Major technology companies signed a pact Friday to voluntarily adopt "reasonable precautions" to prevent artificial intelligence tools from being used to disrupt democratic elections around the world. Executives from Adobe, Amazon, Google, IBM, Meta, Microsoft, OpenAI and TikTok gathered at the Munich Security Conference to announce a new framework for how they respond to AI-generated deepfakes that deliberately trick voters. Twelve other companies – including Elon Musk's X – are also signing on to the accord. "Everybody recognizes that no one tech company, no one government, no one civil society organization is able to deal with the advent of this technology and its possible nefarious use on their own," said Nick Clegg, president of global affairs for Meta, the parent company of Facebook and Instagram, in an interview ahead of the summit. The accord is largely symbolic, but targets increasingly realistic AI-generated images, audio and video "that deceptively fake or alter the appearance, voice, or actions of political candidates, election officials, and other key stakeholders in a democratic election, or that provide false information to voters about when, where, and how they can lawfully vote".

A coalition of 20 tech companies signed an agreement Friday to help prevent AI deepfakes in the critical 2024 elections taking place in more than 40 countries. OpenAI, Google, Meta, Amazon, Adobe and X are among the businesses joining the pact to prevent and combat AI-generated content that could influence voters. However, the agreement's vague language and lack of binding enforcement call into question whether it goes far enough. The list of companies signing the "Tech Accord to Combat Deceptive Use of AI in 2024 Elections" includes those that create and distribute AI models, as well as social platforms where the deepfakes are most likely to pop up. The signees are Adobe, Amazon, Anthropic, Arm, ElevenLabs, Google, IBM, Inflection AI, LinkedIn, McAfee, Meta, Microsoft, Nota, OpenAI, Snap Inc., Stability AI, TikTok, Trend Micro, Truepic and X (formerly Twitter).

This week Microsoft announced that it would invest 3.2 billion ( 3.5 billion) in Germany over the next two years. The U.S. tech giant will use the money to double the capacity of its artificial intelligence and data center infrastructure in Germany and expand its training programmes, according to Microsoft vice chair and president Brad Smith. The move follows a similar announcement from November 2023, when Microsoft said it would invest 2.5 billion ( 3.2 billion) in infrastructure in the U.K. over the next three years. Both countries hailed the investments as significant steps that would permit them to compete on the world stage when it comes to AI. However, the investments are dwarfed by investments made by U.S.-based cloud service providers elsewhere, particularly in the U.S. As AI becomes increasingly economically and militarily important, governments are taking steps to ensure they have control over the technology that they depend on.

Rep. Ronny Jackson reiterated his calls for President Biden to prove his mental fitness for office after it was put into question by Special Counsel Robert Hur. FIRST ON FOX: House Republicans are appealing directly to President Biden demanding that he take a cognitive test to prove his mental fitness for office. Rep. Ronny Jackson, R-Texas, the former White House physician who served as chief medical adviser to former President Trump, led a letter to the president co-signed by 83 House Republicans, including House GOP Conference Chair Elise Stefanik and Chief Deputy Whip Guy Reschenthaler, arguing that the president's many public "gaffes" are a "national security concern." "Following the recent report from Special Counsel Robert Hur, we write to express our grave concerns with your current cognitive state and ability to successfully execute the duties of the Presidency, including as Chief Executive, Head of State, and Commander in Chief," the lawmakers wrote. "The President of the United States must demonstrate sound mental abilities, regardless of gender, age, or political party, which you have not." Texas GOP Rep. Ronny Jackson, a former White House physician, left, is again calling on President Biden to take a cognitive exam.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. New Mexico's Democrat-led Legislature delivered on a handful of the governor's major priorities in her calls for public safety reforms, gun control, housing construction and the use of incentives to forge new solutions to climate change as lawmakers adjourned their 30-day annual session Thursday. Lujan Grisham praised a trio of public safety bills that ban some guns at voting locations, extend a waiting period on gun purchases to seven days and give judges an extra opportunity to deny bail to defendants who are charged with new crimes while already awaiting trial on a felony. But she also delivered a grim assessment of violent crime across the state -- invoking the stabbing death last week of a Las Cruces patrol officer at the hands of a man with a record of crime and mental illness.

Academy of Media Arts Founder Dana Hammond joined'Fox & Friends First' to discuss why the school was forced to close to accommodate the homeless population. An Austin resident who has been documenting the city's homeless crisis has helped develop an AI interactive map to track camps and communities. Jamie Hammonds, who has been documenting the crisis through DASH Media, provided data to the group Nomadik, which developed the full-featured AI map. The map vividly depicts where homeless encampments are concentrated throughout the city. A map, powered by AI, provides a visual representation showing where Austin's homeless are concentrated.

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural network models have proved to be adept at modeling processes with spatial-temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance.Projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

On July 20, 2023, a group of 27 scholars and digital rights advocates with expertise in law, computer science, political science, and other disciplines gathered for the Large Language Models, Law and Policy Roundtable, co-hosted by the NYU School of Law's Information Law Institute and the Center for Democracy & Technology. The roundtable convened to discuss how law and policy can help address some of the larger societal problems posed by large language models (LLMs). The discussion focused on three policy topic areas in particular: 1. Truthfulness: What risks do LLMs pose in terms of generating mis- and disinformation? How can these risks be mitigated from a technical and/or regulatory perspective? 2. Privacy: What are the biggest privacy risks involved in the creation, deployment, and use of LLMs? How can these risks be mitigated from a technical and/or regulatory perspective? 3. Market concentration: What threats do LLMs pose concerning market/power concentration? How can these risks be mitigated from a technical and/or regulatory perspective? In this paper, we provide a detailed summary of the day's proceedings. We first recap what we deem to be the most important contributions made during the issue framing discussions. We then provide a list of potential legal and regulatory interventions generated during the brainstorming discussions.

We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements acting alone.'' Collective Intelligence emerges from new ways of connecting humans and AI to enable decision-advantage, in part by creating and leveraging additional sources of information that might otherwise not be included. Aggregative crowdsourced forecasting (ACF) is a recent key advancement towards Collective Intelligence wherein predictions (X\% probability that Y will happen) and rationales (why I believe it is this probability that X will happen) are elicited independently from a diverse crowd, aggregated, and then used to inform higher-level decision-making. This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios (i.e., sequences of events with defined agents, components, and interactions) and decision-making, and considers whether such a capability could provide novel operational capabilities to enable new forms of decision-advantage.

Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of them at once. Recent works such as AdapterSoup propose to mix not all but only a selective sub-set of domain-specific adapters during inference via model weight averaging to optimize performance on novel, unseen domains with excellent computational efficiency. However, the essential generalizability of this emerging weight-space adapter mixing mechanism on unseen, in-domain examples remains unexplored. Thus, in this study, we conduct a comprehensive analysis to elucidate the generalizability of domain-specific adapter mixtures in in-domain evaluation. We also provide investigations into the inner workings of the mixture of domain-specific adapters by analyzing their weight signs, yielding critical analysis on the negative correlation between their fraction of weight sign difference and their mixtures' generalizability. All source code will be published.