
Explore AI ethics across systems designs and large language models, focusing on human applications, deep fakes, AI limitations, and critical thinking to anticipate future ethical challenges.
Explore how to disclose AI use in research and design decisions, evaluate output quality versus human effort, and navigate evolving ethics and laws in GenAI.
Deepfakes pose a major ethical risk by using AI to recreate people’s images and voices for deceptive, fraudulent ends, demanding verification.
Spot deepfakes by staying skeptical, verifying sources, and examining lip-sync, image details, and language patterns to protect yourself from deceptive media.
GenAI creates new content—text, images, music, code, and more—from patterns in large training data, often via large language models, with ethical bias concerns.
Explore the transparency and the black box problem in AI and large language models, examining inputs, outputs, explainability, accountability, and trust.
Explore information privacy in ai environments, examining who owns personal data, consent, and data security as we train large language models with user inputs, risking leakage and chilling effects.
Explore how training data shapes generative AI outputs, addressing fairness and bias in large language models, with western bias examples in image creation and hiring, and discuss mitigation.
Strengthen system safety with robust human oversight and informed consent for AI-generated content, including disclosure of AI authorship and ethical, legal considerations.
Explore how human actions shape AI ethics, contrasting benefits with misuse, and learn to stay aware of nefarious uses and the responsible application of AI.
Examine AI copyright concerns, from training data and tone versus copied content to image styles, with notes on country differences and real-world examples.
Explore the ethics of AI-created avatars, including Synthesia avatars, their use in training and media, potential to replace actors and background roles, and the need for responsible limits.
Examine AI's impact on jobs and society, identifying vulnerable roles and how AI can augment or replace tasks, then explore upskilling in data science, AI ethics, and collaboration.
Explore how advertising integrated into ai responses can bias answers, as ads at the bottom of Bing chat illustrate, raising ethical concerns about manipulation and calls for regulation.
Explore the major limitations of artificial intelligence, including hallucinations, incorrect information, and biased content, and learn how context, real-time data gaps, and cautious use shape responsible practice.
Apply Bruce Weinstein's five principles of ethical intelligence to ai, focusing on do no harm, making things better, respecting others, fairness, and love, with practical ai scenarios.
Apply the IBM AI ethics model—respect for persons, beneficence, and justice—to assess AI use, highlighting the need for human touch and compassionate, fair communication in termination scenarios.
Explore the Salesforce AI ethical maturity model, guiding ethical adoption from ad hoc to optimized, with executive buy-in, diverse teams, and lifecycle reviews.
AI becomes your personal assistant, expanding across professional and personal life. It will grow rapidly with more specialized tools, while ethics, transparency, and regulation guide its use.
Review course parts as needed and reflect on your AI usage and ethics. Stay watchful for deep fakes and continue lifelong learning, then leave a review.
Master the cutting edge of AI, responsibly.
Generative AI (GenAI) using Large Language Models (LLMs) like ChatGPT and Google Gemini are revolutionizing the way we interact with technology. But with great power comes great responsibility. This course equips you to understand the ethical implications of LLMs and ensure their responsible use.
What You'll Learn:
The potential benefits and drawbacks of Generative AI.
Understanding bias in AI and how it impacts LLM outputs.
The rise of deep fakes and the ethical concerns surrounding them.
Data privacy and security considerations with Generative AI.
Frameworks for responsible development and deployment of LLMs.
What Students Are Saying
"The material is presented in a very human and approachable manner. This is a good use of time for anyone interested in the ethical dilemmas of AI and the changing tech of the modern world." -Daniel G
"Really attention grabbing!" -Flor B
"Very engaging speaker; able to simplify concepts for the audience." -Marion R
Who Should Enroll:
Developers & Programmers: Working with LLMs to create applications, you'll gain the knowledge to mitigate bias, ensure data privacy, and build trust with users.
Business Professionals: Integrating LLMs into marketing, customer service, or content creation? Learn how to leverage their power ethically and responsibly.
Policymakers & Legislators: Shaping the future of AI requires an understanding of ethical considerations. This course equips you to create future-proof regulations.
Educators & Researchers: Developing the next generation of AI? Learn how to promote ethical practices within your field.
Content Creators & Writers: Exploring the use of LLMs for content generation? This course will help you navigate issues of authorship, plagiarism, and responsible use.
Journalists & Media Professionals: Combating misinformation and deepfakes is crucial. Gain the knowledge to identify potential issues and ensure ethical reporting.
Anyone Interested in AI's Future: This course offers a comprehensive understanding of the ethical landscape surrounding LLMs, preparing you for a future driven by AI.
This course is designed to be accessible and informative, regardless of your professional background.
Don't be left behind in the AI revolution. Enroll today and become a leader in the responsible use of Generative AI (GenAI).
Many thanks!
Steve Ballinger
Udemy Instructor
Demystifying AI: Your Frequently Asked Questions Answered
Artificial intelligence (AI) is a rapidly evolving field with a lot of buzz surrounding it. But what exactly is it, and how does it work? This post dives into the world of AI, answering some of your most frequently asked questions.
1. What is Artificial Intelligence (AI)?
AI is a broad term encompassing the intelligence displayed by machines, particularly computer systems. It's about creating intelligent agents that can perceive their environment, learn from data, and take actions to achieve specific goals. There are different types of AI, but most commonly we see:
Narrow AI (Weak AI): This type excels at performing specific tasks, like playing chess or recognizing faces.
General AI (Strong AI): This hypothetical AI would possess human-level intelligence and be able to perform any intellectual task a human can.
2. What is Generative AI?
Generative AI is a subfield of AI that focuses on creating new data, like text, images, or even code. It uses complex algorithms, often inspired by how the human brain works, to analyze existing data and then generate entirely new pieces of content that follow the same patterns.
For example, generative AI can be used to create realistic-looking images of people who don't actually exist, or to write different creative text formats, like poems or scripts.
3. What are LLMs (Large Language Models)?
LLMs are a type of AI model trained on massive amounts of text data. This allows them to process information and respond to questions in a way that simulates human conversation. I, for instance, am a large language model!
LLMs are used in a variety of applications, including chatbots, virtual assistants, and machine translation. They're constantly evolving, becoming better at understanding and responding to complex queries.
4. What is ChatGPT and Google Gemini.
ChatGPT and Gemini are both large language models, but we're developed by different companies. ChatGPT is from OpenAI, while Gemini is a product of Google AI. They share many capabilities, like generating text, translating languages, and writing different kinds of creative content. However, they may have different strengths and weaknesses, as they are trained on different datasets and have unique underlying algorithms.