Dr.Christoph Mittendorf-Beyond Bard and Transformers: Unconventional ML Use Cases

In this keynote, Dr. Christoph Mittendorf, Machine Learning Specialist at Google, will discuss the latest advances in machine learning, GenAI and computer vision for chess and sports. He will present case studies on how these technologies have been used to improve performance and gain insights into the games. He will also discuss the challenges of applying machine learning and computer vision to chess and sports. These challenges include the need for large amounts of data, the difficulty of identifying relevant patterns, and the need to develop algorithms that can be run in real time. Despite these challenges, machine learning and computer vision have the potential to revolutionize the way we play and watch chess and sports. By providing insights into the games that were previously unavailable, these technologies can help us to improve our performance and gain a deeper understanding in various areas.

Transcript

1. Proprietary + Confidential
   Munich Datageeks
   Machine Learning & Data
   June 20th 2023
   Dr. Christoph Mittendorf
   Machine Learning Specialist
   

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2. Proprietary + Confidential
   Google is the pioneer in AI
   2018
   Google’s
   groundbreaking
   large language
   model, BERT
   2017
   Google invents
   Transformer
   kickstarting LLM
   revolution
   2019
   Text-to-Text
   Transfer Transformer
   LLM 10B P Model
   Open Sourced
   2021
   AlphaFold predicts
   structures
   proteins
   2020
   Google LaMDA
   Model Trained to
   converse
   Responsible AI
   3,000 Researchers
   7,000 Publications
   Accountable to People
   Built & Tested for Safety
   Socially Beneficial
   Avoid creating unfair
   bias
   Upholds high scientific
   standards
   Privacy in design
   GenAI / Bard
   2023
   A conversational AI
   powered by LaMDA.
   PaLM2 340 billion
   parameter model
   trained on 3.6 trillion
   tokens
   2022
   PaLM 540 billion
   parameter model
   Imagen realistic
   Text-to-Image
   Diffusion Model
   2015 / 2016
   Google Open Sourced
   TensorFlow
   

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3. Neural Networks
   Building Blocks
   & Parameters
   1
   

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4. Proprietary + Confidential
   Building AI Systems
   Source: Deepmind 2016
   AI
   ● Learns solutions from first principles (data / experience)
   ● Can generalize to new tasks Intuition rather than calculation
   ● Enormous search space / vast amount of combinations
   ● Inspired by Neuroscience (NN) aka our BRAIN
   ● Deepblue (1997) vs AlphaGo (2016)
   1959
   AI Approach Learning Systems
   

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5. Proprietary + Confidential
   Electrocardiogram Performance Diagnostics
   QRS
   Compex
   PR Interval
   QT Interval
   ST
   Segment
   PR
   Segment
   R
   P
   Q
   S
   T
   From Triathlon to Machine Learning
   

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6. Proprietary + Confidential
   Building AI Systems
   Neuroscience Heartbeat
   +
   

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7. Proprietary + Confidential
   Inspiration
   Neuroscience
   Structure
   of Neural Networks
   Cardiology-Science
   Activation
   of Neurons
   !
   

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8. Proprietary + Confidential
   Electrocardiogram
   QRS
   Compex
   PR Interval
   QT Interval
   ST
   Segment
   PR
   Segment
   R
   P
   Q
   S
   T
   Popular Activation Functions
   From Triathlon to Machine Learning
   

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9. Proprietary + Confidential
   Input Layer Hidden Layer 1 Hidden Layer 2 Output Layer
   Structure and Building Blocks
   Neural Networks
   Multilayer Perceptron
   Fully Connected
   Feedforward Network
   Deep Neural Network
   

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10. Proprietary + Confidential
    Input Weights Activation Function Prediction
    x
    1
    w
    1
    Structure and Building Blocks
    Neural Networks
    Number of Parameters?
    x
    2
    x
    3
    w
    2
    w
    3
    Y
    Sum Function
    + Bias
    Hidden Layer
    Input Layer Output Layer
    

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11. Proprietary + Confidential
    Input Weights Activation Function Prediction
    x
    1
    w
    1
    Structure and Building Blocks
    Neural Networks
    Number of Parameters?
    x
    2
    x
    3
    w
    2
    w
    3
    Y
    Sum Function
    + Bias
    Hidden Layer
    Input Layer Output Layer
    

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12. Proprietary + Confidential
    Input Weights Activation Function Output / Prediction
    x
    1
    w
    1
    Structure and Building Blocks
    Neural Networks
    Number of Parameters?
    x
    2
    x
    3
    w
    2
    w
    3
    Y
    Sum Function
    + Bias
    Loss function
    X
    Optimizer
    Ground Truth
    

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13. Proprietary + Confidential
    Input Layer Hidden Layer 1 Hidden Layer 2 Output Layer
    x
    i
    w
    j
    ∑ -> Activation Function
    Structure and Building Blocks
    Neural Networks
    Number of Parameters?
    

