Louise Pasteur de Faria, PhD’s Post

If you wonder why the push for AI feels new and familiar all at once, and why AI ethics is positioned as a trailing concern, consider its framing as a “frontier”—and the extractive tendencies, lack of social norms and responsibilities, and obliteration, oppression, and violence that history contains. By definition, frontiers are lonely, ugly places in which “might makes right”—where people, communities and culture are treated as expendable or as targets for genocide, and lawlessness is a norm. This important article snaps those pieces into place. A technology that necessitates intellectual theft, overlooks the ossification of racism and bias, invades privacy, is built on hours of traumatic labor, incurs massive environmental costs, empowers surveillance, can equip bad actors with frightening tools at scale, and threatens truth and legitimacy in our democratic institutions, is important to unpack outside of mythos like frontier tropes, with their embedded permission to disregard laws and norms. That’s the metaphor we’re up against, so talking about “socio-technical” pieces won’t be powerful enough. We need to consistently use the language of justice and accountability, fairness and harm. And to keep making clear what the social tab is for this tech, and to insist that the talk about responsible AI is matched with action and transparency.

Gary Marcus on Why LLMs Won’t Lead to AGI and What’s Needed Next A compelling critique of the current AI landscape given by Gary Marcus at an AGI conference: 🔹 LLMs Won’t Lead to AGI: Marcus argues that LLMs will not get us to AGI. They can't reason or plan. They don't have a world model. This is pretty obvious, but yet so many insist that if we could just scale up with enough data and parameters then predicting the next word will magically morph into AGI. 🔹 Path to AGI: Achieving AGI requires a hybrid approach—integrating deep learning with symbolic reasoning and cognitive models that grasp real-world knowledge and relationships. 🔹 Roadblocks and AI Winter Ahead: The big bucks are going into scaling LLMs and not enough is going into researching other types of architectures and models. As expectations are dashed another AI Winter is ahead. 🔹 Power in Silicon Valley: Marcus raises alarms about the concentration of power in AI development. He warns that tech giants are prioritizing profits over ethics. No news there! 🔹 Urgent Need for Regulation: With AI's potential for misinformation, surveillance, and ethical violations, Marcus calls for robust regulation and transparent governance to prevent harm. Why Watch This Video? Marcus offers a realistic roadmap for reaching AGI and underscores the ethical and societal challenges that must be addressed. What I disagree with: I don't think an "AI Winter" of the sort that happened around the nineties will happen this time. Even though LLMs have so many limitations, they are still INCREDIBLY useful and many organizations and individuals are deriving a tremendous amount of value from them. I also think that more attention needs to be devoted to how the development of AI is and will impact people's livelihoods and lead to ever greater concentration of wealth and power in the hands of a few. Gary Marcus #GenAI #AIEthics https://lnkd.in/gZKBS8Hi

Top AI researchers are divided on just how close we are to artificial general intelligence (AGI), with predictions ranging from a few years to several decades. 🔍 What is AGI? Often described as AI that can perform any human task without being limited to its programming, AGI could redefine intelligence as we know it. 🚀 Optimistic Forecasts: OpenAI’s CEO Sam Altman anticipates AGI by 2025, while former OpenAI executive Miles Brundage sees "human-level AI systems" within a few years. 🏆 Anthropic’s Vision: CEO Dario Amodei envisions AGI as a “country of geniuses in a data center” by 2026, with the capability to surpass Nobel-level thinking across domains. ⏳ Measured Predictions: Geoffrey Hinton suggests we could see AGI within 5 to 20 years, citing the vast uncertainties, while Google DeepMind’s Demis Hassabis believes it will take at least a decade. 🧠 Skeptical Perspectives: Experts like Andrew Ng and Meta’s Yann LeCun are cautious, with Ng doubting AGI’s arrival soon, and LeCun emphasizing gradual progress over any single "AGI event." 🤖 Economic Impact: Richard Socher links AGI to the automation of 80% of jobs, potentially achievable in 3 to 5 years, though fully human-like AI could still be a century away. #AI #AGI #FutureTech 🌐 A Shared Journey: The race to AGI involves collaboration across AI pioneers, each with unique visions for human-machine synergy. 💡 Ethical Questions: The development of AGI raises ethical questions about control, societal impact, and human identity in an AI-driven world. 🌍 Real-World Applications: Whether it arrives soon or decades from now, AGI will influence fields from healthcare to climate science, amplifying human capability on a global scale. ♻️ Repost if you enjoyed this post and follow me, César Beltrán Miralles, for more curated content about generative AI! https://lnkd.in/gUwRy5V4.businessinsider.com%2Fagi-predictions-sam-altman-dario-amodei-geoffrey-hinton-demis-hassabis-2024-11&utm_campaign=aga&utm_source=agsadl1%2Csh%2Fx%2Fgs%2Fm2%2F4

What is AGI? AGI is when artificial intelligence is successfully applied to a wide range of valuable problems. To be "general" means to be widely applicable. Every problem is specific, so there probably is no "everything button," or one AI model that will do everything. The right algorithm will solve the specific problem that you're focusing on at that moment.

Heya! I have been formalizing my thoughts over the past few weeks about the current state of AI, how we're already in the intelligence era, and what the future looks like, over a broad spectrum of fields, from generative AI, AI agents, to software development, to jobs, to legal, ethical and moral ramifications, to the impending energy crunch we'll find ourselves in, in the near future. Give it a read! https://lnkd.in/gxVFQuSs

We’ve been a leader in brand name development for 80 years, and in a typical brief, we can generate over 2,000 names. But as the limitations of language, trademark, name real estate and other stakes get even higher, AI represents a powerful opportunity to supercharge our naming power. AI could access 217 billion other possibilities for names with 8 letters or less. But there's a sobering counterpoint: efforts to-date point to an AI that is good at generating quantity, but bad at producing or selecting quality.  That's why the next installment in our PLAY series features us experimenting with more sophisticated prompts, deeper training, and test-and-learn methodology for brand naming. Let's go. #AI #brandname #brandnaming

Peter Principles is spot on often in Organizational Behavior How to detect those who are bag of gas loaded with whataboutery and Red herring Was told way back never to use the word "Why" in conversation There are always readymade answers to mask their incompetence Rather say okay what next ? How will you do it ? Put words into their mouth like you will not be late tomorrow What should we do , if you repeat again .. Peter Principles are always a treasure Take the cue from the beneath AI summary https://lnkd.in/gKEs9NQT

AI needs EQ more than IQ.

Ever felt like a fraud even when you know your stuff? Imposter syndrome is real, and it's even more prevalent in the AI space. But here's the twist: Large Language Models (LLMs) can be both a boon and a bane. By using LLMs as aids rather than crutches, we can harness their potential without falling into the trap of imposter syndrome. Imagine leveraging LLMs to enhance your expertise instead of questioning your capabilities. This balance is crucial for maximizing AI's benefits while maintaining confidence in our unique skills. A must-read for anyone navigating the intricate dance between self-assurance and AI tools. Discover more insights in the full article here: https://lnkd.in/gCJYW83E

🤔 Building on my last post: If we want AI agents to be reliable collaborators, shouldn’t we go beyond just detecting “hallucinations” and work on fixing the cause? Most AI agents(rag) relies on specific resources or knowledge bases. To keep it grounded, there should be no room for false statements—creativity should only be there to improve natural language, not distort facts. What if we designed AI agents to flag gaps with metrics and say, “This info may not be trusted”? What’s your take?