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Citation networks are critical infrastructures of modern science, serving as intricate webs of past literature and enabling researchers to navigate the knowledge production system. To mine information hiding in the link space of such networks, predicting which previous papers (candidates) will a new paper (query) cite is a critical problem that has long been studied. However, an important gap remains unaddressed: the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts. Distinguishing these roles requires a deeper understanding of the logical relationships among papers, beyond simple edges in citation networks. The emergence of large language models (LLMs) with textual reasoning capabilities offers new possibilities for discerning these relationships, but there are two major challenges. First, in practice, a new paper may select its citations from gigantic existing papers, where the combined texts far exceed the context length of LLMs. Second, logical relationships between papers are often implicit, and directly prompting an LLM to predict citations may lead to results based primarily on surface-level textual similarities, rather than the deeper logical reasoning required. In this paper, we introduce the novel concept of core citation, which identifies the critical references that go beyond superficial mentions.

Has Your Pledge to Go Paperless Perished? You promised yourself you'd digitize every last receipt, document, and paper record. But the trick to getting rid of paper is to not worry about being perfect. Wanting to get rid of paper in your life is easy. Following through with that promise to yourself is hard.

Citation networks are critical infrastructures of modern science, serving as intricate webs of past literature and enabling researchers to navigate the knowledge production system. To mine information hiding in the link space of such networks, predicting which previous papers (candidates) will a new paper (query) cite is a critical problem that has long been studied. However, an important gap remains unaddressed: the roles of a paper's citations vary significantly, ranging from foundational knowledge basis to superficial contexts. Distinguishing these roles requires a deeper understanding of the logical relationships among papers, beyond simple edges in citation networks. The emergence of large language models (LLMs) with textual reasoning capabilities offers new possibilities for discerning these relationships, but there are two major challenges.

Last year, MIT researchers announced that they had built "liquid" neural networks, inspired by the brains of small species: a class of flexible, robust machine learning models that learn on the job and can adapt to changing conditions, for real-world safety-critical tasks, like driving and flying. The flexibility of these "liquid" neural nets meant boosting the bloodline to our connected world, yielding better decision-making for many tasks involving time-series data, such as brain and heart monitoring, weather forecasting, and stock pricing. But these models become computationally expensive as their number of neurons and synapses increase and require clunky computer programs to solve their underlying, complicated math. And all of this math, similar to many physical phenomena, becomes harder to solve with size, meaning computing lots of small steps to arrive at a solution. Now, the same team of scientists has discovered a way to alleviate this bottleneck by solving the differential equation behind the interaction of two neurons through synapses to unlock a new type of fast and efficient artificial intelligence algorithms.

In an open-access paper last year at the Journal of Artificial Intelligence Research, a research group concluded that a computer superintelligence, if developed, could not be contained. It would be a HAL 9000 that couldn't just be turned off. The catch is that controlling a super-intelligence far beyond human comprehension would require a simulation of that super-intelligence which we can analyze (and control). But if we're unable to comprehend it, it's impossible to create such a simulation. Rules such as'cause no harm to humans' can't be set if we don't understand the kind of scenarios that an AI is going to come up with, suggest the authors of the new paper.

A new research paper from Harvard University suggests that OpenAI’s headline-grabbing text-to-image framework DALL-E 2 has notable difficulty in reproducing even infant-level relations between the elements that it composes into synthesized photos, despite the dazzling sophistication of much of its output. The researchers undertook a user study involving 169 crowdsourced participants, who were presented with […]

A new paper published by the Government on the 18th July 2018 called'Establishing A Pro-Innovation Approach To Regulating AI' states that the regulation of artificial intelligence in the UK will be underpinned by 6 core principles designed to manage the risks that come with the technology. The six core principles will be applied across all sectors of the economy on a non-statutory basis, complemented by context-specific regulatory guidance and voluntary standards that will be implemented by UK regulators such as the Information Commissioner's Office. Hence, there will be no central AI regulator, but instead sector regulators who will apply the 6 core principles to artificial intelligence systems operated within the area they oversee. Given these proposals, the UK is adopting a far more light-touch risk-based approach compared to the more prescriptive and standardized one being pursued by the EU, which published its draft AI Act back in 2021. The UK approach to artificial intelligence will instead focus upon proportionality, with the regulatory framework for artificial intelligence systems being determined by the industry and context in which the system is being deployed.

The tension between technology and work is at least as old as the economics profession itself. A question some people are asking now is: if computers run by artificial intelligence can do the job of humans, will work disappear someday? Two economists are proposing a couple different scenarios in a new paper that is part science fiction and part mathematical models. In one scenario, lower-paid workers who are not highly valued by society – say, McDonald's hamburger flippers – are more readily replaced by computers than a scientist searching for a cure for Alzheimer's disease. This will drive down wages for a larger and larger segment of the lower-paid labor force.

Adobe and Meta, together with the University of Washington, have published an extensive criticism regarding what they claim to be the growing misuse and abuse of user studies in computer vision (CV) research. User studies were once typically limited to locals or students around the campus of one or more of the participating academic institutions, but have since migrated almost wholesale to online crowdsourcing platforms such as Amazon Mechanical Turk (AMT). Among a wide gamut of grievances, the new paper contends that research projects are being pressured to produce studies by paper reviewers; are often formulating the studies badly; are commissioning studies where the logic of the project doesn't support this approach; and are often'gamed' by cynical crowdworkers who'figure out' the desired answers instead of really thinking about the problem. The fifteen-page treatise (titled Towards Better User Studies in Computer Graphics and Vision) that comprises the central body of the new paper levels many other criticisms at the way that crowdsourced user studies may actually be impeding the advance of computer vision sub-sectors, such as image recognition and image synthesis. Though the paper addresses a much broader tranche of issues related to user studies, its strongest barbs are reserved for the way that output evaluation in user studies (i.e. when crowdsourced humans are paid in user studies to make value judgements on – for instance – the output of new image synthesis algorithms) may be negatively affecting the entire sector.