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Hands-On Unsupervised Learning Using Python by Ankur A. Patel

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Chapter 14. Conclusion

Artificial intelligence is in the midst of a hype cycle not seen in the tech world since the advent of the internet age 20 years ago.1 However, that does not mean the hype is not warranted or—to some degree—justified.

While the AI and machine learning work in prior decades was mostly theoretical and academic in nature—with few successful commercial applications—the work in this space over the past decade has been much more applied and industry-focused, led by the likes of Google, Facebook, Amazon, Microsoft, and Apple.

The focus on developing machine learning applications for narrowly defined tasks (i.e., weak or narrow AI) rather than on more ambitious tasks (i.e., strong or AGI) has made the field much more attractive to investors that want to achieve good returns on a shorter 7- to 10-year time frame. More attention and capital from investors, in turn, has made the field more successful, both in progress toward narrow AI as well as in laying the building blocks for strong AI.

Of course, capital is not the only catalyst. The rise of big data, the advancements in computer hardware (especially the rise of GPUs, led by Nvidia, for training deep neural networks), and the breakthroughs in algorithm research and development have played equally meaningful roles in contributing to the recent successes of AI.

Like all hype cycles, the current cycle may lead to some disappointment eventually, but so far the progress in the field has astonished many in the science ...

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