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ML6 talk at Nexxworks Bootcamp

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Karel Dumon

Some weeks ago, our ML6 agent Karel Dumon gave a talk at a Nexxworks Bootcamp. During this week-long event, several speakers are invited to take the floor to inspire a heterogenous group of (senior) business people from a wide range of industries. On the third day, Artificial Intelligence was planned. A broad intro to AI and ML was given by prof. dr. Eric Mannens, after which Karel provided the audience with some hands-on insights through use cases.

1 of 101
Nexxworks Bootcamp Ghent - 27/09/2017 karel.dumon@ml6.eu - @kareldumon
Nexxworks Bootcamp Ghent - 27/09/2017 Building machine learning* solutions with Google Cloud*
Nexxworks Bootcamp Ghent - 27/09/2017 Team of Data Engineers, Data Scientist & Machine Learning Engineers Closing the gap between lots of data - lacking insights Robust & agile ML solutions through scalable APIs Premium Partner of Google Cloud
Nexxworks Bootcamp Ghent - 27/09/2017 Building machine learning* solutions with Google Cloud*
Nexxworks Bootcamp Ghent - 27/09/2017 Rise of ML Cases The Future Get started!
Nexxworks Bootcamp Ghent - 27/09/2017
#cloudconf2016
Confidential & ProprietaryGoogle Cloud Platform 8 2012 20132002 2004 2006 2008 2010 GFS MapReduce Bigtable Colossus Dremel Flume Megastore Spanner Millwheel Pub/Sub F1 2016 Dataflow TensorFlow Google’s 15+ years innovation in data
Confidential & ProprietaryGoogle Cloud Platform 9 Decrease the innovation gap 2012 20132002 2004 2006 2008 2010 GCS Dataproc Bigtable GCS BigQuery Dataflow Datastore Spanner Dataflow Pub/Sub F1 2016 Dataflow Cloud ML NoSQL
ML6 talk at Nexxworks Bootcamp
11 Rapidly Accelerating Use of Deep Learning at Google Number of projects using some form of deep learning 2012 2013 2014 2015 1500 1000 500 0 Used across products:
Nexxworks Bootcamp Ghent - 27/09/2017 What’s going on? The cloud is very good at handling, storing and manipulating large volumes of data. What we really care about now is understanding the data.
Nexxworks Bootcamp Ghent - 27/09/2017 MACHINE LEARNING MACHINE LEARNING EVERYWHERE
Keys to Successful ML Large Datasets Good Models Lots of Computation
15 The Deep Learning Revolution: ImageNet 2012 Deep Learning on Google Trends
Nexxworks Bootcamp Ghent - 27/09/2017 16
Nexxworks Bootcamp Ghent - 27/09/2017 17 The old, algorithmic approach “apple” “orange” “banana” IF (round) THEN IF (orange AND coarse) THEN “orange” ELSE IF (green AND smooth) THEN “apple” ELSE IF ... ... ELSE IF … “banana”
Nexxworks Bootcamp Ghent - 27/09/2017 18 Let the machine find the rules “apple” “orange” “banana” ?
Nexxworks Bootcamp Ghent - 27/09/2017 19
Confidential & Proprietary ConvNets
Confidential & Proprietary
+ ‘The Next Big Thing’ + Sentient AI in the next 10 years (‘The Singularity’) + Will put humans out of a job + Foolproof MACHINE LEARNING in popular culture
+ Been around for 60 years now + ‘Sentient next year’, every year, for the last 60 years + AI winters: 1970, 1990, … ? + Not foolproof MACHINE LEARNING reality
24 A person on a beach flying a kite. A person skiing down a snow covered slope. A group of giraffe standing next to each other.
25 A woman riding a horse on a dirt road. An airplane is parked on the tarmac at an airport. A group of people standing on top of a beach.
26
27
28
Nexxworks Bootcamp Ghent - 27/09/2017 29
Confidential & ProprietaryGoogle Cloud Platform 30
Nexxworks Bootcamp Ghent - 27/09/2017 Use cases to illustrate what we actually do.
Nexxworks Bootcamp Ghent - 27/09/2017 Get inspired, but let’s start simple.
