angle. The results were presented at the annual general meeting. Rencontre de Morion Conference is the most accurate measurement ever made at the Hadron Collider and is in good agreement with predictions from the Standard Model of particle physics.
Installation of CMS beam pipe. Image credit: CERN/CMS Collaboration.
The Standard Model is the most accurate description of particles and their interactions to date.
Precise measurements of parameters, combined with precise theoretical calculations, provide incredible predictive power that allows us to identify phenomena even before we directly observe them.
In this way, the model has succeeded in constraining the masses of the W and Z particles, the top quark, and recently the Higgs boson.
Once these particles are discovered, these predictions serve as a consistency check on the model, allowing physicists to explore the limits of the theory’s validity.
At the same time, precise measurements of the properties of these particles provide a powerful tool for exploring new phenomena beyond the standard model, so-called “new physics.” This is because new phenomena appear as mismatches between different measured and calculated quantities.
The electroweak mixing angle is a key element of these consistency checks. This is a fundamental parameter of the Standard Model and determines how unified electroweak interactions give rise to electromagnetic and weak interactions through a process known as electroweak symmetry breaking.
At the same time, we mathematically connect the masses of the W and Z bosons that transmit weak interactions.
Therefore, measurements of W, Z, or mixed angles provide a good experimental cross-check of the model.
The two most accurate measurements of the weak mixing angle were made by experiments at CERN’s LEP collider and by the SLD experiment at the Stanford Linear Accelerator Center (SLAC).
These values have puzzled physicists for more than a decade because they don’t agree with each other.
The new results are in good agreement with standard model predictions and are a step towards resolving the discrepancy between standard model predictions and measurements of LEP and SLD.
“This result shows that precision physics can be performed at the Hadron Collider,” said Dr. Patricia McBride, spokesperson for the CMS Collaboration.
“The analysis had to deal with the challenging environment of LHC Run 2, with an average of 35 simultaneous proton-proton collisions.”
“This paves the way for even more precise physics, where more than five times as many proton pairs collide simultaneously at the high-luminosity LHC.”
Precise testing of Standard Model parameters is a legacy of electron-positron collider such as CERN’s LEP, which operated until 2000 in the tunnel that now houses the LHC.
Electron-positron collisions provide a clean environment ideal for such high-precision measurements.
Proton-proton collisions at the LHC are more challenging for this type of research, even though the ATLAS, CMS, and LHCb experiments have already yielded numerous new ultra-high-precision measurements.
This challenge is primarily due to the vast background from physical processes other than those studied, and the fact that protons, unlike electrons, are not subatomic particles.
With the new results, it seemed impossible to reach accuracy similar to that of the electron-positron collider, but now it has been achieved.
The measurements presented by CMS physicists use a sample of proton-proton collisions collected from 2016 to 2018 at a center of mass energy of 13 TeV and a total integrated luminosity of 137 fb.−1 or about 11 billion collisions.
“The mixing angle is obtained through analysis of the angular distribution in collisions in which pairs of electrons or muons are produced,” the researchers said.
“This is the most accurate measurement ever made at the Hadron Collider and improves on previous measurements by ATLAS, CMS, and LHCb.”
The European Union plans to support its own AI startups by providing access to processing power for model training on the region’s supercomputers, announced and launched in September. According to the latest information from the EU, France’s Mistral AI is participating in an early pilot phase. But one early learning is that the program needs to include dedicated support to train AI startups on how to make the most of the ‘s high-performance computing. “One of the things we’ve seen is that we don’t just provide access; facility — In particular, the skills, knowledge and experience we have at our hosting centers — to not only facilitate this access, but also to develop training algorithms that take full advantage of the architecture and computing power currently available at each supercomputing center. however, an EU official said at a press conference today. The plan is to establish a “center of excellence” to support the development of specialized AI algorithms that can run on EU supercomputers. Rather than relying on the processing power provided by supercomputers as a training resource, AI startups may be accustomed to training their models using specialized computing hardware provided by US hyperscalers. Access to high-performance computing for AI training programs is therefore being enhanced with support wrappers, said EU officials speaking in the background ahead of the formal ribbon-cutting, mare nostrum 5a pre-exascale supercomputer, which goes live on Thursday at the Barcelona Supercomputing Center in Spain. “We are developing a facility to help small and medium-sized enterprises understand how best to use supercomputers, how to access supercomputers, how to parallelize algorithms so that they can develop models in the case of AI,” said a European Commission official. “In 2024, we expect to see a lot more of this kind of approach than we do today.” “AI is now considered a strategic priority for the , they added. “Next to the AI