In fact, it's been around since the 1950s. The problem back then was that computers lacked the ability to store commands, so they could be told what to do but couldn't remember what they did. We've certainly come a long way since then! What are some of the benefits of AI and ML? One of the best use cases we've seen is with how AI and ML can handle the large volume of data that is being produced. ML and AI can help humans manage that data and process things like security data.

Modern security information and event management and intrusion detection systems leverage ML to correlate network features, identify patterns in data and highlight anomalies corresponding to attacks. Security researchers spend many hours understanding these attacks and trying to classify them into known kinds like port sweep, password guess, teardrop, etc. However, due to the constantly changing attack landscape and the emergence of advanced persistent threats (APTs), hackers are continuously finding new ways to attack systems. A static list of classification of attacks will not be able to adapt to new and novel tactics adopted by adversaries. Also, due to the constant flow of alarms generated by multiple sources in the network, it becomes difficult to distinguish and prioritize particular types of attacks--the classic alarm flooding problem.

The Commerce Department is building a National Artificial Intelligence Advisory Committee to guide President Biden on artificial intelligence's effect on society, on a variety of issues such as economic sectors and racial-justice matters. The personnel chosen by Mr. Biden for the committee will determine U.S. strategy on AI policy not only for the administration but also to shape AI competition with China and other adversaries for years to come. The federal government is recruiting candidates for the committee and submitted a formal call for nominations in the Federal Register last week. TOP STORIES Senate Dems to force vote on bill overriding new elections laws in GOP-led states CNN poll: America turns irate, glum'No SALT, no deal': Vulnerable Dems threaten revolt unless $3.5T bill cuts blue-states taxes "AI presents an enormous opportunity to tackle the biggest issues of our time, strengthen our technological competitiveness, and be an engine for growth in nearly every sector of the economy," said Commerce Secretary Gina Raimondo in a statement. "But we must be thoughtful, creative, and wise in how we address the challenges that accompany these new technologies.

On the 20th anniversary of 9/11, against the backdrop of the rushed U.S.-allied Afghanistan withdrawal, the grisly reality of armed combat and the challenge posed by asymmetric suicide terror attacks grow harder to ignore. But weapons technology has changed substantially over the past two decades. And thinking ahead to the not-so-distant future, we must ask: What if these assailants were able to remove human suicide bombers or attackers from the equation altogether? As someone who has studied and worked in artificial intelligence for the better part of four decades, I worry about such a technology threat, born from artificial intelligence and robotics. Autonomous weaponry is the third revolution in warfare, following gunpowder and nuclear arms.

Having spent more than 25 years working with industry partners to educate and prepare the future workforce, it is not surprising to see that Florida has experienced growth in the technology sector. Across the nation, the U.S. Bureau of Labor Statistics estimates that computer and information technology occupations are projected to grow 11 percent from 2019 to 2029, much faster than the average for all occupations. Additionally, demand for skilled professionals in robotics and artificial intelligence is growing. The World Economic Forum estimates that while 85 million jobs will be displaced, 97 million new jobs will be created across 26 countries by 2025 due to the growth of artificial intelligence technology. From my conversations with industry leaders to the research and data I've studied, all signs lead me to believe that robotics and artificial intelligence will be a significant economic driver, surpassing the impact of Christopher Columbus' exploration of the New World in 1492.

Kentucky Republican weighs in on the Biden admin's handling of the Afghanistan crisis on'The Ingraham Angle' The majority of the Sunday morning newscasts on the liberal networks avoided addressing the explosive New York Times report that the drone strike touted by the Biden administration in response to the deadly terrorist attack in Afghanistan did not actually kill the terrorist plotters. Days after 13 U.S. service members were murdered from a suicide bombing outside of the Kabul airport, the Pentagon announced a drone strike that successfully targeted "two high profile" ISIS-K fighters who were dubbed as "planners and facilitators" of the Aug. 26 attack. The Biden administration praised the Pentagon's swift action to support President Biden's rhetoric that those responsible for the terror attack will be brought to justice. However, the Times published the results of a bombshell investigation on Friday outlining video evidence that not only were ISIS-K terrorists not killed in the drone strike but that Zemari Ahmadi, who was described by the Times as a "longtime worker for a U.S. aid group" was one of ten civilians who were killed, seven children among them. The controversy was apparently not newsworthy enough for ABC's "This Week," NBC's "Meet the Press" and CNN's "State of the Union," all avoiding the damning report.

