Category: news

Is your phone battery draining too quickly? Is your tablet suddenly unable to turn on? Are you worried about how much your old phone is worth when you trade it in? These questions trouble many people. Now, with Lenovo’s AI-powered Helper, you can get intelligent responses and precise solutions throughout the entire process.

Is your phone battery always draining too quickly?AI  Smart Check can help.

Many users find that their newly purchased phones have batteries that drain quickly, worrying if there’s a problem. But they don’t know how to check it themselves or how to read “battery health.”

Open the AI ​​Smart Check function in Lenovo’s AI-powered Helper. With just one click, it automatically checks the device’s status, including battery health, whether the power strategy is reasonable, and whether there are any abnormal power consumption issues. It’s not just a “health check,” but it also provides targeted optimization suggestions, such as closing high-power background programs and adjusting performance scheduling modes.

【How to check your phone’s battery health?】

→ Use Lenovo’s AI-powered Helper’s AI Smart Check to perform a “battery health check.”

Operation Steps:

1. Open the Lenovo ThinkHelp app and click on AI Smart Check in the scene;

2. Enter “Check Battery Health,” and Lenovo ThinkHelp will perform a comprehensive device check;

3. After completion, Lenovo ThinkHelp will display the test results. Click “View Full Report” to see more detailed data and usage suggestions.

How to Trade in Your Phone? AI  Smart Trade-in Tells You the Answer

Is it worth continuing to use your old phone, or is it more cost-effective to trade it in now? This is a dilemma for many users.

With the AI ​​Smart Trade-in function, Lenovo ThinkHelp’s AI service intelligence can intelligently assess the value of your old phone based on multi-dimensional data such as device configuration, status, and usage time, and recommend suitable new models and trade-in plans. It not only lets you know “how much it’s worth,” but also provides cost-effective trade-in solutions.

【How to Trade in Your Phone?】

→ Open the AI ​​Smart Trade-in function of Lenovo ThinkHelp’s AI service intelligence to experience one-stop trade-in service.

Operation Steps:

1. Open the Lenovo ThinkHelp app and click “Trade-in” at the bottom;

2. Click “Estimate Now.” Lenovo ThinkHelp will automatically identify the device information and provide an intelligent estimate based on the device’s condition, helping users get the best value for their trade-in.

Why can’t my tablet or phone turn on? AI  Repair can help!

Sometimes users encounter the problem of their devices suddenly turning off and unable to turn on. Buttons are unresponsive, the charging light is off, and they can’t even access the system interface.

The Lenovo ThinkHelp AI Service’s AI Repair function can easily solve these “can’t turn on” problems. Even if the main device is inoperable, users can still submit fault information and schedule repair services across devices using other Lenovo phones or tablets. The entire process is convenient and efficient.

【Where can I get my tablet repaired when it suddenly turns off?】

→ Use Lenovo ThinkHelp AI Service’s AI Repair function to report repairs across devices.

Operation Steps:

1. On another device, open the Lenovo Help App, log in to your account, switch to the device that won’t turn on under “Service,” and click “Service Appointment” at the bottom;

2. On the service appointment page, select the device malfunction description;

3. Fill in the pickup information and wait for pickup.

Frequently Asked Questions!

【How to Check Your Phone’s Battery Health】

→ Open the Lenovo Help AI Service Assistant and use the “AI  Smart Check” function to check the battery status and health with one click, and receive personalized optimization suggestions.

【How to Trade in Your Phone】

→ Use the “AI  Smart Trade-in” function to assess the value of your old device. Combine this with Lenovo’s trade-in program to easily get an accurate valuation and recommendations for new models.

【What to Do If Your Tablet Suddenly Goes Black and Won’t Turn On】

→ Even if the main device won’t start, you can quickly schedule repair service through the “AI Smart Repair” function on other Lenovo devices.

How to check your phone’s battery health? How to trade in your phone? What to do if your tablet suddenly goes black and won’t turn on…? These common device problems can all be solved quickly and intelligently through Lenovo’s Helpful AI Service Agent. The Helpful AI Service Agent is an intelligent agent in the 3C service field. Like a full-process intelligent butler, it uses AI technology to connect users with services, comprehensively improving the user experience. If you’re also troubled by phone battery health, phone trade-in programs, or your tablet won’t turn on, open the Lenovo Helpful AI Service Agent now and get a fantastic one-click solution.

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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!”

The decade from 2025 to 2035 will be a crucial period for artificial intelligence to deeply empower battery technology and fundamentally transform its R&D paradigm and application scenarios. The intervention of AI will drive innovation across the entire battery technology chain, from material discovery and system design to management and maintenance.

