# Сопутствующие статьи по теме Gemini

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Gemini", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

Apple Sends Siri Staff to AI 'Cram School', Jensen Huang's Bold Prediction Begins to Materialize

Apple is reportedly sending nearly 200 engineers from its Siri team to a multi-week AI programming "boot camp" to retrain them in AI-assisted coding, just two months before its major WWDC event. This unusual move reflects Apple's urgent effort to modernize Siri’s development process amid industry-wide shifts toward AI-powered tools like Claude Code and OpenAI’s Codex. The restructured Siri team now consists of only about 60 core developers, with another 60 assigned to test Siri’s performance and safety. The reshuffle follows internal delays in launching a new AI-powered Siri, originally planned for early 2025. Under new leadership—software chief Craig Federighi and hardware veteran Mike Rockwell—Apple is pushing to integrate Google’s Gemini model to power a more conversational, multi-step capable Siri. The situation underscores a broader trend: engineers who don’t adapt to AI tools risk being sidelined, as companies like Meta and NVIDIA emphasize AI-augmented productivity. While Apple’s retraining approach is relatively supportive compared to outright layoffs, it highlights how rapidly AI is reshaping tech roles—making traditional skills less relevant and accelerating a new industrial standard for software development.

marsbit9 ч. назад

Apple Sends Siri Staff to AI 'Cram School', Jensen Huang's Bold Prediction Begins to Materialize

marsbit9 ч. назад

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

A New York Times investigation, in collaboration with AI startup Oumi, reveals significant accuracy and reliability issues with Google's AI Overviews search feature. Testing over 4,300 queries showed the accuracy rate improved from 85% (powered by Gemini 2) to 91% (Gemini 3). However, given Google's scale of ~5 trillion annual searches, this 9% error rate translates to nearly 57 million incorrect answers generated hourly. A critical finding is the prevalence of "unsubstantiated citations." For correct answers, the rate of citations that do not support the AI's summary surged from 37% to 56% with the Gemini 3 upgrade, making it difficult for users to verify information. The AI heavily relies on low-quality sources, with Facebook and Reddit being among its top-cited websites. Furthermore, the system is highly manipulable. A BBC journalist successfully "poisoned" it by publishing a fabricated article; Google's AI began presenting the false information as fact within 24 hours. Google disputed the study's methodology, criticizing its use of the SimpleQA benchmark and an AI model (Oumi's own) to evaluate another AI. The company maintains its AI Overviews, combined with its search ranking systems, perform better than the underlying model alone. Critics note this defense does little to bolster user confidence in the feature's reliability.

marsbit04/13 02:09

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

marsbit04/13 02:09

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

A New York Times investigation, in collaboration with AI startup Oumi, reveals significant accuracy and reliability issues with Google's AI Overviews search feature. Testing over 4,300 queries showed the accuracy rate improved from 85% (Gemini 2) to 91% (Gemini 3). However, given Google's scale of ~5 trillion annual searches, this 9% error rate translates to over 57 million incorrect answers generated hourly. A more critical issue is the prevalence of unsubstantiated citations. For correct answers, the rate of "unfounded citations"—where provided source links do not support the AI's claims—worsened, rising from 37% with Gemini 2 to 56% with Gemini 3. This makes it difficult for users to verify the information. The AI also heavily relies on low-quality sources, with Facebook and Reddit being its second and fourth most cited domains. Furthermore, the system is highly susceptible to manipulation. A BBC journalist successfully "poisoned" it by publishing a fake article; Google's AI began presenting the false information as fact within 24 hours. Google disputed the study's methodology, criticizing the use of the SimpleQA benchmark and an AI model (Oumi's HallOumi) to evaluate its own AI. The company maintains that its internal safeguards and ranking systems improve accuracy beyond the base model's performance.