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14. Proprietary + Confidential
    Input Layer Hidden Layer 1 Hidden Layer 2 Output Layer
    x
    i
    w
    j
    ∑ -> Activation Function
    Structure and Building Blocks
    Neural Networks
    Number of Parameters = 41
    

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15. Proprietary + Confidential
    Electrocardiogram
    QRS
    Compex
    PR Interval
    QT Interval
    ST
    Segment
    PR
    Segment
    R
    P
    Q
    S
    T
    How to get this in code…
    Neural Networks
    

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16. Proprietary + Confidential
    Workbench is a Jupyter-based development environment
    that is fully managed, scalable, and enterprise-ready.
    ● Easy exploration and data analysis
    ● Rapid prototyping and model development
    Electrocardiogram
    QRS
    Compex
    PR Interval
    QT Interval
    ST
    Segment
    PR
    Segment
    R
    P
    Q
    S
    T
    Neural Networks
    Vertex AI - Workbench
    

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17. Proprietary + Confidential
    from keras import backend as K
    Electrocardiogram
    QRS
    Compex
    PR Interval
    QT Interval
    ST
    Segment
    PR
    Segment
    R
    P
    Q
    S
    T
    Iterative Process
    Neural Networks
    

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18. Proprietary + Confidential
    Electrocardiogram
    QRS
    Compex
    PR Interval
    QT Interval
    ST
    Segment
    PR
    Segment
    R
    P
    Q
    S
    T
    Neural Networks
    Vertex AI - Workbench
    

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19. Proprietary + Confidential
    Heartbeat Activation
    Neural Networks
    Vertex AI - Workbench
    Zoomed in
    Zoomed out
    

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20. Proprietary + Confidential
    Nobel Prize? Vertex AI - Workbench
    Mnist Challenge - Classification of binary images of handwritten digits.
    Total params: 59,526
    Accuracy: Number of correct predictions / Total number of
    predictions.
    97.0% on Validation Data
    using Heartbeat
    99.3% on Validation Data
    using ReLu
    

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21. Key learning
    Maybe your Heartbeat is the Key!
    ML Research can be done by everyone having the heart in the right
    place and good tool box.
    ?
    

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22. Computer Vision
    Running Efficiency
    2
    

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23. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    Tl;dr: Eliud Kipchoge has an exceptional running efficiency.
    Eliud Kipchoge, born 5 November 1984, is a Kenyan long-distance runner who
    competes in the marathon. Widely regarded as one of the greatest marathon
    runners of all time, he is the 2016 and 2020 Olympic marathon champion and
    the world record holder in the marathon with a time of 2:01:09 set at the 2022
    Berlin Marathon. He has run four of the six fastest marathons in history.
    In October 2019 - Eliud ran a 1:59:40 marathon - becoming the first person in
    recorded history to break the two-hour barrier over a marathon distance. He
    did so under experimental conditions including a featured a pace car and
    included rotating teams to maximize his running efficiency.
    Our Benchmark
    World Record Holder
    Eliud Kipchoge
    

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24. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    MoveNet is a pretrained ML model that detects 17 keypoints of a body.
    The model is available on TF Hub, our open repository and library for reusable
    machine learning (https://www.tensorflow.org › hub)
    Goal: Evaluate Running Efficiency
    World Record Holder
    Eliud Kipchoge
    

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25. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    The architecture consists of two components: a feature extractor and a set of
    prediction heads. All models are trained using the TensorFlow Object
    Detection API. The feature extractor in MoveNet is MobileNetV2 with an
    attached feature pyramid network (FPN).
    MoveNet Model architecture
    

