It’s no surprise that defining these two fast growing, all-inclusive techniques is difficult. I have yet to see a widely agreed upon definition for data science and artificial intelligence is increasing in application and effectiveness exponentially. Educba.com, Asia’s largest online learning platform, came up with the most clear cut comparative definition: “Data Science is the collection and curating of mass data for analysis whereas Artificial Intelligence is implementing this data in a machine for understanding this data.”
Educba.com goes on to make a number of interesting comparisons:
- Data Science utilizes statistical techniques for design and development while Artificial Intelligence uses algorithms for design and development.
- Data Science is statistical learning whereas Artificial Intelligence is machine learning.
- Data Science observes a pattern in data for decision making whereas Artificial Intelligence uses an intelligence report for decision making.
- Data Science typically has a medium level of data processing for data manipulation while Artificial Intelligence can process a higher order of scientific data for manipulation.
- Data Science can present data in a variety of graphical formats while Artificial Intelligence uses algorithms for network node presentation.
Cloudfront.net in their Career Guide for Artificial Intelligence and Machine Learning gives examples by industry of artificial intelligence applications already happening.
- Healthcare – robot-assisted surgery, virtual nursing assistants, virtual administrative workflow assistants, dosage error detection, clinical trial participant identifier and preliminary diagnosis.
- Entertainment – Streaming channels recommending content based on prior activity and behavior, interactive gaming experience and real-time analysis of on-field action in sports.
- Banking and Finance – Detection of money laundering patterns, automated AI systems account for 70% of trading today, fraud detection, chatbots and virtual assistants for better customer service.
- Marketing – Smart content curation relevant to customer receiving it, voice search, programmatic media buying, propensity modeling, machine learning on historical ads that performed best and dynamic pricing.
- Retail and E-commerce – Image searches, raising customer satisfaction, engagement and loyalty, manage inventories, improve CRM and develop better sales process.
- Manufacturing – Increased productivity, environmental friendliness and quality of life for employees and company, quality checks, maintenance and creating more reliable designs and layouts for plants and processes.
Why is this important to know? You know how hot the Data Scientist career path has been over the past few years. For 2020 the Artificial Intelligence Specialist was #1 on LinkedIn’s 2020 Emerging Jobs Report. AI Specialists have seen a 74% job growth annually for the past five years. By 2020 AI will create 2.3 million jobs and eliminate 1.8 million jobs. PwC reports that AI could add as much as $15.7 trillion to the global economy by 2030.