This was a solid slidecast. The organization of the slides and pacing of the narration was very well down. This gave your presentation a nice flow. In particular, you did a good job of balancing historical contexts with specific insights into different aspects of neural networks.
My favorite point that you made was the connection between neural network development and 9/11. I had never considered that before.
My only minor criticism is that you didn’t spend a lot of time talking about the concerns that our society has with neural networks. I feel like that is a major unanswered question. Therefore, it could have been something to dive a bit deeper into. For example, you could have transitioned from the terrorist detection to the problems that could arise from governments gaining such powerful tools.
Metz, C. (2018, March 6). Google Researchers Are Learning How Machines Learn. The New
York Times.
Nofre, D. & Priestley, M. & Alberts, G. (2014). When Technology Became Language: The Origins of the Linguistic Conception of Computer Programming, 1950-1960. Technology and Culture 55(1), 40-75. Johns Hopkins University Press. Retrieved April 27, 2018, from Project MUSE database.
Saussure, F. D. (1996). Course in general linguistics: Ferdinand de Saussure (C. Bally &A. Sechehaye, Eds.; A. Reidlinger, Trans.). New York: McGraw-Hill.
Very nice! Who would’ve thought that correctly identifying a vase and some lemons could be so historic! Looks like they will have to change CAPTCHAs pretty soon.
You did a great job putting neural networks in their proper historical context by bringing them back to their roots with the technologies developed during World War 2. I’m sure time constraints played a part, but it would have also been interesting to see how the invention of transistors and then microprocessors helped expedite the process of creating artificial intelligence.
This is a fascinating area of study. It is interesting to think about how trying to get machines to understand language requires humans to think about how they themselves understand languages. I think you did a great job of conveying this conundrum by including the example of how each person thinks of a different identity when they hear the word “cat”. It makes sense how it would be very difficult to get a machine to “think” in this form of association for a single word. let alone entire languages.
Thanks for opening my eyes to this field I kinda knew existed, but never fully appreciated. Great work!
First, I think it’s important to make the disclaimer that before watching your video, I was completely unfamiliar with the concept of neural networks. With that in consideration, I think you gave a very clear background explanation for the topic which I found particularly helpful. The part that interested me the most was how even the creators of some networks have trouble understanding how their own networks are making inferences or decisions. For me it raises the question: Will there be a point where humans as a species are limited by the output their brains can produce?
Returning to your presentation, I enjoyed how you explained the government origins of computing which then shifted into the commercial sector. Your explanation of our social anxieties that have shaped the development of neural networks was also an interesting link between society and technology.
My only recommendation for improvement would be to speak more about The Terminator(haha)! When I saw that image, I got really excited, because it’s The Terminator obviously, and I understood the connection, but felt like you could have explained it just a little more. Regardless, it was still very well done!
This was a solid slidecast. The organization of the slides and pacing of the narration was very well down. This gave your presentation a nice flow. In particular, you did a good job of balancing historical contexts with specific insights into different aspects of neural networks.
My favorite point that you made was the connection between neural network development and 9/11. I had never considered that before.
My only minor criticism is that you didn’t spend a lot of time talking about the concerns that our society has with neural networks. I feel like that is a major unanswered question. Therefore, it could have been something to dive a bit deeper into. For example, you could have transitioned from the terrorist detection to the problems that could arise from governments gaining such powerful tools.
Below are the sources I used for this project:
Alan Turing: Cracking the Code of Computing | EAGLE | Blog. (2018, February 05). Retrieved from https://www.autodesk.com/products/eagle/blog/alan-turing-cracking-code-modern-computing/
Andriana, L. (2017, May 28). Photograph. Retrieved from https://www.pinterest.com/pin/385409680602014651/
Breast Cancer Awareness | SexInfo Online. (n.d.). Retrieved from http://www.soc.ucsb.edu/sexinfo/article/breast-cancer-awareness
Caralle, K., & Boyars/AP, G. (2018, May 02). Iran must pay billions to families of 9/11 victims, federal judge rules. Retrieved from https://www.washingtonexaminer.com/news/iran-must-pay-billions-to-families-of-9-11-victims-federal-judge-rules
Cinemanalysis: The Terminator (1984). (2018, February 12). Retrieved from https://www.retrozap.com/the-terminator-1984/
Computing and the Manhattan Project. (2014, July 18). Retrieved from https://www.atomicheritage.org/history/computing-and-manhattan-project
A. (2017, December 10). Dark side of neural networks explained. Retrieved from https://blog.appliedai.com/how-neural-networks-work/
Gray, S. (2018, February 17). How Teacher Who Survived Columbine Prepares Her Students. Retrieved from http://time.com/5162977/columbine-shooting-survivor-students-advice/
A. (2016, October 26). How automation could impact “Make in India”. Retrieved from http://www.oemupdate.com/cover-story/how-automation-could-impact-make-in-india/
Huawei wants to use AI to make its phones faster than the iPhone. (2017, September 04). Retrieved from https://www.irishnews.com/magazine/technology/2017/09/04/news/huawei-wants-to-use-ai-to-make-its-phones-faster-than-the-iphone-1127917/
K. (2017, October 11). Iconic Open Mind Vector. Retrieved from https://www.vecteezy.com/vector-art/163119-iconic-open-mind-vector
Jeremy Jordan. (2018, April 16). Convolutional neural networks. Retrieved from https://www.jeremyjordan.me/convolutional-neural-networks/
KDnuggets. (n.d.). Retrieved from https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html
Kidwell, P. (1992). Technology and Culture, 33(1), 174-175. doi:10.2307/3105832
Kidwell, P. (2006). Technology and Culture, 47(2), 460-462. Retrieved from
http://www.jstor.org.ezproxy.lafayette.edu/stable/40061106
Kuang, C. (2017, November 26). Can A.I. Be Taught to Explain Itself? The New York Times.
