By Nancy Peaslee
Artificial intelligence (AI) and machine learning (ML) are essential concepts to understand as part of a growth model for business. As defined by John McCarthy in 1956, AI involves machines that can perform tasks that are characteristic of human intelligence. AI is a much broader concept than machine learning, which is a way of achieving certain types of intelligence. Some possible tasks that AI can take on is:
- Understanding languages through natural language processing (NLP)
- Mapping trends for searches and results
- Recognizing voices and objects
- Analyzing large amounts of information, such as medical, financial, or meteorological
Are you interested in implementing AI in your organization?
Here are 10 ideas to assist you in developing your AI strategy.
1. Develop a plan.
Determine whether AI will add value. What is your vision? Is AI the right path to success? Does your plan fit within your business goals?
2. Define the problem you would like to solve.
What types of information do you need? Make sure that you have enough available data for analysis.
3. Research compliance requirements.
Are there legal or regulatory requirements based on your data or data collection methods? Be sure you understand data privacy issues.
4. Implement data collection.
Do you have a plan to gather sufficient data? Are your assumptions valid? Have you established accurate correlations?
5. Apply data cleaning and preprocessing.
Are there inconsistencies or noise in your data? This phase also includes combining multiple data sources as well as data reduction.
6. Utilize data wrangling and feature engineering.
Do you understand the data and data mappings? Have you established correlations between the data? Does your transformed data have the capability to solve your problem?
7. Analyze the data.
This data may include Exploratory Data Analysis (EDA), pattern mining, and feature selection. Understand the data you are using. Define the variables and data relationships. This step may include using data visualization to observe the data’s main characteristics.
8. Develop a training dataset for model selection.
A training set is a subset of your data that contains a known output. This allows you to test the model’s predictive abilities and validate the model.
9. Test the dataset subset of the training dataset.
This evaluation is also called unseen data, model validation, or hyperparameter optimization, and includes testing and fine-tuning the ML algorithm that is best aligned to the training set.
10. Deploy your model.
Understanding these basic steps can allow you to better determine the AI/ML strategy for your organization.
Contact us today to learn how our knowledge experts can help you utilize AI to find and integrate information from across your organization for better decision making.