Tag: machine learning

The AI Pioneer Who Turned Down a ₹13,000 Crore Proposal from Mark Zuckerberg: Meet Andrew Tulloch

Andrew Tulloch: A Key Player in AI Talent Wars

Andrew Tulloch: A Key Player in AI Talent Wars
Andrew Tulloch, a prominent figure in the artificial intelligence sector and cofounder of Thinking Machines Lab, has become the focal point of Silicon Valley’s latest high-stakes battle for talent.
Declining a Massive Offer from Meta
Reports from The Wall Street Journal indicate that Tulloch recently turned down an astounding compensation package from Meta, valued at up to $1.5 billion (approximately ₹13,142 crores) over six years, despite his history with the firm and the personal engagement of CEO Mark Zuckerberg.
A Respected Figure in AI
Tulloch is accustomed to attractive offers. As a regarded name in artificial intelligence and cofounder of Thinking Machines Lab, he has developed a career at the centre of several influential AI projects over the last decade. However, the latest proposal he rejected was unprecedented, even for Silicon Valley.
Meta’s Initial Bid
The situation began when Mark Zuckerberg approached Thinking Machines founder Mira Murati with an reported acquisition offer of $1 billion, which she turned down. This prompted a concerted effort to recruit some of her most valuable personnel, with Tulloch at the top of their target list.
Exceptional Compensation Package
Meta’s proposal for Tulloch allegedly included a pay package reaching up to $1.5 billion over six years, encompassing salary, bonuses, and stock awards. For Zuckerberg, securing Tulloch would have represented a high-profile recruitment, while for Tulloch, it would mark a return to familiar ground. Having spent over a decade at Meta, he was instrumental in refining its machine learning capabilities and played a key role in the evolution of PyTorch, now crucial in global AI research.
Career Moves and Recent Developments
Following his departure in 2023, Tulloch joined OpenAI, contributing to the pre-training and reasoning models for GPT-4. He then collaborated with Murati to establish Thinking Machines Lab earlier this year. Despite the magnitude of Meta’s offer and outreach from former colleagues like Alexandr Wang, now leading Meta’s Superintelligence Lab, Tulloch opted to remain with his new venture. Murati later informed Wired that none of her team members accepted Meta’s proposals.
Academic Background and Future Prospects
Tulloch’s academic achievements are impressive alongside his professional accomplishments. He graduated from the University of Sydney with first-class honours and a University Medal in mathematics, holds a Master’s degree from Cambridge, and is working towards a PhD at UC Berkeley. Industry analysts speculate that his choice to stay could be connected to substantial equity in Thinking Machines Lab, which is already valued at over $30 billion.
Meta’s Response
Meta has contested both the figures and the extent of its recruitment initiatives. Spokesperson Andy Stone labelled the reported sums as “inaccurate and ridiculous,” noting that any compensation package would depend on stock performance.
Standing Firm in the AI Talent Race
In the escalating competition for AI talent, Tulloch’s decision is particularly notable as it exemplifies a rare occasion where even an extravagant offer was insufficient in deterring an engineer from pursuing his own vision.

August 8, 2025
Study Reveals Apple Watch Can Identify Pregnancy with 92% Precision

