This course finishes by revisiting the progress and potential benefits of AI, while suggesting other courses at LinkedIn Learning that may be useful and external organizations and resources that may be beneficial to them.
- [Instructor] As we conclude…our discussion of AI accountability…I want to give you a few suggestions of next steps,…specifically some paths for learning more about…some of the elements we've talked about.…You can learn more about the technical aspects of AI,…about it's real-world applications,…or even about the social and ethical context for AI.…If you're interested in getting hands-on in AI…you'll want to make sure…that you have a solid understanding for computer programming…as well as the principals of data science.…You'll want to learn a programming language.…
Python's a great place to start…because it's a clean, friendly,…general purpose programming language…with excellent support for AI functionality.…The statistical programming language R…is also a very common choice…for many of the machine learning algorithms used in AI.…Either of these would serve as an excellent tool…to learn and really even…to work professionally in computer programming…as well as data science, machine learning and AI.…As for the applications,…
- Bias in AI
- Navigating the social challenges of AI
- Moral reasoning and relational ethics
- General Data Protection Regulation (GDPR) and AI
- Discrimination in data
- Liability and AI
- AI in life-and-death situations
- Confronting the challenges of AI as a developer, executive, and consumer
Skill Level Intermediate
1. The Context for AI
2. Technical Challenges of AI
3. Social Challenges of AI
4. Legal Challenges of AI
5. Safety Challenges of AI
AI in the military4m 36s
6. Confronting the Challenges of AI
Next steps2m 52s
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