Learning, L. (n.d.). US History II (American Yawp). Retrieved from https://courses.lumenlearning.com/ushistory2ay/chapter/gender-and-culture-in-the-affluent-society-2/
Lewis-Kraus, G. (2016, December 14). The Great A.I. Awakening. The New York Times.
Maddox, T. (n.d.). Cisco automation tools make it easier for network admins to improve performance, security. Retrieved from https://www.techrepublic.com/article/cisco-automation-tools-make-it-easier-for-network-admins-to-improve-performance-security/
Metz, C. (2018, March 6). Google Researchers Are Learning How Machines Learn. The New
York Times.
Nofre, D. & Priestley, M. & Alberts, G. (2014). When Technology Became Language: The Origins of the Linguistic Conception of Computer Programming, 1950-1960. Technology and Culture 55(1), 40-75. Johns Hopkins University Press. Retrieved April 27, 2018, from Project MUSE database.
Olson, E. (n.d.). Creating Neural Networks in Python. Retrieved from https://electronics360.globalspec.com/article/8956/creating-neural-networks-in-python
Peters, A. (2017, May 03). How Silicon Valley Companies Can Fix The Enormous Regional Traffic Jam They Created. Retrieved from https://www.fastcompany.com/40411415/how-silicon-valley-companies-can-fix-the-enormous-regional-traffic-jam-they-created
Saussure, F. D. (1996). Course in general linguistics: Ferdinand de Saussure (C. Bally &A. Sechehaye, Eds.; A. Reidlinger, Trans.). New York: McGraw-Hill.
Singh, K., Katte, A., Maladkar, K., Sharma, A., & Bhatia, R. (2018, February 15). Artificial Neural Networks 101. Retrieved from https://analyticsindiamag.com/artificial-neural-networks-101/
The finest flower of comparative philology. (n.d.). Retrieved from https://www.newstatesman.com/culture/culture/2012/09/finest-flower-comparative-philology
Very nice! Who would’ve thought that correctly identifying a vase and some lemons could be so historic! Looks like they will have to change CAPTCHAs pretty soon.
You did a great job putting neural networks in their proper historical context by bringing them back to their roots with the technologies developed during World War 2. I’m sure time constraints played a part, but it would have also been interesting to see how the invention of transistors and then microprocessors helped expedite the process of creating artificial intelligence.
This is a fascinating area of study. It is interesting to think about how trying to get machines to understand language requires humans to think about how they themselves understand languages. I think you did a great job of conveying this conundrum by including the example of how each person thinks of a different identity when they hear the word “cat”. It makes sense how it would be very difficult to get a machine to “think” in this form of association for a single word. let alone entire languages.
Thanks for opening my eyes to this field I kinda knew existed, but never fully appreciated. Great work!
First, I think it’s important to make the disclaimer that before watching your video, I was completely unfamiliar with the concept of neural networks. With that in consideration, I think you gave a very clear background explanation for the topic which I found particularly helpful. The part that interested me the most was how even the creators of some networks have trouble understanding how their own networks are making inferences or decisions. For me it raises the question: Will there be a point where humans as a species are limited by the output their brains can produce?
Returning to your presentation, I enjoyed how you explained the government origins of computing which then shifted into the commercial sector. Your explanation of our social anxieties that have shaped the development of neural networks was also an interesting link between society and technology.
My only recommendation for improvement would be to speak more about The Terminator(haha)! When I saw that image, I got really excited, because it’s The Terminator obviously, and I understood the connection, but felt like you could have explained it just a little more. Regardless, it was still very well done!