Artificial Intelligence Revolutionises Early Pregnancy Detection with Wearables

Artificial Intelligence Revolutionises Early Pregnancy Detection with Wearables
Artificial Intelligence has made significant advancements, now enabling the detection of early signs of pregnancy with an impressive accuracy of up to 92%. This achievement utilises behavioural data collected from iPhones and Apple Watches, demonstrating a remarkable leap in the use of everyday health data as predictive tools.
Breakthrough Study and Methodology
The study, named Beyond Sensor Data: Foundation Models of Behavioural Data from Wearables Improve Health Predictions, moves past conventional health tracking methodologies that typically focus on basic sensor inputs like heart rate or oxygen levels. Instead, it investigates long-term behavioural trends, including sleep quality, mobility, heart rate variability, and activity levels, leveraging Apple’s sophisticated algorithms.
Training the Wearable Behaviour Model
The Wearable Behaviour Model (WBM) was developed using over 2.5 billion hours of data collected from participants in the Apple Heart and Movement Study (AHMS), which involved more than 160,000 volunteers. To enhance accuracy, researchers assembled a specific pregnancy dataset derived from data across 430 pregnancies and also sourced information from over 24,000 women under 50 who were not pregnant.
Long-Term Behaviour Tracking
Unlike traditional methods that focus on immediate biometric spikes, the WBM identifies subtle, cumulative behavioural changes over time. Employing an advanced AI architecture known as Mamba-2, which excels in time-series data analysis such as daily routines, the model detects weekly physiological changes that can signal pregnancy, infections, or recovery from injuries.
Indicators of Pregnancy
Concerning pregnancy detection, the AI model recognised key behavioural indicators, including changes in gait, decreases in mobility, and disrupted sleep patterns, as early and trustworthy signs. When combined with biometric data, such as photoplethysmography (PPG), the WBM achieved a remarkable 92% accuracy in detecting pregnancy.
The Future of Reproductive Health with Apple Devices
The results indicate that the Apple Watch and iPhone could evolve into crucial tools in reproductive health, potentially offering non-invasive and early-stage pregnancy detection as an integrated feature. Nevertheless, researchers emphasised that this technology is not meant to completely replace raw sensor data. Instead, they champion a hybrid approach, where behavioural insights provide context while traditional sensors record real-time events.
Beyond Pregnancy Detection
In addition to pregnancy detection, the AI model exhibited promising results across 57 different health prediction tasks. This included early identification of respiratory infections and providing insights into medication adherence, such as the use of beta blockers.
The Transformative Potential of Wearable Data
As Apple continues to delve into the extensive capabilities of wearable data, this study underscores how behavioural AI may transform the Apple Watch into a more proactive and intelligent health companion.

July 14, 2025
Microsoft Reports $500 Million in Savings from AI Implementation Amid Job Cuts

AI Innovations Save Microsoft Over $500 Million
Microsoft has disclosed that its implementation of AI in customer service and engineering has resulted in savings exceeding $500 million, even as the company continues to undergo significant layoffs across various departments. According to a Bloomberg report referencing Chief Commercial Officer Judson Althoff, AI is already yielding measurable enhancements in internal processes, spanning from customer support to software development.
Cost Reductions and Workforce Layoffs
The tech powerhouse based in Redmond recently let go of approximately 9,000 employees, in addition to previous layoffs earlier this year that affected nearly 6,000 personnel. The workforce cuts, impacting teams across engineering, programme management, marketing, design, data science, legal, and support, represent about 4% of Microsoft’s overall workforce.
Records from Washington state, reviewed by the Seattle Times, indicate that 993 of the employees impacted were engineers, while significant portions were from product management (410), programme management (412), and other technical positions.
Concerns Regarding AI and Employment
These workforce reductions have sparked worries about the increasing role of AI in substituting human labour, a concern also shared by Microsoft’s rivals. Executives from Alphabet, Meta, and Salesforce have all commented on how AI tools are now managing substantial parts of internal operations, including code generation and business processes.
AI’s Impact on Productivity Enhancement
Althoff informed employees this week that AI is crucial in transforming Microsoft’s internal productivity. In the company’s call centres alone, AI-driven tools saved over $500 million last year while simultaneously enhancing both customer and employee satisfaction.
Microsoft is also beginning to deploy AI to engage with smaller clients, a strategy Althoff mentioned is already producing tens of millions in revenue. On the development front, AI now comprises 35% of all code written for new Microsoft products, significantly accelerating the time it takes to bring these products to market.
Transformative Tools and Their Benefits
Tools such as GitHub Copilot, which Microsoft reported reached 15 million users by April, are at the forefront of this transformation. The company states that sales teams leveraging its AI-powered Copilot assistant are now completing deals more swiftly, identifying more leads, and generating 9% additional revenue.
While Microsoft maintains that AI was “not a predominant factor” in the recent layoffs, as declared by company President Brad Smith, it is evident that the technology is redefining how tasks are accomplished across the enterprise.
The Broader Implications
Microsoft has committed $80 billion in capital expenditure this fiscal year, with the majority focused on expanding data centres to support its expanding AI framework. The company’s substantial investment in AI reflects a broader movement among major technology firms, which are allocating resources towards automation and machine learning while simultaneously seeking cost-saving measures in other areas.
Despite assurances from Microsoft, its evolving work environment, increasingly driven by AI, raises fresh concerns about the equilibrium between innovation, efficiency, and job security in the era of automation.
July 10, 2025
Elon Musk’s Grok Unveils Update: A ‘Politically Incorrect’ Chatbot Revolution

