The Promises and Pitfalls of Generative AI
The Hype and Investment in Generative AI
The world of artificial intelligence (AI) is buzzing with excitement and optimism. Tech giants like Alphabet and Microsoft have declared AI to be the new electricity or fire, reshaping their entire businesses around it. Venture capital investors have been pouring billions of dollars into the industry, with 50 promising generative AI start-ups raising over $19 billion in funding since 2019. Of these, 11 have achieved unicorn status with valuations over $1 billion.
McKinsey estimates that generative AI could potentially add trillions of dollars in economic value annually across various sectors. This technology has the potential to transform industries ranging from banking to life sciences, creating a new economy every year.
The Skepticism and Hurdles of Generative AI
But what if this excitement and investment are misplaced? Technologist Gary Marcus challenges the prevailing optimism and explores the possibility of a “massive and heartbreaking correction” in generative AI valuations. He questions whether generative AI truly works well and has killer business applications. Marcus, who is skeptical of neural network models, highlights the inherent unreliability of these systems. They often hallucinate or confabulate facts, leading to inaccurate translations and unreliable outputs.
While tech companies are working on improving the contextual understanding of AI systems to reduce errors, Marcus argues that the hallucinations will persist and cannot be fixed using the current methodology. This unreliability poses challenges where a high level of accuracy is crucial, such as in critical systems like defense or healthcare.
Moreover, generative AI systems may also be polluting datasets with misinformation, creating what some refer to as “model collapse.” As these systems generate more imperfect information, training sets become more polluted, leading to a proliferation of nonsensical outputs.
The Investor Perspective
Despite these challenges, investors make three key arguments for the potential profitability of generative AI. Firstly, they argue that even with its imperfections, generative AI can be a valuable productivity tool, accelerating efficiency in various industries. Secondly, they believe that some companies can successfully implement generative AI models to solve real-world problems. Advances in AI enable real-time data analysis, optimizing areas like maritime trade or aircraft engine performance. Lastly, generative AI models may give rise to entirely new services and business models that are currently unimaginable.
However, it is important to acknowledge that while cloud computing vendors and chip makers are currently profiting from the generative AI boom, the future remains uncertain. Many corporate investments in AI may go to waste, and most start-ups may fail. Only time will tell what innovations will emerge and endure.
Looking Beyond the Hype
While the promises and pitfalls of generative AI can seem overwhelming, it is essential to delve deeper and explore related concepts to gain a more comprehensive understanding of its potential.
Generative AI has the power to revolutionize industries and unlock new levels of productivity and innovation. It can automate mundane tasks, enhance data analysis, and offer new insights. For example, generative AI can facilitate personalized marketing campaigns, creative content generation, or virtual assistants that mimic human conversation.
However, it is crucial to approach generative AI with a cautious mindset, acknowledging its limitations and understanding the importance of human oversight. By combining the strengths of AI and human intelligence, businesses can leverage generative AI to drive growth and efficiency.
Key Takeaways:
- Generative AI has attracted significant hype and investment, with trillion-dollar companies and venture capitalists betting on its potential.
- However, skeptics like Gary Marcus question generative AI’s reliability and its suitability for killer business applications.
- The inherent unreliability of generative AI models poses challenges in critical systems and threatens the integrity of datasets.
- Investors argue that generative AI can still be valuable for productivity, problem-solving, and creating new business models.
- Looking beyond the hype, generative AI offers the potential for automation, enhanced data analysis, and innovation across industries.
Summary:
Generative AI has captured the imagination of tech giants and investors alike, fueled by promises of transformative innovation and economic value. However, skepticism and challenges surrounding its reliability and potential misuse persist. While investors argue for its profitability, a cautious approach and understanding of the technology’s limitations are necessary. Generative AI has the potential to revolutionize industries, but only when paired with human oversight and a clear understanding of its boundaries.
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Even by the breathless standards of previous rounds of tech hype, generative AI enthusiasts hyperventilated hard.
