Lifeboat Foundation

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“These are things that are superhuman, and we think this will be in every industry, will probably replace 50% of human jobs, create a huge amount of wealth for mankind and wipe out poverty,” https://www.cnbc.com/2017/04/27/kai-fu-lee-robots-will-repla…jobs.html” rel=“noopener” class=””>Lee told CNBC at the time, later forecasting that it would happen in the next 10 years.

Fast forward seven years to the Fortune Innovation forum in Hong Kong this past March, when he sat down with Fortune editor-in-chief Alyson Shontell.

Continue reading “Top China VC Kai-Fu Lee says his prediction that AI will displace 50% of jobs by 2027 is ‘uncannily accurate’” »

As the world’s population continues to grow and age, the healthcare system in different geographies is inching closer to the brink of collapse. According to the World Health Organization, the current number of health workers, including physicians, radiologists, and other professionals, is not sufficient to handle the rising caseload. On top of it, the increased stress and burnout stemming from the surge in cases is pushing many to exit the field, further reducing the number of practicing workers. Becker Health estimates show that nearly 72,000 American physicians left the workforce between 2021 and 2022, and some 30,000 who will join the workforce will not be enough to meet the growing demand.

At the core, both these challenges – the rising caseload and dwindling workforce – are leaving one major impact: diminished quality of patient care. This is where the much talked about generative AI can come in, saving healthcare staffers valuable time and resources and enabling them to focus on enhancing clinical outcomes.

First off, it’s important to understand AI is not new in healthcare. Organizations have been experimenting with predictive and computer vision algorithms for a while now, most notably to forecast the success of treatments and diagnose dangerous diseases earlier than humans. However, when it comes to generative AI, things are still pretty fresh, given the technology came to the forefront just a couple of years ago with the launch of ChatGPT. Gen AI models use neural networks to identify patterns and structures in existing data and generate new content such as text and images. They are applicable across sectors, including healthcare – where organizations cumulatively generate about 300 petabytes of data every single day.

StudyFinds’ Dr. Faith Coleman looks at the issue of AI in the field of medicine. Is advanced technology ready to take over healthcare?

Meta and Elon Musk’s artificial intelligence (AI) startup, xAI, are both vying for a strategic partnership with Character.ai, an emerging player in the chatbot industry known for its innovative conversational agents. This competition highlights the escalating race in the AI sector to secure influential collaborations that could redefine user interactions with AI technologies.

Background on Character.ai

Character.ai, co-founded by ex-Google engineers Noam Shazeer and Daniel de Freitas, has gained considerable attention for its advanced conversational AI that allows users to interact with digital versions of various personalities, both real and fictional. The platform aims to deliver engaging and plausible dialogues, setting it apart from traditional AI models focused on factual accuracy.

ABSTRACT. The search for extraterrestrial intelligence is currently being pursued using multiple techniques and in different wavelength bands. Dyson spheres, megastructures that could be constructed by advanced civilizations to harness the radiation energy of their host stars, represent a potential technosignature, that in principle may be hiding in public data already collected as part of large astronomical surveys. In this study, we present a comprehensive search for partial Dyson spheres by analysing optical and infrared observations from Gaia, 2MASS, and WISE. We develop a pipeline that employs multiple filters to identify potential candidates and reject interlopers in a sample of five million objects, which incorporates a convolutional neural network to help identify confusion in WISE data. Finally, the pipeline identifies seven candidates deserving of further analysis. All of these objects are M-dwarfs, for which astrophysical phenomena cannot easily account for the observed infrared excess emission.

From pocket-sized AI models to a warp drive breakthrough, check out this week’s awesome tech stories from around the web.

The Futurists Podcast — Robert Tercek & Brett King.