Goto

Collaborating Authors

Results


On the Opportunities and Risks of Foundation Models

arXiv.org Artificial Intelligence

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


Is Clover Health Stock a Buy?

#artificialintelligence

The company sells Medicare Advantage plans, focusing on customer experience and leveraging machine learning and artificial intelligence to …


Transpara breast AI by ScreenPoint Medical reaches major milestone in the lead up to …

#artificialintelligence

It is the first and remains the only DEEP LEARNING system to be FDA cleared for use on both 2D and 3D mammograms and now is first of its kind to …


FDA leader talks evolving strategy for AI and machine learning validation

#artificialintelligence

And AI is helping healthcare professionals and patients get more insights into how they can translate what we already knew in different silos into …


Patient Safety, Data Privacy Key for Use of AI-Powered Chatbots

#artificialintelligence

… mention of CAs in the US Food and Drug Administration’s (FDA) proposed regulatory framework for AI or machine learning for software as a medical …


Today initial diagnosis comes with high levels of accuracy: Dr. John Danaher - ET HealthWorld

#artificialintelligence

Shahid Akhter, editor, ETHealthworld spoke to Dr. John Danaher, President, Clinical Solutions, Elsevier, to know what role artificial intelligence plays in healthcare and how Elsevier plans to improve diagnostic outcomes by way of AI and machine learning. Clinical errors and role of AI and health analytics There are three examples. The first one is making an initial diagnosis. What can be achieved with artificial intelligence, machine learning and actual language processing is the ability to assist doctors to make more accurate initial diagnosis. Second is the work being done in the area of image recognition with radiology and pathology.


The 2018 Survey: AI and the Future of Humans

#artificialintelligence

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.