This post contains a list of the AI-related seminars that are scheduled to take place between 5 January and 28 February 2026. All events detailed here are free and open for anyone to attend virtually. Fiona Spuler (University of Reading) ECMWF Teams link is here . Iyad Rahwan (Max Planck Institute for Human Development) The Digital Humanism (DIGHUM) Initiative The talk will be livestreamed on YouTube here . Christopher O'Reilly (University of Reading) ECMWF Teams link is here .

These concerns have spread throughout the AI field, leading even large corporations such as Microsoft to develop internal guidelines for using this technology. In June, the company publicly shared its new "Responsible AI Standard" framework that is aimed at "keeping people and their goals at the center of system design decisions and respecting enduring values like fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability," according to a Microsoft blog post. As a result of these standards, the company phased out an emotion recognition tool from its AI facial analysis services following criticism that such software was discriminatory against marginalized groups and not proven to be scientifically accurate. Businesses are not the only organizations looking to solve ethical questions about AI. Multiple colleges and universities are also creating research centers, educational programming, and other efforts that will help develop a new generation of scientists and engineers who are dedicated to using this form of technology to better society.

It is notable, responsive, and appropriate that the majority of the Initiative projects had various interdisciplinary aspects to them--either in their teams or in the people they convened. Grantees felt that such interdisciplinarity was important to continue and in the long term the community will be healthier and more resilient for this. While the "diversity disaster" in the broader AI field is well known.7 Within the sub-field of AI ethics and governance, grantees noted that a field that relied on the same voices, geographies and, often, institutions would result in missing perspectives. With 15% of grantees being non-US based8 and 80% of funding support academic institutions, on this front, and in line with their international ambitions9 the Initiative could have done more. There remains an imperative to push for substantive diversity of thought and experience, both within geographies and across them.

The societal impact of Artificial Intelligence (AI) dwarfs its technological impact. Already, we see AI everywhere in our daily lives; we see it in our grocery shopping app, our entertainment streaming lists, social media feeds, our dating lives, and the list goes on. The use of AI has become so naturally intertwined with our lives that we often forget to think about the future. We should ask ourselves the question of how we can unlock AI's full potential while keeping its risks at a minimum. And to investigate this question, we need to work together.

Artificial intelligence (AI) has already had a profound impact on business and society. Applied AI and machine learning (ML) are creating safer workplaces, more accurate health diagnoses and better access to information for global citizens. The Fourth Industrial Revolution will represent a new era of partnership between humans and AI, with potentially positive global impact. AI advancements can help society solve problems of income inequality and food insecurity to create a more "inclusive, human-centred future" according to the World Economic Forum (WEF). There is nearly limitless potential to AI innovation, which is both positive and frightening.

You're reading the web edition of STAT Health Tech, our guide to how tech is transforming the life sciences. Sign up to get this newsletter delivered in your inbox every Tuesday and Thursday. A growing patchwork of state-level privacy laws could pose a challenge for upstart health tech companies. After California led the way with its consumer privacy law in 2018, Virginia and Colorado followed suit, and now Massachusetts has advanced its own data privacy bill. Each independent piece of legislation could impact consumer-oriented health apps that don't fall under HIPAA -- leading digital health companies to worry about mounting costs to navigate the regulatory thicket and declining revenue for resale of consumer data, Mohana reports.

CIBO, the science-based technology company that supports growers and enterprises on their journey to regenerative agriculture, is issuing an open invitation to partner with enterprises and organizations interested in submitting a project proposal to the USDA's new Climate-Smart Commodities Partnership Initiative. The initiative, part of the USDA's Climate Smart Agriculture & Forestry Project, includes a $1 billion fund for applicants but requires certain criteria to be met upon application. The CSAF partnership initiative will fund pilot projects that promote and incentivize on-farm conservation practices that sequester carbon or reduce greenhouse gas (GHG) emissions. In order to apply for funding, all applications must include the ability to measure/quantify, monitor, and verify the carbon and GHG benefits associated with those practices, which is where CIBO uniquely supports partnership project proposals. CIBO's proprietary technology platform provides detailed impact quantification, reporting, and verification (MRV) of farming practices using AI-enhanced computer vision from satellite imagery, advanced, mechanistic crop modeling, and scaled cloud infrastructure that combines to deliver in real-time a current carbon footprint, the future carbon impact of practices, and historic and in-season management practices at a field or portfolio level.

UNDP has launched a new digital strategy to enhance its support to governments in shifting to this transforming environment including by building digital capacity within the organization. The AI strategy of UNDP seeks to increase the understanding of digital technologies and how they can be used to achieve the Sustainable Development Goals. As well as risks and trade-offs that come with them. On the other hand, it is also working to manage the unique ethical issues that can rise from deploying AI in an international development context. The AI readiness tool for stakeholders is building on UNDP's Digital Readiness Assessment piloted.

One of the co-founders, Martin Nisser, a PhD student from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), explains the digital literacy and self-efficacy focused objectives: "Some of the women haven't had the opportunity to work with a computer for 25 years, and aren't yet accustomed to using the internet. We're working with them to build their capabilities with these modern tools in order to prepare them for life outside," says Nisser. Even for the students who became incarcerated more recently, it can be difficult to keep up with the fast pace of technological advances, since technical programs in correctional facilities are few and far-between. This scarcity of preparatory programs undoubtedly contributes to high and rising recidivism rates: More often than not, those who are released from prison eventually return. While working at TEJI, Nisser had a fortuitous meeting with his two co-founders, Marisa Gaetz (a PhD student from MIT's Department of Mathematics) and Emily Harburg (co-founder of Brave Initiatives, a nonprofit that develops coding bootcamps for young women).

Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values. In this paper, we propose a framework using the recently presented SSHIBA model for jointly learning different tasks on longitudinal data with missing values. The method uses Bayesian variational inference to impute missing values and combine information of several views. This way, we can combine different data-views from different time-points in a common latent space and learn the relations between each time-point while simultaneously modelling and predicting several output variables. We apply this model to predict together diagnosis, ventricle volume, and clinical scores in dementia. The results demonstrate that SSHIBA is capable of learning a good imputation of the missing values and outperforming the baselines while simultaneously predicting three different tasks.