While data science, machine learning and AI have affinities and support each other in analytics applications and other use cases, their concepts, goals, and methods differ in significant ways. To further differentiate between them, consider these lists of some of their key attributes.
- focuses on extracting information needles from data haystacks to aid in decision-making and planning.
- is applicable to a wide range of business issues and problems through descriptive, predictive, and prescriptive analytics applications.
- deals with data at a small scale up through very large data sets.
- uses statistics, mathematics, data wrangling, big data analytics, machine learning and various other methods to answer analytics questions.
- focuses on providing a means for algorithms and systems to learn from experience with data and use that experience to improve over time.
- learns by examining data sets rather than explicit programming, which makes use of data science methods, techniques, and tools a key asset.
- can be done through supervised, unsupervised or reinforcement learning approaches; and
- supports artificial intelligence uses, especially narrow AI applications that handle specific tasks.
- focuses on giving machines cognitive and intellectual capabilities like those of humans.
- encompasses a collection of intelligence concepts, including elements of perception, planning and prediction.
- is capable of augmenting or replacing humans in specific tasks and workflows; and
- currently doesn't address key aspects of human intelligence, such as commonsense understanding, applying knowledge from one context to another, adapting to change and displaying sentience and awareness.
These are some of the core attributes of data science, machine learning and AI.
How data science, machine learning and AI can be combined
The business value of data science on its own is significant. Combining it with machine learning adds even more potential to generate valuable insights from ever-growing pools of data. Used together, data science and machine learning also drive a variety of narrow AI applications and might eventually solve the challenge of general AI.
Here are some specific examples of how organizations are combining data science, machine learning and AI to great effect:
- predictive analytics applications that forecast customer behavior, business trends and events based on analysis of constantly changing data sets.
- conversational AI systems that can engage in highly interactive communications with customers, users, patients, and other individuals.
- anomaly detection systems that underpin adaptive cybersecurity and fraud detection processes to help organizations respond to continually evolving threats; and
- hyper-personalization systems that enable targeted advertising, product recommendations, financial guidance and medical care, plus other personalized offerings to customers.
While data science, machine learning and AI are separate concepts that individually offer powerful capabilities, using them together is transforming the way we manage organizations and business operations -- and how we live, work, and interact with the world around us.