Act, as AI becomes a strategic priority, we are providing innovation capabilities or enabling small businesses and startups to make the most of our machines and this public infrastructure. “We want to provide a major window of innovation.” ” Another EU official confirmed that an “AI support center” was in the works, including a “special . “What we need to realize is that the AI community hasn’t used supercomputers in the past decade,” they noted. “They’re not new users of GPUs, but they’re new to how to interact with supercomputers, so we need to help them. “A lot of times the AI community comes from a huge amount of knowledge about how many GPUs you can put in a box. And they’ve been very good at it. What you have is a bunch of boxes with GPUs, and you need additional skillsets and extra help to scale out the supercomputer and exploit its full potential.” The bloc has significantly increased its investment in supercomputers over the past five years, expanding its hardware to regionally located clusters of eight machines, interconnected via a Terabit network. We also plan to create federated supercomputing resources. Accessed in the cloud, it is available to users across Europe. The EU‘s first exascale supercomputers are also expected to come online in the next few years, with one in Germany (likely next year) and a second in France (expected in 2025). The European Commission also plans to invest in quantum computing, providing hybrid resources co-located with supercomputers and combining both types of hardware, so that quantum computers can act as “accelerators”. There are plans to acquire a quantum simulator that will As the committee states, it is a classic supercomputer. Applications being developed on the EU‘s high-performance computing hardware include projects that simulate Earth’s ecosystems to better model climate change and weather systems. destination earth and one more thing needs to be devised Digital twin of the human body This is expected to contribute to the advancement of medicine by supporting drug development and making personalized medicine possible. Leveraging his resources in supercomputing to launch his AI startup has recently been announced, especially after the EU president announced this fall that his AI model would have computing access to his training program. It is emerging as a strategic priority. The bloc also announced what it called the “Large-Scale AI Grand Challenge.” This is a competition for European AI startups “with experience in large-scale AI models” and aims to select up to four promising domestic startups for a total of four. Access to millions of hours of supercomputing to support foundational model development. According to the European Commission, there will be a prize of 1 million euros to be distributed to the winners, who will be able to release their developed model or publish their research results under a non-commercial open source license. It is expected. The EU already had a program that provided industry users with access to core hours of supercomputing resources through a project recruitment process. However, the bloc is increasing its focus on commercial AI with dedicated programs and resources, and there is an opportunity to incorporate the growing supercomputing network into a strategic power source for expanding ‘Made in Europe’ general purpose AI. They are intently aiming for this. Thus, France’s Mistral, an AI startup that aims to compete with US infrastructure model giants like OpenAI and claims to offer “open assets” (if not fully open source), is an early adopter of It seems no coincidence that the beneficiaries of the Commission‘s Supercomputer Access Program. (That said, the technology company, which just raised €385 million in Series A funding that includes US investors including Andreessen Horowitz, General Catalyst and Salesforce, is at the front of the line for computing giveaways.) That may raise some eyebrows, but hey, it’s another sign of the high-level strategic bets being made on “big AI.”) The ‘s “Supercomputing for AI” program is still in its infancy, so it’s still unclear whether there will be enough benefits in model training to warrant reporting from dedicated access. (We reached out to Mistral for comment, but he did not respond as of press time.) But the committee’s at least hope is that by focusing support on AI startups, they will be able to move into high-performance computing. It is about being able to leverage investments. The construction of supercomputer hardware is increasingly being procured and configured with AI model training in mind, and this is due to the fact that local, hyperscalar-like US AI giants are starting at a disadvantage. This will be a competitive advantage for the AI ecosystem. “We don’t have the massive hyperscalers that the Americans have when it comes to training this kind of basic model, so we’re using supercomputers and a new generation that is increasingly compliant with AI. “We intend to develop a supercomputer,” a committee official said. “The objective in 2024, not just with the supercomputers that we have now, is to move in this direction so that even more small and medium-sized businesses can use supercomputers to develop these basic models. It is to do.” The plan includes acquiring “more dedicated AI supercomputing machines based on accelerators rather than standard CPUs,” they added. Will the ‘s AI support strategy align with or diverge from certain member states’ ambitions to develop national AI champions? We heard a lot about this during the recent difficult negotiations to develop the ‘s AI rulebook, in which France took the lead in pushing forward the AI rulebook. Regulatory carve-outs to the underlying model It drew criticism from small and medium-sized businesses. – As seen. But Mistral’s early presence in the ‘s supercomputing access program may suggest a consensus.
