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
Understanding and Analyzing Financial Statements
Onboarding is Not Orientation: How to Improve Your New Hire…
Managing Toxic & Other Employees Who have Attitude Issues
Do's and Don'ts of Documenting Employee Behaviour, Performa…
Gossip-Free: Leadership Techniques to Quell Office Chatter
Outlook - Master your Mailbox - Inbox Hero Inbox Zero
Harassment, Bullying, Gossip, Confrontational and Disruptiv…
Excel & ChatGPT Synergy Masterclass: Unleashing Financial A…
Introduction to Microsoft Power BI Dashboards
Drive Recruiting Success with the Using Recruiting Metrics …
2025 EEOC & Employers: Investigating Claims of Harassment …
Impact Assessments For Supplier Change Notices
Mastering Job Descriptions: Legal and Practical Insights fo…
Effective Onboarding: How to Welcome, Engage, and Retain Ne…
What is in Store for Employers When Updating Employee Handb…
Designing Employee Experiences to Build a Culture of Compli…
Onboarding Best Practices for 2025: Proven Strategies to Po…
Accounting For Non Accountants : Debit, Credits And Financi…
Creating a Successful Job Rotation Program
The Anti-Kickback Statute: Enforcement and Recent Updates
FDA Compliance And Laboratory Computer System Validation
How To Create Psychological Safety in your Organization
Aligning Your HR Strategy with Your Business Strategy
Transforming Anger And Conflict Into Collaborative Problem …
How to Give Corrective Feedback: The CARE Model - Eliminati…
I-9 Audits: Strengthening Your Immigration Compliance Strat…
Zero Acceptance Sampling to Reduce Inspection Costs
Identifying, Managing, and Retaining High Potential Employe…
AI at Your Service: Enhancing Your Microsoft OfficeSkills w…
Why EBITDA Doesn't Spell Cash Flow and What Does
FDA Recommendations for Artificial Intelligence/Machine Lea…
Project Management for Non-Project Managers - How to commun…
Dealing With Difficult People In Life & Work
Developing and Implementing Quality Culture in the Organiza…
2-Hour Virtual Seminar on the 6 Most Common Problems in FDA…
Enhancing Pivot Tables with Images: Visualize Your Data Lik…
How to Write Effective Audit Observations: The Principles f…
How to Write Contracts for Procurement Professionals
Uplifting the Credibility of HR: How to Build the Credibili…
Strategic Interviewing & Selection: Getting the Right Talen…
Performance of Root Cause Analysis, CAPA, and Effectiveness…
FDA Audit Best Practices - Do's and Don'ts
Unlock Employee Loyalty: Stay Interviews Will Keep Them Eng…
How to Manage the Legal Landmine of the FMLA, ADA and Worke…
Excel Lookup Functions: VLOOKUP, HLOOKUP, and XLOOKUP Made …