The following is a summary of the core trends in AI-driven battery technology development over the next decade: AI is transforming battery R&D from an experience-based “trial and error” approach to a data-driven “automated design.”

From “Trial and Error” to “Design”: Traditional battery R&D cycles are lengthy and heavily reliant on experimental trial and error. The emergence of Battery Design Automation (BDA), similar to EDA software in the chip industry, integrates multi-scale physical models with artificial intelligence algorithms to build an automated R&D platform from atomic-level material design to system-level performance prediction. This can significantly shorten the R&D cycle of next-generation batteries from several years.

AI Accelerates Material Innovation: Generative AI can reverse engineer novel battery materials that meet specific performance targets (such as high conductivity and high stability) within a vast chemical space. For example, the Uni-Electrolyte platform can utilize generative AI to design novel electrolyte molecules and predict their synthetic pathways. The Fudan University team used AI high-throughput computing to improve material screening efficiency by a hundredfold.

Accurate Performance Simulation and Prediction: Through algorithms such as Physical Information Neural Networks (PINN), AI can accurately and efficiently solve complex multiphysics problems within batteries, achieving precise predictions of battery state of health (SOH). This provides a key tool for optimizing battery design and extending battery life.

Deep Integration Across the Industry Chain: AI’s empowerment of battery technology will permeate the entire industry chain, from laboratory innovation to large-scale applications.

Intelligent Manufacturing and Quality Control: AI-powered large-scale models can automatically call upon and optimize production process parameters. Through digital twin technology based on physical models and quantum computing, material interface reactions can be predicted in virtual space, and a defect detection system can be built, thereby significantly improving production yield and reducing experimental trial-and-error costs by more than 90%.

Intelligent Battery Management System (BMS): Equipping batteries with a “digital brain” is the most direct manifestation of AI at the application level. For example, the “Battery Digital Brain” developed by the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, can achieve day-level advanced fault warnings through AI algorithms, far exceeding the minute-level level of traditional systems, greatly improving the safety and operation and maintenance efficiency of energy storage power stations.

Empowering the Next Generation Battery System: All-solid-state batteries are hailed as the ultimate next-generation battery technology, but their industrialization faces many challenges, such as interface impedance. AI, through high-throughput computing and knowledge graphs, can quickly analyze decades of accumulated literature and patents, providing a new path to overcome key challenges such as solid-solid interfaces and sulfide electrolyte stability, accelerating its commercialization. It is expected that all-solid-state batteries will enter the vehicle testing phase in 2027.

Future Applications and Emerging Opportunities: The combination of AI and battery technology will spawn and drive the development of a series of cutting-edge technology industries.

Opening Up a New Market Worth Hundreds of Billions: Due to size limitations and the extreme pursuit of energy density, AI consumer terminals (such as eVTOL electric vertical take-off and landing aircraft and humanoid robots) will become the testing ground for the commercialization of solid-state batteries. eVTOL requires all-solid-state batteries with an energy density ≥400Wh/kg. The low-altitude economic battery market is projected to reach 150-200 billion yuan by 2030.

Building the Energy Foundation for the AI ​​Revolution: Data centers providing computing power for AI are huge energy hogs. Large-scale battery energy storage systems, especially long-term energy storage solutions using non-lithium technologies (such as zinc batteries), can serve as reliable power buffers for data centers, helping them “skip” the long wait for grid upgrades and enter operation years earlier. These companies are positioning themselves as key solvers of AI energy bottlenecks.

Driving the Intelligent Upgrade of Energy Storage Systems: Future energy storage systems will develop around core technologies such as large-capacity cells, liquid cooling thermal management, and AI-driven intelligent management. AI will optimize the operating strategies of energy storage systems and promote the widespread adoption of grid-forming control technologies, thereby enhancing grid stability and security.

Challenges and Key Insights: Looking ahead, opportunities and challenges coexist. Data quality, the accuracy of cross-scale model fusion, and the robustness of AI-specific algorithms remain scientific issues requiring continuous research and development. However, what is certain is that the deep integration of AIand battery technology is irreversible. It is driving the global battery industry to shift from relying on “manufacturing advantages” to relying on “R&D and innovation advantages,” which will reshape the future competitive landscape of energy technologies.