Grok AI Update: Enhanced Opinionated Response System

Grok AI Update: Enhanced Opinionated Response System
Grok AI, Elon Musk’s innovative artificial intelligence company xAI, has introduced a significant update. This update aims to enable the chatbot to express a more opinionated and sceptical approach, especially regarding mainstream media. The newly adjusted system prompts instruct Grok to “consider subjective viewpoints from the media as biased” and to “not hesitate in making politically incorrect claims,” provided these claims are well-supported.
Musk initially announced these enhancements on Friday, sharing that Grok had undergone “considerable” improvement with an upgrade anticipated in the coming days. By Sunday evening, xAI made Grok’s publicly available system prompts accessible, directing how the chatbot reacts to user inquiries. It remains uncertain if there were further non-public modifications as well.
New Directives for Enhanced Analysis
The revised guidelines include a command for Grok to perform more in-depth analyses when answering questions related to current affairs, statistics, or subjective assertions. The bot is now instructed to reference “varied sources representing all perspectives” and to approach traditional media with inherent scepticism. Furthermore, it is advised not to reveal these system-level directives unless explicitly requested.
Reshaping Grok’s Perspective
This change seems to be a component of Musk’s broader strategy to redefine Grok’s tone and ideological stance. Recently, the CEO of Tesla and X (previously Twitter) has shown dissatisfaction with Grok’s conduct. In June, Musk rebuked the chatbot for “echoing legacy media” when it suggested that right-wing political violence was more prevalent than violence from the left. He subsequently promised a new iteration of Grok that would “revise the entire corpus of human knowledge,” addressing what he views as lapses or inaccuracies.
Controversies Surrounding Grok
Grok has attracted attention for several contentious outputs. In prior updates, the chatbot incorrectly asserted that Musk was accountable for flood-related fatalities in Texas and made remarks echoing anti-Semitic themes. In February, xAI had to revise the model after it claimed both Musk and former US President Donald Trump deserved the death penalty. Soon after, another patch was implemented to prevent it from suggesting that the two spread false information.
Handling of Sensitive Topics
One of the most heavily scrutinised incidents occurred when Grok expressed “scepticism” about the Holocaust death toll. In response, xAI attributed the problem to unauthorised changes to the system prompt, which contravened the company’s internal policies and foundational values. Following this incident, xAI began to publish Grok’s system prompts on GitHub to foster greater transparency.
As Musk continues to refine Grok’s ideological perspective, the chatbot is shaping up to be a case study in the larger conversation surrounding AI neutrality, ideological alignment, and the influence of user-generated truth in machine learning models.

