The challenge of AI and deep learning systems

One of the biggest challenges of AI is that it is not plug-n-play. You have to customize AI to your business context. A common pattern in AI is mapping an input to an output. For e.g.
  1. input a picture, output 1 if it is a cat, or 0 if it is a dog
  2. input an audio file, output transcript
  3. input Spanish text, output English text
  4. input customer logs, output 1 if customer will churn, or 0 if they will not churn
  5. input transaction logs, output 1 if transaction is fraud, or 0 if it is not
The challenge and also the opportunity of AI is discovering what input and the corresponding output that will fit in your business context.

Maybe your group writes a lot of SQL, perhaps the request for SQL comes as a ticket created in JIRA in plain english then the input in this case will be the english description of the SQL needed, along with the database schema, and the output will be SQL.

Maybe you spend time deciding if an item should be retired from your catalog. Then the input will be item details, item transaction details, customer logs and output will be 1 if retire, 0 if not retire.

Maybe you spend time deciding when to approve a return and when not to, then the input will be the return info (such as item, price, reason for return, etc) and the output will be 1 if return, 0 if not

Maybe you spend time researching 10k SEC documents for the purposes of deciding if you want to invest in this company or not. Then the input will be 10k SEC documents, and the output will be 1 if invest, and 0 if not.

Just to be clear the output does not have to binary, it can be a probability or confidence score. Also the output need not be just one class, it can be multiple classes.

As must now be clear why customizing AI is exactly what will happen over the next few years. We at SublimeAI started with exactly this belief that AI has to be customized and we are constantly searching with your help fascinating business contexts in which AI can be applied.

Many fortune 500 company CEOs have regretted that they wish they developed their internet strategy earlier, or their mobile strategy earlier. We think the same will happen with AI. Fortune 500 Company CEO's are going to regret not having developed their AI strategy in time. Building an AI strategy requires a deep understanding of AI (where we come in) and a deep understanding of your business.

We encourage you to email us at with the business context that comes to your mind after reading this post and we would love to jump on a free consulting call to discuss if AI can indeed help.


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