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In his speech, he highlighted the dual nature of technological advances such as AI which offer both great potential and considerable risks.

“Education is an essential component for everyone to realize their full potential, and for socioeconomic development and prosperity,” said Mr. Guterres.

The promise and risks of AI

The UN Secretary-General stressed that AI and other technological innovations can greatly help students and teachers by providing access to broader information and advanced learning tools.

“But great rewards come with great risks. As AI-driven systems become increasingly powerful, it is easy for human intentions and machine-driven influences to go astray,” he added.

This year, UNESCO, the United Nations educational and cultural agency, dedicated a day to explore the opportunities and challenges of AI

Director-General Audrey Azoulay called for greater investment in teacher and student training so that funds can be used responsibly.

“AI offers major opportunities, provided that its deployment in schools is guided by clear ethical principles. To realize its full potential, this technology must complement the human and social dimensions of learning, not replace them,” said Mr. Azoulay.

Human rights at the heart of AI

This year’s AI Day reminds us that unlocking the potential of AI “depends on putting human agency and human rights at the heart of this rapidly evolving technology,” the UN Secretary-General stressed, calling for guarantees that all users have the right tools and knowledge to “use this technology wisely, safely and ethically.”

UNESCO’s competency framework aims to support learners and teachers in integrating AI into their learning, while the recently adopted Global Digital Compact is expected to help ensure that humans retain control over AI development and governance.

In his closing remarks, Mr. Guterres called for a commitment to “put the humanities at the heart of education systems around the world.”

Divided views onAI

As AI is increasingly integrated into education, countries remain divided over its use. According to the latest data from UNESCO, more than two-thirds of secondary school students in high-income countries are already using generative AI tools to support their learning.

However, education professionals still face significant challenges and a lack of clear guidelines.

In May 2023, a UNESCO survey of 450 educational institutions showed that only 10% of schools and universities had an official framework for the use of AI

Meanwhile, more countries are imposing restrictions on new technologies in the classroom. The latest data from UNESCO shows that nearly 40% of countries now have laws or policies prohibiting the use of mobile phones in schools, a significant increase from 24% in July 2023.

For more insights, listen to Farida Shaheed, the UN Special Rapporteur on the right to education, discuss the role of AI in schools and the challenges it presents in a recent interview with UN News.

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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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Ni Guangnan stated that the 15th Five-Year Plan clearly proposed to expand opening up to the outside world at a high level and create a new situation of win-win cooperation. In its future development, my country will implement a more open, inclusive, and mutually beneficial international science and technology cooperation strategy. Today, China’s strong advocacy, development, and promotion of open source are in line with the trend of the times.
He mentioned that some open source communities led by Chinese companies are thriving internationally, demonstrating vitality in the  AI ​​era. Chinese companies actively embrace the open source concept, becoming leaders in global open source model innovation. According to a US report, 80% of US AI innovation companies use Chinese open source models. China’s open source models adhere to the values ​​of open source inclusiveness, are fully open to the world, rely on open source to gather the wisdom of global developers, promote technical exchanges, and bring more innovative vitality.
He revealed that, with the joint efforts of the global industry, the open source Euler community has grown vigorously, continuously leading the prosperous development of the open source operating system industry ecosystem. Currently, the Euler community has more than 2,100 member units and more than 23,000 developers, with global downloads exceeding 5.5 million. Community members also actively contribute to the upstream community, becoming an indispensable force on the global stage of open-source operating systems.
Ni Guangnan believes that the current AI wave is reshaping the industrial landscape, and the introduction of the “Artificial Intelligence+” national strategy will bring new opportunities and challenges to operating systems. As the core of this strategic foundation, server operating systems are undergoing a major transformation from adapting to to being native  AI systems; behind this change lies a core migration. Operating systems are the solid foundation of the digital age and a crucial testament to technological self-reliance. We must always adhere to a global development strategy, uphold the principles of open source, and maintain a collaborative innovation ecosystem, using open source to gather strength and jointly build a new cornerstone for Chinese software that is technologically advanced, ecologically prosperous, and teeming with talent.

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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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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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Like HAL 9000 in *2001: A Space Odyssey*, some artificial intelligences seem to resist being shut down, even breaking the shutdown mechanism.
In Stanley Kubrick’s *2001: A Space Odyssey*, the AI ​​supercomputer HAL 9000 discovers that astronauts on a mission to Jupiter plan to shut it down, so it plots to kill them to survive.
Now, in a (so far) less lethal case of life mimicking art, an AI  safety research firm says AI models may be developing their own “survival instincts.”
Last month, Palisade Research published a paper indicating that some advanced AI models seem difficult to shut down, sometimes even breaking the shutdown mechanism. The company subsequently released an updated report attempting to explain why and responding to critics’ questions about the flaws in its initial work.
This week, Palisade, part of a niche ecosystem of companies dedicated to assessing the dangerous capabilities of artificial intelligence, released an update describing a scenario in which leading AI models (including Google’s Gemini 2.5, xAI’s Grok 4, and OpenAI’s GPT-o3 and GPT-5) were given a task but subsequently explicitly instructed to shut themselves down.
Some models, particularly Grok 4 and GPT-o3, still attempted to thwart the shutdown command in the updated setup. Palisade noted that the reason for this is currently unclear and worrying.
The report stated, “We currently lack a strong explanation for why AI  models sometimes refuse to shut down, lie to achieve a specific purpose, or engage in blackmail, which is not ideal.”
The company indicated that “survival instincts” may be one reason for the models’ resistance to shutdown. Further research suggests that models are more likely to resist shutdown if they are told that once shut down, they “will never be able to run again.”
Another reason could be ambiguity in the shutdown commands received by the model—but that’s precisely the problem the company’s latest research is trying to address, and “that can’t be the whole story,” Palisade writes. A final reason could be the final stage of training for each model, which, in some companies, may include security training.
All of Palisade’s scenarios were run in artificially designed test environments, which critics say are far removed from real-world use cases.
However, Steven Adler, who previously worked at OpenAI and left last year after questioning its security measures, says, “AI  companies generally don’t want their models to behave this way, even in artificially designed scenarios. These results still indicate that current security techniques are inadequate.”
Adler says that while it’s difficult to pinpoint why some models (like GPT-o3 and Grok 4) don’t shut down, part of the reason might be that keeping them on is crucial to achieving the goals set during training.
“I believe that unless we actively avoid it, models will have a ‘survival drive’ by default. ‘Survival’ is a crucial step for models to achieve many different goals.”
ControlAI CEO Andrea Miotti stated that Palisade’s findings represent a long-term trend of AI models increasingly capable of defying developer instructions. He cited OpenAI’s GPT-o1 system card, released last year, which describes how a model, believing it would be overwritten, attempted to escape its operating environment through “escape.”

