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"The internet is primarily funded by the collection, analysis, and trade of data-the data economy. Much of that data is personal data-data about you. The trading of personal data as a business model is increasingly being exported to all institutions in society-the surveillance society, or surveillance capitalism."

"DATA LEVERAGE CAN be deployed through at least four avenues: direct action (for instance, individuals banding together to withhold, "poison," or redirect data), regulatory action (for instance, pushing for data protection policy and legal recognition of "data coalitions"), legal action (for instance, communities adopting new data-licensing regimes or pursuing a lawsuit), and market action (for instance, demanding large language models be trained only with data from consenting creators). "

"Imagine having a digital twin that gets ill, and can be experimented on to identify the best possible treatment, without you having to go near a pill or a surgeon's knife. Scientists believe that within five to 10 years, "in silico" trials - in which hundreds of virtual organs are used to assess the safety and efficacy of drugs - could become routine, while patient-specific organ models could be used to personalise treatment and avoid medical complications. Digital twins are computational models of physical objects or processes, updated using data from their real-world counterparts. Within medicine, this means combining vast amounts of data about the workings of genes, proteins, cells and whole-body systems with patients' personal data to create virtual models of their organs - and eventually, potentially their entire body"

"AI models will inevitably have to make value judgments. If we want those judgments to be congruent with our own values (which is, after all, the central goal of AI alignment research) then we need to have ways of testing which values a model expresses in the real world. Our method provides a new, data-focused method of doing this, and of seeing where we might've succeeded-or indeed failed-at aligning our models' behavior."

"or its latest update, which it released Wednesday, the COVID-19 Scenario Modeling Hub combined nine different mathematical models from different research groups to get an outlook for the pandemic for the next six months. "Any of us who have been following this closely, given what happened with delta, are going to be really cautious about too much optimism," says Justin Lessler at the University of North Carolina, who helps run the hub. "But I do think that the trajectory is towards improvement for most of the country," he says. The modelers developed four potential scenarios, taking into account whether or not childhood vaccinations take off and whether a more infectious new variant should emerge. "

"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."

"The research, conducted at Vision and AI Lab of the Department of Computational and Data Sciences, offers a novel approach to mitigating bias in popular image-generative models without the need for additional data or model retraining."

"A pilot study by academics at the University of Oxford found some care providers had been using generative AI chatbots such as ChatGPT and Bard to create care plans for people receiving care. That presents a potential risk to patient confidentiality, according to Dr Caroline Green, an early career research fellow at the Institute for Ethics in AI at Oxford, who surveyed care organisations for the study. "If you put any type of personal data into [a generative AI chatbot], that data is used to train the language model," Green said. "That personal data could be generated and revealed to somebody else." She said carers might act on faulty or biased information and inadvertently cause harm, and an AI-generated care plan might be substandard."

"Liverpool are using incredible data science during matches, and effects are extraordinary Liverpool's sport-leading data science is providing Jürgen Klopp with the tools to change football matches as they're happening."

"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."

"Wooldridge says copyright is a "coming storm" for AI companies. LLMs are likely to have accessed copyrighted material, such as news articles. Indeed the GPT-4-assisted chatbot attached to Microsoft's Bing search engine cites news sites in its answers. "I didn't give explicit permission for my works to be used as training data, but they almost certainly were, and now they contribute to what these models know," he says. "Many artists are gravely concerned that their livelihoods are at risk from generative AI. Expect to see legal battles," he adds."

"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."

"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."

"The company has called for Australian policymakers to promote "copyright systems that enable appropriate and fair use of copyrighted content to enable the training of AI models in Australia on a broad and diverse range of data, while supporting workable opt-outs for entities that prefer their data not to be trained in using AI systems". The call for a fair use exception for AI systems is a view the company has expressed to the Australian government in the past, but the notion of an opt-out option for publishers is a new argument from Google."

This discrepancy has made it relatively easy to connect an anonymous line of data to a specific person: if a private detective is searching for someone in New York City and knows the subject is male, is 30 to 35 years old and has diabetes, the sleuth would not be able to deduce the man's name-but could likely do so quite easily if he or she also knows the target's birthday, number of children, zip code, employer and car model.

"The organisation is a pioneer of the "ransomware as a service" model, whereby it outsources the target selection and attacks to a network of semi-independent "affiliates", providing them with the tools and infrastructure and taking a commission on the ransoms in return. As well as ransomware, which typically works by encrypting data on infected machines and demanding a payment for providing the decryption key, LockBit copied stolen data and threatened to publish it if the fee was not paid, promising to delete the copies on receipt of a ransom."

"Fear of data-mining of healthcare could be costing as many as 100,000 lives a year, according to Google's Larry Page. Speaking out in response to fears over his company's vast haul of personal information, Page made the case that not only is Google not going too far with collecting and analysing such information - it's not going far enough."

"Have you been looking forward to somniferous alkaloid compounds customized to your personal metabolic dependency profile? Good news! 23andMe is selling everyone's DNA to the pharmaceutical industry. GSK Plc will pay 23andMe Holding Co. $20 million for access to the genetic-testing company's vast trove of consumer DNA data, extending a five-year collaboration that's allowed the drugmaker to mine genetic data as it researches new medications."