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Google’s next-generation large-scale language model, Gemini 3.0, is about to be released, with frequent leaks from company employees and CEO Sundar Pichai explicitly stating that the model will launch before the end of this year.

IT Home notes that AI enthusiasts on social media platform X (formerly Twitter) and multiple Discord communities are reaching fever pitch; some “AI truth-seekers” even firmly believe that Gemini 3 has already been quietly launched. Given Google’s tradition of semi-secretly testing new models, such speculation is not entirely unfounded.

It’s not just ordinary users who are eagerly awaiting this; the entire AI  industry is holding its breath, waiting for Google to reveal its trump card. The industry generally expects Gemini 3 to achieve significant leaps in code generation and multimodal content creation; particularly noteworthy is the rumored integration of an upgraded version of “Nano Banana,” a new generation of Google’s previously wildly popular image generation tool.

Since the emergence of ChatGPT in late 2022, Google has been widely perceived as a slow-moving industry giant struggling to catch up. At the time, this assessment was not without merit: facing its most severe survival challenges in years, Google quickly restructured its team, focusing on integrating generative AI into its core product matrix.

Now, this once-dormant tech behemoth has awakened: Gemini’s user base is rapidly expanding; the AI revolution has so far not shaken its core profit pillar, advertising; and calls for Pichai’s resignation have largely subsided.

To achieve this catch-up, Google fully leverages its unique “full-stack” advantage, not only developing large-scale models in-house but also achieving efficient distribution through its vast product ecosystem and building a robust infrastructure using Google Cloud. This has allowed Google to remain outside the increasingly complex “mutual aid network” of the current AI  industry and avoid the widespread concerns about a growing bubble.

Meanwhile, a huge opportunity lies before Google: OpenAI’s highly anticipated ChatGPT 5 has received a lukewarm reception, falling far short of expectations as a “blockbuster.” Is this a sign that the AI ​​industry is entering a “plateau,” or does it mean OpenAI’s edge is waning?

According to multiple insiders speaking to Business Insider, the new model’s performance is “extremely stunning.” If Gemini 3 truly becomes a phenomenon, Google could potentially reclaim the industry leadership it has coveted since the start of the generative AI wave.

For OpenAI, this undoubtedly poses a serious challenge: it lacks Google’s full-stack integration capabilities, and its previous lead was primarily built on “first-mover advantage” and extensive industry alliances.

Google still needs to address a key challenge: currently, “ChatGPT” has become the default synonym for AI technology in the public eye; its cognitive position in the chatbot field is comparable to that of “Google” in web search.

The user base also differs significantly: Google claims Gemini has 650 million monthly active users, while OpenAI’s ChatGPT boasts approximately 800 million weekly active users. Although Gemini’s popularity among younger users continues to rise, bridging the gap remains a long and arduous task.

It’s worth noting that Google’s strategic investments over the years in cloud computing, self-developed chips, and top talent are now bearing fruit. If Gemini 3 truly makes a splash, all Google needs to do is capitalize on this opportunity.

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Artificial Intelligence and Experimental Validation Reveal the Atomic-Scale Basis for Improving the Performance of “Water-in-Salt” Batteries

Upton, New York—A team of scientists from Brookhaven National Laboratory and Stony Brook University have used artificial intelligence (AI) to gain insights into how zinc-ion batteries work and explore ways to improve their efficiency to meet future energy storage needs. Their findings, published in the journal *PRX Energy*, focus on the water-based electrolyte responsible for transporting charged zinc ions during charging and use. The AI ​​model analyzed how these charged ions interact with water in solutions of zinc chloride (ZnCl₂, a water-soluble salt) at different concentrations.

The AI ​​findings were experimentally validated at the National Synchrotron Radiation Facility II (NSLS-II) at Brookhaven National Laboratory, demonstrating why high salt concentrations produce optimal battery performance.

“Artificial intelligence is a crucial tool for advancing scientific progress,” said Esther Takeuchi, Director of the Division of Interdisciplinary Sciences at Brookhaven National Laboratory and William and Jane Knapp Professor of Energy and Environment at Stony Brook University. “This team’s findings demonstrate the profound insights that can be gained by combining experimentation and theory with artificial intelligence.”

