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In the early 1900s, Albert Einstein proposed the theory of general relativity, which challenged everything scientists believed they understood about the universe at the time. Over the years, scientists have questioned whether this theory was true. However, a newly created dark matter map finally gives undeniable proof.
We must first look at Einstein’s original theory to fully understand this new development. Before Einstein proposed the theory of general relativity, scientists believed space to be almost featureless and changeless. Further, they thought that time flowed at its own pace, oblivious to clocks that tried to measure it, as Isaac Newton had suggested two centuries earlier.
However, Einstein proposed that both space and time were one force, spacetime, and that matter within this ever-changing stage was controlled by the curving path that gravity dictated. But to create gravity, we needed mass, a force so strong it could literally curve spacetime around it. This is where dark matter comes into play.
A system for realizing many-photon quantum circuits is presented, comprising a programmable nanophotonic chip operating at room temperature, interfaced with a fully automated control system.
University of Oxford professor explains how conscious machines are possible.
Up next, The intelligence explosion: Nick Bostrom on the future of AI ► https://youtu.be/1WcpN4ds0iY
In his book “A Brief History of AI,” Michael Wooldridge, a professor of computer science at the University of Oxford and an AI researcher, explains that AI is not about creating life, but rather about creating machines that can perform tasks requiring intelligence.
Wooldridge discusses the two approaches to AI: symbolic AI and machine learning. Symbolic AI involves coding human knowledge into machines, while machine learning allows machines to learn from examples to perform specific tasks. Progress in AI stalled in the 1970s due to a lack of data and computational power, but recent advancements in technology have led to significant progress. AI can perform narrow tasks better than humans, but the grand dream of AI is achieving artificial general intelligence (AGI), which means creating machines with the same intellectual capabilities as humans. One challenge for AI is giving machines social skills, such as cooperation, coordination, and negotiation.
— What’s the biggest black hole in the universe?
LISA will consist of a trio of satellites orbiting the sun that will constantly monitor the distances among them. When a gravitational wave comes by, the satellites will detect the telltale signature, like buoys in the ocean recognizing a passing tidal wave.
To search for IMBHs, the astronomers have to hope for a lucky break. If an IMBH in the galactic center happens to capture a wandering dense remnant (like a smaller black hole, a neutron star, or a white dwarf), the process will emit gravitational waves that LISA can potentially detect. Because the IMBH itself will be orbiting around the central supermassive black hole, these gravitational waves will undergo a Doppler shift (like the shifting in frequencies from a passing ambulance) due to the IMBH’s motion.
I propose to consider the question, ‘Can machines think?’ This should begin with definitions of the meaning of the terms ‘machine’ and ‘think’. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words ‘machine’ and ‘think’ are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, ‘Can machines think?’ is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.
The new form of the problem can be described in terms of a game which we call the ‘imitation game’. It is played with three people, a man (A), a woman (B), and an interrogator © who may be of either sex. The interrogator stays in a room apart from the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either ‘X is A and Y is B’ or ‘X is B and Y is A’. The interrogator is allowed to put questions to A and B thus:
C: Will X please tell me the length of his or her hair? Now suppose X is actually A, then A must answer. It is A’s object in the game to try and cause C to make the wrong identification. His answer might therefore be.
Grocery shopping will lead the way in the AI (artificial intelligence) revolution.
Brits aged 15 to 64 spend about 43 percent of all their work and study time on cooking cleaning and other jobs round the home such as looking after children or elderly relatives.
In the UK, working-age men spend around half as much time as working-age women do on them.
Today, most of the universe’s galaxies are already receding faster than the speed of light.
Is time travel FINALLY possible?? Join us… and find out!
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In this video, Unveiled takes a closer look at 3 groundbreaking experiments in time travel and quantum computing! On an international scale, science is starting show how moving forward and back in time really COULD be possible… all it will take is a little manipulation at the atomic and subatomic levels!
This is Unveiled, giving you incredible answers to extraordinary questions!
The gene-editing system CRISPR-Cas9 which has revolutionized genetic engineering over the past decade involves cutting DNA strands which is a process that can be quite hard to control and can result in unwanted genetic changes. Now, thanks to researchers at the Massachusetts Institute of Technology and the University of California, San Francisco (UCSF), a new gene-editing technology called CRISPRoff can change that, according to a press release.
“Fast forward four years [from the initial grant], and CRISPRoff finally works as envisioned in a science fiction way,” says co-senior author Luke Gilbert. “It’s exciting to see it work so well in practice.”
