The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combined process can successfully learn adaptation knowledge applicable to nonsymbolic differences in cases. The CBR system achieves slightly lower performance overall than a baseline deep network regressor, but better performance than the baseline on novel queries.

While previous distribution shift detection approaches can identify if a shift has occurred, these approaches cannot localize which specific features have caused a distribution shift--a critical step in diagnosing or fixing any underlying issue. For example, in military sensor networks, users will want to detect when one or more of the sensors has been compromised, and critically, they will want to know which specific sensors might be compromised. Thus, we first define a formalization of this problem as multiple conditional distribution hypothesis tests and propose both non-parametric and parametric statistical tests. For both efficiency and flexibility, we then propose to use a test statistic based on the density model score function (i.e., gradient with respect to the input)--which can easily compute test statistics for all dimensions in a single forward and backward pass. Any density model could be used for computing the necessary statistics including deep density models such as normalizing flows or autoregressive models. We additionally develop methods for identifying when and where a shift occurs in multivariate time-series data and show results for multiple scenarios using realistic attack models on both simulated and real world data.

Automated writing evaluation systems can improve students' writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the `RER' scheme) and apply it to 5th- and 6th-grade students' argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.

Biodiversity, the variation within and between species and ecosystems, is essential for human well-being and the equilibrium of the planet. It is critical for the sustainable development of human society and is an important global challenge. Biodiversity research has become increasingly data-intensive and it deals with heterogeneous and distributed data made available by global and regional initiatives, such as GBIF, ILTER, LifeWatch, BODC, PANGAEA, and TERN, that apply different data management practices. In particular, a variety of metadata and semantic resources have been produced by these initiatives to describe biodiversity observations, introducing interoperability issues across data management systems. To address these challenges, the InteroperAble Descriptions of Observable Property Terminology WG (I-ADOPT WG) was formed by a group of international terminology providers and data center managers in 2019 with the aim to build a common approach to describe what is observed, measured, calculated, or derived. Based on an extensive analysis of existing semantic representations of variables, the WG has recently published the I-ADOPT framework ontology to facilitate interoperability between existing semantic resources and support the provision of machine-readable variable descriptions whose components are mapped to FAIR vocabulary terms. The I-ADOPT framework ontology defines a set of high level semantic components that can be used to describe a variety of patterns commonly found in scientific observations. This contribution will focus on how the I-ADOPT framework can be applied to represent variables commonly used in the biodiversity domain.

As more and more companies adopt AI, a question arises--can the federal government keep up with all the changes from a regulatory perspective? In some cases, the federal government is behind (see Why Are Technology Companies Quitting Facial Recognition?). In a recent blog post, the FTC has warned companies that they have sufficient laws to enforce truth, fairness, and equity when enforcing the developers and users of AI. The FTC essentially says that companies need to hold themselves accountable for their AI, or the FTC will take enforcement action against them. The FTC's primary focus is on consumer protection.

Artificial intelligence (AI) has steadily become more integrated into daily life, and this is evident through Google maps, face recognition, and autocorrect. Joining their ranks is breakthrough conversational AI technology. Conversational AI entails technologies that allow computers and machines to create automated messages and speech-enabled applications. This allows for human-like interactions between humans and devices. As more and more platforms begin to rely on AI-based customer services, it's no surprise that the conversational AI market is projected to reach $18.02 billion by 2027.

Last year, the Department of Energy announced $2 million in funding for research on neuromorphic computing, which aims to mimic the structure and functionality of the human brain. This new approach could provide a more efficient and malleable way to process data in ways not yet seen in the computing world. For government agencies, that could open new use cases, lower power consumption, and potentially save both money and lives. Agencies are already using traditional artificial intelligence to complete their missions and make decisions. But early generations of AI were built as binary systems.

Although Jupiter's moon Europa may be one of the places in the solar system to look for life due to its ocean, robots could have to dig further than previously thought to find it, a new study suggests. According to the research, any robot landers that enter Europa's atmosphere will have to dig at least 12 inches down. This is a result of the ice moon being hit with'impact gardening,' a phenomenon that is the result of space radiation hitting molecules and bringing some of them to the surface, while pushing other parts down, mixing with the subsurface. 'If we hope to find pristine, chemical biosignatures, we will have to look below the zone where impacts have been gardening,' said the study's lead author, University of Hawaii at Manoa planetary research scientist Emily Costello in a statement. Jupiter's moon Europa may be one of the places in the solar system to look for life due to its ocean, but robots could have to dig further than previously thought to find it, a new study suggests According to the research, any robot landers that enter Europa's atmosphere will have to dig at least 12 inches down as the ice moon is hit with'impact gardening.'

Aidoc, a provider of artificial intelligence (AI) solutions for medical imaging, has announced a $66 million investment, bringing its total funding to $140 million. This Series C round, led by General Catalyst, follows a surge in demand for Aidoc's AI-driven solutions, including the largest clinical deployment of AI in healthcare through its partnership with Radiology Partners. Aidoc co-founder and CEO Elad Walach, said: "This investment comes after significant milestones; expanding our product lines, doubling our FDA clearances and quadrupling our customer base. We are experiencing a huge expansion, which is also a direct result of C-level executives adopting an AI strategy and integrating our platform as a must-have solution across clinical pathways. It is truly rewarding – and a great responsibility – to be the trusted partner of the most innovative health systems and physician practices across the globe." A pioneer in healthcare AI, Aidoc's FDA-cleared solutions analyse medical images for critical conditions and trigger actionable alerts directly in the imaging workflow supporting medical specialists in reducing turnaround time and improving quality of care.

The technologies referred to as "artificial intelligence" or "AI" are more momentous than most people realize. Their impact will be at least equal to, and may well exceed, that of electricity, the computer, and the internet. What's more, their impact will be massive and rapid, faster than what the internet has wrought in the past thirty years. Much of it will be wondrous, giving sight to the blind and enabling self-driving vehicles, for example, but AI-engendered technology may also devastate job rolls, enable an all- encompassing surveillance state, and provoke social upheavals yet unforeseen. The time we have to understand this fast-moving technology and establish principles for its governance is very short. The term "AI" was coined by a computer scientist in 1956.