The document discusses artificial intelligence (AI) and its applications, focusing on algorithms that mimic human intelligence in problem-solving, perception, and natural language processing. It highlights the importance of data transmission, privacy concerns, and the performance of various computing techniques such as machine learning, distributed deep neural networks, and AI accelerators. Key technologies mentioned include GPUs, TPUs, FPGAs, and techniques like quantization, pruning, and federated learning.

1. Fares S. Al-Qunaieer
Engineering Consultant in Artificial Intelligence at SITE | Google
Developer Expert (GDE) in Machine Learning
7 September 2020

2. AI is a field in
Computer Science
that concerns on
developing
algorithms that
mimic human
intelligence

3. Artificial Intelligence
Problem Solving
Perception
Natural Language
Processing
Planning Knowledge
Representation
Machine LearningReasoning

4. Input Consume Compute
request
response

6. Data transmission

7. Data transmission
Latency (Performance)

8. Data transmission
Latency (Performance)
Scalability

9. Data transmission
Latency (Performance)
Scalability
User Experience

10. Data transmission
Latency (Performance)
ScalabilityPrivacy Concerns
User Experience

11. Data transmission
Latency (Performance)
Scalability
Security Risks
Privacy Concerns
User Experience

12. • Performing the computation locally
• Distribute the computation across the network
• Perform computations close to the data source

14. • Security and surveillance
• Removing Personally Identifiable Information (PII)
• Image quality check
• Autonomous cars
• Predictive maintenance
• Medical monitoring devices

15. Data transmission
Latency (Performance)
Scalability
Security Risks
Privacy Concerns
User Experience

16. Input Consume/Compute

17. Algorithms Hardware Capturing

18. Algorithms Hardware Capturing

19. Small networks

20. QuantizationSmall networks

21. Quantization PruningSmall networks

22. Image source: https://towardsdatascience.com/pruning-deep-neural-network-56cae1ec5505

23. Quantization Pruning
Knowledge Distillation
Small networks

24. Image source: https://nervanasystems.github.io/distiller/knowledge_distillation.html

25. Quantization Pruning
Knowledge Distillation
Small networks
Distributed Deep Neural
Networks (DDNN)

26. Image source: Machine Learning at the Network Edge: A Survey, M.G. Sarwar Murshed et. al, 2019

27. Federated and split learning
Quantization Pruning
Knowledge Distillation
Small networks
Distributed Deep Neural
Networks (DDNN)

28. Federated and split learning
Quantization Pruning
Knowledge Distillation
Small networks
Distributed Deep Neural
Networks (DDNN)

29. Image source: https://proandroiddev.com/federated-learning-e79e054c33ef

30. Image source: https://splitlearning.github.io

31. Algorithms Hardware Capturing

32. Graphics Processing
Unit (GPU)

33. Video source: https://www.youtube.com/watch?v=-P28LKWTzrI

34. Graphics Processing
Unit (GPU)
AI Accelerators

35. Graphics Processing
Unit (GPU)
Tensor Processing
Unit (TPU)
AI Accelerators

36. Graphics Processing
Unit (GPU)
Tensor Processing
Unit (TPU)
AI Accelerators
Field Programmable
Gate Array (FPGA)

37. Graphics Processing
Unit (GPU)
Tensor Processing
Unit (TPU)
AI Accelerators
Field Programmable
Gate Array (FPGA)
Application Specific
Integrated Circuit (ASIC)

38. Algorithms Hardware Capturing

39. Embedded Sensors

40. Embedded Sensors
Text / Speech / Images

41. Embedded Sensors
Text / Speech / Images
Event Cameras

44. Fares S. Al-Qunaieer
Website: www.qunaieer.com
Twitter: @qunaieer
LinkedIn: https://www.linkedin.com/in/qunaieer