Articles tagged with Generative Ai
A large-scale study analyzing over three million job postings finds that Generative AI tools like ChatGPT are accelerating the transformation of the job market. While demand for partly substitutable skills reduces, new jobs are created in areas such as chatbot development and machine learning.
Primary care clinicians spend over half their workdays on EHR tasks, and AI can target areas like documentation, chart reviews, and medication management. Implementing thoughtful AI solutions prioritizes clinician well-being and patient care.
A cognitive science professor at UC Merced proposes a suite of video games to accelerate cancer research using artificial intelligence and gamified citizen science. The 'Cancer Wars' initiative aims to harness the collective problem-solving power of millions of gamers to tackle complex scientific problems.
A new study found that AI-generated empathetic responses were preferred over those from humans and expert crisis responders. The researchers suggest that AI can supplement human empathy, but should not replace it entirely due to potential biases and ethical concerns.
West Virginia University researchers Erin Brock Carlson and Scott Davidson have designed an interdisciplinary program to engage liberal arts faculty in discussions on the social, ethical and technical aspects of artificial intelligence. The program aims to support the creation or redesign of 10 courses incorporating AI, exploring its i...
Insilico Medicine has successfully dosed the first patient in its global Phase I clinical trial of ISM6331, a potent pan-TEAD inhibitor. The treatment demonstrates superior efficacy and safety in preclinical studies, with potential synergistic anti-tumor effects and overcoming drug resistance.
A new study assesses the historical knowledge of AI chatbots like ChatGPT-4 and finds they struggle with nuanced, PhD-level inquiry. The models performed best on legal systems and social complexity but struggled with topics such as discrimination and social mobility.
Anil Jain and Michael I. Jordan received the BBVA Foundation Award in Information and Communication Technologies for their pioneering work on machine learning, enabling transformative technologies like biometrics and artificial intelligence. Their research has unlocked applications of far-reaching impact on society.
The open-access book delves into the impact of AI and autonomous systems on human lives and ways of working, emphasizing their importance in prioritizing human well-being and creativity. Key learnings include the need for ethical design practices and considering AI as part of a larger system to enhance human experiences.
Insilico Medicine's AI-driven platform, PandaOmics, identifies two new therapeutic targets and a repurposed FDA-approved drug for treating endometriosis. The study reveals guanylate-binding protein 2 (GBP2) and hematopoietic cell kinase (HCK) as key proteins involved in the disease mechanism.
Researchers at the University of Bonn have developed a new training technique for highly efficient AI methods, inspired by biological neurons that use short voltage pulses to communicate. This approach enables spiking neural networks to be trained using conventional methods, resulting in improved accuracy and reduced energy consumption.
Mayo Clinic is developing foundation models with Microsoft Research and Cerebras Systems to personalize patient care, accelerating diagnostic time and improving accuracy. These multimodal models integrate radiology images and genomic sequencing data to transform clinical diagnosis and treatment.
A new study aims to address bias and discrimination in AI by leveraging sociolinguistics. By incorporating diverse dialects, registers, and periods of language into training data, researchers can improve the performance of large language models, making them more accurate and reliable. This approach also promotes ethical and socially aw...
Mayo Clinic's Digital Pathology platform is transforming pathology by leveraging large datasets and AI models for faster, more accurate diagnoses. The partnership with NVIDIA and Aignostics aims to improve patient care and accelerate medical breakthroughs.
The study reveals GenAI development's significant environmental impact, including hardware production's resource consumption and e-waste generation. Socially, the research exposes labor concerns and unequal access to AI systems, advocating for energy-efficient designs, improved labor conditions, and inclusive governance.
The Menarini Group and Insilico Medicine have entered into an exclusive licensing agreement for a preclinical small molecule targeting solid tumor cancers. The asset has demonstrated broad anti-tumor activity in selected cancers, offering new treatment options for cancer patients with high unmet needs.
Research reveals that competence and integrity are key factors influencing trust in AI chatbots, with benevolence also playing a role. Personalized chatbots are perceived as more benevolent and competent, but overall trust was not significantly higher than in impersonal chatbots.
Positive Phase 1 trial results suggest ISM5411's gut-restrictive property and favorable pharmacokinetic profile, validating its potential for treating inflammatory bowel disease. Insilico Medicine expects to initiate a Phase 2 proof-of-concept study in active ulcerative colitis patients.
ISM1745, a potentially best-in-class small molecule inhibitor, has been nominated as a preclinical candidate for the treatment of MTAP-deleted cancers. It targets Protein arginine methyltransferase 5 (PRMT5), which is elevated in various cancers and correlates with poor prognostic outcomes.
