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Did you hear the news? OpenAI’s newest model can reason across audio, vision, and text in real time.

How does GPT-4o do with math tutoring? 🤔

Sal and his son test it out on a Khan Academy math problem.

You can get AI-powered math tutoring right now with Khanmigo:


AI-powered tutor Khanmigo makes homework time easy. It’s the only AI integrated into nonprofit Khan Academy’s world-class content library.

ChatGPT 4O can now speak and sing in real time. It can even view the real world through your phone’s camera and describe what’s happening in real time.


The AI race has just shifted into high gear, with US artificial intelligence pioneers OpenAI rolling out its new interface that works with audio and vision as well as text. The new model, called GPT-4o, has gone beyond the familiar chat-bot features and is capable of real-time, near-natural voice conversations. The developer OpenAI will also make it available to free users.

ChatGPT was already able to talk to users, but with long pauses to process the data. It often seemed a bit sluggish. This was because the feature required three internal applications, the company explained: transcribing the spoken text, processing and generating, and converting the response to speech. This caused delays.

We talk to computer scientist Mike Cook from the renowned Kings College London about the new Chat GPT-4o development.

#artificialintelligence #chatgpt #openai.

By Rachel Kremen, Princeton Plasma Physics Laboratory

The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) to solve one of humankind’s most pressing issues: generating clean, reliable energy from fusing plasma.

Underwater recon and attack drones are about to enter war zones.


Australia has unveiled ‘Ghost Shark’, an underwater drone that is capable of surveillance, intelligence collection and attacking enemy targets. The U.S. has a ‘Monster Manta’ that can carry a range of payloads, carry out long-range missions. Countries around the world are developing unmanned underwater vehicles for the next war at sea. What about India?

#australia #us #india.

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“Big machine learning models have to consume lots of power to crunch data and come out with the right parameters, whereas our model and training is so extremely simple that you could have systems learning on the fly,” said Robert Kent.


How can machine learning be improved to provide better efficiency in the future? This is what a recent study published in Nature Communications hopes to address as a team of researchers from The Ohio State University investigated the potential for controlling future machine learning products by creating digital twins (copies) that can be used to improve machine learning-based controllers that are currently being used in self-driving cars. However, these controllers require large amounts of computing power and are often challenging to use. This study holds the potential to help researchers better understand how future machine learning algorithms can exhibit better control and efficiency, thus improving their products.

“The problem with most machine learning-based controllers is that they use a lot of energy or power, and they take a long time to evaluate,” said Robert Kent, who is a graduate student in the Department of Physics at The Ohio State University and lead author of the study. “Developing traditional controllers for them has also been difficult because chaotic systems are extremely sensitive to small changes.”

For the study, the researchers created a fingertip-sized digital twin that can function without the internet with the goal of improving the productivity and capabilities of a machine learning-based controller. In the end, the researchers discovered a decrease in the controller’s power needs due to a machine learning method known as reservoir computing, which involves reading in data and mapping out to the target location. According to the researchers, this new method can be used to simplify complex systems, including self-driving cars while decreasing the amount of power and energy required to run the system.