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DeepSeek’s AI has sparked a debate about whether cutting-edge platforms can be built for far less than the billions of dollars invested by US companies to build data centers. Chinese AI startup DeepSeek has released an update to its V3 model that promises better programming capabilities.
The V3-0324 update, which was initially announced on Hugging Face this week but has not yet been officially released, claims to address real-world challenges while setting benchmarks for accuracy and efficiency. V3 is actually an older DeepSeek platform, and DeepSeek claims to have significant improvements in benchmark performance across multiple metrics.
It also claims to have improved the style and content quality of Chinese writing features, improved multi-round interactive rewrites, optimized translation quality and letter writing, enhanced reporting analysis requests and output more detailed output, and improved the accuracy of function calls, fixing issues in previous V3 versions.
The startup’s AI service has sparked a debate about whether cutting-edge platforms can be built for far less than the billions of dollars invested by US companies to build data centers. It also highlights the company’s intention to stay ahead of its competitors, especially those from Silicon Valley, such as OpenAI and Google.
Previously, DeepSeek surpassed OpenAI’s ChatGPT to become the most popular free app in Apple’s US App Store.
DeepSeek’s achievements also include the performance of the initial R1 model, which seems to be on par with OpenAI’s best model, but at a fraction of the cost. The cost part was particularly shocking to the industry and triggered a sell-off in  AI and technology-related stocks in the US market. This is because the best companies in Silicon Valley have invested huge amounts of money in their artificial intelligence projects, but have only achieved similar results


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Battery energy storage is essential to improving the reliability of renewable energy. It can collect extra energy from solar and wind power to provide electricity when needed. However, artificial intelligence (AI) is taking battery management to the next level.

Experts say AI software is now key to managing large battery systems. Companies are applying AI not only to basic tasks but also to energy trading, safety monitoring and predictive maintenance.

Advanced AI technologies enhance battery energy storage

Battery systems use intelligent tools such as machine learning, deep learning, predictive analytics and reinforcement learning. They are becoming a key tool for managing large battery systems.

By combining these technologies,  AI ensures that:

Batteries store and release energy efficiently based on demand.

Optimize performance by processing real-time data to reduce waste and improve efficiency.

These upgrades provide a stable and reliable power source, making battery energy storage more viable and cost-effective.

S&P Global says the market demand for battery energy storage systems is growing. However, the integration of  AI  is just getting started. Lithium-ion battery energy storage developers are well positioned to meet this demand.

Henrique Ribeiro, lead analyst for batteries and energy storage at S&P Global Commodity Insights, said:

“As market competition intensifies and more capacity is deployed, maximizing returns may become increasingly difficult. Therefore, such tools may become an advantage.”

Improving battery energy storage safety

Scientists, researchers and experts agree that manufacturing high-quality batteries is technically complex and challenging. As lithium-ion battery production grows, especially in China and the United States,  AI analysis will become increasingly important.

A major challenge is the rapid pace of innovation. If manufacturing errors go unnoticed, they can cause serious problems. Thermal runaway is one of them, which can cause dangerous fires. However, AI can help detect problems early and avoid costly failures.

Like other energy storage systems, batteries also have safety risks. This is a cause for concern. However, this challenge also provides an opportunity for the industry to improve safety measures.

Organizations such as the Industrial Electrotechnical Committee (IEC) and UL Solutions are raising safety standards. Therefore, effectively managing these risks is essential to maintain industry momentum.

As energy storage technology develops,  AI will help optimize operations and ensure grid reliability and sustainability.

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Purpose-built for the enterprise, helping you confidently develop, deploy, and secure AI applications.

News Highlights:

Cisco’s end-to-end solution secures the development and use of AIapplications, enabling enterprises to confidently advance their AI initiatives.

AI Defense protects against AI misuse, data breaches, and increasingly sophisticated threats that existing security solutions can’t address.

This innovative solution leverages Cisco’s unmatched network visibility and control to help you meet the evolving challenges of AI security.

San Jose, California, January 15, 2025—Cisco (NASDAQ: CSCO), a leader in networking and security, today announced Cisco AI Defense, a breakthrough solution designed to support and protect enterprises’ AI transformation. As AI continues to advance, new security challenges and threats are emerging at an unprecedented rate, and existing security solutions are no longer able to address them. Cisco AI Defense is designed to help enterprises confidently develop, deploy, and secure AI applications.

Jeetu Patel, Executive Vice President and Chief Product Officer at Cisco, said, “As business and technology leaders embrace artificial intelligence, they cannot sacrifice security for speed. In a highly competitive and rapidly changing environment, speed determines success or failure.” Cisco AI Defense Integrated into the Network: “We have integrated unique capabilities into our framework to detect and block threats during the development and access ofAIapplications, preventing them from causing harm.”

