Christian Hein’s Post

Agreed! AI especially #GenAI is not like any other technology adoption paradigm we have seen. “We’re finding that AI requires a paradigm shift, It’s not like a traditional #technology deployment where IT flips a switch. Businesses need to identify areas where AI can make a real impact and strategically deploy AI there.”

The recent maneuvers hint at Microsoft's and Google's struggle to effectively leverage generative artificial intelligence, despite pouring significant resources into partnerships, investments, and product development. Neither company has managed to swiftly translate these efforts into consumer products that drive revenue and secure market dominance. Despite their considerable size and influence, they remain vulnerable to disruption. While engineers work tirelessly to refine the underlying large language models, both companies are actively pursuing alliances and talent worldwide, as well as scouting promising startups. This article explores this story in further detail. Have you got a concentrated position in Big Tech and want help exiting in a tax-efficient manner? Book a time to discuss the options available to you https://lnkd.in/ex9YgEws #bigtech #ai #artificialintelligence

Let's try a Yes-No fact-check question for reality and trust in AI-IP: "Is there any data products you know, that can interpret and answer the following realistic Chinese-English multilingual questions of BI?" With our IP, a copyrighted multilingual metadata, we can provide real time answers, by census geographical locations, as evidence for policy/decision making. "Who, in the Ontario province of Canada, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" "Who, in the "江蘇‘’ province of China, has new US patents granted on the nearest Tuesday, when the USPTO releases the newly granted US patents on a weekly basis?" Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, NVIDIA chips, supercomputers, etc. https://lnkd.in/g-aJFnXR Our IP can also make your information service UNIQUE in the world.

More and more, I think smart investors will begin to distinguish between #generative #AI and practical, useful applications of machine learning (#ML) in medical, scientific, transportation, and financial verticals. Researchers will return their focus on problems that can be solved with a reasonable degree of certainty at a cost that doesn't frighten away all but the biggest whales in the VC community. We have become obsessed by generative AI and blinded to more useful applications of AI in constrained contexts. I believe the pendulum will swing back as investors and adopters realize that the costs of sustaining advanced generative AI is all too high for the results yielded.

The trough of despair is a hard place for any technology to be, but it's also an important stage in that technology's evolution. This is when investment slows down, when the hype gets exposed, and when people get a chance to prove that the technology can actually meet real-world needs. The hype period is when extroverts rule - the technology is the best thing since sliced bread!!! The technology will doom us all!! - but the despair phase is when introverts dig in and chip away at the hype to find the real utility, even as the cheerleaders sit on the sidelines and sulk. There is great potential for generative AI, but it's just that: potential. Like the emergence of the web thirty years ago, it will take time, energy, money, and a lot of missteps for that potential to truly show itself. It will require patience and a willingness to experiment. AGI, whatever the heck that is, won't come about next year, maybe not even in the next ten or twenty years, because it isn't really a viable goal. I'm still not convinced that we've even reached "AI" stage yet, only that we have tapped into aspects of language that are unexpected, and we're still exploring the implications. So, yeah, fewer megaphones, more tinkering. I don't think that's a bad thing. Do you?

Inflection’s co-founders moved to Microsoft, “Google’s AI search tool still constantly makes mistakes,” and “very few companies are turning a profit.” “Drastic warnings about AI posing an existential threat to humanity or taking everyone’s jobs have mostly disappeared, replaced by technical conversations about how to cajole chatbots into helping summarize insurance policies or handle customer service calls.”   “The road to widespread adoption and business success is still looking long, twisty and full of roadblocks, say #tech executives, technologists and financial analysts. “If you compare a mature market to a mature tree, we’re just at the trunk,” said one founder. “We’re at the genesis stage of AI.”   It’s also not clear whether the tech industry is slowing down or not. Stanford’s AI Index Report claims that private investment in AI fell from $130 billion in 2021 to $103 billion in 2022 to $96 billion in 2023, with some growth in generative AI investment.   Google Cloud business is also mixed. Although revenue is growing, “Chief Financial Officer Ruth Porat said the company’s investment in data centers and computer chips to run AI would mean Google’s expenses would be “notably larger” this year than last year.”   “Microsoft has also been trumpeting the interest in its AI tools and says 1.3 million people now use its “GitHub Copilot” AI code-writing assistant.” “But the company has been mum on whether any of the tools are profitable when compared with the cost of running them. “We’re finding that AI requires a paradigm shift, It’s not like a traditional #technology deployment where IT flips a switch. Businesses need to identify areas where AI can make a real impact and strategically deploy AI there.”   #Startups, are “trying to replace customer service agents, writing advertising copy, summarizing doctors’ notes and even trying to detect deepfake AI images made by other AI tools.” Gartner says: “These tools are not yet pervasive, not even close.”   Instead, startups focus on the #future. “We’re at the very, very beginning. AI will work its way into every single industry, but it might take at least three to five years before people really see those changes in their own lives. “We have to take a longer look.” Wasn’t the beginning 12 years ago when Erik Brynjolfsson and Andrew McAffee published their two books on AI or 8 years ago when big consulting claims forecast a $16 trillion market by 2030?   The article also admits “there are still glaring problems with generative #AI. Figuring out how to make sure models that are supposed to be reliable don’t make up false information has vexed researchers.” Google’s solution: “let their bots fact-check themselves by simply looking things up on Google Search.” So much for the claim that AI would replace Google search.   How long are investors willing to wait? #technology #innovation #startups #artificialintelligence https://lnkd.in/gWze77d3 