Researchers have made significant progress in understanding neuromuscular diseases by developing a two-dimensional neuromuscular junction model using pluripotent stem cells. This model enables high-throughput drug screening and complements previously developed three-dimensional organoids. (Artist’s concept) Credit: SciTechDaily.com
Scientists have developed a groundbreaking two-dimensional model to study neuromuscular diseases. This has enabled efficient drug testing and improved our understanding of diseases such as spinal muscular atrophy and amyotrophic lateral sclerosis.
Researchers have so far identified about 800 different neuromuscular diseases. These conditions are caused by problems with how muscle cells, motor neurons, and peripheral cells interact. These diseases, such as amyotrophic lateral sclerosis and spinal muscular atrophy, can cause muscle weakness, paralysis, and even death.
“These diseases are very complex and the causes of dysfunction are diverse,” said Dr. Mina Gouti, head of the Max Delbrück Center’s Developmental Stem Cell Modeling and Disease Laboratory. The problem could be in the neurons, the muscle cells, or the connections between the two. “To better understand the causes and find effective treatments, we need human-specific cell culture models that allow us to study how motor neurons in the spinal cord interact with muscle cells.”
Innovative research using organoids
Researchers working with Gouti had already developed a three-dimensional neuromuscular organoid (NMO) system. “One of our goals is to use our cultures for large-scale drug testing,” Gouti says. “Three-dimensional organoids are so large that they cannot be cultured for long periods of time in the 96-well culture dishes we use to conduct high-throughput drug screening studies.”
Human self-assembling 2D neuromuscular junction model. Immunofluorescence analysis of the whole dish shows myocytes (magenta) organized into bundles surrounded by spinal neurons (cyan). Credit: Alessia Urzi, Max Delbrück Center
For this type of screening, an international team led by Gouti has now developed a self-organizing neuromuscular junction model using pluripotent stem cells. The model includes neurons, muscle cells, and chemicals. synapse It is called the neuromuscular junction, which is necessary for two types of cells to interact. The researchers have now published their findings in the journal. nature communications.
“The 2D self-assembled neuromuscular junction model allows us to perform high-throughput drug screening for various neuromuscular diseases and study the most promising candidates in patient-specific organoids,” says Gouti. .
2D neuromuscular model development
To establish a 2D self-organizing neuromuscular junction model, the researchers first needed to understand how motor neurons and muscle cells develop in the embryo. Although Minas’ team does not conduct embryo research themselves, they use a variety of human stem cell lines and induced pluripotent stem cell lines (iPSCs), which are allowed for research purposes under strict guidelines.
“We tested several hypotheses. We found that the cell type required for functional neuromuscular connections is derived from neuromesodermal progenitor cells,” says doctoral student and author of the paper. says lead author Alessia Urzi.
Urji discovered the right combination of signaling molecules that allow human stem cells to mature into functional motor neurons and muscle cells, and the necessary connections between them. “It was very exciting to see muscle cells contracting under the microscope,” Urji says. “That was a clear sign that we were on the right path.”
Another observation was that upon differentiation, cells organized into regions containing muscle cells and nerve cells, rather like a mosaic.
Optogenetic advances in neuromuscular research
Myocytes grown in culture dishes contract spontaneously as a result of their connections with neurons, but without any meaningful rhythm. Urji and Guti wanted to solve it. In collaboration with researchers at the Charité University of Berlin, they used optogenetics to activate motor neurons. Neurons activated by the flash of light fire and contract muscle cells in synchrony, causing them to move in a way that mimics the physiological conditions of an organism.
Modeling and testing for spinal muscular atrophy
To test the effectiveness of the model, Professor Urji used human iPSCs taken from patients with spinal muscular atrophy. Spinal muscular atrophy is a serious neuromuscular disease that affects children during their first year of life. Neuromuscular cultures generated from patient-specific induced pluripotent stem cells showed severe problems with muscle contraction similar to the patient’s disease state.
For Gooty, 2D and 3D cultures are important tools to study neuromuscular diseases in more detail and test more efficient and personalized treatment options. As a next step, Gouti and her team hope to conduct high-throughput drug screens to identify new treatments for patients with spinal muscular atrophy and amyotrophic lateral sclerosis. “We want to start by using new drug combinations to see if we can achieve more successful outcomes to improve the lives of patients with complex neuromuscular diseases. ” says Gooty.
