Feeding the AI Beast: A Feast Today, Famine Tomorrow?

The data fueling AI boom is running dry

The meteoric rise of models like GPT-3 and ChatGPT seems unstoppable.

But there's a problem - these models are data hungry beasts!

Feeding them is becoming extremely difficult.

Ironically, we're generating more data today than ever before. YouTube videos, smart devices, social media - massive volumes of data created daily.

Yet AI models cannot take full advantage of this firehose of digital information.

Much of it is proprietary or legally off-limits.

And the messiness makes extracting value non-trivial.

So while the world produces ever-growing oceans of data, legal and practical constraints mean model developers cannot access or utilize most of it.

Another irony of the big data age!

Previously mined sources are tapped out

In the past, most training data came from public internet sources.

But we've nearly exhausted these supplies.

Today's models need orders of magnitude more data to satisfy their voracious appetites!

New sources pose big challenges

Finding new sources is proving highly challenging:

• Legal barriers to web scraping

• Logistical nightmares of digitizing offline data

• Licensing and rights clearance at scale nearly impossible

• Governments unwilling to provide unfettered access

Running into the data gathering wall

Between legal obstacles, technical hurdles, and the massive manual effort involved, gathering vastly more training data just isn't feasible.

These data-famished models have hit a wall!

Creativity, not scale, now the key

This bottleneck is forcing a shift - away from pure data scale, towards new techniques:

• Clever architectures and training methods

• Synthetic data generation

• Multimodal transfer learning

• Smarter data sourcing and filtering

With data maxed out, progress must come through ingenuity in model building and training.

Exciting innovations ahead!