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Data is essential when it comes to building machine learning models for business applications. A strong AI strategy is predicated on data that is specific to the business problem a company is trying to solve, as outlined in our recent article: Data Collection and Enhancement Strategies for AI Initiatives in Business. When it comes to executing on that strategy, oftentimes the first thing a company needs to do is collect some or all of the data it will need to build the right machine learning model for its use case.
When it comes to planning an AI initiative, a business will need to determine the method by which to acquire the data necessary to meet their objectives. Data is essential when it comes to succeeding with AI. An effective AI strategy is built on top of data that is specific to the business problem a company is trying to solve.
There’s more to successful AI adoption than picking the right technology. Business leaders should be aware of the technical requirements of the initiative they’re undertaking, and few of those requirements are as important as data.
This week on AI in Industry, we are talking about the ethical consequences of AI in business. If a system were to train itself to act in unethical or legally reprehensible ways, it could take actions such as filtering or making decisions about people in regards to race or gender.
When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data, and limitations. One way executives can judge how successful or appropriate and AI project would be for their company is to examine use cases of businesses that have previously done something similar.
Rather than coming up with completely new processes or products that involve deep learning, companies say they are using this AI technology to expand on functions or tasks that already existed at their organization, according to a new report published by O’Reilly.