Committee

Have you ever tried connecting a language model to your own data or tools? If so, you know it often means writing custom integrations, managing API schemas, and wrestling with authentication.

Cohere AI Labs has released Tiny Aya, a family of small language models (SLMs) that redefines multilingual performance. While many models scale by increasing parameters, Tiny Aya uses a 3.35B-parameter architecture to deliver state-of-the-art translation and generation across 70 languages. The release includes 5 models: Tiny Aya Base (pretrained), Tiny Aya Global (balanced instruction-tuned), and three region-specific variants—Earth (Africa/West Asia), Fire (South Asia), and Water (Asia-Pacific/Europe). https://cohere.com/blog/cohere-labs-tiny-aya The Architecture Tiny Aya is built on a dense decoder-only Transformer architecture. Key specs include: Parameters: 3.35B total (2.8B non-embedding) Layers: 36 Vocabulary: 262k tokenizer designed for equitable language representation. Attention: Interleaved sliding window and full attention (3:1 ratio) with Grouped Query Attention (GQA). Context: 8192 tokens for input and output. The model was pretrained on 6T tokens using a Warmup-Stable-Decay (WSD) schedule. To maintain stability, the team used SwiGLU activations and removed all biases from dense layers. Advanced Post-training: FUSION and SimMerge To bridge the gap in low-resource languages, Cohere used a synthetic data pipeline. Fusion-of-N (FUSION): Prompts are sent to a ‘team of teachers’ (COMMAND A, GEMMA3-27B-IT, DEEPSEEK-V3). A judge LLM, the Fusor, extracts and aggregates the strongest components of their responses. Region Specialization: Models were finetuned on 5 regional clusters (e.g., South Asia, Africa). SimMerge: To prevent ‘catastrophic forgetting’ of global safety, regional checkpoints were merged with the global model using SimMerge, which selects the best merge operators based on similarity signals. Performance Benchmarks Tiny Aya Global consistently beats larger or same-scale competitors in multilingual tasks: Translation: It outperforms GEMMA3-4B in 46 of 61 languages on WMT24++. Reasoning: In the GlobalMGSM (math) benchmark for African languages, Tiny Aya achieved 39.2% accuracy, dwarfing GEMMA3-4B (17.6%) and QWEN3-4B (6.25%). Safety: It holds the highest mean safe response rate (91.1%) on MultiJail. Language Integrity: The model achieves 94% language accuracy, meaning it rarely switches to English when asked to reply in another language. On-Device Deployment Tiny Aya is optimized for edge computing. Using 4-bit quantization (Q4_K_M), the model fits in a 2.14 GB memory footprint. iPhone 13: 10 tokens/s. iPhone 17 Pro: 32 tokens/s. This quantization scheme results in a minimal 1.4-point drop in generation quality, making it a viable solution for offline, private, and localized AI applications. Key Takeaways Efficient Multilingual Power: Tiny Aya is a 3.35B-parameter model family that delivers state-of-the-art translation and high-quality generation across 70 languages. It proves that massive scale is not required for strong multilingual performance if models are designed with intentional data curation. Innovative Training Pipeline: The models were developed using a novel strategy involving Fusion-of-N (FUSION), where a ‘team of teachers’ (like Command A and DeepSeek-V3) generated synthetic data. A judge model then aggregated the strongest components to ensure high-quality training signals even for low-resource languages. Regional Specialization via Merging: Cohere released specialized variants—Tiny Aya Earth, Fire, and Water—which are tuned for specific regions like Africa, South Asia, and the Asia-Pacific. These were created by merging regional fine-tuned models with a global model using SimMerge to preserve safety while boosting local language performance. Superior Benchmark Performance: Tiny Aya Global outperforms competitors like Gemma3-4B in translation quality for 46 of 61 languages on WMT24++. It also significantly reduces disparities in mathematical reasoning for African languages, achieving 39.2% accuracy compared to Gemma3-4B’s 17.6%. Optimized for On-Device Deployment: The model is highly portable and runs efficiently on edge devices; it achieves ~10 tokens/s on an iPhone 13 and 32 tokens/s on an iPhone 17 Pro using Q4_K_M quantization. This 4-bit quantization format maintains high quality with only a minimal 1.4-point degradation. Check out the Technical details, Paper, Model Weights and Playground. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Cohere Releases Tiny Aya: A 3B-Parameter Small Language Model that Supports 70 Languages and Runs Locally Even on a Phone appeared first on MarkTechPost.

