Action-values play a central role in popular Reinforcement Learing (RL) models of behavior. Yet, the idea that action-values are explicitly represented has been extensively debated. Critics had therefore repeatedly suggested that policy-gradient (PG) models should be favored over value-based (VB) ones, as a potential solution for this dilemma. Here we argue that this solution is unsatisfying. This is because PG methods are not, in fact, "Value-free" -- while they do not rely on an explicit representation of Value for acting (stimulus-response mapping), they do require it for learning. Hence, switching to PG models is, per se, insufficient for eliminating Value from models of behavior. More broadly, the requirement for a representation of Value stems from the underlying assumptions regarding the optimization objective posed by the standard RL framework, not from the particular algorithm chosen to solve it. Previous studies mostly took these standard RL assumptions for granted, as part of their conceptualization or problem modeling, while debating the different methods used to optimize it (i.e., PG or VB). We propose that, instead, the focus of the debate should shift to critically evaluating the underlying modeling assumptions. Such evaluation is particularly important from an experimental perspective. Indeed, the very notion of Value must be reconsidered when standard assumptions (e.g., risk neutrality, full-observability, Markovian environment, exponential discounting) are relaxed, as is likely in natural settings. Finally, we use the Value debate as a case study to argue in favor of a more nuanced, algorithmic rather than statistical, view of what constitutes "a model" in cognitive sciences. Our analysis suggests that besides "parametric" statistical complexity, additional aspects such as computational complexity must also be taken into account when evaluating model complexity.

An overreliance on technology by Israel's intelligence agencies and military has continued to shape the current conflict in Gaza, analysts say, while also being partially responsible for the failure to detect the Hamas attack on October 7. Hamas's surprise attack on army outposts and surrounding villages in southern Israel, which resulted in the deaths of 1,200 Israeli and foreign nationals, mostly civilians, took the Israeli intelligence agencies by surprise. Hamas fighters also took about 240 people captive. Israel, in its brutal military response, has killed more than 17,000 Palestinians in Gaza since then. Within both Israel and the wider Arab region, many have asked how Shin Bet, one of the world's most respected and feared intelligence agencies, which is responsible for Israel's domestic security, could have been outmatched by Hamas using bulldozers and paragliders. The world's disbelief has sparked a bounty of conspiracy theories in some quarters.

Amman, Jordan – The Israeli army released footage on October 22 of its Maglan commando unit deploying a new precision-guided 120mm mortar bomb called the Iron Sting, against Hamas in Gaza. The bomb's Haifa-based manufacturer, Elbit Systems, has been advertising its qualities on the public relations page of its website since March 2021, when it was integrated into the Israeli military. Benny Gantz, then Israel's defence minister and now a part of Prime Minister Benjamin Netanyahu's war cabinet, described the Iron Sting as "designed to engage targets precisely, in both open terrains and urban environments, while reducing the possibility of collateral damage and preventing injury to non-combatants". It's a claim echoed by Mark Regev, Netanyahu's former spokesperson, for the country's overall approach to its war on Gaza, in which, he has said, Israel is "trying to be as surgical as humanly possible". Yet, more than one month after Israel launched the aerial bombardment of Gaza following a surprise Hamas attack, it has killed at least 11,400 Palestinian civilians, and injured 30,000 in the besieged strip and the occupied West Bank.

Curiosity for machine agents has been a focus of lively research activity. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that would offer important benefits for machine learners, but that have not yet been well-explored in machine intelligence. In this work, we conduct a comprehensive, multidisciplinary survey of the field of animal and machine curiosity. As a principal contribution of this work, we use this survey as a foundation to introduce and define what we consider to be five of the most important properties of specific curiosity: 1) directedness towards inostensible referents, 2) cessation when satisfied, 3) voluntary exposure, 4) transience, and 5) coherent long-term learning. As a second main contribution of this work, we show how these properties may be implemented together in a proof-of-concept reinforcement learning agent: we demonstrate how the properties manifest in the behaviour of this agent in a simple non-episodic grid-world environment that includes curiosity-inducing locations and induced targets of curiosity. As we would hope, our example of a computational specific curiosity agent exhibits short-term directed behaviour while updating long-term preferences to adaptively seek out curiosity-inducing situations. This work, therefore, presents a landmark synthesis and translation of specific curiosity to the domain of machine learning and reinforcement learning and provides a novel view into how specific curiosity operates and in the future might be integrated into the behaviour of goal-seeking, decision-making computational agents in complex environments.

In a presentation at the EURETINA 2021 Virtual Congress, Anat Loewenstein, MD, MHA, discussed optimizing optical coherence tomography (OCT) and how physicians can determine with even more accuracy precisely what is happening in patients' eyes with neovascular age-related macular degeneration (nAMD) because of the potential afforded by application of artificial intelligence (AI). Loewenstein is professor and director of the Department of Ophthalmology at Tel Aviv Medical Center, the Sidney A. Fox Chair in Ophthalmology, and vice dean, Sackler Faculty of Medicine, Tel Aviv University, Israel. OCT is a major step forward in patient diagnosis, treatment, and monitoring but shortcomings remain. For example, physicians routinely make qualitative assessments of the presence and degrees of intraretinal/subretinal fluid and pigment epithelial detachments, but these are not precise assessment that are likely to result in poor inter-grader agreement and intra-grader consistency; OCT also provides the central subfield thickness, but the retinal fluid and neural tissue are not considered separately. As Loewenstein pointed out, In neovascular AMD, it is important to distinguish between retinal fluid localization in the intraretinal and subretinal compartments and their volumetric information for informing retreatment decisions and predicting visual outcomes.

The impact of continually evolving digital technologies and the proliferation of communications and content has now been widely acknowledged to be central to understanding our world. What is less acknowledged is that this is based on the successful arousing of curiosity both at the collective and individual levels. Advertisers, communication professionals and news editors are in constant competition to capture attention of the digital population perennially shifty and distracted. This paper, tries to understand how curiosity works in the digital world by attempting the first ever work done on quantifying human curiosity, basing itself on various theories drawn from humanities and social sciences. Curious communication pushes people to spot, read and click the message from their social feed or any other form of online presentation. Our approach focuses on measuring the strength of the stimulus to generate reader curiosity by using unsupervised and supervised machine learning algorithms, but is also informed by philosophical, psychological, neural and cognitive studies on this topic. Manually annotated news headlines - clickbaits - have been selected for the study, which are known to have drawn huge reader response. A binary classifier was developed based on human curiosity (unlike the work done so far using words and other linguistic features). Our classifier shows an accuracy of 97% . This work is part of the research in computational humanities on digital politics quantifying the emotions of curiosity and outrage on digital media.

WASHINGTON – It's a scary world out there, risk experts agree, but they say Donald Trump and Hillary Clinton often focus on the wrong dangers -- fixing on hazards that are unlikely, or unlikely to cause massive pain. The Associated Press asked 21 risk experts to analyze the presidential campaign and list what they consider the five biggest threats to the world. Climate change topped the list with 17 mentions, often as the top threat. It was followed by use of nuclear weapons, pandemics, cyberattacks and problems with high technology. Neither Trump's signature issues of immigration and terrorism nor Clinton's major concerns, financial insecurity and gun violence, made the list. "I have not heard or read about any significant deliberations of the major risks that face our country today and tomorrow.