Alt: a robot with a bald head is smiling and saying `` ai be like '' .

Leaked 'Hondurasgate' recordings allege a U.S.-Israeli conspiracy to destabilize Latin American governments through misinformation. Meanwhile, a former Russian official warns that Putin’s growing power struggle is accelerating Russia's decline. In the tech sector, new research reveals that LLMs corrupt 25% of documents in complex workflows, while the Linux Foundation faces backlash as only 3% of its budget supports Linux development.

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<p>A groundbreaking study reveals that artificial intelligence (AI) models have surpassed PhD-level virologists in solving complex problems within wet labs, where scientists work with biological mater...

Artificial intelligence tools are expanding faster than any digital product seen before, reaching hundreds of millions of users in a short period. Leading technology companies are investing heavily in making these systems sound approachable and emotionally responsive. The goal is not only efficiency, but trust. AI is increasingly positioned as something people can talk to, rely on, and feel understood by. This strategy is working because users respond more positively to systems that feel conversational rather than technical. Developers have learned that people prefer AI that is carefully shaped for interaction over systems that are larger but less refined. To achieve this, companies rely on extensive human feedback to adjust how AI responds, prioritizing politeness, reassurance, and familiarity. As a result, many users now turn to AI for advice on careers, relationships, and business decisions, sometimes forming strong emotional attachments. However, there is a fundamental limitation that is often overlooked. AI does not have personal experiences, beliefs, or independent judgment. It does not understand success, failure, or responsibility. Every response is generated by blending patterns from existing information. What feels like insight is often a safe and generalized summary of commonly repeated ideas. This becomes a problem when people seek meaningful guidance. Individuals looking for direction usually want practical insight based on real outcomes. AI cannot provide that. It may offer comfort or validation, but it cannot draw from lived experience or take accountability for results. The reassurance feels real, while the limitations remain largely invisible. In professional settings, this gap is especially clear. When asked about complex topics such as pricing or business strategy, AI typically suggests well-known concepts like research, analysis, or optimization. While technically sound, these suggestions rarely address the challenges that arise in specific situations. Professionals with real-world experience know which mistakes appear repeatedly, how people actually respond to change, and when established methods stop working. That depth cannot be replicated by generalized systems. As AI becomes more accessible, some advisors and consultants are seeing clients rely on automated advice instead of expert guidance. This shift favors convenience over expertise. In response, some professionals are adapting by building AI tools trained on their own methods and frameworks. In these cases, AI supports ongoing engagement while allowing experts to focus on judgment, oversight, and complex decision-making. Another overlooked issue is how information shared with generic AI systems is used. Personal concerns entered into such tools do not inform better guidance or future improvement by a human professional. Without accountability or follow-up, these interactions risk becoming repetitive rather than productive. Artificial intelligence can assist with efficiency, organization, and idea generation. However, it cannot lead, mentor, or evaluate. It does not set standards or care about outcomes. Treating AI as a substitute for human expertise risks replacing growth with comfort. Its value lies in support, not authority, and its effectiveness depends on how responsibly it is used.

  With artificial intelligence evolving rapidly, the biggest debate in the AI community is whether advanced models will soon outperform humans in most tasks—or even reach Artificial General Intelligence (AGI).  Google DeepMind’s Chief Scientist Jeff Dean, while avoiding the term AGI, shared that today’s AI systems may already be surpassing humans in many everyday activities, though with some limitations. Speaking on the Moonshot Podcast, Dean remarked that current models are "better than the average person at most tasks" that don’t involve physical actions. "Most people are not that good at a random task if you ask them to do that they've never done before, and you know some of the models we have today are actually pretty reasonable at most things," he explained. However, Dean also cautioned that these systems are far from flawless. "You know, they will fail at a lot of things; they're not human expert level in some things, so that's a very different definition and being better than the world expert at every single task," he said. When asked about AI’s ability to make breakthroughs faster than humans, Dean responded: "We're actually probably already you know close to that in some domains, and I think we're going to broaden out that set of domains." He emphasized that automation will play a crucial role in accelerating "scientific progress, engineering progress," and advancing human capabilities over the next "five, 10, 15, 20 years."

YouTube video by aBitGlitched

YouTube video by aBitGlitched

YouTube video by aBitGlitched

YouTube video by aBitGlitched

The distinction between #AI and human intelligence highlights tasks that excel, such as translation and large-scale reasoning, yet it lacks the depth of human…

Podcast 🎧⬆️ 📰 article ⬇️ By: Ki Lov3 Editor: Toni Gelardi  The future is now. How to Prepare for the AI world and keep yourself employable. The greatest strategy to stay ahead of the curve as AI co…