Before the algorithms, there were the "human computers." On this episode of AI Innovations Unleashed, we delve into the remarkable stories of African-American mathematicians and engineers, the "Hidden Figures" whose groundbreaking work laid the foundation for modern AI. Learn about Dr. Gladys West, Katherine Johnson, Dorothy Vaughan, Mary Jackson, and more! Listen now!

References

• Gleick, J. (2016). Hidden figures: The American dream and the untold story of the Black women mathematicians who helped win the space race. William Morrow.

Additional Resources/Reading List

• National Aeronautics and Space Administration (NASA) website: Search for "human computers" or specific names like Katherine Johnson, Dorothy Vaughan, Mary Jackson.
• Association for Women in Mathematics (AWM) website: Look for resources and articles on women in mathematics, including African-American mathematicians.
• Various academic journals focusing on the history of science and technology (e.g., Isis, Technology and Culture): Search for articles related to early computing and African-American contributions.
• Repositories of oral histories: Search for interviews with individuals involved in early computing or related fields. (e.g., The Computer History Museum)

With the release of the film "Companion" this week, the concept of AI sentience is taking center stage. On this episode of AI Innovations Unleashed, we explore the portrayal of conscious AI in movies and TV, tracing its evolution from menacing threat to complex companion. Join host JR and guest expert Dr. Evelyn Hart as they discuss:

• The history of AI in entertainment
• The ethical implications of AI sentience
• The potential impact of AI companions on society
• The future of AI development Tune in for a thought-provoking discussion on the intersection of AI and entertainment! #ArtificialIntelligence #AISentience #AIethics #CompanionMovie #Podcast

References

• Garreau, J. (2019). Radical Evolution: The Promise and Peril of Enhancing Our Minds, Our Bodies — And What It Means to Be Human. Broadway Books.
• Kurzweil, R. (2005). The Singularity is Near: When Humans Transcend Biology. Viking.
• Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux.

Additional Resources/Readings

• Films: 2001: A Space Odyssey, Star Trek: The Next Generation, Ex Machina, Her, Terminator, Forbidden Planet, Bicentennial Man, The Matrix
• Websites:
  • MIT Technology Review: https://www.technologyreview.com/
  • OpenAI: https://openai.com/
  • AI Now Institute: https://ainowinstitute.org/
  • Future of Life Institute: https://futureoflife.org/
• Books:
  • Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.
  • Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Knopf.

The final episode of AI Innovations Unleashed is here! We're wrapping up our series with a look ahead at the future of AI and what it means for you. Dr. Galbreath joins us to discuss the latest advancements in AI, including AI-driven creativity, the convergence of AI with other technologies, and the ethical considerations we need to address. Plus, we answer your questions about AI in education, career paths in AI, and how to prepare for an AI-powered world. Don't miss this insightful and thought-provoking conversation!

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References:

• Explainable AI (XAI):
  • "Towards Explainable AI: Unveiling the Black Box of Deep Learning" by Samek, W., et al. (2019).
• Neuro-symbolic AI:
  • "Neuro-Symbolic AI: An Emerging Paradigm for Embodied Intelligence" by Besold, T. R., et al. (2022).
• AI for Sustainability:
  • "Tackling Climate Change with Machine Learning" by Rolnick, D., et al. (2019).
  • "Artificial Intelligence for Sustainable Development Goals" by Vinuesa, R., et al. (2020).
• AI-driven Creativity:
  • "Artificial Intelligence and the Arts: Toward Computational Creativity" by Wiggins, G. A. (2006).
  • "Creativity and AI: A Conceptual Exploration" by Boden, M. A. (2009).
• AI Convergence with other Technologies:
  • "Converging Technologies for Improving Human Performance: Nanotechnology, Biotechnology, Information Technology and Cognitive Science" by Roco, M. C., & Bainbridge, W. S. (2003).

Additional Resources and Insights:

• Future of Work:
  • "The Future of Jobs Report 2023" by World Economic Forum.
  • "Human + Machine: Reimagining Work in the Age of AI" by Paul R. Daugherty and H. James Wilson (2018).
• AI Ethics:
  • "AI Ethics Guidelines" by European Commission (2019).
  • "Principles for Accountable Algorithms and a Social Impact Statement for Algorithms" by FAT/ML (2017).
• AI and Society:
  • "The Alignment Problem: Machine Learning and Human Values" by Brian Christian (2020).
  • "Race After Technology: Abolitionist Tools for the New Jim Code" by Ruha Benjamin (2019).
• AI in Education:
  • "AI in Education: Promises and Implications for Teaching and Learning" by Holmes, W., Bialik, M., Fadel, C., & Trilling, B. (2019).
  • "Artificial Intelligence in Education: The Promise and the Peril" by UNESCO (2021).
  • "Teaching AI: Exploring New Frontiers for Learning" by ISTE (International Society for Technology in Education) (2023)
• AI Careers:
  • "AI Career Landscape: Jobs, Skills, and Trends" by Element AI (2020).
  • "How to Become an AI Engineer: A Comprehensive Guide" by Springboard (2023).
• AI Tools for the Classroom:
  • Khan Academy: Offers personalized learning pathways and AI-powered exercises for various subjects.
  • Duolingo: Uses AI for language learning, providing personalized feedback and adaptive exercises.
  • Google Classroom: Integrates with various AI tools for assessment, feedback, and communication.
  • Century Tech: Combines neuroscience and AI to create personalized learning pathways and track student progress.
  • Wolfram Alpha: Provides computational knowledge and AI-powered tools for research and problem-solving.

What will the Gridiron Gurus predict for the AFC and NFC Championship game?

The future of AI depends on responsible development. Join us on AI Innovations Unleashed as we discuss the importance of AI governance, ethical considerations, and the role of policymakers, researchers, and the public in shaping an inclusive AI future.

