The Best Skills to Obtain to Help Make Generative AI Safe, Secure, and Responsible in 2024
Safe AI in 2024
Generative AI is a branch of artificial intelligence that can create new content, such as images, text, audio, or video, based on existing data. It has many potential applications, such as enhancing creativity, personalizing experiences, generating insights, and solving problems. However, it also poses many challenges, such as ethical, legal, social, and technical issues, that require careful consideration and regulation.
To help make Generative AI safe, secure, and responsible, it is important to acquire the following skills in 2024:
Data Literacy
Data literacy is the ability to understand, analyze, and communicate with data. It is essential for working with Generative AI, as it involves collecting, processing, and evaluating large amounts of data from various sources and domains. Data literacy can help ensure the quality, validity, and diversity of the data used for training and testing Generative AI models, as well as detecting and mitigating potential biases, errors, or anomalies.
Learning resources:
Data Fluency: Exploring and Describing Data: This course teaches you how to explore and describe data using various methods and tools, such as histograms, scatterplots, and summary statistics.
The Data Literacy Course: Learn How to Work with Data: This course covers the basics of data literacy, such as data types, data sources, data quality, and data visualization. It also includes quizzes and exercises to test your knowledge and skills.
Data — What it is, What We Can Do with It from Johns Hopkins University on Coursera: This course introduces you to the concepts and techniques of data science, such as data collection, data analysis, data modeling, and data communication. It also provides examples and case studies from various domains, such as health, education, and business.
Data Literacy for All by Tableau: This is a free online training program that teaches you the fundamentals of data literacy, such as statistics, data types, and storytelling with data. It also offers opportunities to practice your data skills with the Tableau Community.
The Ultimate Guide to Data Literacy Training by Talend: This guide provides a comprehensive list of data literacy courses and online training for different levels and interests, such as data literacy for beginners, data literacy for business, and data literacy for educators.
AI Ethics
AI ethics is the study of the moral and social implications of artificial intelligence. It is crucial for developing and deploying Generative AI, as it involves assessing the impact, risks, and benefits of the generated content, as well as the rights, responsibilities, and values of the stakeholders involved. AI ethics can help establish the principles, guidelines, and best practices for designing and using Generative AI in a fair, transparent, and accountable manner, as well as addressing the potential harms, dilemmas, or conflicts that may arise.
Learning resources:
Ethics of Artificial Intelligence on Coursera: This course deals with the problems created, aggravated or transformed by AI. It is intended to give students a chance to reflect on the ethical, social, and cultural impact of AI by focusing on the issues faced by and brought about by professionals in AI but also by citizens, institutions and societies.
A Practical Guide to Building Ethical AI on Harvard Business Review: This article provides a clear plan to deal with the ethical quandaries of AI. It covers how to identify existing infrastructure, create a data and AI ethical risk framework, change how you think about ethics, optimize guidance and tools for product managers, build organizational awareness, incentivize employees, and monitor impacts and engage stakeholders.
AI Ethics by IBM: This website showcases IBM’s principles, practices, and tools for ethical AI. It also provides resources for enacting AI ethics, such as AlgorithmWatch, AI Now Institute, and Data & Society.
Ethical AI Frameworks, Guidelines, Toolkits by AI Ethicist: This website provides a comprehensive list of data and AI ethics frameworks, guidelines, and toolkits from various organizations and projects, such as All Tech is Human, Amazon, Google, and Microsoft.
Ethical AI for Teaching and Learning by Cornell University: This website explores the ethical and social implications of using generative AI in the classroom. It covers topics such as transparency, political impact, environmental impact, diversity, privacy, and data governance. It also offers tips and strategies for engaging with generative AI tools in a thoughtful, critical, and ethical way.
AI Security
AI security is the protection of artificial intelligence systems and data from unauthorized access, manipulation, or disruption. It is vital for ensuring the reliability, integrity, and availability of Generative AI, as it involves defending against various threats, such as cyberattacks, adversarial examples, or spoofing. AI security can help prevent the misuse, abuse, or theft of Generative AI, as well as the damage, loss, or corruption of the generated content, by applying the appropriate methods, tools, and standards for encryption, authentication, verification, and monitoring.
Learning resources:
Must Learn AI Security: The rise of the AI empire: AI is practically everywhere these days, from your phone's voice assistant to self-driving cars. By understanding how Security and AI intersect, you'll be better equipped to navigate this brave new world and make informed decisions, both personally and professionally.
OWASP AI Security and Privacy Guide: This guide provides clear and actionable insights on designing, creating, testing, and procuring secure and privacy-preserving AI systems. It covers topics such as AI security risks, AI security testing, AI security best practices, and AI privacy principles and techniques.
Artificial Intelligence Resources by ISACA: This website offers a collection of AI resources, guidance, and training for IT professionals. It includes publications, webinars, podcasts, and courses on various aspects of AI, such as AI ethics, AI auditing, AI security, and AI creativity.
Awesome AI Security: This is a curated list of AI security resources inspired by awesome-adversarial-machine-learning and awesome-ml-for-cybersecurity. It features books, papers, blogs, podcasts, videos, courses, tools, frameworks, and datasets related to AI security.
AI Security on GitHub: This is a repository of thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response, artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more. It is maintained by Omar Santos, a principal engineer of the Cisco Product Security Incident Response Team (PSIRT).
AI Creativity
AI creativity is the ability to generate novel, original, and valuable content using artificial intelligence. It is beneficial for enhancing the performance, quality, and diversity of Generative AI, as it involves exploring, combining, and transforming different data, models, and techniques. AI creativity can help improve the usefulness, relevance, and appeal of the generated content, as well as the satisfaction, engagement, and trust of the users, by applying the suitable strategies, skills, and metrics for evaluation, feedback, and improvement.
Learning resources:
Creativity with AI on Hour of Code: This website offers a variety of lessons, videos, and activities that introduce you to how AI works and how it can be used for creative purposes. You can learn about topics such as machine learning, computer vision, neural networks, chatbots, generative images, and algorithmic bias.
Artificial Creativity on Coursera: This course explores the emerging field of creativity in AI from a design perspective, bringing together insights from computer science and creative disciplines. You will survey the history and theories behind today’s creative AI, analyze the unorthodox approaches that have advanced the field, and experience cutting-edge creative AI tools.
AI4K12: This website provides a curated list of books, curriculum materials, course outlines, software, videos, and more for teaching and learning about AI. The list includes professional development courses for educators to learn about AI, as well as resources for sparking curiosity and creativity in AI among students.
Learn AI Made Easy: A Beginner’s Roadmap to Artificial Intelligence: This article provides a comprehensive guide for beginners who want to learn about AI. It covers the basics of AI, the different types of AI, the applications and challenges of AI, and the resources and tools for learning AI. It also includes a section on AI creativity, where you can find examples and tutorials on how to use AI for generating content.
AI 101 for Teachers on Code.org: This website offers a collection of AI resources, guidance, and training for teachers who want to incorporate AI into their classrooms. It includes publications, webinars, podcasts, and courses on various aspects of AI, such as AI ethics, AI auditing, AI security, and AI creativity.
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These are some of the best skills to obtain in 2024 to help make Generative AI safe, secure, and responsible. By acquiring these skills, one can not only leverage the opportunities and advantages of Generative AI, but also address the challenges and limitations of Generative AI, and ultimately contribute to the development and advancement of artificial intelligence in a positive and responsible way.
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