Nexxworks Bootcamp Ghent - 27/09/2017 33 Logistics optimisation Predicting the time packages stay in customs + Better predict airplane cargo loads for an international courier + For each parcel, predict the time under inspection by customs (0 min = not picked up) + Better prediction → Better planning Better planning → Fuller cargos Avoid sudden overload
Nexxworks Bootcamp Ghent - 27/09/2017 34 Training the deep learning network (simplified) A parcel’s attributes (weight, size, text, colours, origin ...) The predicted package delay in customs >100k examples Full case: 3 days!
35 The goal was to predict congestion in London 40 minutes ahead by looking at the stream of data from induction loop road sensors. Congestions usually ripple through the city, so should be predictable. Warnings ahead of time could help prevent gridlocks.
Nexxworks Bootcamp Ghent - 27/09/2017
Nexxworks Bootcamp Ghent - 27/09/2017 37 Architecture overview Cloud Pub/Sub Cloud Dataflow BigQuery Cloud Pub/Sub Tableau analysis Processing Data ingest Data processing Storage Machine Learning Data visualisation Sensor generating data Cloud Machine Learning
Nexxworks Bootcamp Ghent - 27/09/2017 Put linkable screenshot? https://public.tableau.com/profile/oliver.gindele#!/vizhome/LST MPredictionsDashboard_dark/MapView
Nexxworks Bootcamp Ghent - 27/09/2017 Put linkable screenshot? https://public.tableau.com/profile/oliver.gindele#!/vizhome/LST MPredictionsDashboard_dark/MapView
Nexxworks Bootcamp Ghent - 27/09/2017 40
Nexxworks Bootcamp Ghent - 27/09/2017 41 Predictive Maintenance: towards a highly dynamic Digital Twin Design Connect Predict theoretical model collect data create Digital Twin Testing generates theoretical life-time model - in-house testing - remaining life-time in ‘ideal conditions’ Machine Learning model predicts expected failure time Client receives personalized model by including custom environmental factors - usage, temperature, humidity… Cloud computing/storage STATIC MODEL HIGHLY DYNAMIC ML MODEL connected device Digital Twin ML
42 Automated Customer service Building the smartest bots
43 What did we do? + Shipped state-of-the-art system in under one month turnaround time + System is not an ‘Artificial Intelligence’ + System outperforms most solutions currently marketed as AI Why use Google Cloud Platform? + Access to massive computational resources to train our models + Able to dynamically adjust to changing number of requests + Leverage Google powered API’s The solution
44 Architecture
45
ML6 talk at Nexxworks Bootcamp
47 Word embeddings
48 Word embeddings
49 Word embeddings
50 Word embeddings
51 The solution - semantic knowledge
ML6 talk at Nexxworks Bootcamp
53 The problem
54 The solution - context understanding
55 Convolutional Neural Network
56 The problem
57 LSTM “How” “many” Word2Vec embedding “minutes” “left.”... Topic Long Short Term Memory (LSTM) network
58 The solution - memory
59 The problem
60 Architecture
61 Entity Extraction
62 Entity Extraction ● “I got charged 7 dollars for a Christmas Card that I never ordered. Plz remove this from my invoice, my telephone number is 472867486.” ● “My internet connection is no longer working. I have ADSL smart-50 and my address is NY city 52 A on 21st.” Entity Extraction Postprocessing 7 dollars $7.00 Christmas Card Christmas-card 472867486 +32472867486 ADSL smart-50 ADSL smart 50 NY city 52 A on 21st 52A, 21st avenue, New York
Nexxworks Bootcamp Ghent - 27/09/2017 Automatic maintenance detecting faulty solar panels with drones
Nexxworks Bootcamp Ghent - 27/09/2017 64 + Improve maintenance efficiency of a solar panel company + Using drones with thermal image cameras + Classify broken panels vs defect free panels + Deep convolutional network
Nexxworks Bootcamp Ghent - 27/09/2017 Cloud Storage folder to_classify Cloud Storage folders ML6 API defect normal Machine Learning model on Cloud ML Dashboard Upload captured images to Storage Results and interpretation on an interactive dashboard
Nexxworks Bootcamp Ghent - 27/09/2017 66 Other ideas? smart cities? (garbage detection, road conditions, mobile camera’s for anomaly detection...)