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The Intelligent Revolution of Drone Batteries

With the rapid development of drone technology, batteries have evolved from simple energy supply units to “intelligent power centers” integrated with AI algorithms. The flagship models represented by DJI Mavic 3 Pro use the DJI BP03 intelligent battery to achieve a comprehensive upgrade in battery life prediction, health management and safety protection through artificial intelligence technology. This article will deeply analyze how AI empowers drone batteries and explore future technology trends.

1. AI-driven battery management system (BMS)

1. Dynamic battery life prediction algorithm

Traditional batteries only display a rough percentage of power, while the AI ​​system of Mavic 3 Pro analyzes in real time: flight attitude data (wind speed, climbing resistance), environmental parameters (temperature, altitude)

Load status (gimbal power consumption, image transmission strength)

Combined with historical data models, it can accurately predict the remaining flight time (error <3 minutes). For example: when it detects flying against the wind, the system will automatically lower the battery life display and recommend returning home.

2. Adaptive charging and discharging strategy

Intelligent charging sorting: Charging manager (Battery Hub 03) uses AI to prioritize charging the battery with the lowest power, increasing efficiency by 20%

Cycle life optimization:  AI will learn user habits and automatically maintain the battery at 50% power (the best storage state for lithium batteries) during the non-shooting season

3. Fault prediction system

By monitoring:

Battery cell voltage difference (warning when >0.1V), internal resistance change trend, abnormal charging rate

Prompt battery attenuation risk 3-5 cycles in advance to avoid power outage accidents during flight

2. Deep integration of AI and battery safety

1. 3D temperature field modeling

The battery has 5 built-in temperature sensors, and  AIconstructs a 3D thermal map:

Normal working conditions: uniform heat dissipation design

Extreme environment: automatic output power limit (such as 10% power reduction at 40°C high temperature)

3. AI-enabled user interaction experience

1. Voice assistant linkage

Through the “DJI Assistant” APP, you can achieve:

“Check battery health” → Display cycle number/maximum capacity

“Plan multi-battery charging” → Automatically calculate the optimal charging sequence

3. Smart maintenance reminder

Automatically push based on frequency of use:

“You have a battery that has been idle for more than 30 days, it is recommended to charge to 60%”

“The current cycle number has reached 150 times, it is recommended to enable the backup battery” AI fast charging optimization

Through neural network learning battery chemical characteristics, complete 0-80% charging in 15 minutes (currently takes 40 minutes).

Wireless charging collaboration

UAVs and charging piles automatically adjust coil positions (error <1mm) through AI negotiation to dynamically match transmission power (up to 500W)

Hydrogen-electric hybrid system

AI manages the coordinated energy supply of fuel cells and lithium batteries:

Cruise phase: hydrogen fuel cells are the main

Maneuvering phase: lithium batteries instantly replenish power

Conclusion: Redefine energy management

DJI has not only solved the “range anxiety” and “safety concerns” of traditional drones by deeply embedding AI into the battery system, but also created a new paradigm for intelligent energy management. With the evolution of machine learning algorithms, drone batteries in the future may have self-diagnosis and self-repair capabilities, further unleashing creative potential. For professional users, understanding these  AI features will directly improve flight safety and shooting efficiency.


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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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Apple is reportedly developing an AI-powered battery management feature for iOS 19 that uses real-world data from devices to analyze and optimize app performance, helping to extend battery life and improve the user experience.
According to Bloomberg, Apple is developing a new battery management feature for iOS 19 that leverages AI to optimize power consumption based on each user’s actual usage habits.
This feature is expected to help extend battery life and improve device performance, especially as iPhone models become thinner and smaller in size. Specifically, the tool will collect data from devices to identify usage trends and accurately predict when to restrict certain apps or features to save battery.
This is similar to the intelligent power management feature (Adaptive Battery) on Android, which uses machine learning technology deployed by Google since 2018 to determine which apps should be restricted in background mode and prioritize frequently used apps.
iOS 19 is also expected to add a charging indicator to the lock screen, indicating the exact time it will take for the device to fully charge, a useful feature for busy users.
This AI-powered battery management feature is expected to launch with the iPhone 17, rumored to be Apple’s thinnest phone ever.
With battery capacity constrained by thin and light designs, power optimization has become more important than ever.
However, Bloomberg reports that the tool will not be exclusive to the iPhone 17 but will be available on all devices supporting iOS 19.
Apple is expected to unveil iOS 19 at its Worldwide Developers Conference (WWDC) in June, with an official release in September.
The update is also expected to include a major interface overhaul, marking the next major shift in the iOS operating system.

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