AI is defined as a machine that can sense, reason, act, and adapt.
Data is fundamental: collecting, storing, accessing, and labeling data.
Engaging with Business Executives
If given a chance to meet top business executives (e.g., Steve Jobs, Bill Gates), the key question would be:
"How would you tackle my current business challenge?"
Access to their insights is analogous to having a hard disk full of historical data.
Knowing your goals is crucial for extracting valuable insights.
Importance of Data
Owning data is just the beginning; knowing what's in the data is essential.
There are nontrivial challenges in transforming owned data into actionable knowledge.
The Four Vs of Big Data
Volume:
Challenges in storing data, updating information, querying, and backing up data.
Velocity:
Rate at which new data is generated (e.g., from sensors, programs).
Need for an efficient injection of new data into pipelines.
Variety:
Different sources of data that need to be integrated into AI systems.
Veracity:
Reliability of incoming data; issues with faulty sensors producing unreliable data.
Labeling Data
Essential for supervised learning.
A label indicates the category or class of each data point.
The labeling process can often be manual unless automated methods exist.
Approximately 50% of the time in a project may be spent on data preparation and labeling.
Data Formats
Four main categories of data formats:
Images
Time series (audio, forecasts)
Text (documents, social media posts)
Tabular (spreadsheets, tables)
Knowledge of these categories aids in data handling and solution development.
Reusability of AI Solutions
Solutions developed for one category can often be adapted for another industry (e.g., using retail image recognition technology in airport luggage identification).
Recognizing this potential can enhance efficiency in problem-solving across different sectors.
Conclusion
Understanding AI, data handling, and the nature of business challenges are vital for leveraging technology successfully.
Encouragement to join the next section on machine learning and AI.