AI has never been cheap. GPT-4? If you run on GPT, your AI model is not really yours. "Sam Altman’s goal of raising about $7 trillion to make artificial-intelligence chips tells a story beyond his borderline-insane ambitions. First, the infrastructure needed to build AI has become exorbitantly expensive. Second, most of that value is still — still! — held by a handful of large technology companies — and the oligopoly is only going to get worse." For example - "Take Sasha Haco, the chief executive officer of Unitary, which scans videos on social media for rule-breaking content. It would cost her company 100 times more than it charges clients to subscribe to OpenAI’s video-scanning AI tools. So Unitary makes its own models, which is a high-wire balancing act in itself. Her startup needs to rent access to those rare AI chips via cloud vendors like Microsoft Corp. and Amazon.com Inc.’s Amazon Web Services." I guess that takes trillions. "Unitary makes it work, but Haco admits that no generative AI startup has figured out how to run a low-cost business at scale, at least not in the same way that large tech firms have." Another AI founder in San Francisco tells me that some of his peers who have to rent AI chips and cloud computing find that the only way they make money “is if people don’t use the product.” Here is the point that many people are starting to figure out, OpenAI is a great sampler; once you decide to enter the game ... you realise that the barriers to entry is super expensive, to achieve a vague outcome. "Generative AI startups can build their technology in two different ways. They can develop their own version of OpenAI’s GPT-4 or Google’s Gemini for instance, a so-called foundation model that requires hundreds of millions of dollars in investment. Or they can build on top of an existing model, which only needs tens of millions in investment and which the vast majority of AI startups do today." So the peak AI is almost here, and the bubble for AI startups is about to pop.

🚀 Amazon's AWS AI team has introduced RAGChecker, a groundbreaking tool designed to tackle one of AI’s toughest challenges: ensuring AI systems can accurately retrieve and integrate external knowledge into their responses. RAGChecker is a framework for evaluating Retrieval-Augmented Generation (RAG) systems, which combine large language models with external databases to produce more precise and contextually relevant answers. This capability is essential for AI applications in critical fields like legal advice, medical diagnosis, and financial analysis, where up-to-date, factual information is crucial. Unlike traditional metrics, RAGChecker offers a fine-grained, claim-level analysis, enabling a more detailed evaluation of both the retrieval and generation components. This helps identify specific weaknesses in the system, whether in retrieving relevant information or accurately using it. Currently, RAGChecker is used internally at Amazon, with no public release announced. However, if made available, it could revolutionize how enterprises assess and refine their AI systems, offering a significant improvement in accuracy and reliability. #AI #AWS #Innovation #artificialintelligence #startups #founder #businessowners #AIDevelopment #TechInnovation #MachineLearning #TechTrends #AIinBusiness #AIforGood

Investment in AI has surged into the hundreds of billions, with more on the horizon. While giants like OpenAI generate over $3 billion in annual revenue, smaller startups struggle to reach the $100 million mark. The industry is fiercely competitive, and although companies like Microsoft, Google, and Amazon have the capital to invest, the question remains: Will these investments pay off? In my Built In article, I explore the ROI of AI, focusing on a critical metric—the cost of intelligence—that could drive the next technological revolution. If you want to learn more about this topic, my publication is linked in the comments. #AI #ROI #OpenAI

François Chollet's exit from Google after nearly a decade isn't just another corporate shuffle—it's a pivotal moment for the AI industry. As the visionary behind Keras, Chollet has not only transformed deep learning but also challenged the status quo with his advocacy for neuro-symbolic AI. His departure opens the door to new ventures that could redefine how we approach AI. While many have relied on massive data and computational power, Chollet's focus on human-like reasoning and interpretability suggests a shift in paradigm. His upcoming company, although shrouded in mystery, has the potential to spark innovation in a field ripe for evolution. As we witness this transformation, let’s consider: Could the future of AI lie in collaboration rather than competition? Keep an eye on Chollet; his next moves could reshape our understanding of AI and its role in society. The future of intelligent systems might be more promising than we think!