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26. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    There are four prediction heads attached to the feature extractor:
    (1) Person center heatmap: predicts the geometric center of person instances
    (2) Keypoint regression field: predicts full set of keypoints for a person, used
    for grouping keypoints into instances
    (3) Person keypoint heatmap: predicts the location of all keypoints,
    independent of person instances
    (4) 2D per-keypoint offset field: predicts local offsets from each output
    feature map pixel to the precise sub-pixel location of each keypoint
    MoveNet Model architecture
    

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27. Proprietary + Confidential
    Our goal is a custom classification on “running efficiency”
    26-50% Eliud
    51-75% Eliud
    0-25% Eliud
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    Using MoveNet - we have built a Similarity Model using a similarity function
    that measures how similar or related two keypoints are. In detail - we are
    comparing certain movements (position of keypoint during steps) at a
    particular speed(s). Output is the Eliud Efficiency Model (EEM)
    Prediction: Eliud Efficiency
    Meets expectations
    Needs improvement
    Exceeds expectations
    76-100% Eliud Superb
    

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28. Proprietary + Confidential
    One Platform - Google Cloud Architecture & Libraries
    Implementation & Architecture
    Custom loss functions
    - Location loss
    - Contrastive loss
    MoveNet Pretrained
    Model registry
    Model serving
    Training Benchmark
    Eliud Kipchoge
    Inference Data
    Workbench
    Keypoints detection
    Similarity evaluation
    

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29. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    The Perfect Style
    100% Eliud
    Superb
    [160.26083 96.47073 ]
    [166.83864 89.2794 ]
    [154.45134 88.84522 ]
    [169.83588 89.157646]
    [141.07724 87.254 ]
    [171.61955 122.288956]
    [128.79294 126.7425 ]
    [177.90474 166.47029 ]
    [110.68105 188.49167 ]
    [186.85052 190.10062 ]
    [159.1282 183.19426 ]
    [160.78021 235.62056 ]
    [135.02223 236.45792 ]
    [174.34465 313.08237 ]
    [179.8592 317.48926 ]
    [157.75436 383.82657 ]
    [158.09479 394.32785 ]
    [160.26083 96.47073 ]
    [166.83864 89.2794 ]
    [154.45134 88.84522 ]
    [169.83588 89.157646]
    [141.07724 87.254 ]
    [171.61955 122.288956]
    [128.79294 126.7425 ]
    [177.90474 166.47029 ]
    [110.68105 188.49167 ]
    [186.85052 190.10062 ]
    [159.1282 183.19426 ]
    [160.78021 235.62056 ]
    [135.02223 236.45792 ]
    [174.34465 313.08237 ]
    [179.8592 317.48926 ]
    [157.75436 383.82657 ]
    [158.09479 394.32785 ]
    [170.6349 80.21549 ]
    [172.98218 75.30904 ]
    [165.7312 75.021126]
    [166.37975 75.49159 ]
    [147.61896 74.129814]
    [172.6098 106.84404 ]
    [124.246284 112.40009 ]
    [177.53781 147.2778 ]
    [ 86.22109 160.25734 ]
    [177.21365 155.98389 ]
    [128.97253 175.98878 ]
    [157.27747 224.38336 ]
    [131.50974 226.93161 ]
    [119.27184 302.12704 ]
    [190.04367 301.92426 ]
    [ 56.288105 270.6312 ]
    [215.00241 390.95316 ]
    [160.26083 96.47073 ]
    [166.83864 89.2794 ]
    [154.45134 88.84522 ]
    [169.83588 89.157646]
    [141.07724 87.254 ]
    [171.61955 122.288956]
    [128.79294 126.7425 ]
    [177.90474 166.47029 ]
    [110.68105 188.49167 ]
    [186.85052 190.10062 ]
    [159.1282 183.19426 ]
    [160.78021 235.62056 ]
    [135.02223 236.45792 ]
    [174.34465 313.08237 ]
    [179.8592 317.48926 ]
    [157.75436 383.82657 ]
    [158.09479 394.32785 ]
    