To be human is, fundamentally, to be a forecaster. Occasionally a pretty good one. Trying to see the future, whether through the lens of past experience or the logic of cause and effect, has helped us hunt, avoid being hunted, plant crops, forge social bonds, and in general survive in a world that does not prioritize our survival. Indeed, as the tools of divination have changed over the centuries, from tea leaves to data sets, our conviction that the future can be known (and therefore controlled) has only grown stronger.  Today, we are awash in a sea of predictions so vast and unrelenting that most of us barely even register them. As I write this sentence, algorithms on some remote server are busy trying to guess my next word based on those I have already typed. If you’re reading this online, a separate set of algorithms has likely already served you an ad deemed to be one you are most likely to click. (To the die-hards reading this story on paper, congratulations! You have escaped the algorithms … for now.) If the thought of a ubiquitous, mostly invisible predictive layer secretly grafted onto your life by a bunch of profit-hungry corporations makes you uneasy … well, same here. So how did all this happen? People’s desire for reliable forecasting is understandable. Still, nobody signed up for an omnipresent, algorithmic oracle mediating every aspect of their life. A trio of new books tries to make sense of our future-­focused world—how we got here, and what this change means. Each has its own prescriptions for navigating this new reality, but they all agree on one thing: Predictions are ultimately about power and control. The Means of Prediction: How AI Really Works (and Who Benefits)Maximilian KasyUNIVERSITY OF CHICAGO PRESS, 2025 In The Means of Prediction: How AI Really Works (and Who Benefits), the Oxford economist Maximilian Kasy explains how most predictions in our lives are based on the statistical analysis of patterns in large, labeled data sets—what’s known in AI circles as supervised learning. Once “trained” on such data sets, algorithms for supervised learning can be presented with all kinds of new information and then deliver their best guess as to some specific future outcome. Will you violate your parole, pay off your mortgage, get promoted if hired, perform well on your college exams, be in your home when it gets bombed? More and more, our lives are shaped (and, yes, occasionally shortened) by a machine’s answer to these questions. If the thought of a ubiquitous, mostly invisible predictive layer secretly grafted onto your life by a bunch of profit-hungry corporations makes you uneasy … well, same here. This arrangement is leading to a crueler, blander, more instrumentalized world, one where life’s possibilities are foreclosed, age-old prejudices are entrenched, and everyone’s brain seems to be actively turning into goo. It’s an outcome, according to Kasy, that was entirely predictable.  AI adherents might frame those consequences as “unintended,” or mere problems of optimization and alignment. Kasy, on the other hand, argues that they represent the system working as intended. “If an algorithm selecting what you see on social media promotes outrage, thereby maximizing engagement and ad clicks,” he writes, “that’s because promoting outrage is good for profits from ad sales.” The same holds true for an algorithm that nixes job candidates “who are likely to have family-care responsibilities outside the workplace,” and the ones that “screen out people who are likely to develop chronic health problems or disabilities.” What’s good for a company’s bottom line may not be good for your job-hunting prospects or life expectancy. Where Kasy differs from other critics is that he doesn’t think working to create less biased, more equitable algorithms will fix any of this. Trying to rebalance the scales can’t change the fact that predictive algorithms rely on past data that’s often racist, sexist, and flawed in countless other ways. And, he says, the incentives for profit will always trump attempts to eliminate harm. The only way to counter this is with broad democratic control over what Kasy calls “the means of prediction”: data, computational infrastructure, technical expertise, and energy.   