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Google worked to reassure investors and analysts on Thursday during its quarterly earnings call that it’s still a leader in developing AI. The company’s Q4 2022 results were highly anticipated as investors and the tech industry awaited Google’s response to the popularity of OpenAI’s ChatGPT, which has the potential to threaten its core business.
During the call, Google CEO Sundar Pichai talked about the company’s plans to make AI-based large language models (LLMs) like LaMDA available in the coming weeks and months. Pichai said users will soon be able to use large language models as a companion to search. An LLM, like ChatGPT, is a deep learning algorithm that can recognize, summarize and generate text and other content based on knowledge from enormous amounts of text data. Pichai said the models that users will soon be able to use are particularly good for composing, constructing and summarizing.
“Now that we can integrate more direct LLM-type experiences in Search, I think it will help us expand and serve new types of use cases, generative use cases,” Pichai said. “And so, I think I see this as a chance to rethink and reimagine and drive Search to solve more use cases for our users as well. It’s early days, but you will see us be bold, put things out, get feedback and iterate and make things better.”
Continue reading “ChatGPT is about to get even better and Microsoft’s Bing could win big” »
The rise of social media has changed our day to day lives. But more and more reports show that social media and especially social media can impact our brain. Social media addiction might also to a decline in mental health. How does social media changes us? And are the effects by social media addiction reversal?
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Continue reading “How Social Media Addiction Destroys Your Brain” »
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The only things that travel at the speed of light are photons. Nothing with any mass at all can travel at the speed of light because as it gets closer and closer to the speed of light, its mass increases. And if it were actually traveling at the speed of light, it would have an infinite mass. Light does not experience space or time. It’s not just a speed going through something. All of the universe shifts around this constant, the speed of light. Time and space itself stop when you go that speed.
Continue reading “A mind-blowing explanation of the speed of light | Michelle Thaller | Big Think” »
As fantastic as this may seem this is not an impossible occurrence.
Before Einstein, time travel was just a story, but his calculations led us into the quantum world and gave us a more complicated picture of time. Kurt Godel found that Einstein’s equations made it possible to go back in time. What’s up? None of the ideas about how to go back in time were ever physically possible.
Before sending a particle back in time, scientists from ETH Zurich, Argonne National Laboratory, and Moscow Institute of Physics and Technology asked, Why stick to physical grounds?
Continue reading “Scientists Successfully Sent A Particle Back in Time Using A Quantum Computer” »
Researchers have crafted an artificial intelligence (AI) system capable of deciphering fragments of ancient Babylonian texts. Dubbed the “Fragmentarium,” the algorithm holds the potential to piece together some of the oldest stories ever written by humans, including the Epic of Gilgamesh.
The work comes from a team at Ludwig Maximilian University in Germany who have been attempting to digitize every surviving Babylonian cuneiform tablet since 2018.
The problem with understanding Babylonian texts is that the narratives are written on clay tablets, which today exist only in countless fragments. The fragments are stored at facilities that are continents away from each other, such as the British Museum in London and the Iraq Museum in Baghdad.
Robotic systems have become increasingly sophisticated over the past decades, improving both in terms of precision and capabilities. This is gradually facilitating the partial automation of some surgical and medical procedures.
Researchers at Tsinghua University have recently developed a soft robotic tentacle that could potentially be used to improve the efficiency of some standard medical procedures. This tentacle, introduced in IEEE Transactions on Robotics, is controlled through their novel control algorithm, together with the so-called active cooling for shape memory alloy, the actuating candidate for the robot.
Continue reading “A soft robotic tentacle controlled via active cooling” »
Humans are innately able to reason about the behaviors of different physical objects in their surroundings. These physical reasoning skills are incredibly valuable for solving everyday problems, as they can help us to choose more effective actions to achieve specific goals.
Some computer scientists have been trying to replicate these reasoning abilities in artificial intelligence (AI) agents, to improve their performance on specific tasks. So far, however, a reliable approach to train and assess the physical reasoning capabilities of AI algorithms has been lacking.
Cheng Xue, Vimukthini Pinto, Chathura Gamage, and colleagues, a team of researchers at the Australian National University, recently introduced Phy-Q, a new testbed designed to fill this gap in the literature. Their testbed, introduced in a paper in Nature Machine Intelligence, includes a series of scenarios that specifically assess an AI agent’s physical reasoning capabilities.
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A team of researchers have come up with a machine learning-assisted way to detect the position of shapes including the poses of humans to an astonishing degree — using only WiFi signals.
In a yet-to-be-peer-reviewed paper, first spotted by Vice, researchers at Carnegie Mellon University came up with a deep learning method of mapping the position of multiple human subjects by analyzing the phase and amplitude of WiFi signals, and processing them using computer vision algorithms.
“The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input,” the team concluded in their paper.