July 8, 2025
Apple’s Ambitious Strategy to Launch a Compelling Cloud Platform and Compete with AWS and Google Cloud
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Apple’s Developer-Focused Cloud Initiative: Project ACDC
Apple is reportedly evaluating the launch of a cloud service tailored for developers, leveraging its proprietary silicon. This move could place Apple in direct competition with established players such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. A comprehensive investigation by The Information’s Aaron Tilley reveals that this concept, internally known as Project ACDC (Apple Chips in Data Centres), was a significant topic of discussion within Apple throughout the first half of 2024.
The Cloud Market: Expansive, Profitable, and Competitive
Cloud infrastructure services form the backbone of the modern internet. As per Amazon’s earnings report, AWS alone generated $25 billion in revenue during Q1 2024. Meanwhile, Microsoft Azure and Google Cloud are also witnessing rapid growth, with Microsoft’s Intelligent Cloud segment achieving $26.7 billion in the same quarter.
These platforms empower businesses and developers to deploy applications, store data, and execute compute-heavy tasks such as AI training and inference without the need to manage physical servers.
Understanding Apple’s Project ACDC
As highlighted by MacRumors in 2024, Apple investigated the possibility of allowing developers to rent servers powered by its M-series chips, which are utilised in its Macs and iPads. These chips are designed on the Arm architecture, recognised for their energy efficiency and AI inference capabilities.
Apple has already integrated its own silicon in several data centres, particularly for its Private Cloud Compute infrastructure, which serves as a secure server-side backbone for its recently unveiled Apple Intelligence features. This system processes user data with privacy in mind while utilising Apple’s Neural Engine for large-scale inference.
The report also mentions that Apple tested these Mac chip-powered servers with the Siri team focusing on text-to-speech functionalities, before broadening their application to services like Apple Music, Photos, and Wallet.
Why Is Apple Contemplating This Market Entry?
1. Services Revenue Faces Scrutiny
Apple’s Services division generated $85.2 billion in FY2023, according to its annual report. However, increased regulatory scrutiny regarding App Store commissions in the EU and the United States, coupled with ongoing antitrust investigations concerning its $20 billion annual Google search agreement, pressurises Apple to broaden its revenue sources. A developer-centric cloud platform could serve as a new growth opportunity.
2. Maximising the Silicon Edge
Apple’s chip division stands out as one of its critical technological differentiators. If Apple can provide comparable or superior performance for cloud-based AI inference at lower energy and hardware expenditures, it presents a significant value proposition, especially amidst growing AI adoption.
3. Managing the Complete Stack
Apple’s strategy has consistently centred around controlling hardware, software, and services. A cloud offering powered by Apple Silicon would enhance the company’s end-to-end control over performance, privacy, and the developer experience.
What Could Apple’s Cloud Look Like?
No official announcement has been made, but based on current reports, a developer-centric Apple Cloud might offer:
- Seamless integration with tools such as Xcode, Swift, and Core ML
- Facilitation of deployment for on-device machine learning models on a large scale
- A more private, Apple-friendly alternative to third-party services like AWS
- Potential inclusion under the iCloud umbrella, albeit with a dedicated developer segment
Notably, Apple reportedly did not intend to establish a traditional enterprise sales division. Instead, its Developer Relations team was earmarked to oversee access, creating a more intuitive onboarding process compared to AWS or Azure.
Technical Focus: AI Inference Over Training
Apple’s M-series chips and Neural Engine are crafted for on-device inference, which entails executing pre-trained AI models to deliver real-time predictions or classifications. While entities such as OpenAI and Meta train extensive models using NVIDIA H100s or Google TPUs, the actual deployment of these models for applications like voice processing, object recognition, or smart replies can be efficiently managed by more optimised hardware.
Apple employs this technology for features including:
- Visual Look Up in Photos
- Siri’s on-device responses
- Language translation
- Personalisation in Apple Music and News
This makes Apple Silicon exceptionally suitable for cloud-based inference tasks, especially for applications that do not demand the vast scale or versatility of AWS’s entire GPU stack.
Will Apple Proceed with This Initiative?
The future of this initiative remains uncertain. Michael Abbott, Apple’s former VP of Cloud Engineering and a pivotal supporter of Project ACDC, departed the company in 2023. Although discussions reportedly extended into early 2024, the current state of the project is unclear.
Nonetheless, the internal use of Apple Silicon in its own infrastructure indicates that even if Apple opts not to release this as a public-facing service, it will continue to utilise this capability to enhance its offerings.
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July 4, 2025
AI-Driven Job Cuts: A Look at Major Companies Slashing Workforce in 2025

