AI rhetoric is divisive and therefore one camp argues that current technology is excellent at patterns but narrow, brittle, and prone to abuse. We exaggerate "intelligence," underestimate human cognition, and place unwarranted faith in opaque algorithms, thus we need to reassess our approach.
The other camp views AI as a metaphysical achievement, combining quantum physics and prophecy to promote adoption and morality, but it is motivating for some, and offering more than what current technology delivers, so it is crucial to separate fact from fiction.
Result: Policy zigzags, product turmoil, and teams gambling on hype rather than evidence, and this is because we often prioritize hype over substance.
My plea is for epistemic modesty, and in practice, this entails approaching science as methodology, not miracle, and making decisions based on evidence, not intuition, so we can develop more effective solutions.
· Approach science as methodology, not miracle, and make decisions based on evidence, not intuition, because this is essential for progress.
· Separate representation from reasoning, and AI can generate meaning without comprehension, thus we need to be aware of its limitations.
· Inform all teams about ethics, and bias, adversarial attacks, and data lineage are genuine concerns, not footnotes, so we must address them properly.
· Demystify language models, and communicate limitations and shortcomings alongside achievements, therefore we can have a more nuanced understanding.
· Balance vision with stewardship, and think big, deploy securely, because this is vital for success.
As leaders, you establish the culture, and you should prefer clarity over buzz, skepticism over cynicism, and confidence over arrogance, so you can create a positive and productive environment.
The future of AI is not only determined by technological breakthroughs, but it is also influenced by the stories we tell - within our companies and society, and thus let's create better stories, because they can inspire and motivate us.
Call to action question: How does your team reconcile the hype around AI with reality, and what's successful - and what isn't, so we can learn from each other's experiences?
#AI #MachineLearning #Leadership #EthicsInAI #ProductManagement #EngineeringLeadership #LLMs
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