Google Gemini 3 redefines the limits of multimodal artificial intelligence

The model sets a new benchmark in contextual understanding and complex problem solving, surpassing key benchmarks.

Analysis of the Technical Advancement of Gemini 3

The evolution of artificial intelligence experiences a significant milestone with the official launch of Gemini 3 by Google. This event represents a meticulous effort by the technology giant to consolidate its leadership in a cognitive computing ecosystem characterized by intense competition and unprecedented speed of innovation. The presentation of this AI system does not constitute a mere incremental launch, but rather the materialization of a long-term scientific strategy that the company calls the “Gemini era”, which began almost two years ago.

Adoption data provided by Sundar Pichai, CEO of Google and Alphabet, provides quantifiable context for the impact of this strategy. The Overview created by AI accumulates two billion monthly users, while the specific application Gemini exceeds six hundred and fifty million. Further analysis reveals substantial penetration into the enterprise segment: more than seventy percent of Google Cloud customers use its AI services, and thirteen million developers have built tools based on its generative models. These metrics indicate an accelerated transition of AI from the realm of research to practical implementation on a massive scale.

RelatedGoogle integrates its Gemini AI into select televisions

Benchmark Capabilities and Performance Assessment

According to the Google DeepMind research division, Gemini 3 embodies a conceptual technical leap towards general artificial intelligence. Demis Hassabis, CEO of the division, characterized the model as the “smartest” developed by the company, highlighting substantial improvements in depth of analysis, nuanced understanding and ability to infer user intent. The quantified performance of the model, specifically the Gemini 3 Pro variant available in preview, corroborates these claims. It tops the LMArena leaderboard with 1501 points, surpassing its immediate predecessor, Gemini 2.5 Pro by 50 points.

The rigorous examination of your abilities extends to specialized domains of reasoning. The system scored 37.5% on Humanity’s Last Examwithout tool assistance and 91.9% on GPQA Diamond, a high-level test for experts. In the field of mathematical reasoning, it established a new state of the art with 23.4% in MathArena Apex. However, the most distinctive innovation lies in its advanced multimodal understanding. The results of 81% in MMMU-Pro (multidisciplinary task evaluation) and 87.2% in Video-MMMU demonstrate a superior ability to process and contextualize information from different modalities (text, image, video). The 72.1% in SimpleQA Verified also reflects critical progress in factual accuracy, mitigating one of the persistent challenges in extensive language models: the propensity for hallucination.

Strategic Integration and Future Directions

A key strategic dimension of this launch is its immediate and transversal integration into the Google product ecosystem. For the first time, a new Gemini model is implemented in the Search since its opening day. Its incorporation into AI Mode allows for more dynamic search experiences with integrated advanced reasoning. Simultaneously, the model is available in the Gemini application, in the AI Studio and Vertex AI development platforms, and in the agent creation platform, Google Antigravity, where it is expected to enable more complex use cases.

Google has also introduced Gemini 3 Deep Think, a specialized modality that prioritizes longer and more methodical chains of reasoning. This variant will initially be available to security testers before progressively rolling out to Google AI Ultra subscribers. For Pichai, this collective launch symbolizes “a new chapter” in the evolution of intelligence and computational personalization, with the explicit commitment to continue expanding the frontiers of the possible. The company projects that these capabilities will not only improve user-machine interactions, but will also catalyze the creation of applications and solutions that are currently at the limits of technical feasibility.

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Satellite Internet, the bet to close the digital divide in Mexico

Mexican geography limits fiber deployment, but low orbit satellites are emerging as an alternative.

Satellite internet as a solution to the digital divide in Mexico

Mountains, jungles and deserts make Mexico one of the countries with the most complex orography in Latin America. Bringing fiber optics to isolated communities implies an investment that is difficult to recover with few users. As a result, one in five households still lack internet access or receive poor service.

Faced with this scenario, low orbit (LEO) satellite internet is emerging as a strategic alternative. “Deploying physical infrastructure in Mexico is expensive due to geography. In many cases, satellite internet represents the most viable option to connect areas where fiber will not reach in the short term,” explains Ernesto Piedras, director of The CIU. Companies like Starlink, with more than 10,300 active satellites, already operate in the country. Since May 2021, it obtained a concession and closed 2024 with 160,631 subscriptions, making Mexico its second Latin American market, only behind Brazil. Starlink has signed contracts with CFE Telecomunicaciones for up to 1,556 million pesos to connect remote communities.

However, cost remains a barrier. While a fiber optic plan costs an average of 544 pesos per month, satellite internet starts at 899 pesos, plus equipment and installation. “Commercially it is still not as viable because it is still more expensive and requires purchasing an antenna whose price remains high,” says Fernando Esquivel, from The CIU. The service makes economic sense when it is shared between communities or companies, and with the support of public policies.

Experts agree that this technology will not replace fiber optics, but will complement it. “Terrestrial services are faster and have lower latency. Satellite communication reaches areas that fixed internet would not reach,” says Hugo Simg, from MediaTek. Deployment also requires regulation and coordination between governments and companies. The race to connect Mexico is no longer only being fought underground, but from space, where a new generation of satellites promises to reduce the digital divide that keeps millions out of the digital world.

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Banks prepare for biometric facial controls in October

Banks claim to be ready for facial biometrics in branches since October.

The main banking institutions in the country claim to be prepared for the new regulations on facial biometric controls when opening accounts, which will come into force next October.

What did the banks declare?

Marcos Ramírez, general director of Grupo Financiero Banorte, pointed out that the institution has made progress to reinforce the identification and security of its users. “We are in time and form with everything established by law and a little ahead, with the prudential limits of the law,” he indicated.

These measures will strengthen control over customer identification. Since 2018, by regulation, fingerprint biometrics are already required to open accounts.

Implementation details

On July 1, the National Banking and Securities Commission (CNBV) incorporated facial biometrics into the regulation for multiple banking in customer identification and authentication. Institutions that opt ​​for this mechanism must comply with technical, operational and security requirements.

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AI detects breast cancer with 90% accuracy

An autonomous model analyzes thermographs and classifies tissues without human intervention.

An artificial intelligence model developed by Raúl Castro Ortega, a researcher at the Polytechnic University of Tulancingo, manages to identify breast cancer with an accuracy of 90%. The results were presented during the SPIE Photonics Europe 2026 International Congress, in Strasbourg, France.

How the system works

The system uses convolutional neural networks with attention mechanisms to analyze breast thermographs. Unlike traditional methods, the model autonomously learns the patterns of healthy and diseased tissue, without a specialist previously defining the characteristics of the images. This reduces processing time and improves consistency of results.

The study, titled “Breast thermography analysis using convolutional neural networks with attention mechanisms,” uses an algorithm based on the heat diffusion equation. This approach focuses analysis on thermogram regions of interest and classifies tissues with high levels of sensitivity and specificity.

Impact on early detection

The accuracy achieved represents a significant advance for the early detection of breast cancer, which can improve survival rates and facilitate timely treatment. The researcher highlighted that the system does not replace the specialist, but rather works as a support tool for faster and more consistent diagnoses.

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