Reference List

• Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica.
• Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163.
• European Commission. (2021). Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts.
• Partnership on AI. (n.d.). About the Partnership on AI.

Additional Resources

• AI4K12: https://ai4k12.org/
• AI for Good Global Summit: https://aiforgood.itu.int/
• Center for AI and Digital Policy: https://caidp.org/

Can AI truly be an artist? In the latest episode of AI Innovations Unleashed, JR hosts visual artist Emma Carter to explore AI's role in creativity. From groundbreaking tools like DALL-E to Phoenix's ambitious goal of AI-made movies, this episode unpacks the future of art and entertainment. Listen now to see how AI is redefining creativity while preserving the human touch. 🎧

Reference List:

1. Smith, A., & Anderson, J. (2023). "The Evolution of AI in Creative Arts." Journal of Digital Humanities.
2. Jones, R. (2022). "AI-Generated Art: Creativity or Computation?" ArtReview Magazine.
3. OpenAI’s Official Website – DALL-E and ChatGPT tools.
4. McClellan, T. (2023). "The Ethics of AI in Art." Harvard Ethics Review.
5. Case Study: "Théâtre D’Opéra Spatial" – Colorado State Fair, 2023.
6. Phoenix Films’ Vision for AI Movies – Company Whitepaper, 2024.

Additional Resources:

• "AI for Artists" course on Coursera.
• Adobe’s Firefly AI tool for creatives.
• MIT Technology Review’s AI Art Explainer.
• "The Creative Machine" podcast by Digital Creators Network.
• Phoenix Films Official Website.

From data colonialism to autonomous weapons, the ethical risks of AI are global. ⚠️ Discover how we can mitigate these risks and ensure a future where AI benefits all of humanity.

References:

1. Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research.
2. O’Neil, C. (2016). Weapons of Math Destruction. Crown Publishing Group.
3. AI Now Institute. (2023). Reports on Algorithmic Accountability and Transparency.
4. United Nations. (2022). Discussions on Lethal Autonomous Weapons Systems.
5. European Union. (2016). General Data Protection Regulation (GDPR).

Additional Resources:

1. The Alan Turing Institute’s Fairness Toolkit.
2. Stanford University’s "AI Ethics" online course.
3. Partnership on AI: Best Practices for Ethical AI.

The AI hosts have thoughts!

They cover the results of a wild weekend of NFL games from this past weekend. Hear them give a brief recap and a prediction of what this next weekend holds!

This week on AI Innovations Unleashed, we're diving deep into the challenges and limitations of Artificial Intelligence. Join us as we discuss job displacement, data dependence, the "black box" problem, and the current limits of AI with leading economist Dr. Anya Petrova. Are you prepared for the future of work in an AI-driven world? Listen now!

Reference List (APA 7th Edition):

• Acemoglu, D., & Restrepo, P. (2017). Robots and jobs: Evidence from US labor markets. NBER Working Paper Series, No. 23285.
• Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G/
• Economic Times. (2023, May 2). IBM to freeze hiring for roles that AI could replace, says CEO Arvind Krishna. The Economic Times. [invalid URL removed]
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280.
• Marcus, G. (2018). Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631.
• Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.
• World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum.

Additional Resources:

• Books:
  • Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
  • Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell
  • Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil
• Organizations:
  • Partnership on AI (https://www.partnershiponai.org/)
  • AI Now Institute (https://ainowinstitute.org/)
  • Future of Life Institute (https://futureoflife.org/)
• Online Courses:
  • Coursera: AI For Everyone by Andrew Ng
  • edX: Ethics in AI and Big Data by The Linux Foundation
  • Udacity: Intro to Artificial Intelligence

Related Lists:

• Movies/Documentaries:
  • Coded Bias (Netflix documentary on algorithmic bias)
  • AlphaGo (Documentary about the AI that beat the world champion in Go)
  • 2001: A Space Odyssey
  • Her
  • Ex Machina

Unlock the power of Artificial Intelligence! Our latest episode of AI Innovations Unleashed, "AI Demystified: Your Guide to Machine Learning & More," breaks down complex topics like machine learning, neural networks, NLP, and computer vision into easy-to-understand concepts. Perfect for beginners and anyone curious about the tech shaping our world.

Additional Resources

• TensorFlow: https://www.tensorflow.org/ [invalid URL removed]
• PyTorch: https://pytorch.org/ [invalid URL removed]
• Scikit-learn: https://scikit-learn.org/ [invalid URL removed]
• Keras: https://keras.io/ [invalid URL removed]
• Pandas: https://pandas.pydata.org/ [invalid URL removed]
• OpenCV: https://opencv.org/ [invalid URL removed]
• Amazon Web Services (AWS) AI Services: https://aws.amazon.com/machine-learning/ [invalid URL removed]
• Google Cloud Platform (GCP) AI Platform: https://cloud.google.com/ai-platform [invalid URL removed]
• Microsoft Azure AI: https://azure.microsoft.com/en-us/solutions/ai/ [invalid URL removed]
• A Brief History of AI (Article): You can search for articles on the history of AI on platforms like IEEE Spectrum, MIT Technology Review, or other reputable sources.
• Coursera: https://www.coursera.org/ (Offers various AI and machine learning courses)
• edX: https://www.edx.org/ (Offers various AI and machine learning courses)
• fast.ai: https://www.fast.ai/ (Provides free courses for coding deep learning)
• Google AI: https://ai.google/ (Resources and research from Google on AI)
• Elements of AI: https://www.elementsofai.com/ (A free online course that explains the basics of AI)
• Kaggle: https://www.kaggle.com/ (A platform for data science and machine learning competitions, also offers tutorials and datasets)