🔍 Gemini now searches and buys apps in Play Store

Google integrated Play Store into Gemini, letting users find apps by simply telling the AI what they need. For example, saying "map for travel abroad" prompts Gemini to locate and open the app page directly.

Gemini also enables buying Play gift cards and in-game items right in chat. This feature is limited to personal Google accounts, users 18+, and is rolling out gradually on Android.

@datasciencepulse 〰️

💧 A fact-check regarding artificial intelligence’s water consumption, with focus on the impact of data centers, has emerged.

Available data suggests that large AI models and the facilities housing them require notable amounts of water, particularly for cooling equipment during intensive computations. This operational need has led to increased scrutiny of the technology sector’s environmental footprint.

Sources underline that water usage figures can vary depending on the location, the type of cooling technology used, and the demand placed on data centers. Reliable quantification is challenging, as not all companies disclose detailed consumption data. Nonetheless, the topic continues to attract attention as AI development accelerates.

@datasciencepulse 〰️

🧠 Meta unveils Brain2Qwerty v2: AI can turn brain signals into text, no brain implant required

Imagine typing… without actually typing.

Meta has demonstrated Brain2Qwerty v2, an AI system that converts brain activity into text using a non-invasive magnetoencephalography (MEG) helmet instead of a surgically implanted brain chip.

Here’s how it works:

🪖 The user wears an MEG helmet that measures the tiny magnetic signals produced by the brain.
🧠 As the person thinks about typing, the AI filters through the noisy brain activity.
⌨️ The model reconstructs the intended keystrokes and generates coherent sentences.

The technology is still confined to research labs because MEG scanners are large, expensive, and require highly controlled environments. It’s nowhere near replacing a laptop or smartphone keyboard yet.

Still, it’s a major step for non-invasive brain-computer interfaces. While companies like Neuralink rely on implanted electrodes, Meta is exploring whether AI can decode thoughts without surgery.

@datasciencepulse 〰️

🇰🇷 Korea is quietly becoming the world’s humanoid robot factory.

Goldman Sachs Research estimates Korean companies could produce 30% of all humanoid robots by 2035, jumping from virtually zero today to more than 412,000 robots a year. The reason? Decades of automotive manufacturing have given Korea the motors, actuators, supply chains, and factories needed to scale humanoids.

The government is fueling the push with ₩700 billion ($500M) for robotics in 2026, aiming to produce 1,000 domestically built humanoids annually by 2029.

Investors have already noticed. LG Electronics is leveraging its massive motor business to supply humanoid robots, Hyundai Motor is combining its manufacturing muscle with Boston Dynamics, while Hyundai Mobis, Rainbow Robotics, Robotis, and Doosan Robotics are all positioning themselves across the humanoid supply chain.

For broader exposure, Korea’s new humanoid robot ETFs have surged in popularity, with pension funds pouring billions into the sector.

@datasciencepulse 〰️

🧿 We can all relate.

@datasciencepulse 〰️

🇨🇳 Ex Meta PM and AI founder Xiaoyin Qu says “American and European enterprises will ditch OpenAI and anthropic and adopt Chinese models.”

@datasciencepulse 〰️

🔥The race to dominate enterprise AI 

Anthropic's recent surge has propelled it past OpenAI to become the leading paid AI provider for U.S. businesses, marking a shift in the AI race from model superiority to workflow dominance.

@datasciencepulse 〰️

🖥Agibot robots demonstrate high performance in factory trial

Agibot humanoid robots reached a 99% success rate during a six-day live demonstration at a factory. Over 64 hours of operation, the robots completed 64,828 individual tasks and assembled 17,625 tablet units.

Progress in this field is accelerating, as developments of this magnitude were uncommon a year ago but have recently become more frequent.

@datasciencepulse 〰️

📕 Chinese AI models now offer processing at rates as low as 18 cents per million tokens, compared to the $4 average charged by leading models, according to CitiBank Research.

By June, 65% of open-source model processing on OpenRouter was handled by these lower-cost options, up from 34% in January. DeepSeek and other models from China have attracted interest due to their affordable pricing.

As companies shift focus to controlling AI costs, the decision to adopt a specific model is increasingly based on price instead of technical superiority. This trend is prompting leading providers like OpenAI and Anthropic to reassess pricing, as enterprises now compare models according to specific tasks.

Gartner projects that by 2028, spending on AI coding may surpass the average salary of a developer.

@datasciencepulse 〰️

🤖 OpenAI is preparing to reveal new Codex hardware on July 15.

It looks like OpenAI is building a physical control panel for an AI coding agent.

@datasciencepulse 〰️

🤖 Anthropic just unveiled Claude Sonnet 5, its smartest and most agentic Sonnet yet.

The model can plan multi-step tasks, browse the web, use terminals and other tools, and work autonomously on problems that only much larger, more expensive AI models could handle a few months ago.

Compared to Sonnet 4.6, it delivers major gains in reasoning, coding, tool use, and knowledge work while approaching the performance of Opus 4.8 at a much lower cost.

Early testers say Sonnet 5 completes complex tasks that previous versions couldn’t finish, double-checks its own work without being prompted, and offers one of the best price-to-performance ratios for AI agents today.

Source.

@datasciencepulse 〰️

Ⓜ️ Meta is reportedly telling engineers to be careful with Claude and Codex and the reason is surprisingly simple: AI can “catch” another AI’s knowledge.

According to reports, Meta has restricted the use of Anthropic’s Claude Code and OpenAI’s Codex for some engineering work to avoid contaminating its own AI training data.

Here’s the concern: if Meta’s future models are trained on outputs generated by rival AIs, competitors could argue Meta distilled their models instead of developing its own. Both OpenAI and Anthropic prohibit using their AI outputs to build competing models.

That doesn’t mean engineers can’t use these tools for everyday coding. The key is keeping those outputs completely separate from anything that could end up training, evaluating, or improving Meta’s own AI.

The biggest legal risks would likely come from deliberate behavior such as mass scraping, automated extraction, or knowingly using competitors’ outputs as training data not casual productivity use.

@datasciencepulse 〰️