Deep Learning Startups May Encounter a Gotcha
March 14, 2020
Though fragmented, the deep learning AI market is growing rapidly. Anyone wishing to launch (or invest in) such a firm may want to check out Analytics India Magazine’s article, “Common Pitfalls that the Deep Learning Startups Fail to Recognise.” Writer Sameer Balanganur describes prevalent missteps under these headings: Not Investing Enough in Data and Powerful Processors, Not Accounting for the Cloud Charges, Expensive Data Cleansing, The Edge Cases, and Hiring the Right People.
The part that struck me was this description under Expensive Data Cleansing, as it Illustrates something many fail to understand:
“Training the model nowadays to achieve the state-of-the-art results [still] involves a lot of manual cleaning and labelling of large datasets. And the process of manual cleaning and labelling is expensive and is one of the largest barriers the deep learning startups face. … Although as time passes, the AI systems are moving towards complete automation, which will significantly reduce the cost. However, these AI-based automation applications still need human intervention for years to come. Even if there is full automation achieved, it’s not clear how much the margin of cost and efficiency will improve, so this becomes a matter of whether one should invest towards processes like drift learning and active learning to enhance the ability.
We noted:
“Not only expensive, the human intervention sometimes hinders the system’s creativity, but they might also do it by selecting what is essential for an algorithm to process or not using deep learning for a problem it can easily solve. Many times, deep learning is seen as overkill for many problems. The costs incurred by human intervention and cloud are interdependent. Reducing one means an increase in another.”
AI investment could be quite profitable, if one considers carefully. As always, look before you leap. See the write-up for more details.
Cynthia Murrell, March 14, 2020
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