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30. Proprietary + Confidential
    [160.26083 96.47073 ]
    [166.83864 89.2794 ]
    [154.45134 88.84522 ]
    [169.83588 89.157646]
    [141.07724 87.254 ]
    [171.61955 122.288956]
    [128.79294 126.7425 ]
    [177.90474 166.47029 ]
    [110.68105 188.49167 ]
    [186.85052 190.10062 ]
    [159.1282 183.19426 ]
    [160.78021 235.62056 ]
    [135.02223 236.45792 ]
    [174.34465 313.08237 ]
    [179.8592 317.48926 ]
    [157.75436 383.82657 ]
    [158.09479 394.32785 ]
    [160.26083 96.47073 ]
    [166.83864 89.2794 ]
    [154.45134 88.84522 ]
    [169.83588 89.157646]
    [141.07724 87.254 ]
    [171.61955 122.288956]
    [128.79294 126.7425 ]
    [177.90474 166.47029 ]
    [110.68105 188.49167 ]
    [186.85052 190.10062 ]
    [159.1282 183.19426 ]
    [160.78021 235.62056 ]
    [135.02223 236.45792 ]
    [174.34465 313.08237 ]
    [179.8592 317.48926 ]
    [157.75436 383.82657 ]
    [158.09479 394.32785 ]
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    Leveraging MoveNet’s Keypoints
    - we applied a Similarity Function
    [170.6349 80.21549 ]
    [172.98218 75.30904 ]
    [165.7312 75.021126]
    [166.37975 75.49159 ]
    [147.61896 74.129814]
    [172.6098 106.84404 ]
    [124.246284 112.40009 ]
    [177.53781 147.2778 ]
    [ 86.22109 160.25734 ]
    [177.21365 155.98389 ]
    [128.97253 175.98878 ]
    [157.27747 224.38336 ]
    [131.50974 226.93161 ]
    [119.27184 302.12704 ]
    [190.04367 301.92426 ]
    [ 56.288105 270.6312 ]
    [215.00241 390.95316 ]
    [160.26083 96.47073 ]
    [166.83864 89.2794 ]
    [154.45134 88.84522 ]
    [169.83588 89.157646]
    [141.07724 87.254 ]
    [171.61955 122.288956]
    [128.79294 126.7425 ]
    [177.90474 166.47029 ]
    [110.68105 188.49167 ]
    [186.85052 190.10062 ]
    [159.1282 183.19426 ]
    [160.78021 235.62056 ]
    [135.02223 236.45792 ]
    [174.34465 313.08237 ]
    [179.8592 317.48926 ]
    [157.75436 383.82657 ]
    [158.09479 394.32785 ]
    Step 1: Evaluating Keypoint positions for continuous frames (movement)
    Location loss: Euclidean L2 distance between the pixel coordinates.
    Step 2: Evaluating relative position between differences Keypoints (posture)
    Contrastive loss: Keypoint location in relation to other keypoints in the same frame.
    

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31. Proprietary + Confidential
    [344.19147 181.68451]
    [332.36035 171.67693]
    [342.86694 171.40009]
    [292.6452 180.04279]
    [329.6357 174.82632]
    [268.9414 238.52542]
    [341.15375 222.92154]
    [200.4838 278.67752]
    [354.53955 294.47366]
    [248.2235 321.2749 ]
    [381.76706 274.0878 ]
    [272.6782 382.45728]
    [306.644 387.27908]
    [350.14877 487.54935]
    [238.31728 503.66275]
    [306.85602 581.99554]
    [146.82817 608.27313]
    [344.19147 181.68451]
    [332.36035 171.67693]
    [342.86694 171.40009]
    [292.6452 180.04279]
    [329.6357 174.82632]
    [268.9414 238.52542]
    [341.15375 222.92154]
    [200.4838 278.67752]
    [354.53955 294.47366]
    [248.2235 321.2749 ]
    [381.76706 274.0878 ]
    [272.6782 382.45728]
    [306.644 387.27908]
    [350.14877 487.54935]
    [238.31728 503.66275]
    [306.85602 581.99554]
    [146.82817 608.27313]
    [347.23654 182.92076]
    [341.32898 172.06996]
    [345.10815 171.49492]
    [318.03317 171.599 ]
    [328.86688 171.00182]
    [312.2267 226.91055]
    [302.70245 210.58597]
    [319.71695 279.5299 ]
    [239.49994 233.21846]
    [332.75214 271.21823]
    [303.0601 264.2513 ]
    [287.06555 380.84973]
    [312.01486 381.8607 ]
    [221.09059 500.76703]
    [382.81763 485.10123]
    [122.54193 586.4088 ]
    [345.3879 596.1339 ]
    [344.19147 181.68451]
    [332.36035 171.67693]
    [342.86694 171.40009]
    [292.6452 180.04279]
    [329.6357 174.82632]
    [268.9414 238.52542]
    [341.15375 222.92154]
    [200.4838 278.67752]
    [354.53955 294.47366]
    [248.2235 321.2749 ]
    [381.76706 274.0878 ]
    [272.6782 382.45728]
    [306.644 387.27908]
    [350.14877 487.54935]
    [238.31728 503.66275]
    [306.85602 581.99554]
    [146.82817 608.27313]
    70% Eliud
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    Amateur
    Christoph Mittendorf
    Exceeds expectations
    