marsbit04/10 12:30

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

marsbit04/10 12:30

Pichai's 10-Year Tenure as Google CEO: Lows, Reversals, and Regrets

In a wide-ranging interview marking his 10-year anniversary as Google CEO, Sundar Pichai reflects on the company's journey in AI, from being an early innovator with the Transformer architecture to its current leadership position. Pichai addresses the "missed opportunity" narrative, explaining that internal versions of models like LaMDA (a precursor to ChatGPT) existed but were not released due to higher safety thresholds and early "toxicity" issues. He emphasizes that its research was always product-driven, and attributes OpenAI's success to a fortunate combination of factors, including identifying the coding use case early. Looking forward, Pichai asserts that search will not die but will evolve into an "agent manager," where users command AI to complete tasks. He reveals Google's massive capital expenditure, projected to reach $175-185 billion in 2026, is a testament to its belief in the AGI curve. However, he warns of a major supply crunch in 2026, citing critical bottlenecks in wafer capacity, memory, and even a shortage of electricians as fundamental constraints. Pichai also discusses Google's "hidden gems," including early-stage projects like space-based data centers, quantum computing (which he believes will excel at simulating nature), and robotics. He shares a regret: not investing more aggressively in Waymo earlier. Internally, Pichai reveals he personally spends at least an hour each week allocating scarce computing resources (TPU time), which has become the company's most critical allocation decision. He predicts that by 2027, business forecasting at Google will be fully automated by AI agents, marking a major shift in how work is done.

marsbit04/10 00:36

Pichai's 10-Year Tenure as Google CEO: Lows, Reversals, and Regrets

marsbit04/10 00:36

Google's Deep Think Dominates Eight-Language Olympiads, Autonomously Solves Four Unsolved Problems, Research Barriers Collapse

Google DeepMind's "Deep Think" AI system has demonstrated exceptional performance across eight languages in regional academic competitions, including mathematics and informatics Olympiads. It achieved perfect scores in Japanese and French contests, and high results in Chinese, Korean, Hindi, Vietnamese, Russian, and Portuguese exams. This multi-language capability aims to reduce linguistic barriers in scientific research, enabling non-English-speaking researchers to access advanced AI tools equally. Beyond competitions, Deep Think has solved four previously unsolved mathematical problems and contributed to breakthroughs in computer science, physics, and economics. It powers the Aletheia agent, which autonomously generates and verifies research-level mathematical solutions. Despite these achievements, the results are based on internal evaluations without third-party verification or detailed methodology disclosure. Google positions Deep Think as a "human intelligence multiplier," expanding AI's role in global scientific collaboration beyond English-dominated benchmarks.

marsbit04/08 10:30

Google's Deep Think Dominates Eight-Language Olympiads, Autonomously Solves Four Unsolved Problems, Research Barriers Collapse

marsbit04/08 10:30

"Legal" Ponzi Scheme? Uncovering Gemini Exchange and Its Founders' Circular Lending

A recent report scrutinizes Gemini's financial practices, revealing a circular lending scheme involving its founders, the Winklevoss twins. Through their private investment firm, Winklevoss Capital Fund (WCF), they loaned thousands of Bitcoin and Ethereum to Gemini. The exchange then used these assets as collateral to secure hundreds of millions in USD loans from third parties like Galaxy Digital and NYDIG to fund operations. During its September 2025 IPO, Gemini settled $695.6 million of its debt to WCF by converting it into super-voting Class B shares at a 20% discount ($22.40 per share) compared to the $28 price public investors paid. This secured the founders 94.7% of the voting control. As of the end of 2025, Gemini still owes WCF 4,619 BTC (approx. $400M), which can be recalled at any time, posing a significant liquidity risk. Despite this, Deloitte issued an unqualified audit opinion without highlighting these related-party transactions or associated risks. Since its IPO, Gemini's stock price has collapsed by 88% to $4.42, facing multiple analyst downgrades and a shareholder class action lawsuit for allegedly misleading investors about its strategic and financial condition.

marsbit04/06 01:25

"Legal" Ponzi Scheme? Uncovering Gemini Exchange and Its Founders' Circular Lending

marsbit04/06 01:25

Doom Looms For Gemini (GEMI): Expert Predicts Bankruptcy By End Of 2026

Gemini's stock (GEMI) has plummeted 90% from its September 2025 peak, sparking bankruptcy concerns. Market expert Dom Kwok warns the crypto firm could face insolvency by end-2026 due to multiple crises: mounting lawsuits alleging IPO misinformation, a mass exodus of C-suite executives, slowing revenue growth, and accelerating losses. The company is rapidly depleting its IPO cash reserves and may soon require highly dilutive financing, further eroding shareholder value. Operational issues like account freezes and poor customer service compound its troubles. With its stock trading near $4.59 and no recovery catalyst in sight, Gemini's future appears critically endangered.

