As businesses and other organizations undergo digital transformation, they’re faced with a growing tsunami of data that is at once incredibly valuable and increasingly burdensome to collect, process and analyze. New tools and methodologies are needed to manage the vast quantity of data being collected, to mine it for insights and to act on those insights when they’re discovered.
Getting to know AI/ML and why it matters for bussiness?
Artificial intelligence (AI) is a branch of computer science that deals with the creation of intelligent agents, which are systems that can reason, learn, and act autonomously. AI research has been highly successful in developing effective techniques for solving a wide range of problems, from game playing to medical diagnosis.
Machine learning (ML) is a subfield of AI that focuses on the development of algorithms that can learn from data. ML algorithms can be used to make predictions, identify patterns, and make decisions without being explicitly programmed. ML has become an essential tool for many industries, including finance, healthcare, and manufacturing.
Some of the key differences between AI and ML are:
- Scope: AI is a broader field that encompasses ML, as well as other techniques such as expert systems and natural language processing.
- Learning method: AI systems can be programmed with explicit rules, while ML systems learn from data.
- Adaptability: AI systems are typically less adaptable than ML systems, as they are more reliant on pre-programmed rules.
Here are some examples of how AI and ML are being used today:
- Fraud detection: AI and ML are being used to detect fraudulent activity in credit card transactions and other financial data.
- Medical diagnosis: AI and ML are being used to develop algorithms that can help doctors diagnose diseases more accurately.
- Recommendation systems: AI and ML are being used to develop recommendation systems that suggest products or services that users are likely to be interested in.
The difference between machine learning and AI is that machine learning represents one of – but not the only – precursors to creating a narrow AI. Specifically, machine learning is the best and fastest way to create a narrow AI model for the purpose of categorizing data, detecting fraud, recognizing images, or making predictions about the future.
The future of AI and ML is full of potential. These technologies have the ability to revolutionize many aspects of our lives, from the way we work to the way we interact with the world around us.