Nexxworks Bootcamp Ghent - 27/09/2017 67
Medical imaging diagnostics with deep learning Input Output
MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - DATA Camelyon16: ISBI challenge on cancer metastasis detection in lymph node Training & Evaluation: 110 tumor slides, 160 normal slides, 130 evaluation slides Task 1: whole-slide level prediction binary classification problem Task 2: find metastasis location segmentation problem
ML6 talk at Nexxworks Bootcamp
MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - CHALLENGE BREAKDOWN Not just another standard image classifier… Network architecture Training set construction Computing environment Post-processing (classification and segmentation)
MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - TRAINING Patches are used to train a deep learning model ▹ started with simple CIFAR10-model ▹ ended up retraining Inception-v3 to experiment with synchronous updates across multiple GPUs Metastasis patches Normal patches Deep Learning model e.g. Inception-v3
MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - MULTIPLE GPUs Multiple GPUs were used to calculate model updates (based on TensorFlow tutorial) ▹ individual model replica on each GPU ▹ all GPUs process a batch of data and send update of model parameters to CPU, which performs a synchronized update Approach was extended to Cloud ML Engine GPUs during an external ackathon to build a dermatology POC in 1 day (by starting from a.o. pretrained VGG16)
Cooperation always wins…
75 Image Segmentation
Nexxworks Bootcamp Ghent - 27/09/2017 76
77 What about Reinforcement Learning?
78 Supervised: need large amounts of annotated training data Static inference machines Bad transfer learning capabilities to new tasks
79 Policy
80 Third person imitation learning
81
82
Nexxworks Bootcamp Ghent - 27/09/2017 83
84
85
86 3:53
87 Exploration vs Exploitation
#cloudconf2016
Nexxworks Bootcamp Ghent - 27/09/2017 89 Use reinforcement learning to optimize energy usage Reinforcement learning allows for automatic parameter optimization: - e.g. energy optimization: let AI-agent control settings to optimize energy consumption in data center cooling - See also Google Deepmind
Ultimate Machine Learning with Google Cloud 90
Nexxworks Bootcamp Ghent - 27/09/2017 91 Fleet optimization w/ online reinforcement learning
Confidential + Proprietary
Nexxworks Bootcamp Ghent - 27/09/2017 93 Advantages that AlphaGO can leverage 1. Fully deterministic: no noise in the game 2. Fully observed: each player has complete information and there are no hidden variables. (unlike Poker for example) 3. Discrete action space 4. Each game is relatively short (approximately 200 actions) 5. Target function is clear (win/lose) & fast to evaluate 6. Huge datasets of human gameplay are available to bootstrap the learning, so AlphaGo doesn’t have to start from scratch
Nexxworks Bootcamp Ghent - 27/09/2017 94
Nexxworks Bootcamp Ghent - 27/09/2017 95 “If a top 1% CEO today understands how software applications get built and how that changes the way she/he manages, a top 1% CEO in the near future will understand how models get built and how that changes the way she/he builds her/his organization.” James Cham, Bloomberg Data
Nexxworks Bootcamp Ghent - 27/09/2017 96 1.Educate yourself 2.AI strategy 3.TEST! (Get your hands dirty)
Nexxworks Bootcamp Ghent - 27/09/2017 How?
Plenty of problems to solve...
Plenty of solutions around...
Tensorflow Training THANKS! What my friends think I do What other computer scientists think I do What society thinks I do What mathematicians think I do What I think I do What I actually do
Nexxworks Bootcamp Ghent - 27/09/2017 karel.dumon@ml6.eu - @kareldumon

More Related Content

ML6 talk at Nexxworks Bootcamp

  • 1. Nexxworks Bootcamp Ghent - 27/09/2017 karel.dumon@ml6.eu - @kareldumon
  • 2. Nexxworks Bootcamp Ghent - 27/09/2017 Building machine learning* solutions with Google Cloud*
  • 3. Nexxworks Bootcamp Ghent - 27/09/2017 Team of Data Engineers, Data Scientist & Machine Learning Engineers Closing the gap between lots of data - lacking insights Robust & agile ML solutions through scalable APIs Premium Partner of Google Cloud
  • 4. Nexxworks Bootcamp Ghent - 27/09/2017 Building machine learning* solutions with Google Cloud*
  • 5. Nexxworks Bootcamp Ghent - 27/09/2017 Rise of ML Cases The Future Get started!