bitcoinist03/27 12:32

Doom Looms For Gemini (GEMI): Expert Predicts Bankruptcy By End Of 2026

bitcoinist03/27 12:32

Apple Gains Full Access to Google's Gemini, Accelerates On-Device AI Model Development with Distillation Technology

Apple has secured full access to Google's Gemini model, aiming to accelerate the development of its on-device lightweight AI systems using advanced data distillation techniques. The company will utilize Gemini’s high-quality answers and chain-of-thought reasoning as training data to “feed” its own smaller, proprietary models. This approach, known as model distillation, enables compact models to achieve reasoning capabilities comparable to top-tier large models while maintaining computational efficiency. Although Gemini was originally designed for chatbots and enterprise applications—differing from Apple’s system-level integration vision for Siri—this collaboration significantly addresses Apple's need for high-quality synthetic data. In parallel, Apple continues its in-house development efforts through its Apple Foundation Models team. New AI features leveraging this distilled technology are expected to debut at Apple’s Worldwide Developers Conference (WWDC) in June. This partnership highlights a shift in the AI industry from pure computing power competition toward more efficient training strategies. By investing in access to leading model capabilities to enhance its edge computing advantages, Apple illustrates the ongoing balance between general-purpose large models and private on-device AI. This move also signals a future where edge devices will possess stronger local inference and complex task-handling abilities, further advancing the democratization of AI.

marsbit03/27 01:28

Apple Gains Full Access to Google's Gemini, Accelerates On-Device AI Model Development with Distillation Technology

marsbit03/27 01:28

"Borrowing a Chicken to Lay Eggs"? Apple Secures Google Gemini License: Crafting the Strongest On-Device AI Through Model "Distillation"

Apple has reportedly secured full access to Google's Gemini model, not for simple API integration, but to perform "model distillation"—a technique that allows Apple to train smaller, more efficient on-device AI models by learning from Gemini’s advanced reasoning processes. This approach enables Apple to develop localized AI systems that operate entirely on-device, improving speed and safeguarding user privacy. The distilled models are expected to power a significantly upgraded Siri in iOS 27, enabling deeper interaction, complex question-answering, document understanding, and practical task execution—all without requiring cloud connectivity. While leveraging Google’s model in the short term, Apple continues to develop its own foundational AI models internally, adopting a dual strategy of near-term collaboration and long-term independence. This move represents Apple’s effort to balance cutting-edge AI performance with its strict privacy and efficiency standards, potentially reshaping the landscape of on-device artificial intelligence.

marsbit03/26 01:08

"Borrowing a Chicken to Lay Eggs"? Apple Secures Google Gemini License: Crafting the Strongest On-Device AI Through Model "Distillation"

marsbit03/26 01:08

Google DeepMind Releases Lyria 3 Pro: AI Music Transitions from "30-Second Previews" to Full Songs

Google DeepMind has launched Lyria 3 Pro, a significant upgrade from the previous version released just six weeks prior. The key improvement is the extension of AI-generated music from 30 seconds to 3 minutes, alongside a new "structure-aware" capability. This allows the model to understand and construct a song's internal composition—such as intros, verses, choruses, and bridges—based on user's text, image, or video prompts, marking a shift from a simple generator to a creative tool. While competitors like Suno and Udio have offered similar features since early 2025, Google's entry into this competitive space is notable due to its vast distribution reach via the Gemini ecosystem. The model supports multiple languages, includes vocals and instruments, and automatically adds SynthID watermarks for AI content identification. Access is tiered for paid Gemini users (AI Plus, Pro, Ultra), with daily generation limits, while free users remain on the 30-second Lyria 3. It is also available for developers via API and for enterprises through Vertex AI. However, copyright concerns regarding training data remain unresolved, similar to ongoing industry disputes.

marsbit03/26 00:58

Google DeepMind Releases Lyria 3 Pro: AI Music Transitions from "30-Second Previews" to Full Songs

marsbit03/26 00:58

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