U.S. government spending on artificial intelligence has exploded in the past year, driven by increased military investments, according to a report by the Brookings Institution, a think tank based in Washington D.C. The report found that the potential value of AI-related federal contracts increased by almost 1,200%, from 355 million in the period leading up to August 2022, to 4.6 billion in the period leading up to August 2023. This increase was almost entirely driven by the Department of Defense (DoD). The total amount committed by the DoD to AI-related contracts increased from 190 million in the period leading up to August 2022 to 557 million in the period leading up to August 2023. The total that the DoD might spend on AI-related contracts if each contract were extended to its fullest terms grew even faster, from 269 million in the period leading up to August 2022 to 4.3 billion in the period leading up to August 2023.

The New York Times reports that Israel's military intelligence has been using an experimental facial recognition program in Gaza that's misidentified Palestinian civilians as having ties to Hamas. Google Photos allegedly plays a part in the chilling program's implementation, although it appears not to be through any direct collaboration with the company. The surveillance program reportedly started as a way to search for Israeli hostages in Gaza. However, as often happens with new wartime technology, the initiative was quickly expanded to "root out anyone with ties to Hamas or other militant groups," according to The NYT. The technology is flawed, but Israeli soldiers reportedly haven't treated it as such when detaining civilians flagged by the system.

Mr. Abu Toha is one of hundreds of Palestinians who have been picked out by a previously undisclosed Israeli facial recognition program that was started in Gaza late last year. The expansive and experimental effort is being used to conduct mass surveillance there, collecting and cataloging the faces of Palestinians without their knowledge or consent, according to Israeli intelligence officers, military officials and soldiers. The technology was initially used in Gaza to search for Israelis who were taken hostage by Hamas during the Oct. 7 cross-border raids, the intelligence officials said. After Israel embarked on a ground offensive in Gaza, it increasingly turned to the program to root out anyone with ties to Hamas or other militant groups. At times, the technology wrongly flagged civilians as wanted Hamas militants, one officer said.

MUCH is made of intergenerational conflicts, with boomers pitted against millennials or Gen Zers. But however these competing needs are resolved today, in the future, younger people will become an increasingly prized resource, because there will be fewer of them. Populations are slowly being skewed older than ever before by two seemingly unstoppable demographic forces. One is that, as countries become more prosperous, there is a decline in the number of children that people have. When that figure drops below the population replacement level of 2.1 children per woman – unless offset by immigration – the head count shrinks, as…

The Netflix sign-in page displayed on a laptop screen and Netflix logo displayed on a phone screen are seen in this illustration photo taken in Krakow, Poland, on January 2, 2023. 'NO SHORTCUT': Netflix chief Ted Sarandos does not see artificial intelligence as an "existential" threat to creativity, but a powerful tool. DEADLY CHEAP: Iran has made it no secret that it plans to invest heavily in artificial intelligence to help better its military capabilities, but Iranian President Ebrahim Raisi is now turning to Iran's private sector in a move he thinks will boost his crippling economy. 'CRITICAL ISSUE': GOP rep on bipartisan AI task force says group is concerned about impact on 2024 elections. Governor Bill Lee speaks during the signing of the ELVIS Act to Protect Voice & amp; Likeness in Age of AI event at Robert's Western World on March 21, 2024 in Nashville, Tennessee.

Chatbots could take over eight million jobs in the UK - and women will be worst affected, a leading think tank has warned. Analysis has found nearly two-thirds of tasks carried out by workers could be automated by AI, with admin and entry-level jobs most at risk. But the Institute for Public Policy Research (IPPR) claims the'jobs apocalypse' is not inevitable if the Government acts fast to ensure humans are not replaced. With the right regulation and fiscal incentives, it has estimated AI could instead boost the UK economy by 306bn - and even increase salaries for some by over a third. The report is the first of its kind to look at the impact of generative AI - the technology that mimics the human brain in generating text, images, and videos from scratch - on the UK labour market.

Almost 8 million UK jobs could be lost to artificial intelligence in a "jobs apocalypse", according to a report warning that women, younger workers and those on lower wages are at most risk from automation. The Institute for Public Policy Research (IPPR) said that entry level, part-time and administrative jobs were most exposed to being replaced by AI under a "worst-case scenario" for the rollout of new technologies in the next three to five years. The thinktank warned that the UK was facing a "sliding doors" moment as growing numbers of companies adopt generative AI technologies – which can read and create text, data and software code – to automate everyday workplace tasks. The report said this first wave of AI adoption was already putting jobs at risk as growing numbers of companies introduce the technology. However, a second wave could lead to the automation of more jobs amid rapid advances in AI.

As many as 8 million U.K. workers are at risk of losing their jobs to artificial intelligence (AI) with current government policy, the Institute for Public Policy Research warned. AI already impacts 11% of tasks done by U.K. staff, a figure that could rise to almost 60% if firms integrate the technology more deeply, the think tank warned in a report published Wednesday. Part-time, entry level and back-office jobs such as customer service are most at risk but higher-paying jobs will increasingly be affected. The findings highlight the challenges facing the government as it increasingly bets on artificial intelligence to cure the U.K.'s productivity problem. Companies across the board are using AI to drive up efficiency, while the U.K. Chancellor of the Exchequer announced a 800-million ( 1 billion) investment in technology and AI to boost public-sector output.

Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns and spurred recent research on LLM conversation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM conversation safety: attacks, defenses, and evaluations. Our goal is to provide a structured summary that enhances understanding of LLM conversation safety and encourages further investigation into this important subject. For easy reference, we have categorized all the studies mentioned in this survey according to our taxonomy, available at: https://github.com/niconi19/LLM-conversation-safety.

Evaluating the veracity of everyday claims is time consuming and in some cases requires domain expertise. We empirically demonstrate that the commonly used fact checking pipeline, known as the retriever-reader, suffers from performance deterioration when it is trained on the labeled data from one domain and used in another domain. Afterwards, we delve into each component of the pipeline and propose novel algorithms to address this problem. We propose an adversarial algorithm to make the retriever component robust against distribution shift. Our core idea is to initially train a bi-encoder on the labeled source data, and then, to adversarially train two separate document and claim encoders using unlabeled target data. We then focus on the reader component and propose to train it such that it is insensitive towards the order of claims and evidence documents. Our empirical evaluations support the hypothesis that such a reader shows a higher robustness against distribution shift. To our knowledge, there is no publicly available multi-topic fact checking dataset. Thus, we propose a simple automatic method to re-purpose two well-known fact checking datasets. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models, including recent domain adaptation models that use GPT4 for generating synthetic data.