Lifeboat Foundation

Thanks to this new category of algorithms that has proved its power of mimicking human skills just by learning through examples. Deep learning is a technology representing the next era of machine learning. Algorithms used in machine learning are created by programmers and they hold the responsibility for learning through data. Decisions are made based on such data.

Some of the AI experts say, t here will a shift in AI trends. For instance, the late 1990s and early 2000s saw the rise of machine learning. Neural networks gained its popularity in the early 2010s, and growth in reinforcement came into light recently.

Well, these are just a couple of caveats we’re experienced throughout the past years.

A new algorithm created by researchers at Mount Sinai Hospital, New York, has learned to identify subtle changes in electrocardiograms (ECGs) to predict whether a patient is developing heart failure.

Artificial Intelligence is rapidly improving and has recently gotten to a point where it can outperform humans in several highly competetive job markets including the media. OpenAI and Intel are working on the most advanced AI Algorithms that are actually starting to understand the world similar to the way we experience it. They call these models: OpenAI CLIP, Codex, GPT 4 and other things which are all good at certain things. Now they’re trying to combine them to improve their generality and maybe create a real and working Artificial General Intelligence for our future. Whether AI Supremacy will happen before the singularity is unclear, but one thing is for sure: AI and Machine Learning will take over many jobs in the very near future.
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TIMESTAMPS:
00:00 The Rise of AI Supremacy.
01:15 What Text-Generation AI is doing.
03:28 OpenAI is not open at all?
06:12 The Image AI: CLIP
08:52 LastIs AI taking over every job?
10:32 Last Words.
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#ai #agi #intel

Experts in the AI and Big Data sphere consider October 2021 to be a dark month. Their pessimism isn’t fueled by rapidly shortening days or chilly weather in much of the country—but rather by the grim news from Facebook on the effectiveness of AI in content moderation.

This is unexpected. The social media behemoth has long touted tech tools such as machine learning and Big Data as answers to its moderation woes. As CEO Mark Zuckerberg explained for CBS News, “The long-term promise of AI is that in addition to identifying risks more quickly and accurately than would have already happened, it may also identify risks that nobody would have flagged at all—including terrorists planning attacks using private channels, people bullying someone too afraid to report it themselves, and other issues both local and global.”

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Scientists from Heidelberg and Bern have succeeded in training spiking neural networks to solve complex tasks with extreme energy efficiency. The advance was enabled by the BrainScaleS-2 neuromorphic platform, which can be accessed online as part of the EBRAINS research infrastructure.

Developing a machine that processes information as efficiently as the human brain has been a long-standing research goal towards true artificial intelligence. An interdisciplinary research team at Heidelberg University and the University of Bern led by Dr Mihai Petrovici is tackling this problem with the help of biologically-inspired artificial neural networks.

Continue reading “Human Brain Project researchers demonstrate highly efficient deep learning on a spiking neuromorphic chip” »

This is the first installment blog summarizing AI Theology’s panel discussion on how to make Facebook algorithms accountable to users.

A commonly available oral diuretic pill approved by the U.S. Food and Drug Administration may be a potential candidate for an Alzheimer’s disease treatment for those who are at genetic risk, according to findings published in Nature Aging. The research included analysis showing that those who took bumetanide — a commonly used and potent diuretic — had a significantly lower prevalence of Alzheimer’s disease compared to those not taking the drug. The study, funded by the National Institute on Aging (NIA), part of the National Institutes of Health, advances a precision medicine approach for individuals at greater risk of the disease because of their genetic makeup.

The research team analyzed information in databases of brain tissue samples and FDA-approved drugs, performed mouse and human cell experiments, and explored human population studies to identify bumetanide as a leading drug candidate that may potentially be repurposed to treat Alzheimer’s.

“Though further tests and clinical trials are needed, this research underscores the value of big data-driven tactics combined with more traditional scientific approaches to identify existing FDA-approved drugs as candidates for drug repurposing to treat Alzheimer’s disease,” said NIA Director Richard J. Hodes, M.D.

In Optica, The Optical Society’s (OSA) journal for high impact research, Qiu and colleagues describe a new approach for digitizing color. It can be applied to cameras and displays — including ones used for computers, televisions and mobile devices — and used to fine-tune the color of LED lighting.

“Our new approach can improve today’s commercially available displays or enhance the sense of reality for new technologies such as near-eye-displays for virtual reality and augmented reality glasses,” said Jiyong Wang, a member of the PAINT research team. “It can also be used to produce LED lighting for hospitals, tunnels, submarines and airplanes that precisely mimics natural sunlight. This can help regulate circadian rhythm in people who are lacking sun exposure, for example.”

The company believes its Codex machine learning algorithm is the next step in programming—a sidekick for coders to speed up the work and ease the drudgery.