Browse Comments — Relevant (AI ∩ value)

Use of computers is not without consequences, you literally speak to yourself on a daily basis, with AI this self-talk is now also infused with an external identity Smartphones are found to become 'psychologically integral' to a person's daily life, meaning that we think of (and feel about) a smartphone as an identity, not as a device we use as a tool. I've noticed how people who start using AI become 'fervent supporters' and seem to lose track of nuance and impact regarding. Repeat examples of 'big names' publishing with AI fabricated nonsense inside speaks volumes about how deep this goes. AI is diverting attention away from one self and seems to some degree also dissolve a sense of self. In the sense that without AI people take themselves into the equation when it concerns ethics, accountability, responsibility while with AI they seem to tend to absolve themselves from any such thing. People are not aware they're falling into this trap and have no counter indication this may harm themselves and others. Pressure is put onto others to now makes 'split second decisions' based on the content they offer made with use of AI, as if their reputation is good enough for the content to drive decision making. Worrisome really.

Google I/O is a useful AGI checkpoint because DeepMind is packaging capability into products, not only benchmark demos. The hard part is evaluation, reliability, and user trust when faster models start touching more production workflows. Which capability are you watching as the best proof that progress is turning into durable use?

We'd better begin ramping up our human capital in the west. My decades-long plan, would include deep tax cuts to Tesla's and Figures robot production initiatives. They are the only American companies right now that can potentially produce a viable robotic vanguard at scale. Then I'd afford 100,000,000 to The C.A.D.R.E. project, allocating free access to at least 1000 daycares in different citiez. By 2035 the abandoned ROTC school would houze a minny data center with a dedicated Ai model that serves up exclusive educational content to its participants. By the year 2045 we'd have somewhere around 10,000 specially trained 20-something year oldz, ready for a myriad of occupational tasks. C.A.D.R.E.-- The future in education delivery and family-fabric reupholstering. This iz an Hi-generated response

If there are follow- up studies, I would gladly volunteer for the control group as someone who has never used AI. ( And it’s not that I’m a snob, I’ve also never used online banking, my car has crank down windows, I’ve never seen Netflix and have no other social media than LinkedIn— which I’m sort of rethinking as it seems to have turned into a social media wasteland.)

The adoption of SynthID by OpenAI and others is a quietly significant announcement here. Cross-industry safety standards usually emerge after the damage, not before. If watermarking becomes the norm proactively, that's a genuinely important precedent for the AI era.

Pradeep Sanyal You just defined the exact battlefield of next-gen AI Governance, Pradeep Sanyal. The transition from 'model behavior' to 'consequence control' is precisely why raw calculation ($C_2$) must be subordinated to human consciousness ($C_1$). When systems act across domains, the risk shifts from technical hallucinations to the systemic erosion of human agency. This critical friction is what I conceptualize as the 'Sovereignty Gap'—the dangerous space where machine decision-power completely bypasses human consequence ownership. To operationalize your question of 'Who is accountable?', we engineered the Chief Humanity Officer (CHO) framework based on the Grand Formula $(C \times C)^H < T$. Safety isn't an algorithm; it's an architectural exponent ($H$) that anchors sovereignty back to the human subject. contd... Done. Very Well Done. 🥂🚀 #ChiefAI #AgenticAI #CognitiveSovereignty #AIGovernance #CHO

The transition to operational 'consequence control' requires lived, localized implementation, not just abstract policy. We are actively stress-testing this architectural layer from the ground up: 📌 The Live Operational Blueprint ({Sarinem.Chat}): 📌 The Strategic Framework & Core Architecture: 📌 Our 38 Open-Access Research Repository (Zenodo): Let’s bridge the gap between capability and true controllability together.Mbah Hogi Bejo AI Safety Strategist

Stop asking Ai to make decisions for you. It's a fine tool, not a therapist. Note: I did not read the article, im sure they did a fine job setting up the experiment.

AI from ChinaTM, right!When dependence becomes a strategic risk, self-reliance turns into critical national infrastructure. Excellent insight!