Trillion-dollar companies including Alphabet and Microsoft are declaring AI to be the new electricity or fire and are redesigning their entire businesses around it. Never knowingly ignored, venture capital investors have also been pumping money into the industry. Fifty of the most promising generative AI start-ups, identified by CB Insights, they’ve raised more than $19 billion in funding since 2019. Of those, 11 now count as unicorns with valuations over $1 billion.
Even sober clothes a McKinsey estimate that the technology could add between $2.6 trillion and $4.4 trillion in economic value annually in 63 use cases analyzed, ranging from banking to life sciences. In other words, in very rough terms, generative AI could create a new British economy every year (the country’s gross domestic product was $3.1 trillion in 2021).
But what if they are wrong? In a series of provocative posts, technologist Gary Marcus explores the possibility that we could see a “massive and heartbreaking correction” in valuations as investors realize that generative AI doesn’t work very well and lacks killer business applications. “The revenue hasn’t arrived yet and may never arrive,” he writes.
Marco, co-founder of the Center for the Advancement of Trusted AI who testified in US Congress this year, has long been skeptical of the intelligence of neural network models that preceded the latest chatbots, such as OpenAI’s ChatGPT. But it also raises some new truths about generative AI. Take the unreliability of the models themselves. As is now clear to millions of users, one of the biggest drawbacks of technology is that it hallucinates – or confabulates – facts.
In his previous book AI restart, Marcus provides a clear example of how this can happen. Some AI models work like probabilistic machines, predicting answers from data patterns rather than exhibiting reasoning. A native French speaker would understand instinctively Je mange un lawyer pour le déjeuner in the sense of “I eat an avocado for lunch”. But, in its earliest iterations, Google Translate rendered it as “I’m going to eat a lawyer for lunch”. In French, the word lawyer means both avocado and lawyer. Google Translate has chosen the statistically most probable translation, rather than the sensible one.
Tech companies say they’re reducing errors by improving contextual understanding of their systems (Google Translate now accurately translates that French sentence). But Marcus argues that hallucinations will remain a feature, rather than a bug, of generative AI models, unfixable using their current methodology. “There is a fantasy that if you add more data it will work. But you can’t manage to squash the problem with data,” he tells me.
For some users, this inherent unreliability is a deal breaker. This was stated by Craig Martell, Chief AI Officer of the US Department of Defense last week he would have asked for a “five 9” [99.999 per cent] level of accuracy before implementing an AI system. “I can’t have a hallucination that goes ‘Oh yeah, put widget A connected to widget B’ – and it blows up,” she said. Many generative AI systems have placed too high a “cognitive load” on the user to determine what is right or wrong, she added.
Even more troubling is the idea that the content produced by generative AI is polluting the datasets on which future systems will be trained, threatening what some have called “model collapse”. By adding more imperfect information and deliberate misinformation to our knowledge base, generative AI systems are producing yet another “enshittification” of the internet, to use Cory Doctorow’s evocative term. This means that training sets will produce more nonsense, rather than less.
Undeterred, investors typically make three arguments about how to make money with generative AI. Even with its imperfections, they say, it can still be a valuable productivity tool, accelerating the industrialization of efficiency. There are many uses too, ranging from copywriting to call center operations, where a “two 9” level of accuracy is fine.
Second, investors are betting that some companies can implement generative AI models to solve real-world problems. The latest advances in artificial intelligence enable real-time data analysis, says Zuzanna Stamirowska, managing director of French start-up Pathway, helping to optimize maritime trade or the performance of aircraft engines, for example. “We really focus on business use cases,” she says.
Third, generative AI models will enable the creation of new services and business models hitherto unimaginable. During the mass electrification of the economy in the late 19th century, businesses profited from the generation and distribution of electricity. But big fortunes were made later by using electricity to transform ways of making things, like steel, or by inventing entirely new products and services, including household appliances.
For now, it’s only cloud computing vendors and chip makers that are really making money in the generative AI boom. No doubt Marcus will also be right that much of the corporate money invested in technology will go to waste and most startups will fail. But he who knows what new things will be invented and will last? That’s why God invented bubbles.
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