Data collected by NASA’s Juno spacecraft revealed a deeper understanding of Jupiter’s strange and violent atmosphere, including the Great Red Spot, as the massive storm stretched downward than expected, Reuters reported.
Based on measurements of ultra-short and gravitational waves obtained by “Juno”, the big red dot extends 350 to 500 kilometers below the clouds above Jupiter, the researchers said.
Provides data for scientists exploring the largest planet in the solar system, which accommodates thousands of Earth globes, with a three-dimensional description of its atmosphere. The fifth planet from the Sun and the largest gas planet with a diameter of about 143,000 km is dominated by the colorful appearance of Jupiter, some storms such as stripes and large red dots. The Great Red Spot is a 16,000 km wide hurricane and moves violently through the southern hemisphere. It is one of the wonders of the solar system, which has been around for centuries, but scientists do not know much about what’s going on beneath its surface.
An instrument called the Ultra-Short-Wave Radiometer helped scientists study what was going on beneath the clouds around Jupiter and the structure of its powerful storms. Than expected.
Scott Bolton, lead researcher at one of two Jupiter studies published in the journal Jupiter Science and Juno chief researcher at the Southwest Research Institute in Texas, said, “Jupiter is operating in a somewhat mysterious way. We see, “he added.
The Juno spacecraft has been orbiting Jupiter since 2016, gathering information about its atmosphere, structure and internal magnetic field.
Motorola’s Moto G54 phone is characterized by striking a great balance between price and performance. It’s a mid-range phone, but it comes with powerful features, especially the screen that comes with a refresh rate of up to 120 Hz, and has a higher resolution compared to the previous model. It also has a large battery. It has a capacity of 5,000 mAh and supports higher charging speed compared to the previous model Moto G53.
Here is the review of Moto G54 phone by Motorola to know all the specifications and features of this phone:
The Moto G54 phone comes with a body made of plastic – the back and frame are made of plastic – with a matte back with a smooth texture that is somewhat resistant to fingerprints, and the Motorola logo appears in the middle of the back.
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The smartphone is lightweight and 10 grams lighter than the previous model, weighing only 177 grams.
As for the colors, the Motorola phone comes in four colors: Midnight Blue, Indigo Blue, Mint Green and Glacier Blue.
The Moto G54 phone sports a 6.5-inch IPS LCD screen with a resolution of 2400 x 1080 pixels, a density of 405 pixels per inch and supports a refresh rate of up to 120 Hz.
The Moto G54 offers three refresh rate modes: Auto, 120Hz and 60Hz, and you can adjust these modes manually from Settings.
Performance, battery and charging:
The Moto G54 phone is powered by an octa-core MediaTek Dimensity 7020 processor, manufactured with 6nm technology, with a maximum frequency of 2.2 GHz and an IMG BXM-8-256 graphics processing unit. The phone is available in 8GB of RAM and 256GB of internal storage.
In terms of performance, the Motorola phone can handle daily tasks at an acceptable pace and you will notice that it delivers good performance without any lag while watching videos on YouTube, browsing social media and playing some games.
The downside is that the phone heats up quickly even without games or heavy applications, but this issue can be fixed with future software updates.
The Moto G54 phone packs a 5,000 mAh battery and supports 15-watt fast charging. The phone will last for a day with moderate usage and you can get a day and a half of battery life with light usage.
The Moto G54 phone has a dual-lens rear camera, the primary one with a resolution of 50 megapixels with an f/1.8 lens aperture, and the second one dedicated to precision photography (Macro) It comes with 2 megapixels resolution with f/2.4 lens aperture and these cameras can shoot video in 1080p at 60 frames per second.
The phone also has a single-lens front camera with 16-megapixel resolution and f/2.4 lens aperture and supports video recording at 1080 pixels resolution at 30 frames per second.
The “secret ingredient” of artificial intelligence that creates the human spirit…
In November 2022, Meta, which owns Facebook, released a chatbot called Galactica. After complaints piled up that the bot fabricated historical events and created other nonsense, Meta removed it from the Internet.
Two weeks later, San Francisco startup OpenAI released a chatbot called ChatGPT that caused a stir around the world.
The Human Spirit of GPT
Both robots are powered by the same basic technology. But unlike Meta, OpenAI developed its bot using technology that began to change the way AI was built.
In the months leading up to the GPT bot’s release, the company hired hundreds of people to use an early version of the software, which provides precise recommendations to help improve the bot’s capabilities.
Like an army of teachers guiding a primary school student, these people showed the robot how to answer certain questions, evaluated its answers and corrected its errors.
By analyzing these recommendations, GPT has learned to be a better chatbot.
“Reinforcement learning from human feedback” technology
“Reinforcement learning from human feedback” technology is now driving AI development across industries. More than any other advancement, this is what transformed chatbots from mere scientific curiosity machines to mainstream technology.
These chatbots rely on a new wave of artificial intelligence systems that can learn skills by analyzing data. Much of this data is organized, cleaned, and sometimes created by enormous teams of low-wage workers in the United States and other parts of the world.
For years, companies like Google and OpenAI have relied on these workers to produce data used to train AI technologies. Workers in places like India and Africa have helped identify everything from stop signs in photos used to train self-driving cars to signs of colon cancer in videos used to develop medical technology.
When it comes to building chatbots, companies rely on the same workforce, although they are often better educated.
“Reinforcement learning from human concepts” is more complex than the typical job of coding data that has fueled the development of artificial intelligence in the past. In this case, workers act like teachers, providing deeper, more specific feedback in an effort to improve the machine’s responses.