  • 8. Confidential & ProprietaryGoogle Cloud Platform 8 2012 20132002 2004 2006 2008 2010 GFS MapReduce Bigtable Colossus Dremel Flume Megastore Spanner Millwheel Pub/Sub F1 2016 Dataflow TensorFlow Google’s 15+ years innovation in data
  • 9. Confidential & ProprietaryGoogle Cloud Platform 9 Decrease the innovation gap 2012 20132002 2004 2006 2008 2010 GCS Dataproc Bigtable GCS BigQuery Dataflow Datastore Spanner Dataflow Pub/Sub F1 2016 Dataflow Cloud ML NoSQL
  • 11. 11 Rapidly Accelerating Use of Deep Learning at Google Number of projects using some form of deep learning 2012 2013 2014 2015 1500 1000 500 0 Used across products:
  • 12. Nexxworks Bootcamp Ghent - 27/09/2017 What’s going on? The cloud is very good at handling, storing and manipulating large volumes of data. What we really care about now is understanding the data.
  • 13. Nexxworks Bootcamp Ghent - 27/09/2017 MACHINE LEARNING MACHINE LEARNING EVERYWHERE
  • 14. Keys to Successful ML Large Datasets Good Models Lots of Computation
  • 15. 15 The Deep Learning Revolution: ImageNet 2012 Deep Learning on Google Trends
  • 16. Nexxworks Bootcamp Ghent - 27/09/2017 16
  • 17. Nexxworks Bootcamp Ghent - 27/09/2017 17 The old, algorithmic approach “apple” “orange” “banana” IF (round) THEN IF (orange AND coarse) THEN “orange” ELSE IF (green AND smooth) THEN “apple” ELSE IF ... ... ELSE IF … “banana”
  • 18. Nexxworks Bootcamp Ghent - 27/09/2017 18 Let the machine find the rules “apple” “orange” “banana” ?
  • 19. Nexxworks Bootcamp Ghent - 27/09/2017 19
  • 22. + ‘The Next Big Thing’ + Sentient AI in the next 10 years (‘The Singularity’) + Will put humans out of a job + Foolproof MACHINE LEARNING in popular culture
  • 23. + Been around for 60 years now + ‘Sentient next year’, every year, for the last 60 years + AI winters: 1970, 1990, … ? + Not foolproof MACHINE LEARNING reality
  • 24. 24 A person on a beach flying a kite. A person skiing down a snow covered slope. A group of giraffe standing next to each other.
  • 25. 25 A woman riding a horse on a dirt road. An airplane is parked on the tarmac at an airport. A group of people standing on top of a beach.
  • 26. 26
  • 27. 27
  • 28. 28
  • 29. Nexxworks Bootcamp Ghent - 27/09/2017 29
  • 30. Confidential & ProprietaryGoogle Cloud Platform 30
  • 31. Nexxworks Bootcamp Ghent - 27/09/2017 Use cases to illustrate what we actually do.
  • 32. Nexxworks Bootcamp Ghent - 27/09/2017 Get inspired, but let’s start simple.
  • 33. Nexxworks Bootcamp Ghent - 27/09/2017 33 Logistics optimisation Predicting the time packages stay in customs + Better predict airplane cargo loads for an international courier + For each parcel, predict the time under inspection by customs (0 min = not picked up) + Better prediction → Better planning Better planning → Fuller cargos Avoid sudden overload
  • 34. Nexxworks Bootcamp Ghent - 27/09/2017 34 Training the deep learning network (simplified) A parcel’s attributes (weight, size, text, colours, origin ...) The predicted package delay in customs >100k examples Full case: 3 days!
  • 35. 35 The goal was to predict congestion in London 40 minutes ahead by looking at the stream of data from induction loop road sensors. Congestions usually ripple through the city, so should be predictable. Warnings ahead of time could help prevent gridlocks.