A little more than half of The Means of Prediction is devoted to explaining how this might be accomplished—through mechanisms including “data trusts” (collective public bodies that make decisions about how to process and use data on behalf of their contributors) and corporate taxing schemes that try to account for the social harm AI inflicts. There’s a lot of economist talk along the way, about how “agents of change” might help achieve “value alignment” in order to “maximize social welfare.” Reasonable, I guess, though a skeptic might point out that Kasy’s rigorous, systematic approach to building new public-serving institutions comes at a time when public trust in institutions has never been lower. Also, there’s the brain goo problem.  To his credit, Kasy is a realist here. He doesn’t presume that any of these proposals will be easy to implement. Or that it will happen overnight, or even in the near future. The troubling question at the end his book is: Do we have that kind of time? Reading Kasy’s blueprint for seizing control of the means of prediction raises another pressing question. How on earth did we reach a point where machine-mediated prediction is more or less inescapable? Capitalism, might be Marx’s pithy response. Fine, as far as it goes, but that doesn’t explain why the same kinds of algorithms that currently model climate change are for some reason also deciding whether you get a new kidney or I get a car loan. The Irrational Decision: How We Gave Computers the Power to Choose for UsBenjamin RechtPRINCETON UNIVERSITY PRESS, 2026 If you ask Benjamin Recht, author of The Irrational Decision: How We Gave Computers the Power to Choose for Us, he’d likely tell you our current predicament has a lot to do with the idea and ideology of decision theory—or what economists call rational choice theory.

“We’re out of airspace now. We can do whatever we want,” Jean-Paul Thorbjornsen tells me from the pilot’s seat of his Aston Martin helicopter. As we fly over suburbs outside Melbourne, Australia, it’s becoming clear that doing whatever he wants is Thorbjornsen’s MO.  Upper-middle-class homes give way to vineyards, and Thorbjornsen points out our landing spot outside a winery. People visiting for lunch walk outside. “They’re going to ask for a shot now,” he says, used to the attention drawn by his luxury helicopter, emblazoned with the tail letters “BTC” for bitcoin (the price tag of $5 million in Australian dollars—$3.5 million in US dollars today—was perhaps reasonable for someone who claims a previous crypto project made more than AU$400 million, although he also says those funds were tied up in the company).  Thorbjornsen is a founder of THORChain, a blockchain through which users can swap one cryptocurrency for another and earn fees from making those swaps. THORChain is permissionless, so anyone can use it without getting prior approval from a centralized authority. As a decentralized network, the blockchain is built and run by operators located across the globe, most of whom use pseudonyms.  During its early days, Thorbjornsen himself hid behind the pseudonym “leena” and used an AI-generated female image as his avatar. But around March 2024, he revealed that he, an Australian man in his mid-30s, with a rural Catholic upbringing, was the mind behind the blockchain. More or less.  If there is a central question around THORChain, it is this: Exactly who is responsible for its operations? Blockchains as decentralized as THORChain are supposed to offer systems that operate outside the centralized leadership of corruptible governments and financial institutions. If a few people have outsize sway over this decentralized network—one of a handful that operate at such a large scale—it’s one more blemish on the legacy of bitcoin’s promise, which has already been tarnished by capitalistic political frenzy.    