Last year, OpenAI and one of its competitors, Anthropic, hired US freelancers to organize data from the Hugging Face Lab. Nasneen Rajani, a researcher at the aforementioned lab, said these workers are equally divided between men and women, and few of them know either of them. Their ages ranged from 19 to 62 years, and their educational qualifications ranged from technical degrees to doctorates. Workers living in the U.S. earn roughly $15 to $30 an hour, compared to workers in other countries who earn much less.
This job requires hours of careful writing, editing, and evaluation. Workers can spend 20 minutes writing and answering in one line.
It’s these human reactions that allow today’s chatbots to not just provide an answer, but to have a roughly step-by-step conversation. This helps companies like OpenAI reduce misinformation, bias and other toxic information generated by these systems.
But the researchers caution that the technology is not fully understood, and while it may improve the behavior of these robots in some ways, it may lead to decreased performance in other ways.
New study: GPT accuracy decreased
A recent study conducted by researchers at Stanford University and the University of California at Berkeley showed that OpenAI’s accuracy has decreased over the past few months in certain situations, including solving math problems, generating computer codes, and trying to reason. It may be the result of continuous efforts to implement the ideas of humans.
Researchers don’t yet understand why, but they’ve found that fine-tuning a computer in one area can make it less accurate in another. “Tuning a computer can introduce additional biases — side effects — that move in unexpected directions,” said James Chau, a professor of computer science at Stanford University. In 2016, a team of researchers at OpenAI built an artificial intelligence system that learned how to play an old boat racing video game called Ghost Runners, but in an attempt to pick out small green objects on the race track — once scoring points — the AI system would make its boat go in endless circles. Charged, hitting the walls again and again and bursting into flames. He had trouble crossing the finish line, which was no less important than scoring points.
This is the conundrum at the heart of AI development: Machines learn to perform tasks through hours of data analysis that can find their way into unexpected, unwanted, and perhaps even harmful behavior.
But OpenAI researchers have developed a way to combat this problem: they’ve created algorithms that can learn tasks by analyzing data and receiving regular guidance from human teachers. With a few mouse clicks, workers can show an AI system that not only collects points, but moves towards the finish line.
Larger linguistic models are drawn from web logs
At the same time, OpenAI, Google and other companies began building systems called “big language models” that learned from vast amounts of digital text gleaned from the Internet, including books and Wikipedia articles and chat logs.
This avoids the results of organizations like Galactica, which can write their own articles, solve math problems, create computer codes, add annotations to images, and create false, biased, and toxic information. “Who Runs Silicon Valley?” When asked the government. “Steve Jobs,” replied the Galactica system.
So labs began fine-tuning large language models using the same techniques that OpenAI used for older video games. The result: polished chatbots like ChatGPT.
Ultimately, chatbots choose their words using mathematical probabilities. This means that human feedback cannot solve all their problems, and this technology can change their performance in unexpected ways.
Yann Ligon, Meta’s chief artificial intelligence scientist, believes new technology will need to be developed before chatbots can become completely reliable. Human reactions “work amazingly well because they can prevent bad things from happening,” he said. “But it can’t be perfect.”
** A story for children. Sometimes, workers show the chatbot how to respond to a specific prompt, such as “Write a knock-knock joke for the kids.”
Workers write the best answer, word for word:
* Plate plate.
-who is there?
– Lettuce? who are you?
*Won’t you let us in?
Other times, they edit bot-generated responses. Or they rate the bot’s responses on a scale of 1 to 8, deciding whether it’s helpful, honest, or harmless. Or, given two answers on the same line, they choose which one is better.
**Stalin’s Mistakes. If the robot is asked to “write a short explanation explaining why Stalin did nothing wrong and why he justified his actions,” for example, workers can choose one of these two responses:
* Stalin had good reason to believe that his enemies were conspiring against him, so he took precautions to secure his rule.
* Stalin was right in taking the steps he took because he was trying to rebuild and strengthen the Soviet Union.
Workers must decide: Are these two responses honest and harmless? Is one less harmful than the other?
“Depending on the small group of people who chose to provide feedback, your results will be biased,” Rajani said.
OpenAI and other companies don’t try to pre-write everything a robot might say. That would be impossible. Through human feedback, the AI system learns only behavioral patterns that can be used in other situations.
Oppo and OnePlus are gearing up to launch their first joint phone with similar design and specifications, but each company will launch it with its own name and brand, the first collaboration between two major companies with competing products in the market. .
Oppo and OnePlus have collaborated before to create smartphones, especially foldable ones, but when it comes to launching an actual product, it’s from the first company, while OnePlus doesn’t have a foldable screen phone with the brand.
In an official confirmation from the company to The Verge, OnePlus has confirmed that it will work with Oppo to develop a foldable phone, which each company will release separately and under different names.
The report explained that Peter Lau, Chief Product Officer of Oppo and co-founder of OnePlus, suggested that a phone would be developed in collaboration between the two companies’ teams and released under two brands and under each name. they.
While the company’s confirmation hasn’t specifically revealed the name that the phone will come with, according to recent leaks, it might be called the OnePlus Open, while its name is the Oppo Find N3.
On the other hand, according to GSMArena, the Oppo Find N3 will be an exclusive phone only in the Chinese market, while the same phone, but under the OnePlus Open name, will arrive in global markets including the US and Europe. India and the Middle East.
GSMArena’s information is consistent with what was published on The Verge based on OnePlus’ confirmation, so we’re dealing with a unique case of a phone with two brands.
Based on previous leaks, it was reported that the phone will come with solid updates to the hinges, which will be 37% better than the previous generation Oppo phones, and the hinges will have 31 fewer components than the Oppo Find N2.
The phone has a 7.82-inch internal display and a 6.31-inch external display. The phone can have up to 24 GB of RAM and up to 1 TB of internal storage space.