  • 36. Nexxworks Bootcamp Ghent - 27/09/2017
  • 37. Nexxworks Bootcamp Ghent - 27/09/2017 37 Architecture overview Cloud Pub/Sub Cloud Dataflow BigQuery Cloud Pub/Sub Tableau analysis Processing Data ingest Data processing Storage Machine Learning Data visualisation Sensor generating data Cloud Machine Learning
  • 38. Nexxworks Bootcamp Ghent - 27/09/2017 Put linkable screenshot? https://public.tableau.com/profile/oliver.gindele#!/vizhome/LST MPredictionsDashboard_dark/MapView
  • 39. Nexxworks Bootcamp Ghent - 27/09/2017 Put linkable screenshot? https://public.tableau.com/profile/oliver.gindele#!/vizhome/LST MPredictionsDashboard_dark/MapView
  • 40. Nexxworks Bootcamp Ghent - 27/09/2017 40
  • 41. Nexxworks Bootcamp Ghent - 27/09/2017 41 Predictive Maintenance: towards a highly dynamic Digital Twin Design Connect Predict theoretical model collect data create Digital Twin Testing generates theoretical life-time model - in-house testing - remaining life-time in ‘ideal conditions’ Machine Learning model predicts expected failure time Client receives personalized model by including custom environmental factors - usage, temperature, humidity… Cloud computing/storage STATIC MODEL HIGHLY DYNAMIC ML MODEL connected device Digital Twin ML
  • 43. 43 What did we do? + Shipped state-of-the-art system in under one month turnaround time + System is not an ‘Artificial Intelligence’ + System outperforms most solutions currently marketed as AI Why use Google Cloud Platform? + Access to massive computational resources to train our models + Able to dynamically adjust to changing number of requests + Leverage Google powered API’s The solution
  • 45. 45
  • 51. 51 The solution - semantic knowledge
  • 54. 54 The solution - context understanding
  • 57. 57 LSTM “How” “many” Word2Vec embedding “minutes” “left.”... Topic Long Short Term Memory (LSTM) network
  • 62. 62 Entity Extraction ● “I got charged 7 dollars for a Christmas Card that I never ordered. Plz remove this from my invoice, my telephone number is 472867486.” ● “My internet connection is no longer working. I have ADSL smart-50 and my address is NY city 52 A on 21st.” Entity Extraction Postprocessing 7 dollars $7.00 Christmas Card Christmas-card 472867486 +32472867486 ADSL smart-50 ADSL smart 50 NY city 52 A on 21st 52A, 21st avenue, New York
  • 63. Nexxworks Bootcamp Ghent - 27/09/2017 Automatic maintenance detecting faulty solar panels with drones
  • 64. Nexxworks Bootcamp Ghent - 27/09/2017 64 + Improve maintenance efficiency of a solar panel company + Using drones with thermal image cameras + Classify broken panels vs defect free panels + Deep convolutional network
  • 65. Nexxworks Bootcamp Ghent - 27/09/2017 Cloud Storage folder to_classify Cloud Storage folders ML6 API defect normal Machine Learning model on Cloud ML Dashboard Upload captured images to Storage Results and interpretation on an interactive dashboard
  • 66. Nexxworks Bootcamp Ghent - 27/09/2017 66 Other ideas? smart cities? (garbage detection, road conditions, mobile camera’s for anomaly detection...)
