5 reasons why your business is struggling to use deep learning

So, you’ve heard the dazzling sales pitch on deep learning (DL) and are wondering whether it actually delivers the promised business benefits.

In a previous article, I wrote a simple introduction to DL, a technology that seems to solve every problem.

But what happens when the rubber hits the road?

At Gramener AI Labs, we’ve been studying advances in deep learning and translating them into specific projects that map to client problems. I’ll share some of our learnings from implementing DL solutions over the past year. It’s been a mixed bag, with successes and setbacks.

Here are five reasons why DL projects come to a screeching halt.

1. Expectations bordering on science fiction

Yes, AI is fast becoming a reality, with self-driving cars, drones delivering pizzas, and machines reading brain signals. But many of these are still in research labs and work only under carefully curated scenarios.

There’s a thin line between what’s production-ready and where it’s still a stretch of the imagination. Businesses often misread this, and teams wade deep into the tech.

This is where businesses can experience AI disenchantment, prompting them to become over-cautious and take many steps back.

With some due diligence, the DL use cases that are business-ready must be identified. One can be ambitious and push boundaries, but the key is to under-promise and over-deliver.

2. A lack of data to satiate the giant’s appetite

Performance of analytics techniques with data volume / Photo credit: Andrew Ng

Analytics delivers magic because of data, not in spite of its absence. And DL does not solve the festering challenge of data unavailability. If anything, DL’s appetite for data is all the more insatiable.

3. A lot of unlabeled training data

4. The cost-benefit tradeoff

5. Creepy insights

Summary