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49. Nvidia A100, Training Game AI, Neural Networks | AI Developer Interview
Episode 49

49. Nvidia A100, Training Game AI, Neural Networks | AI Developer Interview

Hot off the heels of the announcement of Nvidia A100, we have an AI Developer on to talk about what these massive GPU’s are actually used for – AI & ML applications. SPONSOR: https://www.cdkoffers.com 25% Windows Code discount: brokensilicon 3% off all software: dieshrink Win10 pro oem key 13$: https://bit.ly/2Wdfghh Win10 Home oem key 10$: https://bit.ly/3dsbSFi Office 2016 key 27$: https://bit.ly/2Wb3sMx Office 2019 key 44$: https://bit.ly/2WdfBAz Win10 pro oem+Office 2016 35$: https://bit.ly/3bkJeVb 1) 1:21 AI Buzzwords & Introductions (some sound issues) 2) 6:03 What is a Neural Network? 3) 12:53 Challenges in Training Neural Networks 4) 16:21 Sparsity and Pruning 5) 24:03 How will AI Improve our Lives? 6) 35:42 Bottlenecks to AI Research 7) 42:03 AI & ML in Gaming 8) 47:38 CUDA Intrenchment, AMD’s ability to enter the market 9) 50:58 Cerebras & Graphcore 10) 55:53 AMD vs Nvidia Graphics 11) 1:03:13 Comparing Synthetic AI to Biological Brains https://www.thispersondoesnotexist.com/ https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/ https://www.cerebras.net/cerebras-wafer-scale-engine-why-we-need-big-chips-for-deep-learning/ https://cirrascale.com/graphcore-cloud.php?utm_term=graphcore&utm_source=adwords&utm_medium=ppc&utm_campaign=Graphcore&hsa_net=adwords&hsa_grp=86815837387&hsa_mt=p&hsa_tgt=kwd-606839681642&hsa_kw=graphcore&hsa_src=g&hsa_acc=2793442874&hsa_cam=8112712212&hsa_ver=3&hsa_ad=397149594120&gclid=CjwKCAjwwYP2BRBGEiwAkoBpAuc9L_R5wt9rbYuJ7jeopg5W5YdYxaFBkNVpQz336TjcNJYg9jyZ2BoCX0MQAvD_BwE https://arxiv.org/pdf/1803.03635.pdf https://arxiv.org/pdf/1807.01281.pdf https://podcasts.google.com/feed/aHR0cHM6Ly9jaGFuZ2Vsb2cuY29tL3ByYWN0aWNhbGFpL2ZlZWQ/episode/Y2hhbmdlbG9nLmNvbS83LzcwNA?hl=en&ved=2ahUKEwiuzYfG_7jpAhUIHs0KHSavAhAQjrkEegQIBxAE&ep=6 https://twimlai.com/upside-down-reinforcement-learning/?fbclid=IwAR18e99xWPGUN0_SVG84IKUaT5KYSKVhzwMdq37goiOdDwRuUf8s9Z_8GSI https://en.wikipedia.org/wiki/Reinforcement_learning https://en.wikipedia.org/wiki/Concept_learning https://en.wikipedia.org/wiki/Unsupervised_learning#cite_note-2 https://arxiv.org/abs/1912.02875 https://arxiv.org/pdf/1912.02877.pdf https://www.alexirpan.com/2018/02/14/rl-hard.html https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/ https://ai.facebook.com/blog/state-of-the-art-open-source-chatbot/ https://openai.com/blog/jukebox/ https://www.pnas.org/content/pnas/116/43/21854.full.pdf

Broken Silicon · Moore's Law Is Dead

May 20, 20201h 15m

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Show Notes

Hot off the heels of the announcement of Nvidia A100, we have an AI Developer on to talk about what these massive GPU’s are actually used for – AI & ML applications.

SPONSOR: https://www.cdkoffers.com
25% Windows Code discount: brokensilicon
3% off all software: dieshrink
Win10 pro oem key 13$: https://bit.ly/2Wdfghh
Win10 Home oem key 10$: https://bit.ly/3dsbSFi
Office 2016 key 27$: https://bit.ly/2Wb3sMx
Office 2019 key 44$: https://bit.ly/2WdfBAz
Win10 pro oem+Office 2016 35$: https://bit.ly/3bkJeVb

  1. 1:21 AI Buzzwords & Introductions (some sound issues)
  2. 6:03 What is a Neural Network?
  3. 12:53 Challenges in Training Neural Networks
  4. 16:21 Sparsity and Pruning
  5. 24:03 How will AI Improve our Lives?
  6. 35:42 Bottlenecks to AI Research
  7. 42:03 AI & ML in Gaming
  8. 47:38 CUDA Intrenchment, AMD’s ability to enter the market
  9. 50:58 Cerebras & Graphcore
  10. 55:53 AMD vs Nvidia Graphics
  11. 1:03:13 Comparing Synthetic AI to Biological Brains

https://www.thispersondoesnotexist.com/
https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/
https://www.cerebras.net/cerebras-wafer-scale-engine-why-we-need-big-chips-for-deep-learning/
https://cirrascale.com/graphcore-cloud.php?utm_term=graphcore&utm_source=adwords&utm_medium=ppc&utm_campaign=Graphcore&hsa_net=adwords&hsa_grp=86815837387&hsa_mt=p&hsa_tgt=kwd-606839681642&hsa_kw=graphcore&hsa_src=g&hsa_acc=2793442874&hsa_cam=8112712212&hsa_ver=3&hsa_ad=397149594120&gclid=CjwKCAjwwYP2BRBGEiwAkoBpAuc9L_R5wt9rbYuJ7jeopg5W5YdYxaFBkNVpQz336TjcNJYg9jyZ2BoCX0MQAvD_BwE
https://arxiv.org/pdf/1803.03635.pdf
https://arxiv.org/pdf/1807.01281.pdf
https://podcasts.google.com/feed/aHR0cHM6Ly9jaGFuZ2Vsb2cuY29tL3ByYWN0aWNhbGFpL2ZlZWQ/episode/Y2hhbmdlbG9nLmNvbS83LzcwNA?hl=en&ved=2ahUKEwiuzYfG_7jpAhUIHs0KHSavAhAQjrkEegQIBxAE&ep=6
https://twimlai.com/upside-down-reinforcement-learning/?fbclid=IwAR18e99xWPGUN0_SVG84IKUaT5KYSKVhzwMdq37goiOdDwRuUf8s9Z_8GSI
https://en.wikipedia.org/wiki/Reinforcement_learning
https://en.wikipedia.org/wiki/Concept_learning
https://en.wikipedia.org/wiki/Unsupervised_learning#cite_note-2
https://arxiv.org/abs/1912.02875
https://arxiv.org/pdf/1912.02877.pdf
https://www.alexirpan.com/2018/02/14/rl-hard.html
https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/
https://ai.facebook.com/blog/state-of-the-art-open-source-chatbot/
https://openai.com/blog/jukebox/
https://www.pnas.org/content/pnas/116/43/21854.full.pdf


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