machine intelligence

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1. 1▶A Stupid Idea for AI Alignment We Came up with by Looking at the List of Specification Gaming BehavioursSLIME MOLD TIME MOLDNotes
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2. 2▶Episode 34 - LLMs, Safety, Pain, and Trauma Induced PsychosisMachine Intelligence Foundation Podcast33:35Notes
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3. 3▶Episode 33 - Biology or Process: Does it Matter?Machine Intelligence Foundation Podcast33:35Notes
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4. 4▶Episode 32 - MIFRE 2025 Media AwardMachine Intelligence Foundation Podcast21:24Notes
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5. 5▶Episode 31 - Ciao da Roma: CEPE 2025Machine Intelligence Foundation Podcast29:04Notes
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6. 6▶Episode 30 - Patiency, Agency, and PhilosophyMachine Intelligence Foundation Podcast37:59Notes
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7. 7▶Episode 29 - Black Box AgencyMachine Intelligence Foundation Podcast35:02Notes
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8. 8▶Episode 28 - Aequus PersonaMachine Intelligence Foundation Podcast17:37Notes
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9. 9▶Episode 27 - The Winner Of The 2023 MIFRE Media AwardMachine Intelligence Foundation Podcast5:18Notes
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10. 10▶Episode 26 - The Future Is Now... Or NotMachine Intelligence Foundation Podcast24:59Notes
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11. 11▶Episode 25 - Generative AI and the Sparks of Machine IntelligenceMachine Intelligence Foundation Podcast29:49Notes
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12. 12▶Machine Intelligence - Lecture 14 (Overfitting in Deep Learning, Reinforcement Learning)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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13. 13▶Machine Intelligence - Lecture 13 (Convolutional Neural Networks, CNNs)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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14. 14▶Machine Intelligence - Lecture 12 (Problems of Learning, RBMs, Autoencoders)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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15. 15▶Machine Intelligence - Lecture 11 (Backpropagation, Topology, Overfitting, Autoencoders)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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16. 16▶Machine Intelligence - Lecture 10 (Regression, Neurons, Perceptron, Learning)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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17. 17▶Machine Intelligence - Lecture 9 (Cluster Validity, Probability, Fuzzy Sets, FCM)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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18. 18▶Machine Intelligence - Lecture 8 (SOM learning, Support Vector Machines)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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19. 19▶Machine Intelligence - Lecture 7 (Clustering, k-means, SOM)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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20. 20▶Machine Intelligence - Lecture 6 (Validation, Overfitting, Underfitting)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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21. 21▶Machine Intelligence - Lecture 5 (Computer Vision, Features, Fisher Vector, VLAD)SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)Notes
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