Cybersecurity researchers have contributed to the automated extraction of CTI from textual sources, such as threat reports and online articles, where cyberattack strategies, procedures, and tools are described. The goal of this article is to aid cybersecurity researchers understand the current techniques used for cyberthreat intelligence extraction from text through a survey of relevant studies in the literature. We systematically collect "CTI extraction from text"-related studies from the literature and categorize the CTI extraction purposes. We propose a CTI extraction pipeline abstracted from these studies. We identify the data sources, techniques, and CTI sharing formats utilized in the context of the proposed pipeline. Our work finds ten types of extraction purposes, such as extraction indicators of compromise extraction, TTPs (tactics, techniques, procedures of attack), and cybersecurity keywords. We also identify seven types of textual sources for CTI extraction, and textual data obtained from hacker forums, threat reports, social media posts, and online news articles have been used by almost 90% of the studies. Natural language processing along with both supervised and unsupervised machine learning techniques such as named entity recognition, topic modelling, dependency parsing, supervised classification, and clustering are used for CTI extraction. We observe the technical challenges associated with these studies related to obtaining available clean, labelled data which could assure replication, validation, and further extension of the studies. As we find the studies focusing on CTI information extraction from text, we advocate for building upon the current CTI extraction work to help cybersecurity practitioners with proactive decision making such as threat prioritization, automated threat modelling to utilize knowledge from past cybersecurity incidents.

Intrinsic evaluations of OIE systems are carried out either manually -- with human evaluators judging the correctness of extractions -- or automatically, on standardized benchmarks. The latter, while much more cost-effective, is less reliable, primarily because of the incompleteness of the existing OIE benchmarks: the ground truth extractions do not include all acceptable variants of the same fact, leading to unreliable assessment of models' performance. Moreover, the existing OIE benchmarks are available for English only. In this work, we introduce BenchIE: a benchmark and evaluation framework for comprehensive evaluation of OIE systems for English, Chinese and German. In contrast to existing OIE benchmarks, BenchIE takes into account informational equivalence of extractions: our gold standard consists of fact synsets, clusters in which we exhaustively list all surface forms of the same fact. We benchmark several state-of-the-art OIE systems using BenchIE and demonstrate that these systems are significantly less effective than indicated by existing OIE benchmarks. We make BenchIE (data and evaluation code) publicly available.

Commonsense is defined as the knowledge that is shared by everyone. However, certain types of commonsense knowledge are correlated with culture and geographic locations and they are only shared locally. For example, the scenarios of wedding ceremonies vary across regions due to different customs influenced by historical and religious factors. Such regional characteristics, however, are generally omitted in prior work. In this paper, we construct a Geo-Diverse Visual Commonsense Reasoning dataset (GD-VCR) to test vision-and-language models' ability to understand cultural and geo-location-specific commonsense. In particular, we study two state-of-the-art Vision-and-Language models, VisualBERT and ViLBERT trained on VCR, a standard multimodal commonsense benchmark with images primarily from Western regions. We then evaluate how well the trained models can generalize to answering the questions in GD-VCR. We find that the performance of both models for non-Western regions including East Asia, South Asia, and Africa is significantly lower than that for Western region. We analyze the reasons behind the performance disparity and find that the performance gap is larger on QA pairs that: 1) are concerned with culture-related scenarios, e.g., weddings, religious activities, and festivals; 2) require high-level geo-diverse commonsense reasoning rather than low-order perception and recognition. Dataset and code are released at https://github.com/WadeYin9712/GD-VCR.

Artificial Intelligence (AI) is rapidly becoming integrated into military Command and Control (C2) systems as a strategic priority for many defence forces. The successful implementation of AI is promising to herald a significant leap in C2 agility through automation. However, realistic expectations need to be set on what AI can achieve in the foreseeable future. This paper will argue that AI could lead to a fragility trap, whereby the delegation of C2 functions to an AI could increase the fragility of C2, resulting in catastrophic strategic failures. This calls for a new framework for AI in C2 to avoid this trap. We will argue that antifragility along with agility should form the core design principles for AI-enabled C2 systems. This duality is termed Agile, Antifragile, AI-Enabled Command and Control (A3IC2). An A3IC2 system continuously improves its capacity to perform in the face of shocks and surprises through overcompensation from feedback during the C2 decision-making cycle. An A3IC2 system will not only be able to survive within a complex operational environment, it will also thrive, benefiting from the inevitable shocks and volatility of war.