Former Israeli Amb. to the U.S. Michael Oren discusses Iran's nuclear capabilities and negotiations with Hamas to release more hostages on'Fox Report.' The idea that Middle East peace cannot and should not advance without a formal agreement between Israel and the Palestinian Authority is outdated and demonstrably untrue. Indeed, it has done little but exacerbate conflict over the last 30 years and undermine U.S. interests in the region. They provide a new paradigm for peace between Israel and all of its neighbors, including the Palestinians. President Donald Trump obviously deserves the Nobel Peace Prize for breaking with the failed Oslo peace process paradigm still sanctified by legacy media and a bipartisan community of foreign policy elites, and for building new bridges of mutually-beneficial cooperation between Israel and its Arab neighbors.

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet their direct application to NP-hard combinatorial problems (CPs) remains underexplored. In this work, we systematically investigate the reasoning abilities of LLMs on a variety of NP-hard combinatorial optimization tasks and introduce ACCORD: Autoregressive Constraint-satisfying generation for COmbinatorial optimization with Routing and Dynamic attention. ACCORD features a novel dataset representation and model architecture that leverage the autoregressive nature of LLMs to dynamically enforce feasibility constraints, coupled with attention-based routing to activate problem-specific LoRA modules. We also present the ACCORD-90k supervised dataset, covering six NP-hard combinatorial problems: TSP, VRP, Knapsack, FlowShop, JSSP, and BinPacking. Extensive experiments demonstrate that our ACCORD model, built on an 8B-parameter Llama backbone, consistently outperforms standard prompting and input-output methods, even when compared to much larger LLMs, such as gpt-4. Ablation studies further show that our output structure enhances solution feasibility. To the best of our knowledge, this is the first large-scale, end-to-end framework for exploring the applications of LLMs to a broad spectrum of combinatorial optimization problems. The codes are publicly available at https://github.com/starjob42/ACCORD

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".

Building machine learning versions could be compared to creating a house. Obviously, a hammer is a fantastic tool if you come across a nail, however it's unnecessary to use it if digging a pit. The same holds for machine learning model improvement -- there's not any"only tool to rule them all" but a comprehensive set of resources to use to fix a specific issue. Machine learning is a multidisciplinary field spanning the bounds of maths, technology, and applications development. But that is not all -- that the data scientist wants not just to be aware of the issue but to have the domain knowledge to provide a usable answer. The same is true for a builder that wants to construct a home -- not merely the understanding of placing bricks together is demanded, but also a vision of a home and the basic understanding of its objective is essential.

What you'll find below is my attempt at talking about my game in the most palatable way possible as I finish grad school! So, get ready for some academic conversation on artificial intelligence, genetic algorithm, data, thesis, accuracy, prediction, all that good stuff! Science is great, and I love it! Despite my degree being unrelated to game development, I decided to inject my passion into the project by applying machine learning to a tower defense game. I hope this article about my research process helps others to incorporate machine learning into their games.

ABU DHABI - President Donald Trump's national security adviser warned Iran on Wednesday that any attacks in the Persian Gulf will draw a "very strong response" from the U.S., taking a hard-line approach with Tehran after his boss only two days earlier said America wasn't "looking to hurt Iran at all." John Bolton's comments are the latest amid heightened tensions between Washington and Tehran that have been playing out in the Middle East. Bolton spoke to journalists in Abu Dhabi, the capital of the United Arab Emirates, which only days earlier saw former Defense Secretary Jim Mattis warn there that "unilateralism will not work" in confronting the Islamic Republic. The dueling approaches highlight the divide over Iran within American politics. The U.S. has accused Tehran of being behind a string of incidents this month, including the alleged sabotage of oil tankers off the Emirati coast, a rocket strike near the U.S. Embassy in Baghdad and a coordinated drone attack on Saudi Arabia by Yemen's Iran-allied Houthi rebels. On Wednesday, Bolton told journalists that there had been a previously unknown attempt to attack the Saudi oil port of Yanbu as well, which he also blamed on Iran.

The US government averted another shutdown when Donald Trump instead opted to declare a national emergency to fund his border wall dreams--a wall which raises huge privacy and security concerns and will cause more problems than it solves. As the country digested the national emergency, cybersecurity workers were still scrambling to clean up the security nightmare wrought by the longest shutdown in history. Amid all the border wall news this week, you'd be forgiven for missing that the president also signed an executive order creating the American AI Initiative. In an op-ed for WIRED, White House deputy assistant to the president for technology policy Michale Kratsios explained why AI strategy is a security issue. Speaking of AI, to combat the growing threat of deep fakes, a new tool uses the blockchain to monitor video for tampering and manipulation.

The latest version is 2.0.1. TensorFlow -- Machine Learning Software, is an open source software library for machine learning. It was developed by the Google team for sorts of perceptual tasks. Also, to conduct sophisticated research on machine learning and deep neural networks. TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors.

Foreign Minister Taro Kono agreed Wednesday with his German counterpart to promote free trade amid a rising protectionist tide, while supporting a rules-based international order. During talks in Tokyo, Kono and German Foreign Minister Heiko Maas stressed the importance of closer economic ties just days after the signing of a free trade agreement between Japan and the European Union. "The free, open and rules-based international order faces a serious challenge," Kono said during a joint press briefing with Maas. "Closer cooperation between Japan and Germany, (countries) that share the same values such as democracy, and lead Asia and Europe … is taking on greater importance than ever." The signing earlier this month of the free trade deal, which covers about a third of the world's economy, has been seen as symbolic of the concerted effort to counter the increasingly protectionist steps taken by U.S. President Donald Trump.

Using data from large clinical trials, UT Southwestern researchers developed a way to predict which patients will benefit most from aggressive high blood pressure treatment. The machine learning algorithm they devised combines three variables routinely collected during clinic visits and demonstrates how the emerging field of bioinformatics could transform patient care. Their work, available online now and publishing July 15 in the American Journal of Cardiology, describes a risk prediction model in which patient age, urinary albumin/creatinine ratio (UACR), and cardiovascular disease history successfully identified hypertensive patients for whom the benefits of intensive therapy outweigh the risks. "Large randomized trials have provided inconsistent evidence regarding the benefit of intensive blood pressure lowering in hypertensive patients," said corresponding author Dr. Yang Xie, Director of the Quantitative Biomedical Research Center at UT Southwestern and of the University's Bioinformatics Core Facility. "To the best of our knowledge, this is the first study to identify a subgroup of patients who derive a higher net benefit from intensive blood pressure treatment."