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"Therefore even Ofqual's best model significantly worsened grade accuracy for most A-level subjects when the cohort size is below 50, which is common (almost 62% of the total in 2019). For GCSEs, even with larger cohorts, the best model would have worsened the grade accuracy for Maths and Sciences. A very conservative figure of 25% of wrong grades would have amounted to 180,000 wrong A-level grades and 1.25 million wrong GCSE grades."

"ChatGPT can also give entirely wrong answers and present misinformation as fact, writing "plausible-sounding but incorrect or nonsensical answers", the company concedes. OpenAI says that fixing this issue is difficult because there is no source of truth in the data they use to train the model and supervised training can also be misleading "because the ideal answer depends on what the model knows, rather than what the human demonstrator knows"."

"Some experts see platforms like Appen as a new form of data colonialism, says Saiph Savage, director of the Civic AI lab at Northeastern University. "Workers in Latin America are labeling images, and those labeled images are going to feed into AI that will be used in the Global North," she says. "While it might be creating new types of jobs, it's not completely clear how fulfilling these types of jobs are for the workers in the region." Due to the ever moving goal posts of AI, workers are in a constant race against the technology, says Schmidt. "One workforce is trained to three-dimensionally place bounding boxes around cars very precisely, and suddenly it's about figuring out if a large language model has given an appropriate answer," he says, regarding the industry's shift from self-driving cars to chatbots. Thus, niche labeling skills have a "very short half-life." "From the clients' perspective, the invisibility of the workers in microtasking is not a bug but a feature," says Schmidt. Economically, because the tasks are so small, it's more feasible to deal with contractors as a crowd instead of individuals. This creates an industry of irregular labor with no face-to-face resolution for disputes if, say, a client deems their answers inaccurate or wages are withheld. The workers WIRED spoke to say it's not low fees but the way platforms pay them that's the key issue. "I don't like the uncertainty of not knowing when an assignment will come out, as it forces us to be near the computer all day long," says Fuentes, who would like to see additional compensation for time spent waiting in front of her screen. Mutmain, 18, from Pakistan, who asked not to use his surname, echoes this. He says he joined Appen at 15, using a family member's ID, and works from 8 am to 6 pm, and another shift from 2 am to 6 am. "I need to stick to these platforms at all times, so that I don't lose work," he says, but he struggles to earn more than $50

"Researchers involved in a recent study trained an artificial intelligence (AI) model to diagnose type 2 diabetes in patients after six to 10 seconds of listening to their voice. Canadian medical researchers trained the machine-learning AI to recognise 14 vocal differences in the voice of someone with type 2 diabetes compared to someone without diabetes. The auditory features that the AI focussed on included slight changes in pitch and intensity, which human ears cannot distinguish. This was then paired with basic health data gathered by the researchers, such as age, sex, height and weight. Researchers believe that the AI model will drastically lower the cost for people with diabetes to be diagnosed."

"A Taiwanese American model says a well-known fashion designer uploaded a digitally altered runway photo that made her appear white. In a TikTok about the incident that has been viewed 1.8m times in the last week, Shereen Wu says Michael Costello, a designer who has worked with Beyoncé, Jennifer Lopez, and Celine Dion, posted a photo to his Instagram from a recent Los Angeles fashion show. The photo depicts Wu in the slinky black ballgown that she walked the runway in - but her face has been changed, made to appear as if she is a white woman."

"Do the potential paying customers for these large models add up to enough money to keep the servers on? That's the 13 trillion dollar question, and the answer is the difference between WorldCom and Enron, or dotcoms and cryptocurrency. Though I don't have a certain answer to this question, I am skeptical. AI decision support is potentially valuable to practitioners. Accountants might value an AI tool's ability to draft a tax return. Radiologists might value the AI's guess about whether an X-ray suggests a cancerous mass. But with AIs' tendency to "hallucinate" and confabulate, there's an increasing recognition that these AI judgments require a "human in the loop" to carefully review their judgments. In other words, an AI-supported radiologist should spend exactly the same amount of time considering your X-ray, and then see if the AI agrees with their judgment, and, if not, they should take a closer look. AI should make radiology more expensive, in order to make it more accurate. But that's not the AI business model. AI pitchmen are explicit on this score: The purpose of AI, the source of its value, is its capacity to increase productivity, which is to say, it should allow workers to do more, which will allow their bosses to fire some of them, or get each one to do more work in the same time, or both. The entire investor case for AI is "companies will buy our products so they can do more with less." It's not "business custom­ers will buy our products so their products will cost more to make, but will be of higher quality.""

