UNBEARABLE LIGHTNESS OF THE AI BUBBLE

The AI investment bubble is much bigger than those around dot-coms or sub-prime assets in the 2000s. Many aspects-from the technology to its finances-are not squaring up

- SATYAJIT DAS

A RTIFICIAL intelligence is trac ing the familiar, weary boomand-bust trajectory identified in 1837 by Lord Overstone of quiescence, improvement, confidence, prosperity, excitement, overtrading, convulsion, pressure, stagnation, and distress.

There are three primary concerns. First, there are doubts about the technology Building on earlier technologies such as neural networks, rule-based expert systems, big data, pattern recognition, and machine learning algorithms, generative AI, the newest iteration, uses large language models (LLMs) trained on massive data sets to create text and imagery. The holy grail is 'singularity", a hypothetical point where machines surpass human intelligence.

It would, in Silicon Valley speak, lead to 'the merge', when humans and machines come together, potentially transforming creativity and technology.

LLMS require enormous quantities of data. Existing firms in online search, sales platforms, and social media platforms can exploit their own troves. This is frequently supplemented by aggres sive and unauthorised scraping of online data, sometimes confidential, leading to litigation around access, compensation, and privacy. In practice, most AI models must rely on incomplete data, which is difficult to clean to ensure accuracy.

Despite massive scaling up of computing power, genAI consistently fails in relatively simple factual tasks due to er rors, biases, and misinformation in datasets. AI models are adept at interpolating answers between things within the data set, but poor at extrapolation. This means they struggle with novel problems and their ability to act autonomously interacting within dynamic environments remains questionable. Cognitive scientists argue that simply scaling up LLMs based on sophisticated pattern-matching built to autocomplete will disappoint. The claimed progress is difficult to measure, as the benchmarks are inconclusive.