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32. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    Amateur
    Christoph Mittendorf
    What are the best shoes?
    

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33. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    World Record Holder
    Eliud Kipchoge
    Amateur
    Christoph Mittendorf
    ?
    70%
    ?
    What can I improve immediately?
    10 Euro -
    Flip Flops
    50 Euro -
    Running Shoe
    160 Euro -
    Running Shoe
    

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34. Proprietary + Confidential
    The Eliud Model - Inferences
    50 Euro -
    Running Shoe
    160 Euro -
    Running Shoe
    10 Euro -
    Flip Flops
    Efficiency
    49% 70% 76%
    Suberb
    Exceeds expectations
    Meets expectations
    Type
    Benchmark
    

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35. Diffusion Models
    ML Shoe improvement
    3
    

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36. Proprietary + Confidential
    From Research to Practice…
    Encoder Network Decoder Network
    “Encoding means to convert
    data into a lower-dimensional
    format.”
    “Decoding means to recreate
    the input data (or an association)
    from the encoded
    representation.”
    'context vector'
    

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37. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    Carbon Fibre Goal: Update Shoes
    for Chris World Record
    Using GenAI
    160 Euro -
    Running Shoe
    Closing the gap (+24%)
    “Make the shoe
    lighter and faster
    so that I can
    fly like a bird!”
    

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38. Proprietary + Confidential
    How to become a Marathon World Champion (using ML)?
    Carbon Fibre Updated Shoes
    for Chris World Record
    “Here is your improved shoe”
    160 Euro -
    Running Shoe
    Closing the gap (+24%)
    Feathers for Flying
    Breathable material
    Beautiful color
    combination
    

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39. Proprietary + Confidential
    Getting to a 120kmh with the New ML Feather Shoes!
    Old Carbon Fibre Updated Shoes
    for Chris World Record
    Fast and Flying
    Slow and clumsy
    1
    

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40. Proprietary + Confidential
    Seek Inspiration - A shoe for every Occasion!
    The Gold Medal
    The Best of Nike & Hoka
    MindCraft Gaming Shoe
    Christopher Street Day
    Original - Base Model
    

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41. Proprietary + Confidential
    Limitations
    Model and Result Limitations
    1. Human limitations:
    a. Human attributes are different
    b. Running speed might vary over time
    c. Stride length / Step size is individual
    2. Technical limitations:
    a. Camera angle(s) influence keypoints
    b. Lightning conditions decrease accuracy
    c. Frame rate consistency is required
    3. Model & Data limitation:
    a. Efficiency measurement is subjective
    b. Training data is limited (1x Elite) & (1x Nerd)
    c. Similarity function has model bias
    d. Only short video sequences were used
    e. Custom loss functions & customer weights for keypoints
    are biased
    Missing evaluation of
    Bundeswehr-Boots?
    

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42. Proprietary + Confidential
    Key learning
    1. Let’s Challenge the State of Possible
    2. Seek inspiration from your personal passion
    3. Combine different technologies and iterate
    

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43. Proprietary + Confidential
    Key learning
    1. Let’s Challenge the State of Possible
    2. Seek inspiration from your personal passion
    3. Combine different technologies and iterate
    4. If we are honest - Flip Flops suck for running!
    

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44. Deep Learning Model
    Transformers in
    a nutshell
    4
    

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45. Proprietary + Confidential
    From Research to Practice…
    Research Paper The Architecture
    2017
    Source: https://arxiv.org/pdf/1706.03762.pdf
    

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46. Proprietary + Confidential
    From Research to Practice…
    Explanation Embeddings
    Word embeddings (i.e., distributed
    representations, word vectors) are dense feature
    vector representations of words in a specific
    dimensional space, which are usually learned by an
    unsupervised algorithm when fed with large
    amounts of tokens.
    

