Biased data representation in AI marginalizes up to 75 million autistic people worldwide through medical applications viewing autism as a deficit of neurotypical social skills rather than an aspect of human diversity, and this perspective is grounded in research questioning the humanity of autistic people. Turing defined artificial intelligence as the ability to mimic human communication, and as AI development increasingly focuses on human-like agents, this benchmark remains popular. In contrast, we define Neuro-Inclusive AI as datasets and systems that move away from mimicking humanness as a benchmark for machine intelligence. Then, we explore the origins, prevalence, and impact of anti-autistic biases in current research. Our work finds that 90% of human-like AI agents exclude autistic perspectives, and AI creators continue to believe ethical considerations are beyond the scope of their work. To improve the autistic representation in data, we conduct empirical experiments with annotators and LLMs, finding that binary labeling schemes sufficiently capture the nuances of labeling anti-autistic hate speech. Our benchmark, AUTALIC, can be used to evaluate or fine-tune models, and was developed to serve as a foundation for more neuro-inclusive future work.

The role of geographical proximity in facilitating inter-regional or inter-organizational collaborations has been studied thoroughly in recent years. However, the effect of geographical proximity on forming scientific collaborations at the individual level still needs to be addressed. Using publication data in the field of artificial intelligence from 2001 to 2019, in this work, the effect of geographical proximity on the likelihood of forming future scientific collaborations among researchers is studied. In addition, the interaction between geographical and network proximities is examined to see whether network proximity can substitute geographical proximity in encouraging long-distance scientific collaborations. Employing conventional and machine learning techniques, our results suggest that geographical distance impedes scientific collaboration at the individual level despite the tremendous improvements in transportation and communication technologies during recent decades. Moreover, our findings show that the effect of network proximity on the likelihood of scientific collaboration increases with geographical distance, implying that network proximity can act as a substitute for geographical proximity.

As computing and AI advancements spanning decades are enabling incredible opportunities for people and society, they're also raising questions about responsible development and deployment. For example, the machine learning models powering AI systems may not perform the same for everyone or every condition, potentially leading to harms related to safety, reliability, and fairness. Single metrics often used to represent model capability, such as overall accuracy, do little to demonstrate under which circumstances or for whom failure is more likely; meanwhile, common approaches to addressing failures, like adding more data and compute or increasing model size, don't get to the root of the problem. Plus, these blanket trial-and-error approaches can be resource intensive and financially costly. Learn how this suite of tools can help assess machine learning models through a lens of responsible AI.

An app called TapCounter records each time I touch my phone's screen. My swipes and jabs are averaging about 1,000 a day, though I notice that's falling as I steer shy of social media to meet my deadline. The European company behind it, QuantActions, promises that through capturing and analysing the data it will be able to "detect important indicators related to mental/neurological health". Arko Ghosh is the company's cofounder and a neuroscientist at Leiden University in the Netherlands. "Tappigraphy patterns" – the time series of my touches – can, he says, confidently be used not only to infer slumber habits (tapping in the wee hours means you are not sleeping) but also mental performance level (the small intervals in a series of key-presses represent a proxy for reaction time), and he has published work to support it.

NIHR awards £12 million to artificial intelligence research to help understand multiple long-term conditions. Professor Bruce Guthrie will lead one of three new Research Collaborations. The NIHR has awarded almost £12 million to new research that will use advanced data science and artificial intelligence (AI) methods to identify and understand clusters of multiple long-term conditions and develop ways to prevent and treat them. An estimated 14 million people in England are living with two or more long-term conditions, with two-thrids of adults aged over 65 expected to be living with multiple long-term conditions by 2035. People who develop multiple long-term conditions often do not have a random assortment of diseases but rather a largely predictable cluster of conditions.

Congratulations to Dr Fabrizzio Horta on receiving the 2021 Monash Data Futures Institute Seed Grant - AI and Data Science for Monash Global Challenges. This $50,000 grant will help in the research collaboration between the Department of Obstetrics and Gynaecology (Dr Fabrizzio Horta, Prof Beverley Vollenhoven) and the Department of Data Science and AI (A/Prof Hamid Rezatofhigi, Prof Jianfei Cai). "This grant will help us to support our current research, aiming to develop a clinical decision support system in IVF through deep learning algorithms. Particularly this grant aims to target one of the global challenges we are facing by introducing Artificial Intelligence technology into clinical practice. Thus, it will not just have a local impact, but a global impact in the IVF field through strong international research collaboration".

Auransa Inc., an artificial intelligence (AI) company developing precision medicines in areas of unmet medical needs, and Polaris Quantum Biotech (POLARISqb), a quantum drug design company, announced a research collaboration addressing therapeutics for neglected diseases disproportionately affecting women. The partnership seeks to discover treatments that may tackle many such diseases, and their complementary expertise promises to seek solutions that elude medical research. Auransa is an AI-driven biotech company, with a pipeline of novel compounds for various diseases. Auransa's proprietary predictive computational platform, SMarTR Engine, uses computational approaches to tackle disease heterogeneity to predict targets and compounds, generating insights from molecular data. POLARISqb built the first drug discovery platform using quantum computing, making the process ten times faster.

The search for novel therapies has long been a trial-and-error process that costs drug companies a vast amount of time and money. Now, with artificial intelligence (AI) set to transform the pharmaceutical industry more than any other emerging technology, a growing number of pharma and biotech groups are harnessing the cutting-edge tech to minimise the hit-and-miss nature of R&D and discover new therapies with previously impossible speed and accuracy. Pharmaceutical Technology delves into the new generation of drug discovery companies leveraging AI to uncover novel treatments. Founded in 2018 by life sciences venture capital company Flagship Pioneering, Massachusetts-based Generate Biomedicines uses machine learning to accelerate the discovery of protein therapeutics. The company's AI-powered Generative Biology platform analyses hundreds of millions of known protein structures, and uses the learned patterns to create novel protein sequences that form the basis of new therapies.

With a slew of partnerships with large pharmaceutical companies under its belt and the successful spin out of at least one new company, Atomwise has already proved the value of its machine learning platform for discovering and commercializing potential small molecule therapies for a host of conditions. Now the company has raised $123 million in new funding to accelerate its business. "Scaling the technology and scaling the team and scaling what we've been doing with it," says chief executive officer Abe Heifets when asked about what comes next for the eight-year-old business. Atomwise has already signed contracts worth $5.5 billion with corporate partners that include Eli Lilly & Co., Bayer, Hansoh Pharmaceuticals and Bridge Biotherapeutics. Smaller, earlier-stage companies like StemoniX and SEngine Precision Medicine are also using Atomwise's tech.

Insilico Medicine is pleased to announce that it has entered into a research collaboration with Boehringer Ingelheim to utilize Insilico's generative machine learning technology and proprietary Pandomics Discovery Platform with the aim of identifying potential therapeutic targets implicated in a variety of diseases. "Insilico Medicine is very impressed with the Research Beyond Borders group at Boehringer Ingelheim capabilities in the search of potential drug targets. In this collaboration, Insilico will provide additional AI capabilities to discover novel targets for a variety of diseases to benefit the patients worldwide. We are very happy to partner with such an advanced group," said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine. "We believe that Insilico's exclusive Pandomics platform will provide huge boost to our ability to explore and identify drug targets. We look forward to using AI to significantly improve the drug discovery process and contribute to human health," said from Dr. Weiyi Zhang, Head of External Innovation Hub, Boehringer Ingelheim Greater China.