Who’s responsible for THORChain matters because in January last year, its users lost more than $200 million worth of their cryptocurrency in US dollars after THORChain transactions and accounts were frozen by a singular admin override, which users believed was not supposed to be possible given the decentralized structure. When the freeze was lifted, some users raced to pull their money out. The following month, a team of North Korean hackers known as the Lazarus Group used THORChain to move roughly $1.2 billion of stolen ethereum taken in the infamous hack of the Dubai-based crypto exchange Bybit.  Thorbjornsen explains away THORChain’s inability to stop the movement of stolen funds, or prevent a bank run, as a function of its decentralized and permissionless nature. The lack of executive powers means that anyone can use the network for any reason, and arguably there’s no one to hold accountable when even the worst goes down. But when the worst did go down, nearly everyone in the THORChain community, and those paying attention to it in channels like X, pointed their fingers at Thorbjornsen. A lawsuit filed by the THORChain creditors who lost millions in January 2025 names him. A former FBI analyst and North Korea specialist, reflecting on the potential repercussions for helping move stolen funds, told me he wouldn’t want to be in Thorbjornsen’s shoes. THORChain was designed to make decisions based on votes by node operators, where two-thirds majority rules. That’s why I traveled to Australia—to see if I could get a handle on where he sees himself and his role in relation to the network he says he founded. According to Thorbjornsen, he should not be held responsible for either event. THORChain was designed to make decisions based on votes by node operators—people with the computer power, and crypto stake, to run a cluster of servers that process the network’s transactions. In those votes, a two-thirds majority rules. Then there’s the permissionless part. Anyone can use THORChain to make swaps, which is why it’s been a popular way for widely sanctioned entities such as the government of North Korea to move stolen money. This principle goes back to the cypherpunk roots of bitcoin, a currency that operates outside of nation-states’ rules. THORChain is designed to avoid geopolitical entanglements; that’s what its users like about it. But there are distinct financial motivations for moving crypto, stolen or not: Node operators earn fees from the funds running through the network. In theory, this incentivizes them to act in the network’s best interests—and, arguably, Thorbjornsen’s interests too, as many assume his wealth is tied to the network’s profits. (Thorbjornsen says it is not, and that it comes instead from “many sources,” including “buying bitcoin back in 2013.”) Now recent events have raised critical questions, not just about Thorbjornsen’s outsize role in THORChain’s operations, but also about the blockchain’s underlying nature. If THORChain is decentralized, how was a single operator able to freeze its funds a month before the Bybit hack? Could someone have unilaterally decided to stop the stolen Bybit funds from coming through the network, and chosen not to?  Thorbjornsen insists THORChain is helping realize bitcoin’s original purpose of enabling anyone to transact freely outside the reach of purportedly corrupt governments. Yet the network’s problems suggest that an alternative financial system might not be much better. Decentralized?  On February 21, 2025, Bybit CEO Ben Zhou got an alarming call from the company’s chief financial officer. About $1.5 billion US of the exchange’s ethereum token, ETH, had been stolen.  The FBI attributed the theft to the Lazarus Group. Typically, criminals will want to convert ETH to bitcoin, which is much easier to convert in turn to cash. Knowing this, the FBI issued a public service announcement on February 26 to “exchanges, bridges … and other virtual asset service providers,” encouraging them to block transactions from accounts related to the hack.  Someone posted that announcement in THORChain’s private, invite-only developer channel on Discord, a chat app used widely by software engineers and gamers. While other crypto exchanges and bridges (which facilitate

For years, GitHub Copilot has served as a powerful pair programming tool for programmers, suggesting the next line of code.

