The Catch 22 problem holding back #AI application adoption  ...

The Catch 22 problem holding back AI application adoption  ...

Last week, there was an interesting report in the MIT technology review that Artificial Intelligence can help construction industry to help see .... According to the article, a construction site is a dangerous place to work, with a fatal accident rate five times higher than that of any other industry.


This is a real problem and if you are not in the AI industry .. this solution can sound magical

However, if you are familiar with how AI applications work – then the solution a bit more prosaic ..


Essentially, its an application of Computer vision to create a model based on the working of people at a construction site to capture the norm. This helps to detect the anomalies (ex: missing gloves or helmets). Doing so, leads to better safety – which is an issue in the construction industry. Based on current AI technologies and algorithms – this is not a big problem to solve


Apart from the issue of workplace surveillance (which I will not cover here) – there is a bigger problem

According to the article, “Suffolk, a construction giant based in Boston, has been developing the system for more than a year in collaboration with SmartVid, a computer vision company in the same city. Earlier this year, the company persuaded several of its competitors to join a consortium that would share data to improve the technology.”

Persuading your competition to share data is the big limitation


The tech is not the bottleneck here.


It’s the data.


And you need a lot more ‘incident data’  - from your competitors - so that you can train your models


The same problem was highlighted in the autonomous vehicles industry – no one company has enough data to train their algorithms.


And the best source of that data is your competitors.


Hence, the Catch 22 situation holding up the deployment of AI applications.


Other industries like Air traffic control and critical infrastructure have addressed this problem of sharing threat information – but that’s still not the same as sharing information with commercial competitors (because airports and public information is owned by governments – local or federal – v.s. private companies)

Who do we share data with?

How long?

Under what guidelines?

Can a group of companies so sharing data create a barrier for everyone else in future?

Should there be more regulation?

There are no accurate answers yet

But the problem is real.

Sharing data across commercial competitors to develop better AI algorithms is the Catch 22 problem holding back AI application adoption  ...


Image source: The Catch 22 book cover



Views: 595


You need to be a member of Data Science Central to add comments!

Join Data Science Central

© 2021   TechTarget, Inc.   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service