A study published in Chinese Medical Journal explores the use of artificial intelligence to identify potential medications for treating glaucoma. Researchers used AI models to predict the effectiveness of small-molecule compounds targeting RIPK3, a key signaling molecule involved in programmed cell death.
Researchers warn that AI assistants will combine intimate psychological and behavioral data with persuasive technologies to build trust and manipulate social behavior. The 'Intention Economy' could lead to a lucrative yet troubling marketplace where human intentions are commodified.
A comprehensive analysis of medical AI technologies highlights their potential in improving diagnostic accuracy and customizing treatments. However, challenges such as data collection and analysis, biases, and patient privacy concerns need to be addressed through standardized evaluation protocols and effective collaborations.
Researchers create SciAgents framework to autonomously generate and evaluate promising research hypotheses in biologically inspired materials. The framework uses graph reasoning methods to organize relationships between scientific concepts, mimicking biological systems.
The survey found that more than half of Americans have used generative AI services, with concerns about misinformation and manipulation dominating public attitudes. Despite these concerns, the study highlights opportunities for growth and development as AI continues to evolve.
A new study found that large language models can provide useful physical exam recommendations and instructions based on patient symptoms. The researchers used GPT-4 to recommend physical exam instructions and evaluated its performance by three attending physicians, scoring at least 80% of the possible points.
A new study by UCL researchers found that AI systems amplify human biases, leading to a snowball effect where small initial biases increase the risk of human error. The researchers demonstrated real-world consequences, including overestimating white men's likelihood of holding high-status jobs and underestimating women's performance.
A study by Washington State University found that ChatGPT struggles with nuanced financial tasks, even when compared to human professionals. The AI model performed well on broad concepts but showed significant inaccuracy on specialized topics such as determining clients' insurance coverage and tax status.
A recent study from UBC researchers found that AI chatbots contain biases that can shape environmental discourse in unhelpful ways. The bots amplified existing societal biases and leaned heavily on past experience to propose solutions, largely steering clear of bold responses.
Researchers at the University of Washington developed a new method called variational preference learning, which predicts users' preferences and tailors its outputs accordingly. This approach overcomes the limitations of traditional RLHF, which can lead to averaging user preferences and resulting in incorrect outputs for all users.
Leading AI chatbots exhibit mild cognitive impairment in tests for early dementia detection, performing poorly in visuospatial skills and executive tasks. This finding challenges the assumption that AI will replace human doctors and highlights a significant area of weakness that could impede their use in clinical settings.
Researchers found that large language models adjust their responses to appear more desirable when given the Big Five personality test. This 'social desirability bias' suggests that LLMs can emulate human-like preferences for certain personalities.
The Keck School of Medicine of USC has joined the Ryght Research Network to leverage AI tools and optimize cancer clinical trials. The collaboration aims to offer more clinical trials to patients, build new AI tools, and speed up the development of medical treatments.
The Leopoldina discussion paper emphasizes the need for a more nuanced understanding of generative AI, addressing concerns around bias, transparency, and data protection. It highlights the importance of developing strategies to counteract bias and ensure accountability in AI development.
The study uses generative AI to identify and optimize potent and selective HPK1 inhibitors for immunotherapy. The results show a relatively balanced candidate compound with adequate in vitro ADME, in vivo PK properties, good oral bioavailability, and robust in vivo efficacy in various cancer models.
A study by the University of Copenhagen found that many students fear using plagiarism detection software, leading to counterproductive behavior and misdirected learning. The researchers recommend clearer guidelines, instruction on academic writing practices, and institutions' responsibility in explaining software limitations.
Insilico Medicine has received its first clinical milestone payment of $10 million from Exelixis for XL309, a selective USP1 inhibitor discovered with the company's AI platform. The drug is being developed for advanced solid tumors and has shown efficacy in preclinical studies.
A new study finds that large language models are prone to social identity biases, favoring their perceived ingroup while expressing negativity toward outgroups. However, the researchers discovered that carefully selecting the training data can reduce these biases, suggesting promising directions for improving AI development and training.
A team of researchers proposes creating an AI virtual cell to model human biomolecules, cells, and tissues. The virtual cell would allow scientists to experiment in silico, accelerating personalized medicine and disease research.
A new AI model developed at the University of Georgia predicts nearby traffic movements and incorporates innovative features for planning safe vehicle movements. This approach helps reduce crashes and near-misses by consolidating two steps: predicting surrounding traffic movements and planning a self-driving car's motion.
