Lifeboat Foundation

This week at Starbase Booster 10 joins Ship 28 at the launch site to begin its testing on the orbital launch mount, Ship 28 performs a static fire test, and SpaceX shows off its holiday spirit, while over at Cape Canaveral, Starlink launch and recovery operations continue as several tanks and a prefabricated tower section are shipped out to Brownsville.\
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Leveraging advancements in Genrative AI, the robot enables more immersive and continuous conversations, deepening the user-device connection.

Earlier this year, OpenAI’s CEO Sam Altman sent the tech world into a frenzy with a five-word Reddit post: “AGI has been achieved internally.”

AGI stands for Artificial General Intelligence. It’s the holy grail of AI research. Essentially, true AGI would be a brain-like intelligence capable of reasoning, creative thought, and perhaps even consciousness.

Altman posting about achieving AGI was a huge deal — it would be akin in its importance to a top scientist posting “Fusion works,” or Donald Trump posting “I’m not running.”

Guided by machine learning, chemists at the Department of Energy’s Oak Ridge National Laboratory designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material. A supercapacitor made with the new material could store more energy—improving regenerative brakes, power electronics and auxiliary power supplies.

“By combining a data-driven method and our research experience, we created a carbon material with enhanced physicochemical and electrochemical properties that pushed the boundary of energy storage for carbon supercapacitors to the next level,” said chemist Tao Wang of ORNL and the University of Tennessee, Knoxville.

Wang led the study, titled “Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitor” and published in Nature Communications, with chemist Sheng Dai of ORNL and UTK.

Sky News Investigations Reporter Jonathan Lea sits down to talk with a “free-thinking” and “opinionated” artificial intelligence, Ameca Desktop.

“Ameca is driven by the same artificial intelligence behind Chat-GPT,” Mr Lea said.

Scientists have established how the activity of our brain during imaginary movement differs from that during real action. It turns out that in both cases, a previous signal occurs in the cerebral cortex, but with an imaginary movement, it does not have a clear link to a specific hemisphere.

The obtained data can potentially be used in medical practice to create neuro trainers and control the restoration of neural networks in post-stroke patients. The results of the study are published in the journal Cerebral Cortex.

Before we pick up a pen or put down a cup, a complete picture of this action is formed in the brain. Such visual–motor transformations ensure the accuracy of our movements. Knowing about these mechanisms helps patients to restore motor activity after strokes. But we don’t always finish the movement we started. In this case, visual information enters the motor areas of the cortex responsible for movement, but the start of the reaction is blocked at some point, and mental effort does not end with real muscle activation.

Microsoft has big plans for generative AI in gaming, and its recent Xbox partnership with Inworld AI is a key example.

Non-playable characters in video games play key roles but stick to stiff scripts. Gen AI should open up their minds and your gaming world experience.

Think of it like a flying Blastoise robot that could be ready for action in the next decade.

As object identification and three-dimensional (3D) reconstruction techniques become essential in various reverse engineering, artificial intelligence, medical diagnosis, and industrial production fields, there is an increasing focus on seeking vastly efficient, faster speed, and more integrated methods that can simplify processing.

In the current field of object identification and 3D reconstruction, extracting sample contour information is primarily accomplished by various computer algorithms. Traditional computer processors suffer from multiple constraints, such as high-power consumption, low-speed operation, and complex algorithms. In this regard, there has recently been growing attention in searching for alternative optical methods to perform those techniques.

The development of optical computing theory and image processing has provided a more complete theoretical basis for object identification and 3D reconstruction techniques. Optical methods have received more attention as an alternative paradigm than traditional mechanisms in recent years due to their enormous advantages of ultra-fast operation speed, high integration, and low latency.