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- 🤖 Is AI just “marketing hype”?
Good morning and welcome to the latest edition of neonpulse!
Today, we’re talking about whether AI is just marketing hype. Because is it as good as we think, or all the people behind it just selling it very well…?
Is AI just “marketing hype”?
AI has undoubtedly moved beyond the confines of tech jargon and is now an everyday term. However, as AI's popularity soars to unprecedented heights, cautionary voices are reminding consumers and investors to tread carefully amidst the hype.
One such voice is Duke University professor Sultan Meghji, formerly the Chief Innovation Officer of the Federal Deposit Insurance Corporation (FDIC). He warns against getting carried away by the glossy marketing campaigns and lofty promises surrounding AI.
Meghji says that a substantial portion of the current AI landscape is driven more by marketing teams than actual groundbreaking technologies. While the hype cycle is nothing new in the tech world, AI seems to have taken the crown for one of the most sensationalized fields.
Numerous glittering logos, captivating press releases, and heated social media discussions have dominated the AI narrative, overshadowing the reality of its current applications, according to Meghji.
Yet, he is not a skeptic; he firmly believes that AI will eventually prove to be transformative.
However, he contends that the most significant impacts of AI will not necessarily make sensational headlines. Instead, the initial wave of AI developments is more likely to focus on streamlining back-office processes and other less glamorous but equally crucial tasks.
The issue, as Meghji highlights, lies in the tendency to overlook the less dramatic advancements in AI. Major tech companies like Alphabet and Microsoft have enthusiastically embraced the AI craze, plunging headfirst into its potential. Conversely, companies like Apple have adopted a more measured and engineering-centric approach.
Meghji argues that Apple's cautious strategy is unsurprising, given its massive consumer base and the complex infrastructure needed to accommodate sudden surges in AI usage.
However, while Apple's approach may be an exception, the larger AI conversation seems to be consumed by extreme scenarios, both positive and negative. In contrast, the real technological challenges that will dictate AI's success or failure are far more mundane. One such challenge, especially in the case of large language models like ChatGPT, is the meticulous curation of training data.
Meghji explains that many AI systems, including LLMs, heavily rely on publicly available internet data for training. Unfortunately, not all internet data is of high quality, and AI systems may inadvertently learn from unreliable or biased sources. Moreover, as AI consumes vast amounts of internet data, it may eventually start generating its own data, leading to a feedback loop that could be detrimental to the system's performance.
The key, according to Meghji, is to be extremely cautious about the data used to train AI models. Once AI reaches a point where its returns diminish, where it "stops getting smarter", it becomes essential to take a step back and reassess the training process.
Do you agree with Meghji? |
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