Artificial intelligence, machine intelligence and machine learning are often used as if they mean the same thing, which causes confusion. This 60-minute webinar on the artificial intelligence landscape explains what each one is and how they differ.
Machine learning and machine intelligence get less attention than AI, yet they are what make it work. The session covers the principles behind these components, deep learning and neural networks, how data is structured, the methodologies that bring the elements together, and how big data fits in.
You'll also see use cases for each, the limitations of the technology and how it shows up in tools such as ChatGPT, social media and industry.
WHY SHOULD YOU ATTEND?
The aim is an unbiased understanding of AI, machine intelligence, machine learning and deep learning: what the parts are, how they differ and what makes them up. The key takeaway is knowing what AI is and what it is not.
It suits engineers, product managers, technicians, IT staff, C-level, sales and marketing professionals, teachers, students and social media professionals who want a fundamental grasp of the technology.
AREA COVERED
- Artificial intelligence (AI)
- Machine intelligence (MI)
- Machine learning (ML)
- Deep learning and neural networks
- Underlying principles of the components and elements
- Structuring of data
- Use cases
- Methodologies: how the elements flow and come together
- Limitations
- How big data plays into AI
- AI chat tools
LEARNING OBJECTIVES
- Define AI and its related elements, machine learning and machine intelligence
- Identify use cases for each
- Explain how functions such as algorithms and models work
- Describe deep learning and neural networks
- Outline common methodologies and techniques
- Explain the role of big data in AI and how AI chat fits in
WHO WILL BENEFIT?
- Technical - engineers
- Semi-technical – product managers, technicians
- Non–technical – C-level, Sales, and marketing (to gain a fundamental knowledge of the technology)
- Students
- IT individuals
- Teachers
- Social media
The aim is an unbiased understanding of AI, machine intelligence, machine learning and deep learning: what the parts are, how they differ and what makes them up. The key takeaway is knowing what AI is and what it is not.
It suits engineers, product managers, technicians, IT staff, C-level, sales and marketing professionals, teachers, students and social media professionals who want a fundamental grasp of the technology.
- Artificial intelligence (AI)
- Machine intelligence (MI)
- Machine learning (ML)
- Deep learning and neural networks
- Underlying principles of the components and elements
- Structuring of data
- Use cases
- Methodologies: how the elements flow and come together
- Limitations
- How big data plays into AI
- AI chat tools
- Define AI and its related elements, machine learning and machine intelligence
- Identify use cases for each
- Explain how functions such as algorithms and models work
- Describe deep learning and neural networks
- Outline common methodologies and techniques
- Explain the role of big data in AI and how AI chat fits in
- Technical - engineers
- Semi-technical – product managers, technicians
- Non–technical – C-level, Sales, and marketing (to gain a fundamental knowledge of the technology)
- Students
- IT individuals
- Teachers
- Social media