Making sense of AI from an Executives point of view
Next big wave will be Artificial Intelligence. Nowadays, a lot of enterprises or startups are claiming themselves as AI technology companies or AI driven companies. According to CBINSIGHTS, since 2010, there have been 635 AI acquisitions.
Being an executive or part of the management team, how can we prepare for catching this big wave? If you don’t want to miss this opportunity, please follow me and understand and learn what you should do.
My name is Billy Tang, one of the executives in a famous mobile game company now. I want to share my experiences on how to apply Machine Learning in your business especially for the role of executives.
My background is established two pioneer biometric startups in Hong Kong in 1998. The first focus was fingerprint recognition and the second one was face recognition. All our founders were research students/assistants in universities, especially for myself graduation in Mathematics with Scientific Computation for fluid mechanics and computer science.
I started my first technology company in the wave of Internet. We built fingerprint recognition software in an age where there were no iPhone, no fingerprint sensor and technology embedded in your phones.
After the fingerprint company was acquired, we moved our focus to another biometric technology — face recognition. It’s not hardware dependency. About face recognition, there are two core processes, 1) Face detection (identifies/extract all faces from an image) and 2) Face recognition (matches two faces). We used machine learning technologies on these two processes by different training datasets.
With the experience from these two companies, I have a healthy understanding of how ML can be used in business.
6 Steps to Apply Machine Learning in Your Business
The following steps are all going to be their own articles over the course of the next 6 weeks. Please clap, like, or comment on this article to give me the motivation for the next articles 🙂
Machine Learning is a subset of Artificial Intelligence. ML is a predefined programming model which is trained by a huge number of data to make predictions. ML can help you to automate daily human processes and make a decision/judgement.
Step 2: Study your business processes and Identify which processes can be ML-enabled (More is coming..)
Before applying ML, you have to study all your internal business processes.
- Which process is human-intensive?
- Which process is highly repeatable?
- Which process needs human to review a large amount of data?
Step 3: Collect data, Extract data features (More is coming..)
Data Collection and Feature Extraction are the keys of Machine Learning. Best practice is storing all data in a database for future better data analysis and management.
Step 4: Find the best model (More is coming..)
Now, you have training data and then run different models and tests to find the best model based on the training data.
Step 5: Verify the accuracy of the model (More is coming..)
Whenever you work out a model, you have to find a way to verify its accuracy.
Step 6: Measure the ROI (More is coming..)
The last and most important step is to measure the ROI of whole Machine Learning implementation.