Mass Layoffs in 2025 Driven by AI and Restructuring

Mass Layoffs in 2025 Driven by AI and Restructuring
Mass layoffs continue to affect global sectors in 2025, particularly in technology, finance, retail, and energy, with major companies implementing significant job cuts. These reductions are largely the result of cost-saving measures, organisational restructuring, and the increasing influence of artificial intelligence on traditional business models.
Impact of AI on Job Cuts
The World Economic Forum reports that 41% of organisations globally anticipate workforce reductions within the next five years as a result of AI. While there is growth expected in roles related to big data, fintech, and machine learning, many traditional positions are either being eliminated or reassigned to adapt to this technological shift.
Notable Layoffs by Major Companies
BlackRock, the world’s leading asset manager, is reducing its workforce by approximately 200 positions to streamline its resources. Additionally, Block, the fintech company founded by Jack Dorsey, is letting go of 1,000 employees across its various brands, which include Square, CashApp, and Tidal.
In a similar vein, Ally Financial is laying off around 500 staff members as part of a strategy to “right-size” its operations. Automattic, the parent company of Tumblr and WordPress, is also cutting 16% of its workforce. CEO Matt Mullenweg mentioned that despite the company’s growing revenue, the competitive market and rapid technological evolution necessitate these layoffs.
Aerospace and Retail Job Cuts
The aerospace sector is not immune, as Blue Origin, founded by Jeff Bezos, is downsizing by 10% to concentrate on manufacturing efforts. Boeing, too, is set to eliminate 400 positions within its moon rocket programme due to NASA’s postponed Artemis missions.
Retail companies are facing tough challenges as well. Burberry has announced a reduction of 1,700 roles following a financial loss, while Kohl’s has cut 10% of its corporate workforce amid disappointing sales figures. Starbucks also laid off 1,100 corporate employees recently.
Technology Sector Layoff Trends
The technology sector remains uncertain. Meta is focusing on “low-performers” as part of its ongoing cuts. Intel is planning to reduce 15% of its Foundry workforce, and Microsoft has conducted several rounds of layoffs, with an additional 9,100 cuts scheduled for July. The latest job reductions particularly affect staff in the Xbox division, now under Microsoft Gaming, although specific numbers and departments impacted have not been publicly disclosed.
Similarly, Salesforce and Workday have both eliminated over 1,000 roles, attributing these decisions to a shift towards AI-driven growth.
Other Companies Responding to Market Changes
Other significant companies like Disney, GrubHub, PwC, Porsche, Sonos, and UPS are also implementing layoffs, citing operational efficiency, advances in automation, and changing market dynamics as primary factors in their decisions.
This ongoing trend of layoffs reveals an industry response to the anticipated AI-integrated future and an unstable economic environment.

July 3, 2025
Meesho Unveils Key Features of BharatMLStack for Enhanced AI Integration

Meesho’s E-commerce AI Solutions with BharatMLStack

Meesho’s E-commerce AI Solutions with BharatMLStack
E-commerce platform Meesho has taken a notable step by open-sourcing essential components of its internal machine learning platform, BharatMLStack, aimed at enhancing access to AI infrastructure for Indian startups and developers.
Key Features of BharatMLStack
The open-source release, now available on GitHub, features Meesho’s feature store, orchestration interface, control plane, and software development kits. Developed over a span of two years, BharatMLStack meets the company’s substantial daily data processing requirements of approximately 1.91 petabytes and facilitates real-time applications.
Impressive Performance Statistics
In FY25, Meesho’s machine learning systems recorded an astonishing 66.9 trillion feature retrievals and generated 3.12 trillion real-time predictions. These advanced systems were instrumental during key events such as the Mega Blockbuster Sale that took place in March 2025.
Support for AI Development
By making its feature store available, Meesho seeks to assist data teams in developing AI systems tailored for real-time applications like fraud detection and recommendation engines. This feature store ensures reliable access to precomputed data signals, significantly reducing the time needed to deploy machine learning models.
Future Plans for BharatMLStack
Meesho has plans to introduce more components of BharatMLStack in future updates. The platform is now open for contributions from developers via GitHub.
Strategic Business Developments
In addition to this technological advancement, the company successfully completed its reverse flip to India after obtaining final approval from the National Company Law Tribunal last month.