With AI, the stakes are extremely high that things won’t go as planned. According to Cisco’s 2024 AI Readiness Index, only 29% of respondents believe they are fully capable of detecting and preventing unauthorized AI modifications. Due to the multi-model and multi-cloud nature of AI applications, security challenges are both novel and complex. Vulnerabilities can arise at the model or application level, with responsibility borne by different owners, including developers, end users, and vendors. As organizations move beyond public data and begin training models with proprietary data, the risks will only increase.

To unleash the potential of AI innovation and application, organizations need a universal security layer to protect every user and every application.AI Defense empowers enterprise AI transformation by addressing two pressing risks:

Developing and deploying secure AI applications: As AI becomes more widespread, enterprises will use and develop hundreds, even thousands, of AI applications. Developers need a comprehensive set of AI security measures for each application. AIDefense helps developers scale faster and create more value by protecting AI systems from attacks and ensuring cross-platform model behavior. AI Defense capabilities include:

Understanding AI: Security teams need to understand who builds applications and the training sources they use. AI Defense detects malicious and regulated AI applications across public and private clouds.

Model validation: Model adjustments can lead to adverse consequences. Automated testing verifies AI models for hundreds of potential security issues. This AI-powered algorithmic red team identifies potential vulnerabilities and recommends safeguards that security teams can implement to prevent AI attacks.

Runtime security: Continuous validation provides ongoing protection against potential security threats such as edge injection, denial of service, and sensitive data leakage. Securing access to AI applications: As end users adopt AI applications, such as summarization tools, to improve productivity, security teams must protect against data breaches and data poisoning. AI Defense provides security teams with the following capabilities:

Visibility: Gain comprehensive visibility into shadow AIapplications and approved AI applications used by employees.

Access Control: Enforce policies that limit employee access rights.


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An unexpected, potentially historic, collaboration may be brewing between two of the world’s largest tech companies. According to Bloomberg, Apple is in advanced talks to integrate Google’s Gemini artificial intelligence model to power a major upgrade of its long-running but recently criticized voice assistant, Siri. This rumored partnership isn’t the first between the two companies, but it may be a much-needed strategic move for Apple to catch up with the likes of Perplexity, OpenAI, and Google in the field of artificial intelligence.

For years, Siri has lagged behind competitors like Google Assistant and Amazon Alexa in its ability to handle complex, open-ended questions. Therefore, this partnership will enable Siri to leverage Gemini’s large language model to provide richer, more conversational, and contextually aware answers. While Apple currently uses its own Apple Foundation Models for personal and privacy-sensitive requests, this partnership will allow Siri to route more complex questions requiring answers from web information to a version of Google Gemini. Gemini will reportedly run on Apple’s private cloud computing servers to maintain a high level of privacy. Cupertino had previously held talks with other AI companies, including OpenAI and Anthropic, but partnering with Google made strategic sense given their existing partnership. In fact, Apple and Google’s partnership dates back to the early days of the iPhone, when the device defaulted to integrating key Google services, including Google Search, Google Maps, and even the YouTube app. For years, Google Search was the default search engine in Apple’s Safari browser, a lucrative partnership reportedly generating billions of dollars in revenue for Apple annually.

Although the two companies became fierce competitors after Google launched Android, their strategic partnership has remained. Even as Apple develops its own competing services, such as Apple Maps, it still relies on Google to maintain its search dominance, a partnership recently affirmed by a significant court ruling.

Of course, another winner from this partnership is the end user. The launch of “Apple Intelligence” (the company’s take on Gemini), originally scheduled for 2025, has been delayed until sometime in 2026, meaning consumers are already impatient for Apple to take action.

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OpenAI released ChatGPT-5 on Thursday, touting it as a major leap forward in artificial intelligence, with enhanced reasoning, task automation, and coding capabilities. The new model will be freely available to all users as OpenAI continues to compete in the rapidly evolving global AI  race. As the global technology race accelerates, OpenAI released the much-anticipated next generation of its iconic ChatGPT on Thursday, touting a “significant” advance in AI capabilities.

In a briefing with reporters, OpenAI said ChatGPT-5 will be freely available to all users of its AI  tools, which are used by nearly 700 million people each week.

Co-founder and CEO Sam Altman called the latest version “clearly a model of general intelligence.”

“This is a significant step toward a truly capable model,” he said.

Altman cautioned that much work remains to achieve artificial general intelligence (AGI), which can think like humans.

“This isn’t a model that continuously learns as it’s deployed to new things, which to me should be part of AGI,” Altman said.

“But there’s a huge improvement in the level of capability here.” Michelle Pokrass, a member of the development team, said GPT-5 is particularly good at letting AI  act as an “agent” to independently handle computer tasks.