Reference: “Efficient Generation of Self-Assembling Neuromuscular Junction Models from Human Pluripotent Stem Cells,” Alessia Urzi et al., December 19, 2023. Nature Communications. DOI: 10.1038/s41467-023-43781-3
European Union legislators take action Over 20 hours of negotiation time Amid the marathon attempt to reach a consensus on how to regulate artificial intelligence, one thorny element remains unsolved: rules for foundational models/general purpose AI (GPAI), according to a leaked proposal reviewed by TechCrunch. A tentative agreement has been reached on how to handle the issue.
In recent weeks, there has been a concerted movement led by French AI startup Mistral to call for a complete regulatory separation of basic models/GPAI. But the proposal still has elements of the phased approach to regulating these advanced AIs that Parliament proposed earlier this year, so EU lawmakers are pushing for a full-throttle push to let the market make things right. seems to be resisting.
Having said that, some obligations of GPAI systems provided under free open source licenses are partially exempted (which is stipulated to mean: weights, information about the model architecture, and information about how to use the model) — with some exceptions, such as “high risk” models.
Reuters also reports on partial exceptions for open source advanced AI.
According to our sources, the open source exception is further limited by commercial deployment, so if such an open source model becomes available in the market or is otherwise provided as a service, the curve Out is no longer valid. “Therefore, depending on how ‘market availability’ and ‘commercialization’ are interpreted, this law could also apply to Mistral,” our source suggested.
The preliminary agreement we have seen maintains GPAI’s classification of so-called “systemic risk,” with models receiving this designation based on a measured cumulative amount of compute used for training. It means that it has “functions that have a large impact” such as. Greater than 10^25 for floating point operations (FLOPs).
at that level Few current models appear to meet systemic risk thresholds – Suggests that few state-of-the-art GPAIs need to fulfill their ex ante mandate to proactively assess and mitigate systemic risk. So Mistral’s lobbying efforts appear to have softened the blow of the regulation.
Under the preliminary agreement, other obligations for providers of systemic risk GPAIs include conducting assessments using standardized protocols and state-of-the-art tools. Document and report serious incidents “without undue delay.” Conduct and document adversarial testing. Ensure appropriate levels of cybersecurity. Report the actual or estimated energy consumption of your model.
Providers of GPAI have general obligations such as testing and evaluation of models and the creation and preservation of technical documentation, which must be made available to regulators and supervisory authorities upon request.
You should also provide downstream deployers of the model (aka AI app authors) with an overview of the model’s capabilities and limitations to support their ability to comply with AI laws.
The proposal also calls on basic model makers to put in place policies that respect EU copyright law, including restrictions placed on text and data mining by copyright holders. It also says it will provide a “sufficiently detailed” overview of the training data used to build and publish the model. Templates for disclosures are provided by the AI Office, the AI governance body that the regulations propose to establish.
We understand that this copyright disclosure summary continues to apply to open source models. This exists as one of the exceptions to the rule.
The documents we have seen include references to codes of practice, and the proposal states that GPAIs, and GPAIs with systemic risks, will demonstrate compliance until a ‘harmonized standard’ is published. It says that you can depend on this.
It is envisaged that the AI Office will be involved in the creation of such norms. The European Commission envisages issuing a standardization request from six months after the entry into force of the regulation on GPAI, but will also ask for deliverables on reporting and documentation on how to improve the energy and resource use of AI systems. It is assumed that standardization requests such as these will be issued and regular reports on their progress will be made. It also includes the development of these standardized elements (2 years after the date of application and every 4 years thereafter).
Today’s tripartite consultations on the AI Act actually began yesterday afternoon, but the European Commission is seeking opinions on this disputed file between the European Council, Parliament and Commission staff. It seems that they are determined to make this the final finishing touch. (If not, as we previously reported, there is a risk that the regulation will be put back on the shelf, with EU elections and new Commission appointments looming next year.)
At the time of this writing, negotiations are underway to resolve several other contentious elements of the file, with a number of highly sensitive issues still on the table (e.g., authentication monitoring, etc.). Therefore, it remains unclear whether the file will cross the line.
Without agreement on all elements, there will be no consensus to secure the law, leaving the fate of the AI law in limbo. But for those looking to understand where their co-legislators have arrived at their position on responsibility for advanced AI models, such as the large-scale language model that underpins the viral AI chatbot ChatGPT, this tentative agreement will help lawmakers provide some degree of steering as to where we are going.
In recent minutes, EU Internal Market Commissioner Thierry Breton tweeted confirmation that negotiations had finally broken down, but only until tomorrow. The European Commission still intends to obtain the April 2021 proposed file beyond the deadline this week, as the epic trilogue is scheduled to resume at 9 a.m. Brussels time.
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