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"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 algorithm at play is performing what's commonly referred to as predictive policing. Using years - and sometimes decades - worth of crime reports, the algorithm analyses the data to identify areas with high probabilities for certain types of crime, placing little red boxes on maps of the city that are streamed into patrol cars."

"It's not that Google wants to do this, it's that they didn't anticipate this outcome, and compounded that omission by likewise omitting a way to overrule the algorithm's judgment. As with other examples of algorithmic cruelty, it's not so much this specific example as was it presages for a future in which more and more of our external reality is determined by models derived from machine learning systems whose workings we're not privy to and have no say in. "

"The founders of Predictim want to be clear with me: Their product-an algorithm that scans the online footprint of a prospective babysitter to determine their "risk" levels for parents-is not racist. It is not biased. "We take ethics and bias extremely seriously," Sal Parsa, Predictim's CEO, tells me warily over the phone. "In fact, in the last 18 months we trained our product, our machine, our algorithm to make sure it was ethical and not biased. We took sensitive attributes, protected classes, sex, gender, race, away from our training set. We continuously audit our model. And on top of that we added a human review process.""

"No prominent developer of AI foundation models - a list including companies like OpenAI and Meta - is releasing sufficient information about their potential impact on society, determines a new report from Stanford HAI (Human-Centered Artificial Intelligence). Today, Stanford HAI released its Foundation Model Transparency Index, which tracked whether creators of the 10 most popular AI models disclose information about their work and how people use their systems. Among the models it tested, Meta's Llama 2 scored the highest, followed by BloomZ and then OpenAI's GPT-4. But none of them, it turned out, got particularly high marks."

"This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions."

"With all the information together, they created a prediction model which can guess with 80% accuracy whether or not that user will go on to be banned from just their first five posts. "

"Amid mounting financial pressure, at least a dozen police forces are using or considering predictive analytics, despite warnings from campaigners that use of algorithms and "predictive policing" models risks locking discrimination into the criminal justice system."

"What Hamilton and his colleagues found surprising was that this approach could also be applied to helping find problems with existing deep networks, related to the surge of "deepfakes" that have recently cropped up. They applied this data structure to find areas where probabilistic models, such as the generative adversarial networks (GANs) that are often used to create deepfakes, break down. They coined these problematic areas "blind spots," and note that they give us insight into how GANs can be biased. Such blind spots further show that GANs struggle to represent particular areas of a dataset, even if most of their fakes can fool a human. "

"The use for humans in the world of robotics, even as teachers, is shrinking thanks to AI. NVIDIA Research has announced the creation of Eureka, an AI agent powered by GPT-4 that has trained robots to perform tasks using reward algorithms. Notably, Eureka taught a robotic hand to do pen spinning tricks as well as a human can (honestly, as you can see in the YouTube video below, better than many of us)."

"Most AI programs function like a "black box." "We know exactly what a model does but not why it has now specifically recognized that a picture shows a cat," said computer scientist Kristian Kersting of the Technical University of Darmstadt in Germany to the German-language newspaper Handelsblatt. That dilemma prompted Kersting-along with computer scientists Patrick Schramowski of the Technical University of Darmstadt and Björn Deiseroth, Mayukh Deb and Samuel Weinbach, all at the Heidelberg, Germany-based AI company Aleph Alpha-to introduce an algorithm called AtMan earlier this year. AtMan allows large AI systems such as ChatGPT, Dall-E and Midjourney to finally explain their outputs."

"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."

"So they trained a deep-learning neural net on tons of examples of deepfaked videos, and produced a model that's better than any previous automated technique at spotting hoaxery. (Their paper documenting the work is here.)"

"In a paper titled "Towards Causal Representation Learning," researchers at the Max Planck Institute for Intelligent Systems, the Montreal Institute for Learning Algorithms (Mila), and Google Research discuss the challenges arising from the lack of causal representations in machine learning models and provide directions for creating artificial intelligence systems that can learn causal representations."

"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.""

"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."

"As the miles grow, the odds shrink. At some point, a car driving autonomously or semi-autonomously will cause a fatal accident. If their performance is remotely comparable to a human's, that moment could come within the next 18-24 months. If so, by the law of averages it will probably involve a Tesla Model 3. Self-driving cars may be about to have their Driscoll moment."

"Similarly to how voice cloning raises fears about the consequences of its potential misuse, increasingly many people worry that the proliferation of AI technology will put professionals' livelihoods at risk. At Eleven, we see a future in which voice actors are able to license their voices to train speech models for specific use, in exchange for fees. Clients and studios will still gladly feature professional voice talent in their projects and using AI will simply contribute to faster turnaround times and greater freedom to experiment and establish direction in early development. The technology will change how spoken audio is designed and recorded but the fact that voice actors no longer need to be physically present for every session really gives them the freedom to be involved in more projects at any one time, as well as to truly immortalize their voices."

"Green Light uses machine learning systems to comb through Maps data to calculate the amount of traffic congestion present at a given light, as well as the average wait times of vehicles stopped there. That information is then used to train AI models that can autonomously optimize the traffic timing at that intersection, reducing idle times as well as the amount of braking and accelerating vehicles have to do there. It's all part of Google's goal to help its partners collectively reduce their carbon emissions by a gigaton by 2030."

"OpenAI has "discussed and debated quite extensively" when to release a tool that can determine whether an image was made with DALL-E 3, OpenAI's generative AI art model, or not. But the startup isn't close to making a decision anytime soon. That's according to Sandhini Agarwal, an OpenAI researcher who focuses on safety and policy, who spoke with TechCrunch in a phone interview this week. She said that, while the classifier tool's accuracy is "really good" - at least by her estimation - it hasn't met OpenAI's threshold for quality."