  • 67. Nexxworks Bootcamp Ghent - 27/09/2017 67
  • 68. Medical imaging diagnostics with deep learning Input Output
  • 69. MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - DATA Camelyon16: ISBI challenge on cancer metastasis detection in lymph node Training & Evaluation: 110 tumor slides, 160 normal slides, 130 evaluation slides Task 1: whole-slide level prediction binary classification problem Task 2: find metastasis location segmentation problem
  • 71. MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - CHALLENGE BREAKDOWN Not just another standard image classifier… Network architecture Training set construction Computing environment Post-processing (classification and segmentation)
  • 72. MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - TRAINING Patches are used to train a deep learning model ▹ started with simple CIFAR10-model ▹ ended up retraining Inception-v3 to experiment with synchronous updates across multiple GPUs Metastasis patches Normal patches Deep Learning model e.g. Inception-v3
  • 73. MEDICAL IMAGE CLASSIFICATION TECHNICAL DETAILS - MULTIPLE GPUs Multiple GPUs were used to calculate model updates (based on TensorFlow tutorial) ▹ individual model replica on each GPU ▹ all GPUs process a batch of data and send update of model parameters to CPU, which performs a synchronized update Approach was extended to Cloud ML Engine GPUs during an external ackathon to build a dermatology POC in 1 day (by starting from a.o. pretrained VGG16)
  • 76. Nexxworks Bootcamp Ghent - 27/09/2017 76
  • 78. 78 Supervised: need large amounts of annotated training data Static inference machines Bad transfer learning capabilities to new tasks
  • 81. 81
  • 82. 82
  • 83. Nexxworks Bootcamp Ghent - 27/09/2017 83
  • 84. 84
  • 85. 85
  • 89. Nexxworks Bootcamp Ghent - 27/09/2017 89 Use reinforcement learning to optimize energy usage Reinforcement learning allows for automatic parameter optimization: - e.g. energy optimization: let AI-agent control settings to optimize energy consumption in data center cooling - See also Google Deepmind
  • 90. Ultimate Machine Learning with Google Cloud 90
  • 91. Nexxworks Bootcamp Ghent - 27/09/2017 91 Fleet optimization w/ online reinforcement learning
  • 93. Nexxworks Bootcamp Ghent - 27/09/2017 93 Advantages that AlphaGO can leverage 1. Fully deterministic: no noise in the game 2. Fully observed: each player has complete information and there are no hidden variables. (unlike Poker for example) 3. Discrete action space 4. Each game is relatively short (approximately 200 actions) 5. Target function is clear (win/lose) & fast to evaluate 6. Huge datasets of human gameplay are available to bootstrap the learning, so AlphaGo doesn’t have to start from scratch
  • 94. Nexxworks Bootcamp Ghent - 27/09/2017 94
  • 95. Nexxworks Bootcamp Ghent - 27/09/2017 95 “If a top 1% CEO today understands how software applications get built and how that changes the way she/he manages, a top 1% CEO in the near future will understand how models get built and how that changes the way she/he builds her/his organization.” James Cham, Bloomberg Data
  • 96. Nexxworks Bootcamp Ghent - 27/09/2017 96 1.Educate yourself 2.AI strategy 3.TEST! (Get your hands dirty)
  • 97. Nexxworks Bootcamp Ghent - 27/09/2017 How?
  • 98. Plenty of problems to solve...
  • 99. Plenty of solutions around...
  • 100. Tensorflow Training THANKS! What my friends think I do What other computer scientists think I do What society thinks I do What mathematicians think I do What I think I do What I actually do
  • 101. Nexxworks Bootcamp Ghent - 27/09/2017 karel.dumon@ml6.eu - @kareldumon

Editor's Notes

  1. Talk about AI → ML is a technique that is currently most used in the context of AI Frame Machine Learning: what is it, why is it working now and not before? What can it do? Where does it fail? Cases to get a very good grasp of the concepts; mostly supervised learning What’s Next: Reinforcement Learning Close with some tips on how to do ‘AI’ yourself
  2. Note: limit my talk to ML (and not general AI), because that’s our scope atm Also: not unsupervised learning
  3. Note: limit my talk to ML (and not general AI), because that’s our scope atm Also: not unsupervised learning
  4. Note: limit my talk to ML (and not general AI), because that’s our scope atm Also: not unsupervised learning
  5. Note: limit my talk to ML (and not general AI), because that’s our scope atm Also: not unsupervised learning
  6. When you think of Google, you probably think of this: a simple and elegant search box that delivers an instant answer
  7. But when I think of Google, I think of this: infrastructure, network, datacenters, servers, all designed to enable developers to build innovative software and services And the reason they had to build all this is because back when they started, the technology they needed simply didn’t exist. And of course, having built that infrastructure, it also enabled them to build new products like GMail, Maps, YouTube, Chrome
  8. Over the years there have been running some interesting technologies inside of Google. And they have published papers describing these technologies to the outside world.
