Making Sense of Artificial Intelligence: A Practical Guide

Transcription

1 Making Sense of Artificial Intelligence: A Practical Guide JEDEC Mobile & IOT Forum Copyright 2018 Young Paik, Samsung Senior Director Product Planning

2 Disclaimer This presentation and/or accompanying oral statements by Samsung representatives collectively, the Presentation ) is intended to provide information concerning the SSD and memory industry and Samsung Electronics Co., Ltd. and certain affiliates (collectively, Samsung ). While Samsung strives to provide information that is accurate and up-to-date, this Presentation may nonetheless contain inaccuracies or omissions. As a consequence, Samsung does not in any way guarantee the accuracy or completeness of the information provided in this Presentation. This Presentation may include forward-looking statements, including, but not limited to, statements about any matter that is not a historical fact; statements regarding Samsung s intentions, beliefs or current expectations concerning, among other things, market prospects, technological developments, growth, strategies, and the industry in which Samsung operates; and statements regarding products or features that are still in development. By their nature, forward-looking statements involve risks and uncertainties, because they relate to events and depend on circumstances that may or may not occur in the future. Samsung cautions you that forward looking statements are not guarantees of future performance and that the actual developments of Samsung, the market, or industry in which Samsung operates may differ materially from those made or suggested by the forward-looking statements in this Presentation. In addition, even if such forward-looking statements are shown to be accurate, those developments may not be indicative of developments in future periods. 2

3 Deep Learning Has Changed AI Artificial Intelligence Rules Based Data Driven Machine Learning Example: Speech recognition Few words ~ 99% accurate Many words ~ 60% accurate Not accurate enough to be commercially viable Deep Learning Approaching Human Level Accuracy

4 This Is Not That Kind of Talk Deep Learning has a lot of jargon This talk will skip as much as possible. Deep Neural Net Convolutional Neural Net Recurrent Neural Net CUDA Pooling LSTM GPU Supervised Training Stochastic Gradient Descent Fully connected OpenCL Backpropagation Variable precision math Softmax ReLu

5 Deep Learning Simplified High-Level AI Flow Training Inference Data Storage Data Processing DDR Deployment Phones AI Assistants Home Devices IoT SSD GDDR Auto Many TB - PB+ HBM >1 TBps BW GPU Memory DL Model < 1 MB 10 GB Mobile CPU LPDDR NPU Hours - Weeks

6 3 Takeaways Data is King More data = more accuracy Deep Learning is hard Leave it to the professionals You don t have to use one AI Many, smaller AIs are better than one big one

7 Circle of (DL) Life DL models need to be constantly be fed data. When designing new products find ways to feed back data to improve AIs. Deploy Create AI Gather Data Example: Home thermostat: User override of settings should be seen as an AI fault and feed back.

8 Real World Considerations Q: How would you implement mobile speech recognition? DL Model Size In general, larger models are more accurate, but may be hard to fit on mobile/iot devices. Power Constantly running DNNs on devices may take a lot of power. Compute Mobile/IoT resources may not be enough to run ondevice. Decentralized DB Some apps require additional data that are best centrally stored. Network Device Data Training DL Model DL Model on-device DL Model on-server Server Training Duration Training may take days or even weeks. Hard to parallelize. Latency Keeping models on servers will increase latency. Bandwidth End users may not want to constantly use bandwidth to servers. Privacy Constantly feeding data back to servers may have privacy concerns.

9 Tricks For Improvement Transfer Learning Model Compressions Take a pre-trained DL model and retrain it with new data. Many weights and nodes may not be important and can be removed. Pros Trains much faster than first model. No need for AI scientist. Easy frameworks available in cloud. Cons Use case must be very similar. Not as accurate as original. Pros DL models may compress by 500x. Accuracy may not be impacted. May decrease power needed to infer. Cons Requires additional processing after initial training.

10 Real World Considerations Q: How would you implement mobile speech recognition? DL Model Size In general, larger models are more accurate, but may be hard to fit on mobile/iot devices. Power Constantly running DNNs on devices may take a lot of power. Compute Mobile/IoT resources may not be enough to run ondevice. Decentralized DB Some apps require additional data that are best centrally stored. Network Device Data Training DL Model DL Model on-device DL Model on-server Server Training Duration Training may take days or even weeks. Hard to parallelize. Latency Keeping models on servers will increase latency. Bandwidth End users may not want to constantly use bandwidth to servers. Privacy Constantly feeding data back to servers may have privacy concerns.

11 Find New Ways Of Using Old Tech Artificial Intelligence Rules Based Data Driven Machine Learning Example: Speech recognition Few words ~ 99% accurate Many words ~ 60% accurate New uses? Deep Learning Approaching Human Level Accuracy

12 Making Use of Multiple AIs Data Training Model Network Device What is HOTWORD, What is Server Decentralized DB Centralized DB can store data that can be used by AI. Latency Latency for full AI is still longer, but now hotword recognition masks issue. Privacy and Bandwidth Use of hotword minimizes data sent back to server. Power Specialized AI for hotwords require much less power.

13 Q: How would you implement Other Use Cases Facial Recognition? Autonomous Driving? Security Cameras? Drone Delivery Network Device Data Training DL Model DL Model on-device Server DL Model on-server

14 Conclusions AI is still early in its development. Design of AI systems is evolving. You may find new uses for old ideas.

15 Thank you! Young Paik