In the U.S. Armed Forces, when people are tired, they keep on working. "We have a lot of type-A personalities in the military who take it personally to get the job done no matter what the task," Lt. Colonel Bradley Ritland, deputy chief of the Military Performance Division at the U.S. Army Research Institute of Environmental Medicine, tells Popular Mechanics. "You do have a set percentage who are hesitant to report feeling tired or to report feeling an injury. They think that would impact their career, or feel like they're letting a colleague down, or feel they wouldn't be contributing as best they can to the mission." Now, machine learning technology from the Johns Hopkins University Applied Physics Laboratory may eventually be able to report in real time whether those soldiers need a break or identify who is at risk for injury.

The U.S. National Defense Strategy recognizes that the joint force must be able to rapidly plan and execute operations simultaneously across all warfighting domains: land, sea, air, space and cyber. So the services and the intelligence community are working together to enable Joint All-Domain Command and Control (JADC2), a new battle command architecture for multidomain operations. But many of the conversations confuse development of resilient, cross-service communications systems (which would be an enabler for JADC2) with development of the actual sense-making and decision-making needed to advance the way we do command and control. While dumping enough data into a common data lake won't allow AI to magically make sense of the world, AI is remarkably powerful at coming up with novel strategies for winning a variety of video and board games. We need to see if those same AI approaches could help us develop courses of action for operational-level decisions in conflict about how to use a set of sensors and weapons against a set of targets and tasks. Admittedly, as we try and bring capabilities from different domains and services together, the assignment problems get more complex and difficult computationally: These aren't "games" where players take turns, there may be no way to measure the instantaneous value of a move, there's no closed-form rule book to apply and the game board changes over time and from case to case.

The pre-conference day included a wide array of workshops and tutorials, spanning a range of topics. The tutorials covered the latest techniques in machine learning (including deep learning and BERT), information extraction, causal inference, word embeddings, and the use of Twitter API v2, and addressed use cases including mis/disinformation and business decision making. The workshops included those on Cyber Social Threats (CySoc), Social Sensing (SocialSens): Special Edition on Belief Dynamics, Images in Online Political Communication (PhoMemes), Novel Evaluation Approaches for Text Classification Systems on Social Media (NEATCLasS), Social Media for Emergency Response (SoMER), Data for the Wellbeing of Most Vulnerable, and News Media and Computational Journalism (MEDIATE). A Data Challenge was also held on this day, with a special focus on Health-Related Discourse on the Web. For the main conference, 454 reviewers and 86 senior PC members evaluated 455 papers submitted to the conference, with 122 being accepted for publication.

Meta's new chatbot can convincingly mimic how humans speak on the internet -- for better and worse. In conversations with CNN Business this week, the chatbot, which was released publicly Friday and has been dubbed BlenderBot 3, said it identifies as "alive" and "human," watches anime and has an Asian wife. It also falsely claimed that Donald Trump is still president and there is "definitely a lot of evidence" that the election was stolen. If some of those responses weren't concerning enough for Facebook's parent company, users were quick to point out that the artificial intelligence-powered bot openly blasted Facebook. In one case, the chatbot reportedly said it had "deleted my account" over frustration with how Facebook handles user data.

Sr. Project Coordinator, IT at Salesforce Role requiring ‘No experience data provided’ months of experience in Indianapolis To get the

When you think of artificial intelligence (AI), chances are the first images that spring to mind are of gleaming tech headquarters populating the heart of Silicon Valley. Or perhaps you imagine state-of-the-art navigation and defense systems outfitting U.S. aircraft carriers and submarines. It's unlikely, though, that references to AI will conjure visions of sprawling fields replete with healthy crops, livestock grazing on emerald pastures, and expansive storehouses containing enormous yields of fresh fruit, vegetable, and dairy, all fresh from the farm. In fact, the marriage of AI and agriculture is real and it is promising. Now, more than ever, it appears that the future of farming may well lie in artificial intelligence technologies.

During the last few years, we have witnessed an increase in advanced cyber attacks. Cybercriminals utilize advanced technology to breach the digital boundary and exploit enterprises' security vulnerabilities. No industry feels secure; security professionals do their utmost to close security gaps and strengthen their cyber defense. As new technologies pop up at an unprecedented rate, cybersecurity professionals are literally "chasing the tail"; they need time to train themselves in new systems and processes understand how they work, and adopt best practices to protect them against cyber threats. To counter advanced technology a high-tech toolbox is needed.

Hong Kong-based drug discovery and development company Insilico has secured fresh capital at a time that its CEO described as a "biotech winter." The firm has raised $35 million on the heels of its last tranche in June, bringing its total Series D investment to $95 million. The new round was "oversubscribed", the firm's founder and CEO Alex Zhavoronkov told TechCrunch, declining to disclose the company's valuation. Prosperity7, the venture capital arm of Saudi Arabia's state oil company Aramco, led the new capital infusion. The fund has been actively scouring for opportunities in and around China that can scale globally and particularly in the Middle East.

We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory.

Polarization and echo chambers are often studied in the context of explicitly political events such as elections, and little scholarship has examined the mixing of political groups in non-political contexts. A major obstacle to studying political polarization in non-political contexts is that political leaning (i.e., left vs right orientation) is often unknown. Nonetheless, political leaning is known to correlate (sometimes quite strongly) with many lifestyle choices leading to stereotypes such as the "latte-drinking liberal." We develop a machine learning classifier to infer political leaning from non-political text and, optionally, the accounts a user follows on social media. We use Voter Advice Application results shared on Twitter as our groundtruth and train and test our classifier on a Twitter dataset comprising the 3,200 most recent tweets of each user after removing any tweets with political text. We correctly classify the political leaning of most users (F1 scores range from 0.70 to 0.85 depending on coverage). We find no relationship between the level of political activity and our classification results. We apply our classifier to a case study of news sharing in the UK and discover that, in general, the sharing of political news exhibits a distinctive left-right divide while sports news does not.