Over the past year or so, generative AI models such as ChatGPT and DALL-E have made it possible to produce vast quantities of apparently human-like, high-quality creative content from a simple series of prompts.
Though highly capable – far outperforming humans in big-data pattern recognition tasks in particular – current AI systems are not intelligent in the same way we are. AI systems aren’t structured like our brains and don’t learn the same way.
AI systems also use vast amounts of energy and resources for training (compared to our three-or-so meals a day). Their ability to adapt and function in dynamic, hard-to-predict and noisy environments is poor in comparison to ours, and they lack human-like memory capabilities.
Pneumonia is a potentially fatal lung infection that progresses rapidly. Patients with pneumonia symptoms – such as a dry, hacking cough, breathing difficulties and high fever – generally receive a stethoscope examination of the lungs, followed by a chest X-ray to confirm diagnosis. Distinguishing between bacterial and viral pneumonia, however, remains a challenge, as both have similar clinical presentation.
Mathematical modelling and artificial intelligence could help improve the accuracy of disease diagnosis from radiographic images. Deep learning has become increasingly popular for medical image classification, and several studies have explored the use of convolutional neural network (CNN) models to automatically identify pneumonia from chest X-ray images. It’s critical, however, to create efficient models that can analyse large numbers of medical images without false negatives.
Facebook and Instagram owner Meta will release a commercial A.I. product, following moves from Google, Microsoft, and OpenAI, in 2023 confirmed Reality Labs boss Andrew Bosworth.
Rupert Sheldrake, PhD, is a biologist and author best known for his hypothesis of morphic resonance. At Cambridge University he worked in developmental biology as a Fellow of Clare College. He was Principal Plant Physiologist at the International Crops Research Institute for the Semi-Arid Tropics in Hyderabad, India. From 2005 to 2010 he was Director of the Perrott-Warrick project for research on unexplained human and animal abilities, administered by Trinity College, Cambridge. Sheldrake has published a number of books — A New Science of Life (1981), The Presence of the Past (1988), The Rebirth of Nature (1991), Seven Experiments That Could Change the World (1994), Dogs That Know When Their Owners are Coming Home (1999), The Sense of Being Stared At (2003), The Science Delusion (Science Set Free) (2012), Science and Spiritual Practices (2017), Ways of Going Beyond and Why They Work (2019).
Rupert gave a talk entitled The Science Delusion at TEDx Whitechapel, Jan 12, 2013. The theme for the night was Visions for Transition: Challenging existing paradigms and redefining values (for a more beautiful world). In response to protests from two materialists in the US, the talk was taken out of circulation by TED, relegated to a corner of their website and stamped with a warning label.
This video is from a presentation at a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model AIs. This presentation was given before the launch of GPT-4.
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Since its launch in November 2022, ChatGPT, an artificial intelligence (AI) chatbot, has been causing quite a stir because of the software’s surprisingly human and accurate responses.
The auto-generative system reached a record-breaking 100 million monthly active users only two months after launching. However, while its popularity continues to grow, the current discussion within the cybersecurity industry is whether this type of technology will aid in making the internet safer or play right into the hands of those trying to cause chaos.
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The explosion of new generative AI products and capabilities over the last several months — from ChatGPT to Bard and the many variations from others based on large language models (LLMs) — has driven an overheated hype cycle. In turn, this situation has led to a similarly expansive and passionate discussion about needed AI regulation.
The AI regulation firestorm was ignited by the Future of Life Institute open letter, now signed by thousands of AI researchers and concerned others. Some of the notable signees include Apple cofounder Steve Wozniak, SpaceX, Tesla and Twitter CEO Elon Musk; Stability AI CEO Emad Mostaque; Sapiens author Yuval Noah Harari; and Yoshua Bengio, founder of AI research institute Mila.
Summary: It may be possible to optimize the stimulation parameters of brain implants in animals without human intervention. The study highlights the potential for autonomous optimization of prostheses implanted in the brain. The advance may prove to be beneficial for those with spinal cord injury and diseases that affect movement.
Source: University of Montreal.
Scientists have long studied neurostimulation to treat paralysis and sensory deficits caused by strokes and spinal cord injuries, which in Canada affect some 380,000 people across the country.
There are a lot of reasons why we think technological singularity will happen sooner than 2045. With technology advancing at a rapid pace, an abundance of data, increased investment, collaboration, and potential breakthroughs, we might just wake up one day and realize that the robots have taken over. But hey, at least they’ll do our laundry.
Do you think singularity will happen sooner than 2045? Why or why not? Answer in the comment section below.
