Researchers at the University of New Orleans are using machine learning to analyse autistic children's behaviour patterns. The project aims to create an AI system to interpret behaviours and advise caregivers. With £300,000 in funding, the study focuses on children aged 2-5, creating a database of behaviour patterns. The goal is to develop an app to help caregivers understand autistic children's behaviour by analysing images or videos, potentially benefiting thousands of families.
NeuroAI, combining neuroscience and AI, has become a key research field. It applies AI to neuroscience and vice versa, accelerating both fields. This connection traces back to early computer science. Developments like perceptrons, convolutional neural networks, and reinforcement learning showcase the symbiosis. AI has also advanced neuroscience, with artificial neural networks improving models of brain function and generating new hypotheses.
Monash University and Apollo Hospitals in India are collaborating on clinical AI research, using data from 200 million patients to train disease-detection algorithms. Separately, the Australian government has allocated A$13 million to support AI and digital technology implementation in aged care.
AI model compression techniques optimise large language models for faster, cheaper predictions whilst maintaining performance. Key methods include model pruning, quantization, and knowledge distillation. These enable deployment in resource-constrained environments, reduce latency, lower costs, and improve energy efficiency, particularly for real-time AI applications and edge devices.
A new AI tool developed by Mass General Brigham has identified long COVID in 22.8% of patients, much higher than previous 7% estimates. The tool analyses health records from nearly 300,000 patients, using 'precision phenotyping' to distinguish long COVID symptoms from pre-existing conditions. It improves diagnostic accuracy and addresses demographic biases. Researchers plan to release the algorithm publicly for global use, potentially advancing research on long COVID subtypes.
AI is being widely adopted in healthcare to address challenges from ageing populations and limited resources. Virtual healthcare platforms use AI to support clinical decisions, improve patient outcomes, and boost efficiency. They automate routine tasks, provide real-time insights, and facilitate team communication. AI-powered virtual nursing and predictive monitoring are gaining traction. One healthcare institution reported a 40% reduction in nursing documentation time using AI. AI is also being integrated with wearables and home monitoring systems for post-discharge care.
A startup called Conflixis has developed AI software to help hospitals identify risky behaviour by doctors. The company uses AI to analyse data from various sources to detect potential conflicts of interest and regulatory risks. Conflixis has secured $4.2 million in seed funding and aims to assist hospitals in reducing risks, increasing transparency, and improving procurement decisions.
A Reddit user fabricated a story about ChatGPT prompting them to seek medical care for a heart attack. The incident, which gained significant attention before being debunked, demonstrates how AI can be used to create convincing false content online. This serves as a reminder to critically evaluate information, especially in the age of generative AI.
Yandex, a leading Russian AI company, plans to invest in Indonesia's AI ecosystem and expand its search engine there. This comes as Indonesia attracts investments from global tech firms like Nvidia and Microsoft, while Russia faces Western technology restrictions due to the Ukraine conflict. The investment details were not disclosed.
The global Multimodal Learning market is set to grow from £1.2 billion in 2024 to £3.5 billion by 2032, with a 14% annual growth rate. This technology combines multiple input types to improve learning and AI applications. The market covers various learning types and applications across industries. North America and Asia-Pacific currently lead, with Asia-Pacific and Europe expected to grow fastest.
A major AI-ready dataset on type 2 diabetes has been released, containing diverse data from over 1,000 participants. The AI-READI study aims to collect data from 4,000 people for global analysis. The dataset is accessible through an online platform and has already been downloaded by numerous research organisations worldwide. Multiple institutions are involved in this project, based in Seattle.
Microsoft has enhanced Azure AI with new features: Data Zone for geographic data control, a 99.9% latency SLA, new AI models including medical imaging and language models, and GitHub Marketplace API access. These updates improve data privacy, reliability, and accessibility for Azure AI users.
Zoom and Suki have partnered to integrate an AI notetaker into Zoom's telehealth service. The AI will record, transcribe, and extract key details from doctor-patient video appointments. After doctor approval, notes will be added to electronic health records, potentially reducing paperwork time by up to 70%. This aligns with a broader trend of AI integration in healthcare administration, with other major tech companies developing similar tools.
Andreessen Horowitz, a venture capital firm, has announced a new investment focus on AI-powered parenting tools. They are interested in companies developing AI applications for various parenting tasks, including sleep monitoring and pregnancy guidance. The firm suggests AI could potentially act as a constant parenting companion, though this field is still in its early stages.
ChatGPT visits rose 116% year-over-year to 3.7 billion in October 2023. Other AI tools like NotebookLM, Microsoft CoPilot, Perplexity, Claude, and Google's Gemini also saw significant growth. Business leaders are increasingly familiar with generative AI, with 72% using it weekly. Average budgets for generative AI more than doubled to £8.2 million. Many firms are expanding AI teams, with some appointing Chief AI Officers. Concerns about accuracy, privacy, and integration remain.
Generative AI technologies like ChatGPT could improve patient-oriented clinical practice guidelines in Japan, which are currently underdeveloped. AI could help simplify complex medical information and customise content, potentially benefiting other countries too. However, expert review remains necessary to ensure accuracy.
AI companies are using machine learning to improve clinical trial design and execution for existing drugs, aiming to accelerate drug development. Current AI applications in clinical trials focus on process optimisation rather than advanced concepts like full trial simulation. The industry is not yet implementing more speculative AI applications in this area.
Researchers have developed an AI-enhanced electronic tongue that can detect subtle variations in liquids with over 95% accuracy. The device uses a graphene-based sensor linked to an artificial neural network, enabling it to distinguish between liquids, identify products, and detect spoilage. The technology shows promise for applications in food safety, production, and medical diagnostics.
ChatGPT 4 Omni, an AI system, accurately identified pills in a controlled test, showing potential for EMS training in overdose scenarios. However, its real-world application faces limitations and privacy concerns. EMS professionals are advised to understand the technology's capabilities and limitations before considering integration into practice.
Argonne National Laboratory researchers have developed MProt-DPO, an AI-driven computing framework for accelerating protein design. It combines various data sources and uses large language models and supercomputers to process billions of parameters, achieving over one exaflop of performance. The framework incorporates a Direct Preference Optimization algorithm to improve design reliability and is being tested on protein design challenges, with plans for laboratory validation.
