Adapting to Change: How Machine Intelligences Adapt to a Changing World • Robert Crowe • GOTO 2022
This presentation was recorded at GOTO Amsterdam 2022. #GOTOcon #GOTOams
Robert Crowe - TensorFlow Developer Advocate at Google
ABSTRACT
Change is perhaps the only constant in our world, and it requires us to adapt to new realities. Depending on the rate of change and the amount of change, the requirement to adapt often results in our brains increasing levels of stress hormones as they try to adjust. To adapt to changes in the world that it lives in, our brain needs to make changes in what it’s learned, which means making changes in the neural structures that have captured that learning. Sometimes in fact it can be more difficult for our brains to relearn something than it is for them to learn it for the first time. Failing to adapt to change has been the downfall of many people, and even whole species.
While this is true for human intelligence, it’s also true for machine intelligence. But because of the isolated nature of machine intelligences it’s even more difficult for them to detect and adapt to change. In the vast majority of cases these intelligences are very limited in their ability to perceive and adapt to change, even in very limited, specialized areas of learning, which requires humans to design and direct their adaptation to change. Failing to adapt to change can lead to poor decision making, and even catastrophic consequences.
This talk will explore:
• How both human and machine intelligences adapt to change, including the state of the art and industry best practices for adapting machine intelligence to change
• Techniques for making change less stressful for both machines and their humans [...]
TIMECODES
00:00 Intro
00:55 Change can be stressful
01:55 Do I need to adapt?
02:51 Mental adaptation
04:01 Neocortex
05:47 Cortical columns
06:45 Reference frames
09:54 Machine intelligence
10:05 Artificial neural networks
11:00 Supervised learning
12:32 Prediction aka inference
14:26 Expectations
15:27 Machine expectations
16:45 Labeling data
17:50 Consequences
18:17 MLOps
19:41 Production ML
21:35 Tales from the trenches
21:53 Production ML research
22:29 Process
22:40 What is MLOps?
23:10 CI, deployment & training
24:35 Training & deploying models
25:29 TFX production components
26:12 MLOps Level 0
29:46 Outro
Read the full abstract here:
RECOMMENDED BOOKS
Phil Winder • Reinforcement Learning •
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) •
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns •
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision •
Aurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow •
#TensorFlow #TFX #AI #ML #MLOps #DeepLearning #ArtificialIntelligence #MachineLearning #ReinforcementLearning #DataScience #MachineIntelligence #Programming #Change #NeuralNetworks #ArtificialNeuralNetwork #Inference #Data #CI #ContinuousIntegration #Deployment
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