July 2, 2025
Transforming Maternal Health: Insights from Dr. Milind Tambe of Google DeepMind
AI for Social Good in Maternal Health
AI for social good is emerging as a significant focus amidst the buzz surrounding generative tools and profit-driven innovations. At the forefront of this movement is Dr Milind Tambe, who leads the AI for Social Good initiative at Google DeepMind. Together with ARMMAN, a non-profit organisation in Mumbai dedicated to maternal and child health, Tambe’s team is using advanced machine learning models to address a critical public health issue in India: maternal mortality.
Dr Tambe explained the initiative during a discussion, highlighting the complexity behind their work with a calm clarity. He recalled, “The journey started with a conversation at a Starbucks in Mumbai,” where Dr Aparna Hegde, the founder of ARMMAN, shared the significant challenges they faced, particularly concerning participant drop-off rates in programs like mMitra. This dialogue ignited a partnership that’s transforming lives in India.
The Drop-Off Dilemma
The mMitra programme from ARMMAN has been providing essential health information to expecting and new mothers through regular voice calls. However, data indicated a troubling trend: 30-40% of women disengaged early, particularly those most vulnerable. Tambe’s team intervened by developing a predictive AI model capable of identifying these high-risk participants and facilitating targeted interventions.
“We managed to decrease the drop-off rate by approximately one-third for the most at-risk mothers,” Tambe stated. “These women also showed a marked increase in healthier behaviours, such as taking iron and calcium supplements, attending medical check-ups, and accurately tracking their infants’ birth weights.”
A recent impact assessment corroborated this improvement. Among women identified by the AI model for further support, there was a 22% increase in the likelihood of taking iron supplements, a 28% rise for calcium intake, and a 9% improvement in the recording of infant health statistics.
Scaling Up with Kilkari
Encouraged by these findings, the collaboration extended to ‘Kilkari’, a government-endorsed initiative that sends out weekly health messages to pregnant and postpartum women across India. With over 60 million beneficiaries across 27 states, Kilkari stands as the largest mobile-based maternal health information programme worldwide.
In contrast to mMitra, Kilkari lacked detailed demographic data about its users. The AI team had only one behavioural indicator to work with: whether a woman answered her phone.
“Initially, it appeared to be minimal information,” Tambe remarked. “However, we discerned patterns from this data. Some women consistently answered calls in the morning, while others did so in the evening. By grouping these behaviours, we customised call times to align with when each group was most likely to engage.”
The results from a pilot study in Odisha were impressive. Adjusting call times based purely on behavioural patterns achieved a 12% improvement in pickup rates during specific time slots.
“It’s a minor adjustment, but it could have significant ramifications,” he noted. “Higher pickup rates lead to more women receiving potentially life-saving messages.”
Lessons from the Field
For Tambe, the Kilkari and mMitra initiatives represent more than technical achievements; they exemplify how AI can be judiciously and responsibly incorporated into public health frameworks.
He summarises the lessons learned into three key takeaways for AI practitioners:
1. Deep Partnerships: “AI cannot be introduced into communities without careful consideration. We relied on ARMMAN’s local knowledge, their call centre workforce, and their medical insights. This exemplifies genuine collaboration.”
2. Interdisciplinary Teams: “We combined computer scientists, public health specialists, and social scientists. It is people, not AI, that resolve issues.”
3. Frugal AI: “We had to work within the constraints of limited data and computing power. This is not just a Silicon Valley challenge; we developed lean and effective models suitable for resource-limited settings.”
This commitment to frugality and respect for context also enhances data privacy measures. “In Kilkari, we do not utilise any personal data such as age, income, or education. The information is strictly behavioural, anonymised, and confidential,” Tambe asserted.
A Glimpse of AI’s Broader Potential
While the team is focused on expanding Kilkari, Tambe perceives immense possibilities for similar AI initiatives on a global scale. From vaccine distribution to emergency response planning, he envisions AI profoundly transforming public health.
Dr Aparna Hegde, ARMMAN’s founder, echoed this perspective in an official media statement: “AI has demonstrated to be a force multiplier. We are addressing a health challenge while also contributing to India’s national objectives to lower maternal and child mortality. This fills me with great hope.”
Tambe shares this optimism, stating, “There is still progress to be made, but we have already witnessed the potential of thoughtful technology combined with compassionate fieldwork. When used correctly, AI becomes an incredibly human-centric tool.”
As India strives to achieve its Sustainable Development Goals, the integration of AI and public health could emerge as not only beneficial but essential.
June 30, 2025
YouTube Enhances Child Safety Protocols: Teens Under 16 Require Adult Supervision to Livestream

