You’re already using AI—every time you unlock your phone, stream music, or shop online. In Episode 2 of The Human Guide to AI, we reveal how artificial intelligence is quietly woven into your everyday life (and even write a toaster love song). Discover the tech behind your day.

Key Terms & Concepts Covered

• Computer Vision – AI that interprets and understands visual input (e.g., Face ID, Google Photos)
• Natural Language Processing (NLP) – How machines understand and generate human language (e.g., Gmail Smart Compose)
• Recommendation Algorithms – Used in Netflix, Spotify, Amazon to suggest content/products based on your behavior
• Generative AI – AI that creates content: text, images, music, video (e.g., ChatGPT, DALL·E, Suno)
• Machine Learning – Algorithms that learn from data and improve over time
• Collaborative Filtering – Technique for making personalized recommendations based on similar users
• Dynamic Pricing – AI-driven pricing that adjusts based on demand, behavior, or location
• Predictive Analytics – AI that forecasts future behavior based on data patterns
• Style Transfer – AI technique that applies one artistic style to another image or medium

Artificial Intelligence isn’t just for data scientists. In Episode 1 of WTF is AI?, we make AI human—with dogs, toddlers, Netflix, and pizza analogies that actually make sense. 🎧 A must-listen for professionals navigating the AI revolution.

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Reference List & Further Reading

General AI Concepts:

• Russell & Norvig. Artificial Intelligence: A Modern Approach
• Stanford’s “AI Index Report” (https://aiindex.stanford.edu)
• Google’s “AI for Anyone” Guide: https://ai.google/education/

On Transformer Models:

• “Attention Is All You Need” (Vaswani et al., 2017)
• OpenAI’s GPT-3 and GPT-4 papers
• Illustrated Transformer Guide by Jay Alammar: https://jalammar.github.io/illustrated-transformer/

Ethics & Society:

• Kate Crawford – Atlas of AI
• Ruha Benjamin – Race After Technology
• Timnit Gebru’s work on algorithmic bias
• AI Now Institute: https://ainowinstitute.org

Accessible AI Tools (Try it Yourself):

• ChatGPT: https://chat.openai.com
• DALL·E: https://openai.com/dall-e
• Google Teachable Machine (for kids/educators): https://teachablemachine.withgoogle.com

Dive into the future of AI! In this episode of AI Innovations Unleashed, we explore how AI is transforming the workplace and why focusing on the next 5 years is key to staying ahead.

References, Additional Resources, and Readings

Books

1. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.
   • This book discusses how algorithms and AI can perpetuate bias and inequality, making the case for ethical AI and responsible data use.
2. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.
   • A detailed look at how technology, particularly AI, is transforming work and the economy. The authors discuss the challenges and opportunities AI presents in reshaping industries and society.
3. West, D. M. (2018). The Ethics of Artificial Intelligence. Brookings Institution Press.
   • West offers an overview of the ethical dilemmas posed by AI, including bias, privacy, and accountability, and provides suggestions for addressing these issues.
4. Harari, Y. N. (2018). 21 Lessons for the 21st Century. Spiegel & Grau.
   • A broad exploration of the societal changes brought about by AI, focusing on how technology, including AI, is reshaping politics, economics, and our daily lives.

Research Articles & Journals

1. O'Neil, C. (2017). "How to fight the dangerous rise of AI." The Guardian.
   • This article provides a critical look at the potential dangers of AI and the importance of keeping AI systems transparent and accountable.
2. Brynjolfsson, E., & McAfee, A. (2024). "The impact of artificial intelligence on the workforce." MIT Sloan Management Review.
   • A comprehensive report on the evolving relationship between AI and work, highlighting which jobs are being augmented and which are being replaced.
3. Raji, I. D., & Buolamwini, J. (2019). "Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products." Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.
   • This research paper highlights the role of bias in AI technologies and the need for fairness auditing to prevent racial bias in AI products like facial recognition.
4. Dastin, J. (2018). "Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against Women." Reuters.
   • A real-world example of AI bias in recruitment tools and its repercussions for organizations that do not consider the ethical ramifications of deploying AI systems.
5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. A., Kaiser, Ł., & Polosukhin, I. (2017). "Attention is All You Need." Proceedings of Neural Information Processing Systems (NeurIPS).
   • The seminal paper introducing the Transformer model, which has since become foundational to many modern AI systems like GPT-3 and other natural language processing technologies.