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Best Kept Secret In AI? Think Huge, Act Tiny

  • 2 mths ago

We were days away from IPO. We had raised $100 million in funding and exploded from a team of 50 in a garage to 600 in 18 months. One million technologists joined our platform. We were the next big deal.

It was the turn of a new millennium. And anything was possible. We were betting on a big deal called the internet. Fast-forward to today. AI is the new internet. Cloud is currency. And “data is the new dollar.”

By the end of this decade, economic output from AI is poised to eclipse the entire economies of China and India combined — nearly $16 trillion.

And yet, to CEOs and technology leaders, the promise of AI can feel at times both overwhelming and underwhelming. Underwhelming given the track record of many AI projects. Overwhelming because leaders are drowning in a sea of data. The sea is deep. And it’s roaring with noise.

A massive paradigm shift is accelerating among artificial intelligence gurus: to solve precision problems, a tiny, precise dataset (as small as 50 images) beats millions of images of noisy data.

All AI is custom. The challenge is, how do you make it systematic and scalable? The answer: good data, not big data.

Andrew Ng, co-founder of Google Brain and named to the Time 100 most influential people, really nailed it at the ScaleUp:AI conference. At its most basic level, AI is simply data + code. The problem is, nearly every company out there is scrambling to fix the code. Engineers are working furiously to write better algorithms. Better algorithms can bring incremental improvement. And yet if you want exponential, scalable gains, you need to focus on the data.

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