Lifeboat Foundation

“Recall uses Copilot+ PC advanced processing capabilities to take images of your active screen every few seconds,” Microsoft says on its website. “The snapshots are encrypted and saved on your PC’s hard drive. You can use Recall to locate the content you have viewed on your PC using search or on a timeline bar that allows you to scroll through your snapshots.”

By performing a Recall action, users can access a snapshot from a specific time period, providing context for the event or moment they are searching for. It also allows users to search through teleconference meetings they’ve participated in and videos watched using an AI-powered feature that transcribes and translates speech.

At first glance, the Recall feature seems like it may set the stage for potential gross violations of user privacy. Despite reassurances from Microsoft, that impression persists for second and third glances as well. For example, someone with access to your Windows account could potentially use Recall to see everything you’ve been doing recently on your PC, which might extend beyond the embarrassing implications of pornography viewing and actually threaten the lives of journalists or perceived enemies of the state.

Yann LeCun argues current AI methods are flawed as he pushes for ‘world modelling’ vision for superintelligence.

An international research team including Los Alamos National Laboratory and Tel Aviv University has developed a unique, mechanical metamaterial that, like a computer following instructions, can remember the order of actions performed on it. Named Chaco, after the archaeological site in northern New Mexico, the new metamaterial offers a route to applications in memory storage, robotics, and even mechanical computing.

OpenAI has landed itself in hot water for pushing out an update to ChatGPT that features a virtual assistant with an uncanny vocal resemblance to Scarlett Johansson — and it could be staring down the barrel of a compelling lawsuit.

Almost instantly, comparisons to the movie “Her” abounded, in which the actress plays a chatbot named Samantha that falls in love with a lonely man. Had OpenAI just aped her role — and her voice? Officially, it said no. Then, Johansson dropped a bombshell: leadership at the AI startup had in fact asked permission to use her voice last year. She said no, and they did it anyway.

The codes to replicate the simulations of the paper: Available at: https://arxiv.org/abs/2405.12832 and also: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4835325

For now, we just added the codes to…

In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance. Traditional multilayer perceptrons (MLPs) and even recent advancements like Spl-KAN \cite{kan} face challenges related to interpretability, training speed, robustness, computational efficiency, and performance. Wav-KAN addresses these limitations by incorporating wavelet functions into the Kolmogorov-Arnold network structure, enabling the network to capture both high-frequency and low-frequency components of the input data efficiently. Wavelet-based approximations employ orthogonal or semi-orthogonal basis and also maintains a balance between accurately representing the underlying data structure and avoiding overfitting to the noise. Analogous to how water conforms to the shape of its container, Wav-KAN adapts to the data structure, resulting in enhanced accuracy, faster training speeds, and increased robustness compared to Spl-KAN and MLPs. Our results highlight the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields. This work sets the stage for further exploration and implementation of Wav-KAN in frameworks such as PyTorch, TensorFlow, and also it makes wavelet in KAN in wide-spread usage like nowadays activation functions like ReLU, sigmoid in universal approximation theory (UAT).

editor of[Possible Minds: Twenty-Five Ways of Looking at AI], talked about the future of artificial intelligence with two contributors to the book, David Chalmers and Daniel Dennett.

Memristors mimic timekeeping in the brain, which is done with neurons that relax at different rates after receiving a signal.

Leading AI scientists are calling for stronger action on AI risks from world leaders, warning that progress has been insufficient since the first AI Safety Summit in Bletchley Park six months ago.

The US Department of Justice arrested a Wisconsin man last week for generating and distributing AI-generated child sexual abuse material (CSAM). As far as we know, this is the first case of its kind as the DOJ looks to establish a judicial precedent that exploitative materials are still illegal even when no children were used to create them. “Put simply, CSAM generated by AI is still CSAM,” Deputy Attorney General Lisa Monaco wrote in a press release.

The DOJ says 42-year-old software engineer Steven Anderegg of Holmen, WI, used a fork of the open-source AI image generator Stable Diffusion to make the images, which he then used to try to lure an underage boy into sexual situations. The latter will likely play a central role in the eventual trial for the four counts of “producing, distributing, and possessing obscene visual depictions of minors engaged in sexually explicit conduct and transferring obscene material to a minor under the age of 16.”

The government says Anderegg’s images showed “nude or partially clothed minors lasciviously displaying or touching their genitals or engaging in sexual intercourse with men.” The DOJ claims he used specific prompts, including negative prompts (extra guidance for the AI model, telling it what not to produce) to spur the generator into making the CSAM.