"In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life's molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy."

"Automatically produced texts use language models derived from statistical analysis of vast corpuses of human-generated text to produce machine-generated texts that can be very hard for a human to distinguish from text produced by another human. These models could help malicious actors in many ways, including generating convincing spam, reviews, and comments -- so it's really important to develop tools that can help us distinguish between human-generated and machine-generated texts."

"Blockchain technology has also opened the way to new models whereby endless micropayments can be made in return for particular online services or content; and, if people voluntarily allow elements of their data to be used, rewards can flow the other way. Here perhaps lies the key to a system beyond the current, Google-led model, in which services appear to be free but the letting-go of personal data is the actual price."

"But, as he and other modellers warn, much information about how SARS-CoV-2 spreads is still unknown and must be estimated or assumed - and that limits the precision of forecasts. An earlier version of the Imperial model, for instance, estimated that SARS-CoV-2 would be about as severe as influenza in necessitating the hospitalization of those infected. That turned out to be incorrect."

"A natural alternative to symbolic AI came to prominence: Instead of modeling high-level reasoning processes, why not instead model the brain? After all, brains are the only things that we know for certain can produce intelligent behavior. Why not start with them?"

"If true, this would be a big deal. But, said OpenAI, "due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper.""

"Our social media platforms are powered by a surveillance-based business model designed to mine, manipulate, and extract our human experiences at any cost, causing a breakdown of our information ecosystem and shared sense of truth worldwide. This extractive business model is not built for us but built to exploit us."

"Some ideas include building mechanisms into devices or platforms that cause them to shut down automatically after two hours, or nudges that make it more difficult to keep spending money in an app. The problem is that tech companies are ruled by a profit model built on capturing more of our attention to sell to advertisers. Designing platforms so they limit the amount of time we spend on social media runs counter to this model."

"DeepMind is trying to combine deep learning and algorithms, creating the one algorithm to rule them all: a deep learning model that can learn how to emulate any algorithm, generating an algorithm-equivalent model that can work with real-world data."

"describing how they had created an A.I. model to instantly translate the ancient glyphs. The team, led by a Google software engineer and an Assyriologist from Ariel University, trained the model on existing cuneiform translations using the same technology that powers Google Translate."

"The intriguing echo of Eliza in thinking about ChatGPT is that people regard it as magical even though they know how it works - as a "stochastic parrot" (in the words of Timnit Gebru, a well-known researcher) or as a machine for "hi-tech plagiarism" (Noam Chomsky). But actually we do not know the half of it yet - not the CO2 emissions incurred in training its underlying language model or the carbon footprint of all those delighted interactions people are having with it. Or, pace Chomsky, that the technology only exists because of its unauthorised appropriation of the creative work of millions of people that just happened to be lying around on the web? What's the business model behind these tools? And so on. Answer: we don't know."

"The post sparked a debate in the comments about the ethics of fine-tuning an AI on the work of a specific living artist, even as new fine-tuned models are posted daily. The most-upvoted comment asked, "Whether it's legal or not, how do you think this artist feels now that thousands of people can now copy her style of works almost exactly?""

"Now researchers have taken a step forward by combining fMRI's ability to monitor neural activity with the predictive power of artificial intelligence language models. The hybrid technology has resulted in a decoder that can reproduce, with a surprising level of accuracy, the stories that a person listened to or imagined telling in the scanner. The decoder could even guess the story behind a short film that someone watched in the scanner, though with less accuracy."

"In fact, generating an image using a powerful AI model takes as much energy as fully charging your smartphone, according to a new study by researchers at the AI startup Hugging Face and Carnegie Mellon University. However, they found that using an AI model to generate text is significantly less energy-intensive. Creating text 1,000 times only uses as much energy as 16% of a full smartphone charge. "