When Sam Zahr first saw the gray Rolls-Royce Dawn convertible with orange interior and orange roof, he knew he’d found a perfect addition to his fleet. “It was very appealing to our clientele,” he told me. As the director of operations at Dream Luxury Rental, he outfits customers in the Detroit area looking to ride in style to a wedding, a graduation, or any other event with high-end vehicles—Rolls-Royces, Lamborghinis, Bentleys, Mercedes G-Wagons, and more. But before he could rent out the Rolls, Zahr needed to get the car to Detroit from Miami, where he bought it from a used-car dealer.  His team posted the convertible on Central Dispatch, an online marketplace that’s popular among car dealers, manufacturers, and owners who want to arrange vehicle shipments. It’s not too complicated, at least in theory: A typical listing includes the type of vehicle, zip codes of the origin and destination, dates for pickup and delivery, and the fee. Anyone with a Central Dispatch account can see the job, and an individual carrier or transport broker who wants it can call the number on the listing. Zahr’s team got a call from a transport company that wanted the job. They agreed on the price and scheduled pickup for January 17, 2025. Zahr watched from a few feet away as the car was loaded into an enclosed trailer. He expected the vehicle to arrive in Detroit just a few days later—by January 21.  But it never showed up. Zahr called a contact at the transport company to ask what happened.  “He’s like, I don’t know what you’re talking about.”  Zahr told me his contact angrily told him they mostly ship Coca-Cola products, not luxury cars. “He was yelling and screaming about it,” Zahr said. Over the years, people have broken into his business to steal cars, or they’ve rented them out and never come back. But until this day, he’d never had a car simply disappear during shipping. He’d expected no trouble this time around, especially since he’d used Central Dispatch—“a legit platform that everyone uses to transport cars,” he said.  “That’s the scary part about it, you know?” Wreaking havoc Zahr had unwittingly been caught up in a new and growing type of organized criminal enterprise: vehicle transport fraud and theft. Crooks use email phishing, fraudulent paperwork, and other tactics to impersonate legitimate transport companies and get hired to deliver a luxury vehicle. They divert the shipment away from its intended destination and then use a mix of technology, computer skills, and old-school chop shop techniques to erase traces of the vehicle’s original ownership and registration. These vehicles can be retitled and resold in the US or loaded into a shipping container and sent to an overseas buyer. In some cases, the car has been resold or is out of the country by the time the rightful owner even realizes it’s missing. “Criminals have learned that stealing cars via the web portals has become extremely easy, and when I say easy—it’s become seamless,” says Steven Yariv, the CEO of Dealers Choice Auto Transport of West Palm Beach, Florida, one of the country’s largest luxury-vehicle transport brokers. Individual cases have received media coverage thanks to the high value of the stolen cars and the fact that some belong to professional athletes and other celebrities. In late 2024, a Lamborghini Huracán belonging to Colorado Rockies third baseman Kris Bryant went missing en route to his home in Las Vegas; R&B singer Ray J told TMZ the same year that two Mercedes Maybachs never arrived in New York as planned; and last fall, NBA Hall of Famer Shaquille O’Neal had a $180,000 custom Range Rover stolen when the transport company hired to move the vehicle was hacked. “They’re saying they think it’s probably in Dubai by now, to be honest,” an employee of the company that customized the SUV told Shaq in a YouTube video. “Criminals have learned that stealing cars via the web portals has become extremely easy, and when I say easy—it’s become seamless.” Steven Yariv, CEO, Dealers Choice Auto Transport of West Palm Beach, Florida But the nationwide epidemic of vehicle transport fraud and theft has remained under the radar, even as it’s rocked the industry over the past two years. MIT Technology Review identified more than a dozen cases involving high-end vehicles, obtained court records, and spoke to law enforcement, brokers, drivers, and victims in multiple states to reveal how transport fraud is wreaking havoc across the country. RICHARD CHANCE It’s challenging to quantify the scale of this type of crime, since there isn’t a single entity or association that tracks it. Still, these law enforcement officials and brokers, as well as the country’s biggest online car-transport marketplaces, acknowledge that fraud and theft are on the rise.  When I spoke with him in August, Yariv estimated that around 8,000 exotic and high-end cars had been stolen since the spring of 2024, resulting in over $1 billion in losses. “You’re talking 30 cars a day [on] average is gone,” he said. Multiple state and local law enforcement officials told MIT Technology Review that the number is plausible. (The FBI did not respond to a request for an interview.) “It doesn’t surprise me,” said J.D. Decker, chief of the Nevada Department of Motor Vehicles’ police division and chair of the fraud subcommittee for the American Association of Motor Vehicle Administrators. “It’s a huge business.” Data from the National Insurance Crime Bureau (NICB), a nonprofit that works with law enforcement and the insurance industry to investigate insurance fraud and related crimes, provides further evidence of this crime wave. NICB tracks both car theft and cargo theft, a broad category that refers to goods, money, or baggage that is stolen while part of a commercial shipment; the category also covers cases in which a vehicle is stolen via a diverted transport truck or a purloined car is loaded into a shipping container. NICB’s statistics about car theft show that it has declined following an increase