It may be a buzzword, but does funding AI require a sober approach?

Earlier this month, Amazon made headlines for retiring “Just Check Out.” This technology was introduced in Amazon Fresh in the US in 2016. Customers can simply add their purchased items to the cart and skip checkout if they scan her QR code upon entering the store. This seemed noticeable as everything seemed automated. Over time, flaws began to appear. The wait time for the receipt was long. And sometimes people receive incorrect receipts. It turns out that no technology was actually in place, just a thousand or so Indian employees monitoring people and their purchases with cameras. This was another way to outsource checkout to a cheaper cost center, such as IT, a few years ago.

Amazon has since come clean and introduced more advanced technology. But a Bangalore-based public policy expert watching India's situation from the ringside was not at all surprised. “AI is like magic pixie dust. One robot advisory firm I contacted said they were using AI, but when I spoke to someone within the organization, they said, He laughed and said all he had was a spreadsheet.''He declined to go on the record, citing his affiliation.

So, what should we make of the Amazon story and what public policy experts are testifying to? AI's roots can be traced back to the 1940s, when visionaries like Alan Turing Back in the day, I was pondering the possibility of machines exhibiting intelligent behavior. Early milestones include the development of neural networks inspired by the structure and function of the human brain. However, these early efforts were limited by computational power and an early understanding of how to train these networks.

The following decades were a period of both excitement and disillusionment. The 1960s saw a surge in AI research funding, but then the “AI winter” of the 1970s and his 1980s occurred, when technology limitations and unrealistic expectations led to a significant decline in research. But the AI ​​embers never really went out. Advances in computer science and the explosion of available data in the digital age revitalized the field in the late 20th century.

An important turning point occurred with the advent of machine learning (ML), a subfield of AI that allows algorithms to “learn” from data without explicit programming. Technologies such as deep learning, which uses artificial neural networks with multiple layers, have further improved capabilities. These advances have transformed AI from a theoretical concept to a practical tool. We've also improved accessibility. Most of us can easily use applications including ChatGPT, Gemini, Grok, and chatbots and virtual assistants.

The current situation can be compared to the early days of the Internet, when basic websites were hailed as revolutionary. Human-level AI is still on the distant horizon. But this is not to suggest that the current generation of tools does not have the potential to revolutionize fields ranging from medicine to finance. No one knows yet when that leap will occur.

That's why the Bangalore-based public policy expert is proposing a sober approach to AI. “Every technology has a hype cycle. Fraudsters exploit investors’ fear of missing out (FOMO) and use buzzwords to raise money. , he says, are keen to brand themselves as bastions of AI innovation. But beneath the glossy surface of AI lies a complex reality. Much of what is touted as intelligence is not true intelligence but clever adaptations of established technologies.

But investor Krishna Jha is optimistic. Jha, who founded the technology startup ITFinity, which was sold to Infosys spinoff OnMobile, said there is no need to be overly cautious about investing in AI. “At this stage, the limitations of AI are well known and to be expected. At the same time, the advances in technology are unprecedented and will give us more reliable use cases in the coming years. Currently. and, as expected in the future, technology is indistinguishable from magic.”