AI Developers: How to Solve the “Garbage In – Garbage Out” Problem

By Socialgist on May 1 2023 on

The Rude Awakening Coming for AI / ML Devs

We’re swimming in an ocean of data. From social posts to reviews, news stories to message boards, the digital landscape provides an unprecedented amount of information. Businesses who leverage this data well are more likely to reach their target audience, build successful products, and optimize their customer experience. 

These same companies are taking note of how the recent advances in generally available artificial intelligence tools like ChatGPT will support everything from copywriting to product development.

Behind ML / AI is a robust dataset that has trained that machine to think, respond – even ideate – like a human. We’re seeing the next wave of innovators entering ML / AI with the energy – and now the resources – to make big things happen. Digital data is the treasure trove for training the next wave of AI and machine learning models. But they’re in for a rude awakening.

Garbage In, Garbage Out 

Assembling and integrating training data comes with its own set of challenges. Web-sourced data can be inconsistent, irrelevant, low-quality, and uniquely formatted, creating problems as the data is streamlined for ingestion by model training systems. And if the data isn’t coherent, the model will follow suit.  

A model trained on irrelevant data decelerates go-to-market and wastes crucial development time when the results are less powerful and less accurate.  

Clean, Relevant, Compliant

For over 20 years, SocialGist has provided clean and compliant data to enterprise companies and analysts. We’re uniquely positioned to deliver the data developers need to train the next generation of machine learning models.

Sourcing training data from SocialGist guarantees: 

  • Relevance: Training data should be closely related to the problem the model is trying to solve. For example, if the model is built to understand customer sentiment on products and services, relevant review data should be used for training. 
  • Cleanliness: High-performing models require data that is free of noise, inconsistencies, and irrelevant information. The data also needs to be easy to integrate into the application. Clean data helps a model learn more efficiently, thus speeding development.
  • Maintenance:  Training data needs to be continually updated and maintained.   

With SocialGist, customers can focus on what they do best: creating cutting-edge ML solutions. We see the demand for quality data expanding as more players enter this space, and our platform is prepared to meet this demand and help the trailblazers create the next wave of solutions that shape the world.

Do What You Do Best. We’ll Cover the Data.

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