Going into 2024, generative AI technology has transitioned from a shiny new solution to an everyday part of life. The question most people want answered today is no longer, “What is generative AI?” The typical digital citizen has already experimented with GenAI tools, and a fair number of us are using them on a daily basis.
Now, what folks really want to know is what’s coming next in the realm of GenAI. How will it evolve beyond its current strengths and limitations? How far will it really go in changing the way we work and think? To what extent do regulators need to impose guardrails around GenAI technology?
I’ve spent months in the trenches helping my company build a tool that integrates generative AI into cloud applications. My experience with GenAI runs deeper than chatting with ChatGPT or asking GitHub Copilot to write some code. I’ve actually seen how the GenAI sausage is made, and I’ve developed some strong perspectives on where generative AI stands today and what’s coming next. Here are my thoughts.
4 Generative AI Issues to Stop Worrying About
- Whether we’re close to achieving artificial general intelligence.
- AI hallucinations.
- Slapping as many regulations on AI as possible.
- AI stealing our jobs.
The Debate Around AGI Misses the Point
There has been a lot of chatter lately about artificial general intelligence, which is an AI model or tool capable of simulating all facets of human intelligence. Some folks have even speculated that the drama in late 2023 at OpenAI stemmed from the company’s having achieved AGI, although there’s no hard evidence that this actually happened.
To me, asking how close we are to AGI is not the right question. The answers will always vary because there are different takes on what exactly counts as AGI. I think we’re far from true AGI because large language models are not capable of reasoning, and I consider reasoning to be a critical component of AGI. But a model incapable of basic reasoning could still count as AGI.
Debates like those, though, are mostly beside the point of what matters in practice. What we should really do is analyze AI solutions based on how useful they actually are to the people they are intended to serve, not how closely they resemble AGI, however we choose to define it. It doesn’t really matter whether a given tool is capable of AGI or not if it’s serving its intended purpose well.
The debate about AGI, although interesting from an intellectual standpoint, misses the point about what matters to actual people. We should fixate less on achieving AGI and more on improving the quality of the AI solutions we already have.
AI Hallucinations Aren’t Always Bad
Ask most folks about the shortcomings of GenAI, and one of the first things they’re likely to mention are hallucinations. Hallucinations happen when GenAI models produce information that is false.
AI hallucinations are a problem if people take the resulting data as fact, but they’re not always a bad thing. On the contrary, hallucinations are an important part of the ability of AI models to generate novel stories or ideas. Sometimes, you do want your model to make stuff up if your goal is to get it to say something no one has said before.
Rather than seeking to prevent hallucinations, AI developers should focus on controlling them. AI models incapable of hallucinating would be a bad thing because they’d never say anything original. As long as users can reliably control when a model hallucinates and when it presents only true information, they can leverage it to suit varying needs.
Strict Regulations Risk Hindering Innovation
GenAI regulations remain fluid. There has been much discussion about how to regulate GenAI models, but to date, very few regulatory frameworks have actually appeared. The best path forward on the regulatory front is to ensure that regulations prevent harmful use of AI technologies, while simultaneously keeping the door open for new inventions and innovations.
To do this, regulations should focus on specific models rather than concepts or practices. Policies that categorically forbid a certain type of AI development or prevent AI from being used in certain contexts run the risk of strangling innovation. But if a model already exists and we know its capabilities and limitations, it makes sense to regulate what the model is and is not allowed to do.
AI Will Complement Jobs, Not Threaten Them.
Worries that humans will lose jobs to AI are understandable given the powerful capabilities of GenAI technology. AI can already do many things faster and more effectively than humans, and it’s only going to get better with time.
But that doesn’t mean we should resign ourselves to a future where human workers are irrelevant. Instead, we should focus on upskilling humans so that they can work more effectively with help from AI. AI will make some types of jobs irrelevant, but it also creates opportunities for many workers to become much more efficient.
Smart employees should focus their energy and skills on learning how to use AI tools to become better at doing things that AI can’t do on its own. As long as enough people take that approach — as opposed to assuming that AI is ushering in a dystopian future where humans serve no useful purpose — AI will become a net benefit for workers.
Looking toward the future, we should focus on improving the AI tools we already have and not being to hasty with new regulations. AI hallucinations aren’t inherently bad and energy spent worrying about AI threatening our jobs would be better used learning how to use AI to improve our work processes.