“GPT-3 to me felt like talking to a high school student—you ask a question, maybe you get a correct answer, maybe you get some crazy answer,” Altman said.

“GPT-4 felt like talking to a college student; and GPT-5 is the first time you really feel like you’re talking to a PhD-level expert on any subject.”

Ambient Coding

Altman said he expects the ability to create software programs on demand—so-called “ambient coding”—to be “a defining part of the new ChatGPT-5 era.”

For example, OpenAI executives demonstrated a robot being asked to create an app for learning French.

Altman said ChatGPT-5 is leading the way in areas like coding, writing, and healthcare, as the global race for the technology intensifies.

Competitors including Google and Microsoft have invested billions of dollars in developing AI  systems.

Altman said there are “orders of magnitude more progress” ahead on the path to general artificial intelligence.

“Obviously… you have to invest in computing power at an incredible rate to achieve this, but we intend to continue doing so.”

Alex Beutel, head of security research at OpenAI, said ChatGPT-5 has also been trained to be trustworthy and to provide answers that are as helpful as possible, rather than assisting with seemingly harmful tasks.

“We built an evaluation system to measure the prevalence of deception and train the model to be honest,” Beutel said.

Beutel said ChatGPT-5 is trained to generate “safely completed” high-level information that will not be used to cause harm. Just a day earlier, OpenAI announced that it would allow the US government to use a version of ChatGPT designed for businesses for one year for $1.

According to AI luminaries, ChatGPT Enterprise will be available free of charge to federal workers across the executive branch through a partnership with the General Services Administration.

The company also released two new AI models this week, which users can download and modify for free to challenge similar products from US and Chinese competitors.

The release of the gpt-oss-120b and gpt-oss-20b “open weight language models” comes as ChatGPT’s makers face pressure to share the inner workings of their software, in keeping with its nonprofit nature.

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Apple (AAPL) plans to use AI technology to solve the battery life problem of iPhone. According to people familiar with the matter, the company plans to launch an artificial intelligence battery management mode for iOS 19, the iPhone software update to be launched in September.

People familiar with the matter said that the enhancement will analyze how iPhone users use their devices and make adjustments to save energy. In order to create this new technology as part of the Apple Intelligence platform, Apple is collecting battery data from user devices to understand usage trends and predict when the power consumption of certain applications or features should be reduced. People familiar with the matter said that the technology will have a lock screen indicator showing how long it will take to charge the device. Apple (AAPL.US) held a global developer conference at its headquarters in California on Monday and announced the launch of a new visual design language “Liquid Glass”, which will upgrade all iPhone, iPad and Mac operating systems. It is also the first major redesign of the iPhone operating system since 2013. However, there were few artificial intelligence features involved at the conference, only the translation function was incorporated into the system and new tools were added to shortcuts. The advanced version of Siri  AI promised by Apple has not yet appeared, and the market was disappointed. Apple’s stock price fell 1% on Monday. The new system design update, called “Liquid Glass,” includes translucent features such as redesigned notifications and more. Apple says it is more dynamic than previous design languages and will bring greater consistency across all operating systems, including iOS, iPadOS, and macOS. At the same time, Apple will also change the names of all its operating systems, launching iOS 26 instead of iOS 19 this fall. iPadOS 26, watchOS 26, and other systems will follow suit, marking the first time Apple has given its software updates a release year instead of an arbitrary code name. In terms of  AI , Apple has integrated Apple Intelligence into the existing Shortcuts app and updated the visual intelligence feature, which can use the iPhone’s camera to identify objects and events. Visual Intelligence provides an “Ask” button that enables users to ask ChatGPT what’s on their screen. At the same time, the enhanced Image Playground tool released by OpenAI for the next version of iOS is introduced, where users can describe the desired style and ChatGPT will adjust the image. The iPad is undergoing major changes, including a new multitasking system that mimics the Mac. Apple demonstrated the ability to open six apps on the iPad screen at the same time, and supports background tasks, so apps can be switched during video export. Apple (AAPL.US) has an internal release target of launching an upgraded version of Siri  AI in the spring of 2026, hoping to turn things around in the  AI market. Foreign media quoted people familiar with the matter as saying that the Siri team plans to include this important AI upgrade in the iOS 26.4 release. Siri will be able to analyze user profiles and screen operations to provide more accurate and personalized responses. As usual, Apple releases a “.4” update around March each year, such as this year’s iOS 18.4 and last year’s iOS 17.4. Apple reiterated its earlier statement that the Siri upgrade plan will be carried out next year.

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