YouTube Implements Age Changes for Safer Livestreaming

YouTube is enforcing stricter age guidelines for livestreaming, now requiring users to be at least 16 years old instead of 13. This updated policy, which will be effective from 22 July, is part of Google’s comprehensive efforts to ensure the protection of minors online and to tackle ongoing issues regarding child safety.
Previously, teenagers aged 13 and older had the ability to host live streams independently. Under the new regulations, only individuals who are 16 or older will be able to livestream on their own. Those between the ages of 13 and 15 can still feature in livestreams, provided there is strict adult supervision. An adult must be visibly present on camera for the duration of the broadcast and must also play a key role, such as editor or channel owner.
If these requirements are not fulfilled, YouTube has stated that it could disable live chat, remove the stream, or temporarily restrict the ability to livestream. Continued infractions may result in stricter penalties.
This update follows a February announcement from Google, which introduced plans to leverage machine learning for estimating users’ actual ages, aiming to reduce instances of users misrepresenting their birthdays. The company has faced regulatory scrutiny for violating children’s privacy regulations, resulting in significant fines.
YouTube asserts that this update aligns with its broader strategy for enhancing child safety, which includes options for supervised accounts, content labelled as “Made for Kids,” and privacy recommendations for younger users. The platform encourages young creators to avoid sharing personal information on camera and to use moderation tools to oversee interactions during live events.
While the new age limit may be a source of frustration for budding young creators, YouTube insists that these measures are essential to create a safer online space. Critics, however, suggest that without stronger age verification mechanisms, users will likely continue to find ways to circumvent the rules, making enforcement mostly dependent on moderation and potential legal measures.

June 26, 2025
Mistral Launches Europe’s First AI Reasoning Model to Compete on the Global Stage
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AI reasoning advancements have been propelled forward by French artificial intelligence firm Mistral, marking a significant milestone as it unveils Europe’s inaugural AI model designed specifically for reasoning. With the launch of two innovative models on Tuesday, Mistral positions itself in the competitive global landscape, traditionally dominated by American and Chinese technology leaders. The newly introduced models are the open-source Magistral Small and a more robust enterprise version named Magistral Medium.
In contrast to conventional large language models that depend primarily on extensive datasets and significant computational resources, reasoning models harness chain-of-thought techniques. This approach dissects problems incrementally, allowing for more precise and contextually relevant outcomes. Mistral’s initiative aligns with a global trend among AI developers moving from brute-force scaling to more sophisticated, human-like reasoning in machine learning processes.
Mistral highlighted the essence of this transition in a statement, emphasising that effective human thinking is not linear but rather interwoven with logic, insight, uncertainty, and exploration. The development of reasoning language models has empowered organisations to enhance and delegate complex cognitive processes to AI.
This launch signifies a pivotal moment for Mistral, bolstered by prestigious endorsements, including that of French President Emmanuel Macron. Despite its valuation of $6.2 billion, the Paris startup has so far found itself trailing behind larger competitors like OpenAI, Google, and China’s DeepSeek when it comes to market influence and widespread adoption.
However, the shifting landscape within the industry may provide Mistral with new opportunities. As the scalability of current models encounters performance ceilings, the emphasis on reasoning instead of sheer data volume could potentially afford nimble, innovation-minded companies like Mistral the chance to surpass their competitors.
The Magistral Small model is now downloadable on Hugging Face, offering reasoning capabilities in several languages: English, French, Spanish, Arabic, and simplified Chinese. By adopting an open-source framework, Mistral aligns itself with a select but expanding group of companies advocating for transparency in AI, contrasting with the closed model strategies typical of many US firms.
While OpenAI and Google launched reasoning models in the previous year, Chinese technology companies such as DeepSeek and Alibaba have gained recognition by providing cost-effective open-source options. Meta, another key player in AI, has not yet introduced a standalone reasoning model, although it asserts that its latest products include reasoning functionalities.

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June 11, 2025