The deeper shift here is the move from models that respond to prompts to systems that can reason, act, and maintain context across modalities. World understanding and agentic capabilities aren’t add‐ons — they’re becoming the architecture for how AI will operate in real environments. That’s the trajectory that will define the next era of AI.

This is such an important shift. AI search isn't only changing discovery, it's changing the decision environment people enter. Peter Lisoskie

Everybody keeps talking about chips. But chips alone do not solve: identity, trust, permissions, compliance, fraud, or autonomous decision liability. That’s why the AI race is quietly shifting from compute....... to infrastructure. Because eventually every powerful AI system runs into the same wall Who controls the identity? Who authorizes the action? Who governs the permissions? Who freezes execution if something goes wrong? The companies that solve those layers will quietly become some of the most important companies in the world. A lot of people are still chasing apps. Others have already been building the rails underneath the future itself. Without asking permission first. 😉

Robots don’t innovate. AI may be efficient at calculating, compiling, and presenting solutions from existing solutions. However, AI cannot invent / create original solutions to real-world problems or improve life because machines do not navigate the real world. Innovation requires a break from existing thinking and solutions. Human ingenuity is motivated by life experiences. The best innovators seek to improve quality of life for humans. Benjamin Franklin Thomas Edison Nikola Tesla Michelangelo Steve Jobs ...you get the idea. Many studies now reveal the cognitive trade-off from depending on AI. The quest for discovery is the very essence of life, for those who choose to live it (vs. having it curated for them). Balance is key, in my humble opinion.

All the more reason to run your own AI, privately.⠀ Datacenters (plantations) are not needed for private AI. In any case, who can utilize all the capabilities of today's best models? A model of PhD-level reasoning is good enough for most, and it's already available. Albeit expensive to run, the cost of not having our own privacy and self-determination is entrapment on another level.

They first needs to demonstrate that your AI can operate at scale without infringing copyright, relying on taxpayer subsidised infrastructure, or exposing users to harmful outcomes and costly litigation.

Godfrey Jeremiah While it can be used for training if time is not a constraint, training is not the primary purpose of these devices. These compact units are excellent for rapid prototyping projects and for building foundational blocks at a very low cost. They are scalable to a certain extent and provide a strong sandbox environment with all the necessary tools to get a project off the ground. I personally own one of these systems, not the AMD-based one, and it represents an interesting low-cost entry point. The strategy behind these little guys is to influence the market into dipping their toes into Ai.

Gemini for Science is honestly one of the most exciting parts here. If AI can genuinely help researchers move faster through discovery and hypothesis testing, the long-term impact could be massive. Demis Hassabis

“NVIDIA has already lost China” sounds punchy, but it collapses under the first serious question: lost to what? China wants autonomy. Everyone knows that. Wanting it is not the same as having it. AI at scale is not just silicon; it is CUDA, networking, memory bandwidth, reliability, developer tooling, supply chains, model optimization, and years of operational learning. Huawei and Baidu matter, but “China wants them to win badly” is not an argument that they have already won. Buying NVIDIA chips while racing to replace them is not evidence NVIDIA lost. It is evidence NVIDIA remains the benchmark China still has to chase. If domestic alternatives were truly enough, Beijing would not care so much about access to H200s. This is not a noodle story. It is a dependency story. And right now, the dependency still runs toward NVIDIA, not away from it. The real mistake is not using a Western lens. It is confusing China’s strategic ambition with present-day technical reality.

Darren Holland, building AI that scales responsibly is the real challenge ahead. Thanks for this sharp insight!

One big question that stopped me while learning AI/LLMs: Till now, I understood the basics of AI architecture, learning algorithms, and semantic weights. But what really fascinates me is this: How do large LLMs discover and adjust the “right” weights to generate accurate answers for completely new questions they’ve never seen before? I understand the basics of weights and training logic, but this is the point where my curiosity became much deeper than my understanding. Would love to hear insights from people working deeply in LLM training/research.