DeepSeek has ripped away AI’s veil of mystique. That’s the real reason the tech bros fear it; The Observer via The Guardian, February 2, 2025

 Kenan Malik, The Observer via The Guardian ; DeepSeek has ripped away AI’s veil of mystique. That’s the real reason the tech bros fear it

"DeepSeek, sponsored by a Chinese hedge fund, is a notable achievement. Technically, though, it is no advance on large language models (LLMs) that already exist. It is neither faster nor “cleverer” than OpenAI’s ChatGPT or Anthropic’s Claude and just as prone to “hallucinations” – the tendency, exhibited by all LLMs, to give false answers or to make up “facts” to fill gaps in its data. According to NewsGuard, a rating system for news and information websites, DeepSeek’s chatbot made false claims 30% of the time and gave no answers to 53% of questions, compared with 40% and 22% respectively for the 10 leading chatbots in NewsGuard’s most recent audit.

The figures expose the profound unreliability of all LLMs. DeepSeek’s particularly high non-response rate is likely to be the product of its censoriousness; it refuses to provide answers on any issue that China finds sensitive or about which it wants facts restricted, whether Tiananmen Square or Taiwan...

Nevertheless, for all the pushback, each time one fantasy prediction fails to materialise, another takes its place. Such claims derive less from technological possibilities than from political and economic needs. While AI technology has provided hugely important tools, capable of surpassing humans in specific fields, from the solving of mathematical problems to the recognition of disease patterns, the business model depends on hype. It is the hype that drives the billion-dollar investment and buys political influence, including a seat at the presidential inauguration."

Less Noise but More Money in Data Science; New York Times, 4/28/15

Steve Lohr, New York Times; Less Noise but More Money in Data Science:

"There is an apparent contradiction between the buoyant job market for big data practitioners and Gartner’s judgment that, on the perception scale, big data has moved from high expectations to what Gartner calls the “trough of disillusionment.” But, in fact, it fits a familiar pattern of technology absorption and use. Significant new technologies always take time to move into the mainstream as people and organizations learn to exploit them. It takes years.

The classic study of the phenomenon, “The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox,” by Paul David, an economic historian at Stanford University, was published in 1990. In it, Mr. David noted, the electric motor was introduced in the early 1880s, but its real payoff in productivity was not evident until the 1920s. It took that long for businesses to reorganize work around the industrial production line, the efficiency breakthrough of its day, made possible by the electric motor.

Similarly, it took a while for personal computers and the Internet to deliver big gains. And so too for big data, which harnesses computing, modern digital data and the software tools of artificial intelligence.

A report this week from Forrester Research described the challenge ahead. “Businesses are drowning in data but starving for insights,” the report began. “Worse, they have no systematic way to turn data into action.”"