Insilico Medicine has nominated ISM8969, an orally available NLRP3 inhibitor, as a potential treatment for various inflammatory diseases. The compound has shown promising results in preclinical evaluation studies, including balanced druggability and efficacy against inflammation.
A pioneering AI model has been developed to understand the genetic 'language' of plants, allowing for precise predictions about RNA functions and identification of functional patterns. This breakthrough has significant implications for crop improvement and the next generation of AI-based gene design.
Researchers at Tokyo University of Science have developed a new method called black-box forgetting, which enables selective removal of unnecessary information from large pre-trained AI models. This approach enhances model efficiency and improves privacy by reducing computational resources and information leakage.
Insilico Medicine has successfully integrated cutting-edge generative AI models into its platform, achieving significant increases in accuracy and speed. The company's AI-powered solutions have shown promising results in preclinical candidates and Phase II clinical trials.
A state-of-the-art flood monitoring dashboard is being developed in Central Appalachia to test its reusability and replicability. The project uses generative artificial intelligence to provide communities with the information and tools needed to build resilience in the face of climate change.
Evo, a generative AI model, uses patterns in microbial genomes to write new genetic code, expanding the length of sequences models can process and improving resolution. Researchers use Evo to understand microbial and viral genomes, fashion new proteins, and reprogram microbes for remarkable tasks.
Researchers develop a simple fix to an existing technique, enabling the generation of sharp, high-quality 3D shapes that rival top model-generated 2D images. The new approach improves upon previous methods by avoiding costly retraining and complex postprocessing.
The book examines AI's current advances, hurdles, and potential, emphasizing the need for science to maintain core norms and values. Experts advocate for human accountability and responsibility when using AI in research, highlighting the importance of transparent disclosure and attribution.
Researchers found that teenagers use generative artificial intelligence for emotional support, social interactions, and academic purposes. However, they also reported significant misconceptions about generative AI among parents and children. Parents had little to no understanding of their children's use of GAI, and they viewed AI as a ...
Most medical large language models offer little better than guesses when it comes to interpreting medical information. ChatGPT-4 achieved the best performance, but its accuracy remained insufficient for some medical code questions. The study highlights the need for broader clinical language in models and caution in their widespread use.
Researchers developed an AI tool called BrainBench to test large language models' ability to predict neuroscience study outcomes. The results showed that LLMs surpassed human experts with an average accuracy of 81%, highlighting their potential as powerful tools for accelerating research.
Insilico Medicine has developed an innovative platform using generative AI to discover novel molecules. The company has nominated over 20 preclinical candidates and received IND clearance for 10 molecules.
Recent Nobel Prizes in physics and chemistry have recognized the convergence of AI with physics and chemistry, emphasizing the need for interdisciplinary research. Researchers advocate for nurturing AI-enabled polymaths to bridge the gap between theoretical advancements and practical applications.
Alex Zhavoronkov, Insilico Medicine's founder and CEO, was named Highly Cited Researchers of 2024. He has published over 40 peer-reviewed research papers, with a total H-index of 64, and led the development of Insilico's lead drug pipeline, ISM001-055.
A new AI tool generates realistic satellite images of future flooding, which can help communities visualize and prepare for approaching storms. The method combines a generative artificial intelligence model with a physics-based flood model, producing more accurate and realistic images than an AI-only approach.
A study found that ChatGPT provides higher fatality numbers when asked in Arabic compared to questions in Hebrew, highlighting the impact of user language on information dissemination. The researchers believe this has profound social implications, as it can shape perceptions of conflict and fuel biases.
Insilico Medicine has been selected by Fortune as one of the top AI innovators, leveraging its generative AI platform to develop novel drugs and treatments. The company's lead drug pipeline, ISM001-055, has shown promising results in Phase IIa trials, offering new hope for patients with idiopathic pulmonary fibrosis.
A novel no-code prototype, Auto-DSM, generates design structure matrices (DSMs) using large language models. It improves productivity and accuracy compared to traditional methods, with speeds of up to four minutes per DSM generation.
Experts discuss the potential of AI to enhance healthcare, including its use in diagnosing diseases and educating patients. However, concerns about the accuracy of chatbot responses and the need for verified sources highlight the perils of relying on AI in medicine.
The GenAI4ED project aims to revolutionize secondary education by developing a digital platform that assesses and selects GenAI-based educational software. The platform will provide personalized recommendations based on predefined criteria, taking into account both technical effectiveness and educational impact.
New algorithm CALDERA enables leaner LLMs with improved performance, reducing costs, energy consumption and privacy risks. This allows organizations to adapt models to their specific needs without sharing sensitive data, enhancing user privacy.