ECE 490/590
Neural networks
Vikas Dhiman
Barrows Hall Rm 279,
[email protected]
## Vikas Dhiman (he/him/his) #### Career + BTech in Electrical Engineering + Worked as an IT Engineer for 4 years + MS in Computer Science + PhD in Robotics #### Personal + Hobbies: running, hiking, skiing, biking, video games  
Terminology
## ImageNet moment <img src="/ECE490-S24-Neural-Networks/assets/0000-00-03-intro/imagenet-challenge.jpg" style="width:700px"/> 
## The Turing awardees 
## Similar courses at UMaine + ECE 491/591: Deep Learning (Dr Yifeng Zhu) - 25-30% overlap but we will dive deeper on backpropagation. We will implement your on Pytorch library. + COS 498/598: Machine Learning (Dr Salimeh Y Sekeh) - 25-30% overlap but we will be focused on Neural Networks + COS 498/598: Explainable AI (Dr Chaofan Chen) - Focuses on explinabliity of AI
## Similar courses elsewhere 1. Machine Learning Specialization (2022) [Website](https://www.coursera.org/specializations/machine-learning-introduction) | Instructor: Andrew Ng 2. [DEEP LEARNING NYU Fall 2022](https://atcold.github.io/NYU-DLFL22) Instructor : Alfredo Canziani, Yann LeCun 3. [Deep Learning in Computer Vision with Prof. Kosta Derpanis (York University)](https://www.eecs.yorku.ca/~kosta/Courses/EECS6322/) 4. [Stanford CS231n](https://cs231n.github.io/); Instructors: Fei-Fei Li This list can go on.
## Syllabus [vikasdhiman.info/ECE490-S24-Neural-Networks](https://vikasdhiman.info/ECE490-S24-Neural-Networks)
## AI in the media * Geoffrey Hinton https://youtu.be/qpoRO378qRY?t=1166 * 60 minutes https://youtu.be/880TBXMuzmk?t=724 * Octopus paper explained by Emily Bender https://youtu.be/VaxNN3YRhBA?t=1560 https://aclanthology.org/2020.acl-main.463.pdf * Chomsky https://youtu.be/PBdZi_JtV4c?t=167 * https://www.youtube.com/watch?v=miLLeyQyBtg * https://www.weforum.org/agenda/2020/11/productivity-workforce-america-united-states-wage * Roodney brooks http://rodneybrooks.com/predictions-scorecard-2023-january-01/
# Questions for discussion 1. What is intelligence (human or artifical) 2. What is artificial intelligence? 3. What is a good measure of artificial intelligence? 4. What is happening now? 5. Are we going in the right direction? 6. What is the right direction for AI research? What are some wrong directions? 7. What about the job replacement?
## Ethical concerns because of high-accuracy * Face recognition * Deepfakes[^1] ## Ethical concerns (summary) * Assigned reading: https://fairmlbook.org/introduction.html [^1]: https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf
## Fundamentally dubious * Predicting criminal recidivism * Predicting job performance * Predictive policing * Predicting terrorist risk * Predicting at-risk kids[^1] [^1]: https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf
## Case of lie detectors * "Overall, the cumulative research evidence suggests that when used in criminal investigations, the polygraph test detects deception better than chance, but with error rates that could be considered significant."<br/>  * Keeps coming back: "Tracy Harpster, had ... a miracle method to determine when 911 callers are actually guilty of the crimes they are reporting."<br/> 
## Thank you * Prereq homework posted
## Thank you * Prereq homework posted