Amy Marschilok, Professor of Chemistry at Stony Brook University (SBU) and Manager of the Energy Storage Division at ISD, added, “This research helps advance the development of robust zinc-ion batteries for large-scale energy storage. These batteries are particularly attractive for applications requiring highly reliable energy because their water-based electrolytes are inherently safe, and the materials used to manufacture them are abundant and inexpensive.”

Deryu Lu, a scientist in the Theoretical and Computational Group at the Center for Functional Nanomaterials (CFN) at Brookhaven National Laboratory who led the research, explained that zinc-ion batteries, like all batteries, convert the energy generated by chemical reactions into electrical energy.

“However, competing chemical reactions, such as the splitting of water molecules to produce hydrogen, can severely degrade battery performance,” he said. “If this energy is used for side reactions, then the energy that should have been used for work is lost.”

Lu and his collaborators knew that previous research had found that the water-breaking reaction was inhibited in a special zinc chloride electrolyte. This electrolyte had a very high salt concentration and was called a “water-in-salt” electrolyte, in contrast to the more common “salt-in-water” electrolyte. To investigate why the high-salt electrolyte was superior, they wanted to capture the atomic-scale details of how zinc and chloride ions move and interact with water at different salt concentrations, and how this interaction affects the electrolyte’s conductivity.

However, observing these atomic-scale details is extremely difficult. Therefore, the research team turned to a computer modeling method enhanced by artificial intelligence vision.

Developing AI Vision

Professor Lu stated, “These complex details cannot be observed using traditional computing techniques. Traditional simulation methods cannot handle the large number of atomic interactions with the required precision, thus failing to capture the timescale of evolution in such systems. Such calculations require enormous computing power and can easily take years.”

Therefore, instead of performing all the complex calculations required to simulate ion-water interactions, the research team used traditional simulation methods to generate a small amount of simulated data (called a “training set”) and fed it into the AI ​​program. They utilized computing resources at the Theory and Computation Facility (CFN) (a user facility of the U.S. Department of Energy’s Office of Science) and the Scientific Computing and Data Facility (CDS) within Brookhaven National Laboratory’s Division of Computation and Data Science (CDS).

“We need a small amount of data, collecting data by computing a small number of interactions to initiate the initial model training process,” said Cao Chuntian of CDS, the paper’s first author. “Then, we run the model to generate more data, continuously improving the model’s predictive capabilities.”

At each step, the scientists fed the results into a set of machine learning (ML) models to evaluate the accuracy of the predictions. Lu likened the process to calling several friends and asking them to help answer questions from the once-popular TV game show “Who Wants to Be a Millionaire?”. “If the friends/models all agree, then your prediction is likely accurate,” he noted.

But as Cao pointed out, “When we find that some predictions in the machine learning model ensemble are significantly biased, we re-perform traditional calculations to get the correct answers. Then, we add these new corrected data points back into the training data to further improve the machine learning model.”

This iterative “active learning” process minimizes the high computational demands required to train the machine learning model. After several rounds of training, the AI ​​model is able to predict interactions between a larger number of atoms over longer timescales.

“Springfield performed simulations for hundreds of nanoseconds on a massive system of thousands of atoms—a task that would have been impossible using traditional methods. AI/machine learning has truly transformed the landscape of complex materials research,” Lu said.

Stabilizing Water AI models developed by scientists at Brookhaven National Laboratory and Stony Brook University show that high concentrations of zinc chloride play a crucial role in stabilizing water molecules and preventing their fragmentation.

Professor Lu explained that in pure water, the oxygen atom in a water molecule (H₂O) forms two hydrogen bonds with the hydrogen atom in an adjacent water molecule. These hydrogen bonds connect water molecules into a continuous network, making them more reactive and easier to break down.

The research team found that as the concentration of zinc chloride increases, the number of hydrogen bonds decreases rapidly, disrupting the hydrogen bond network. In the water-in-salt system, only about 20% of the hydrogen bonds remain.

Cao said, “Stabilizing water molecules is the key factor behind the remarkable effect of high-concentration water-in-salt electrolytes.”Browse the great range of batteries & chargers at Batterypcs.co.uk in UK.Low prices, big inventory, expert advice. Find your battery here!”