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47. Proprietary + Confidential
    From Research to Practice…
    Function Positional Encoder
    Source: https://arxiv.org/pdf/1706.03762.pdf
    PE is a vector that gives context based on the position of the
    word in the sentence (a unique representation).
    Since we have no recurrent networks that can remember how
    sequences are fed into a model, we need to somehow give every
    item in our sequence a relative position since a sequence depends on
    the order of its elements.
    These positions are added to the embedded representation
    (n-dimensional vector) of each item.
    This is done using positional encoding which can be any function that
    attributes numerical position values to different parts of the input
    sequence.
    

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48. Proprietary + Confidential
    From Research to Practice…
    Function Positional Encoder
    Source: https://arxiv.org/pdf/1706.03762.pdf
    

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49. Proprietary + Confidential
    From Research to Practice…
    Self Attention Attention
    Source: https://arxiv.org/pdf/1706.03762.pdf
    A self-attention module works by comparing every input in
    the sequence to every other input in the same sequence,
    including itself, and reweighing the embeddings of each input
    to include contextual relevance.
    At a high level, the Self-attention block is comprised of three
    steps/parts:
    ● Dot product similarity to find alignment scores
    ● Normalization of the scores to get the weights
    ● Reweighting of the original embeddings using the
    weights
    

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50. Proprietary + Confidential
    From Research to Practice…
    Research Paper The Architecture
    “The Transformer is a magnificent neural network architecture
    because it is a general-purpose differentiable computer.”
    Advantages
    ● Transformers process the entire input all at once –
    > do not forget
    ● Transformers allow for more parallelization
    > excellent training on our hardware GPUs & TPUs
    ● Transformers allow backpropagation + gradient descent
    > allow great optimization
    Source: https://arxiv.org/pdf/1706.03762.pdf
    Andrej Karpathy
    

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51. Google Cloud PaLM 2
    A true next generation LLM
    Multilingual
    Understand, translate and
    generate across +100
    languages
    Elastic
    Natively multi-sized with
    optimized scaling
    architecture
    Scientific Reasoning
    Next level of deep
    understanding from
    mathematics to logics
    Advanced Coding
    From code generation &
    completion to advanced
    code translation
    https://ai.google/static/documents/palm2techreport.pdf
    PaLM 2 LLM sizes
    Gecko: Small
    sized-model designed
    for specific use-cases -
    incl. interactive
    applications. Can run
    off-line incl. mobile
    devices
    Otter: Medium-sized
    model designed for
    general-purpose use
    including generating
    text, translating
    languages and
    answering question
    Bison: A large-sized
    model designed for very
    complex tasks incl.
    industry specific
    use-cases and
    advanced natural
    language processing
    Unicorn: Full- sized model
    designed for the most
    demanding tasks, incl.
    generating large amounts
    of very complex text or
    translating between
    multiple languages.
    

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52. AI Systems
    Safety & Ethics overview
    

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53. Proprietary + Confidential
    AI systems can only benefit the world if
    we make them reliable and fair.”
    Google
    Source: Google - https://blog.google/technology/ai/update-work-ai-responsible-innovation/
    

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54. Proprietary + Confidential
    AI systems can only benefit the world if
    we make them reliable and fair.”
    Fairness refers to the
    attempt of correcting bias.
    Reliability is the overall consistency of a
    measure - it produces similar results under
    consistent conditions.
    *As it is the case with many ethical concepts, definitions of fairness and bias are always controversial.
    

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55. Proprietary + Confidential
    A reliable and fair AI System, like all
    technology, needs to be built and used
    responsibly.”
    Google
    Source: Google - https://blog.google/technology/ai/update-work-ai-responsible-innovation/
    

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56. Proprietary + Confidential
    Google AI Principles
    AI should:
    be socially beneficial
    avoid creating or reinforcing unfair bias
    be built and tested for safety
    be accountable to people
    incorporate privacy design principles
    uphold high standards of scientific excellence
    be made available for uses that accord with these principles
    likely to cause overall harm
    principal purpose to direct injury
    surveillance violating internationally accepted norms
    purpose contravenes international law and human rights
    Applications we will not pursue:
    1
    2
    3
    5
    6
    7
    4
    1
    2
    3
    4
    2018-today
    

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57. Cloud. Nach deutschen Maßstäben.
    Thank you.
    

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