The marketing hype surrounding AI broadly — and generative AI (genAI) more specifically — is becoming tiresome. You can’t open an article or watch a news video without running into at least a reference to it. We may be approaching the point at which we stop breathlessly extolling its virtues (and dreading some of its outcomes).
The hype is so extreme that a fall-out, which Gartner describes in its technology hype cycle reports as the “trough of disillusionment,” seems inevitable and might be coming this year. That’s a testament to both genAI’s burgeoning potential and a sign of the technology’s immaturity.
The outlook for deep learning for predictive models and genAI for communication and content generation is bright. But what’s been rarely mentioned amid the marketing blitz of recent months is that the challenges are also formidable.
Machine learning tools are only as good as the data they’re trained with. Companies are finding that the millions of dollars they’ve spent on genAI have yielded lackluster ROI because their data is filled with contradictions, inaccuracies, and omissions. Plus, the hype surrounding the technology makes it difficult to see that many of the claimed benefits reside in the future, not the present.
In short, we’re not all the way there yet, especially with genAI-based chatbots, which have the tendency to “hallucinate
Google, Microsoft, and OpenAI have rushed to develop and release genAI tools, but that haste has caused an exceptional level of immaturity from many tools. Chatbots create content, but staking your company’s reputation on the content they’re able generate right now could be career limiting. Here are some of the ways a genAI chatbot can get into trouble:
Enterprises and biz tech workers should at least be experimenting with machine learning, deep learning, and genAI, but 2024 may not be the time for your company to go all-in. Wait for the fake news and possible disinformation of the election to shake out. Wait for the tools to have their rough edges smoothed out and additional rounds of training. Wait for government regulations to be invoked (or at least until you get a better sense of what they’re aiming to regulate). If productivity is the goal, wait for the promised productivity gains to be realized by others.
GenAI is still the new big thing, but it hasn’t advanced as much as the hype might make you think.
Still confused about the difference between AI, generative AI, machine learning and LLMs? The links below can get you up to speed.
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Artificial intelligence (AI), as defined by Coursera, is an umbrella term for computer software that mimics human cognition to perform complex tasks and learn from them. AI, machine learning, deep learning, and generative AI are sometimes used interchangeably, but they are each distinct terms with separate meanings.
Machine learning is a subfield of AI that uses algorithms trained on data to produce adaptable models that can perform a variety of complex tasks.
Deep learning is a subset of machine learning that uses several layers within neural networks to do some of the most complex ML tasks without any human intervention.
Generative AI, also referred to as genAI, is the technology behind chatbots and other tools. It’s a type of AI that generates images, text, videos, and other media in response to inputted prompts.
Large language model (LLM) is the term for the algorithmic foundation of chatbots like OpenAI’s ChatGPT and Google’s Gemini. An LLM is a computer algorithm that processes natural language inputs and predicts the next word based on what it’s already seen. Then it predicts the next word, and the next word, and so on until its answer is complete.