The AI hype has progressed massively over the past year, yet the expectations seem to be that we should solve all of them today or maybe yesterday already!
But can we just pause for a moment? AI — as of now — is “just another approach to automation.” Automation is not new, and it's not yet to be "solved."
And here’s the thing – we all know that GPT-3 was vastly better than GPT-2. And we all know that GPT-4 (released thirteen months ago) was vastly better than GPT-3. But what has happened since?
I could be persuaded that on some measures there was a doubling of capabilities for some set of months in 2020-2023, but I don’t see that case at all for the last 13 months.
Instead, I see numerous signs that we have reached a period of diminishing returns.
— Gary Marcus on AI
There's definitely some discussion about AI progress slowing down or running into challenges. Here are a couple of things to consider:
Hype vs. reality: There's been a lot of excitement about AI, and some experts argue that it's outpaced actual progress1. This can lead to disappointment when people expect superhuman AI, and we're still working on getting machines to understand complex concepts.
Technical hurdles: Deep learning, a powerful technique in AI, may be limited in certain tasks. For example, AI systems might struggle with open-ended problems that require reasoning and understanding the real world2.
However, it's important to remember that AI research is ongoing. Even if there are setbacks, researchers are constantly exploring new approaches and applications. So, while the hype might settle down, progress in AI is likely to continue.
Here are three key takeaways for decision-makers regarding AI hype:
Focus on AI as a tool, not a silver bullet: AI can be incredibly powerful, but it's important to understand its limitations. Don't expect AI to solve every problem or make perfect decisions on its own. Instead, AI can be seen as a tool that can augment human decision-making by providing data analysis, automation, and new perspectives.
Prioritize clear goals and well-defined problems: AI works best when the task is clearly defined and the data is high-quality. Focus on using AI for tasks with measurable goals and avoid getting caught up in the hype of AI for complex, subjective areas.
Be aware of potential bias and fairness issues: AI systems can inherit biases from the data they're trained on. Decision makers should be aware of these potential biases and take steps to mitigate them to ensure fair and ethical use of AI.
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🚨❓ Does the AI hype hit a wall?
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The AI hype has progressed massively over the past year, yet the expectations seem to be that we should solve all of them today or maybe yesterday already!
But can we just pause for a moment? AI — as of now — is “just another approach to automation.” Automation is not new, and it's not yet to be "solved."
There's definitely some discussion about AI progress slowing down or running into challenges. Here are a couple of things to consider:
Hype vs. reality: There's been a lot of excitement about AI, and some experts argue that it's outpaced actual progress1. This can lead to disappointment when people expect superhuman AI, and we're still working on getting machines to understand complex concepts.
Technical hurdles: Deep learning, a powerful technique in AI, may be limited in certain tasks. For example, AI systems might struggle with open-ended problems that require reasoning and understanding the real world2.
However, it's important to remember that AI research is ongoing. Even if there are setbacks, researchers are constantly exploring new approaches and applications. So, while the hype might settle down, progress in AI is likely to continue.
Here are three key takeaways for decision-makers regarding AI hype:
Focus on AI as a tool, not a silver bullet: AI can be incredibly powerful, but it's important to understand its limitations. Don't expect AI to solve every problem or make perfect decisions on its own. Instead, AI can be seen as a tool that can augment human decision-making by providing data analysis, automation, and new perspectives.
Prioritize clear goals and well-defined problems: AI works best when the task is clearly defined and the data is high-quality. Focus on using AI for tasks with measurable goals and avoid getting caught up in the hype of AI for complex, subjective areas.
Be aware of potential bias and fairness issues: AI systems can inherit biases from the data they're trained on. Decision makers should be aware of these potential biases and take steps to mitigate them to ensure fair and ethical use of AI.
Does the AI hype hit a wall?
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