  9. Historically, the open source community would read these papers and built out technologies based on the described concepts. One important thing to note here is that Google has had Hadoop in production since early 2000s. Most enterprises did not start using Hadoop at-scale until at least 2010. That’s 10 years. In 10 years the Dow Jones has lost 50% of companies from its index. What kind of impact does not having a potentially competitive technology for 10 years have on your business? Well all this has changed with the growth of Google Cloud Platform. Everything that Google will build for the future will be built with external customers in mind. So this 5 to 10 year gap from when Google has something to when customers have something, is now erased down to 0 - if you are on Google Cloud. 2002: cool stuff but industry lagging behind Now: open source - research/ MapReduce in 2004: paper that was basis for Hadoop MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. BigTable → H Base Now: TensorFlow & Kubernetes directly
  10. Google’s case also nicely shows where we are now: understanding the data
  11. That is why wachine learning is everywhere already… What are some of the ways that Machine Learning is having a significant impact at Google? All of these examples implemented uses of TensorFlow or our predecessor system
  12. With Machine Learning We have the general purpose Cloud ML platform which runs TensorFlow models at scale Then the pre-built ML APIs Like Natural Language for parsing text and extracting entities, sentiment, etc Speech API takes spoken word and turns it into text And finally Vision API - you give it images, it gives you back a ton of metadata, like places, objects, faces, etc.
  13. Computer sets the rule, mini brain, black box Loads of oranges bananas...
  14. Computer sets the rule, mini brain, black box Loads of oranges bananas...
  15. How we are about to achieve this? by using Machine Intelligence. (and deep neural nets) Making sense of (lots of) existing data to make predictions about new data. And getting smarter from that new data to make ever better predictions.
  16. Insight is very important to leverage it for your business Is it just ‘A Machine’?
  17. Luckily there are teams like us to guide you through the AI/ML jungle :-) AI and ML can really empower your business, help you today, tomorrow, and definitely the day after tomorrow Become ‘AI first’ and make your company future proof Start small, learn the basics, build a POC and go on from there
  18. How to model this? Which attributes? How to present the input vector to the network? Which metric to predict? Weight by time? Actual delay? Which implementation? LSTM, convolutional? How many layers? Just using Importance of reusing and retraining data Text, weight (very important), colours, origin >100000 in 3 days
  19. Demand & Supply forecasting
  20. Demand & Supply forecasting
  21. Demand & Supply forecasting
  22. A customer collects images of solar panels with thermal cameras attached to drones. We were asked to develop a solution able to classify them as broken or defect free. We want the technicians to spend time efficiently, fixing the broken panels instead of looking for problems. 1000 images
  23. We build full solutions. The model is just 1 part.. It also needs to be retrained BigQuery!
  24. Other use cases: smart cities? (garbage detection, road conditions, mobile camera’s for anomaly detection,...)
  25. A customer collects images of solar panels with thermal cameras attached to drones. We were asked to develop a solution able to classify them as broken or defect free. We want the technicians to spend time efficiently, fixing the broken panels instead of looking for problems.
  26. + you don’t need to be an expert, but you might still need one!
  27. Decision making
  28. From simulation to real-life?
  29. Meerdere agents Toon pacman hierna
  30. It’s not only in robotics!
  31. In our fund, where we invested exclusively in the future of work from our first check, we see a new kind of company leader emerging. She does not see her role in just the old way — deciding and translating decisions into software and people workflows, or even as nurturing her people to do that. She sees her role in a new way — creating the conditions (in talent and data) to build the right models that can make better decisions than any of us, and — critically — understanding when not to trust models and when to update them. This creates an organization that can make decisions more rapidly (and improve more rapidly) than any pointy-haired boss could through tedious training or tenuous anecdotes. The previous generation understood how to lead people and how to harness IT. The new generation will understand how to manage models, which are the flux capacitor of making software go beyond workflows to decisions. If a top 1% CEO today understands how software applications get built and how that changes the way she manages, a top 1% CEO in the near future will understand how models get built and how that changes the way she builds her organization. A whole way of thinking about leadership, and about the economics of companies, with as many maxims and guidelines as the old way, is to come. We’re waiting for the Peter Drucker of machine intelligence. James Cham Partner, Bloomberg Beta
  32. https://www.youtube.com/watch?v=BgxIJw0osME&t=25s
  33. BLUE?