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
Speaker Profile
Ernest Worthman is an analyst and SME in several segments of high technology and the VP of content and Technology for AGL Information and Technology, LLC.He is also a nationally and internationally published technical editor/writer for wireless, semiconductor, cybersecurity, and other industries and regularly speaks at industry events.As well, he is a guest lecturer at Colorado State University’s College of Electrical Engineering.Ernest has over 25 years of experience in high-tech print and online publishing. He has held several editorial positions across several high-tech publications including Semiconductor Engineering’s cybersecurity and Internet of Everything/Everyone (IoX) channels, Editor of RF Design, Editorial Director …
Upcoming Webinars
Surviving and Thriving Organizational Change and Loss: The …
Impact Assessment and Risk Management for Change Control
Excel Deep Dive: Advanced Tips & Techniques – A 3-hour Work…
How to Write Effective Audit Observations: The Principles f…
Coming Soon - New Minimum Salary Levels for Exempt Employee…
Marijuana: Compliance and Safety in the Workplace
FDA Regulation of Artificial Intelligence/ Machine Learning
Stressed Out: How to Handle Conflict, Difficult People and …
2025 Top Employment Regulations That Will Impact Employers!
How to Handle Workplace Conversations Around Politics and R…
Data Integrity: Compliance with 21 CFR Part 11, SaaS-Cloud,…
How to Give Corrective Feedback: The CARE Model - Eliminati…
Improving Employee Engagement & Retention Through Stay Inte…
SOPs - How to Write Them to Satisfy those Inspectors
Why EBITDA Doesn't Spell Cash Flow and What Does
With Mandatory Paid Leave Gaining Ground Is It Time To Do A…
Marketing to Medicare or Medicaid Beneficiaries - What You …
Human Error Reduction Techniques for Floor Supervisors
Documenting Misconduct that Will Stand Up in Court
Trial Master File (TMF)/eTMF, & FDAs Draft Guidance for Ele…
Tattoos, hijabs, piercings, and pink hair: The challenges …
Project Management for Non-Project Managers - How to commun…
OSHA Requirements for Supervisors, Project Leaders & HR - W…
Humane Layoffs: How to Let People Go with Compassion and De…
Unlock Employee Loyalty: Stay Interviews Will Keep Them Eng…
Sunshine Act Reporting - Clarification for Clinical Research
FFIEC BSA/AML Examination Manual: What Compliance Officers …
Female to Female Hostility @Workplace: All you Need to Know
Onboarding is NOT Orientation - How to Improve the New Empl…
FDA Technology Modernization Action Plan (TMAP) and Impact …
Excel - Pivot Tables - The Key To Modern Data Analysis and …
Managing Toxic & Other Employees Who Have Attitude Issues
Building GMP Excellence: A Guide to Implementing Compliant …
Excel Power Skills: Master Functions, Formulas, and Macros …