Artificial Intelligence, Machine Intelligence, and Machine Learning are hot buzzwords today and they are, sometimes used interchangeably.
The perception that they are the same often leads to some confusion. But, under the covers, they are different. ML and MI do not get as much attention as AI, yet they are the underlying enablers of it. As they evolve, the differences will become more obvious and this webinar will unpack not only AI but MI and ML as well.
This presentation takes a look at these platforms, what they are, and how they differ. Their infusion into platforms such as ChapGPT, social media, industry, and societal segments change the landscape as significantly as the splitting of the atom.
WHY SHOULD YOU ATTEND?
To give the attendee an unbiased understanding of the AI/MI/ML and the deep learning landscape. This is a semi-deep dive into the elements of AI, what they are, how they differ, and what components make it up. The key takeaway is to understand what it is, and what it is not.
AREA COVERED
- AI
- MI
- ML
- Deep learning
- Underlying principles of the components and elements
- Structuring of data
- Use cases
- Methodologies (how things flow and how the various elements come together)
- Limitations
- How big data plays into it
LEARNING OBJECTIVES
- Defining AI and its tangential elements (ML and MI)
- Use cases for each
- How are functions (algorithms, models)
- Deep learning
- Methodologies and techniques
- Neural networks
- The AI case for big data
- AI Chat
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
To give the attendee an unbiased understanding of the AI/MI/ML and the deep learning landscape. This is a semi-deep dive into the elements of AI, what they are, how they differ, and what components make it up. The key takeaway is to understand what it is, and what it is not.
- AI
- MI
- ML
- Deep learning
- Underlying principles of the components and elements
- Structuring of data
- Use cases
- Methodologies (how things flow and how the various elements come together)
- Limitations
- How big data plays into it
- Defining AI and its tangential elements (ML and MI)
- Use cases for each
- How are functions (algorithms, models)
- Deep learning
- Methodologies and techniques
- Neural networks
- The AI case for big data
- AI Chat
- 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