Overcome artificial intelligence challenges and embrace data science as a way to get value from AI and machine learning.
You have data, and you have questions to answer and problems to solve. How do you go about using your data to answer those questions and solve those problems? Due to the power and popularity of big data, machine learning (ML), and artificial intelligence (AI), many organizations leap to the conclusion that choosing machine learning is the best approach. However, older, less sophisticated options may deliver better results, depending on the purpose. Sometimes, a spreadsheet or database program is all you need.
The following is a list of options along with suggestions of when each option may be most appropriate for any given data product:
When you're trying to decide between machine learning and an expert system, ask the following question: Does the task require sequential reasoning or pattern matching? If it requires sequential reasoning and the task can be mapped out, go with an expert system. If it requires pattern matching, either to make a prediction or to help uncover hidden meaning in the data, machine learning is probably best.
Prior to deciding which approach is the best match for the problem you're trying to solve or the question you're trying to answer, consult your data science team. Other people on the team may be able to offer valuable insights based on their unique perspectives and training. Encourage your team to ask questions, so they begin to develop an exploratory mindset. Team members should challenge one another's ideas and recommendations, so, together, the team can choose the best approach. (During this process, you may even discover that the question or problem you have identified is not the one you should be seeking to answer or solve. Instead, there may be a more compelling path to explore.)
Keep in mind that two distinctly different approaches may be effective in answering the question or solving the problem, and that a combination of approaches (an ensemble) may be the best approach. If two different approaches seem to be equally effective, opt for the easiest, most cost-effective option.
What is important is that you and your data science team carefully consider the different approaches before starting your work. Choosing the right approach and the right tools will make your job that much easier and deliver superior results.
Overcome artificial intelligence challenges and embrace data science as a way to get value from AI and machine learning.
In AI machines learn by looking through your data using machine learning algorithms.
Deep learning is a machine learning technique that creates an artificial neural network that is many layers "deep."