Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for status assessment). This knowledge-reasoning gap, evident across all models, suggests inherent architectural constraints, not just data limitations. Furthermore, models exhibited systematic biases favoring charismatic vertebrates, potentially amplifying existing conservation inequities. These findings delineate clear boundaries for responsible LLM deployment: they are powerful tools for information retrieval but require human oversight for judgment-based decisions. A hybrid approach is recommended, where LLMs augment expert capacity while human experts retain sole authority over risk assessment and policy.

Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation spanning 10 to a few hundred million years. Observations from the $\textit{Spitzer}$ Space Telescope have unveiled thousands of debris disks, yet systematic studies remain scarce, let alone those with unsupervised clustering techniques. This study introduces $\texttt{CLUES}$ (CLustering UnsupErvised with Sequencer), a novel, non-parametric, fully-interpretable machine-learning spectral analysis tool designed to analyze and classify the spectral data of debris disks. $\texttt{CLUES}$ combines multiple unsupervised clustering methods with multi-scale distance measures to discern new groupings and trends, offering insights into compositional diversity and geophysical processes within these disks. Our analysis allows us to explore a vast parameter space in debris disk mineralogy and also offers broader applications in fields such as protoplanetary disks and solar system objects. This paper details the methodology, implementation, and initial results of $\texttt{CLUES}$, setting the stage for more detailed follow-up studies focusing on debris disk mineralogy and demographics.

Design biases in NLP systems, such as performance differences for different populations, often stem from their creator's positionality, i.e., views and lived experiences shaped by identity and background. Despite the prevalence and risks of design biases, they are hard to quantify because researcher, system, and dataset positionality is often unobserved. We introduce NLPositionality, a framework for characterizing design biases and quantifying the positionality of NLP datasets and models. Our framework continuously collects annotations from a diverse pool of volunteer participants on LabintheWild, and statistically quantifies alignment with dataset labels and model predictions. We apply NLPositionality to existing datasets and models for two tasks -- social acceptability and hate speech detection. To date, we have collected 16,299 annotations in over a year for 600 instances from 1,096 annotators across 87 countries. We find that datasets and models align predominantly with Western, White, college-educated, and younger populations. Additionally, certain groups, such as non-binary people and non-native English speakers, are further marginalized by datasets and models as they rank least in alignment across all tasks. Finally, we draw from prior literature to discuss how researchers can examine their own positionality and that of their datasets and models, opening the door for more inclusive NLP systems.

Google Bard, the search giant's ChatGPT rival, is already available in 180 countries and territories. But even though it's been widely available for months and was the centerpiece of Google's recent I/O event, it's missing one big region. The 450 million people living in the European Union are still unable to access Bard, or any of the company's other generative AI technologies. It's a move that has surprised lawmakers, and even Google won't say why it's holding back. Brando Benifei, the MEP leading the negotiations on Europe's new artificial intelligence rules, is not sure why the bloc had been excluded, describing the omission of the EU from Bard's rollout as a "big issue."

Learn Computer Vision. Introduction course to Computer Vision with Python. Make Computer Vision Apps? Learn Computer Vision theory? Build a strong portfolio with Computer Vision & Image Processing Projects? Looking to add Computer Vision algorithms in your current software project ? Whatever be your motivation to learn Computer Vision, I can assure you that you’ve come to the right course. You get. Complete course with 1 hour of video tutorials, Source code for all examples in the course. What you'll learn. Use basic Computer Vision techniques. Do image processing. Build: Image Similarity app, Face Detection app and Object Detection app! Master Computer Vision! .

"Artificial intelligence" (AI) has become one of the buzzwords of the decade, as a potentially important part of the answer to humanity's biggest challenges in everything from addressing climate change to fighting cancer and even halting the ageing process. It is widely seen as the most important technological development since the mass use of electricity, one that will usher in the next phase of human evolution. At the same time, some warnings that AI could lead to widespread unemployment, rising inequality, the development of surveillance dystopias, or even the end of humanity are worryingly convincing. States would, therefore, be well advised to actively guide AI's development and adoption into their societies. For Europe, 2019 was the year of AI strategy development, as a growing number of EU member states put together expert groups, organised public debates, and published strategies designed to grapple with the possible implications of AI. European countries have developed training programmes, allocated investment, and made plans for cooperation in the area. Next year is likely to be an important one for AI in Europe, as member states and the European Union will need to show that they can fulfil their promises by translating ideas into effective policies. But, while Europeans are doing a lot of work on the economic and societal consequences of the growing use of AI in various areas of life, they generally pay too little attention to one aspect of the issue: the use of AI in the military realm. Strikingly, the military implications of AI are absent from many European AI strategies, as governments and officials appear uncomfortable discussing the subject (with the exception of the debate on limiting "killer robots"). Similarly, the academic and expert discourse on AI in the military also tends to overlook Europe, predominantly focusing on developments in the US, China, and, to some extent, Russia. This is likely because most researchers consider Europe to be an unimportant player in the area.

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Mika Lintilä the Minister of Economic Affairs in Finland appointed a steering group in May 2017 to figure out how they could become one of the world's top countries within the field of Applied AI. In October 2017, Finland was the first European Union country to put a national action plan on AI into writing. This seems quite a lot earlier than most other countries in Scandinavia. At around that time they were scheduled to release their final report April 2019. However they also released a report at the time called Finland's Age of Artificial Intelligence that touched upon their strengths and weaknesses in AI with eight specific recommendations to turn the country into a global leader.

The Government Artificial Intelligence (AI) Readiness Index, compiled by Oxford Insights and the International Development Research Centre, ranks the governments of 194 nations according to how prepared they are to utilise AI in the provision of public services. According to global consulting firm PriceWaterhouseCooper, AI technologies are forecast to add an additional $15.7 trillion to the global economy by 2030, with $6.6 trillion to come from an increase in productivity and $9.1 trillion from consumption-side effects. The score that Oxford Insights provides for each country comprises of 11 input metrics grouped under four high-level topics: governance; infrastructure and data; skills and education; and government public services. On a global level, the top ranking countries (and their scores) were: Singapore (9.186), The likes of India (7.515) and China (7.37) were ranked 17th and 20th respectively.

Vikram Mahidhar reminds us all that AI is only as good as the humans supervising it and programming it. The biases and artefacts that come out of the processing are reflective of the biases programmed in at the beginning. A program trained to recognise totalled car bodies for insurance purposes, for example, will need close supervision of its decision-making outputs, for regulatory and consumer confidence and acceptance of the decision. There is a call and a growth in a new class of AI--one that is explainable, and that builds trust by providing evidence. Vikram also reminds us that a governance strategy is key to engendering trust in our organisation, processes and people.