Quick Start with AI for Businesses

Transcription

1 Quick Start with AI for Businesses ML Conference 2018, Dr. Ulrich Bodenhausen, AI Coach V

2 About me PhD in Machine Learning from KIT. Application of neural networks to speech and gesture recognition at Carnegie Mellon University and KIT. Strategic business consulting including applied predictive analysis. Strategic business planning at a 1st tier automotive supplier. Responsibility for innovation management, development processes and knowledge management at 1st tier automotive supplier. Managing a team of consultants for transition programs and optimization of product development (e.g. agile methods, development methods, functional safety, crisis management) in the automotive industry. 2/34

3 Introduction AI is changing our world with tremendous pace. Many companies are exploring paths to become a driving player in the usage of AI. If there is not yet experience and expertise in the company, there are several ways to go: Building up a new AI team inside the company and integrating it in the overall business development. This can take a very long time. Buying a smaller company with proven expertise. This is becoming a more and more costly approach. Founding a Start Up outside of the main company and later integrating it. This can be risky approach due to many unforeseen challenges in the integration process. This presentation has strong focus on the view on the AI world from perspective of non-ai, established companies, who want to get into AI. 3/34

4 Content AI and Business Value AI and Data Embedded Approach Summary 4/34

5 AI and Business Value: Experiences from Industry Experiences from Successful Collaboration between Established Automotive Supplier and AI Startup Case study: 1 st Tier Automotive Supplier in Driver Assistance Excellent access to market Very good reputation, established No AI expertise Long term collaboration with B2B AI-Type of company, emerging from startup: What is the business case? What are the benefits for the participating companies? What is the development of business indicators over time? Advanced Development Phase Product Development Phase Market Application Phase - Beginning Application Phase - Mature Product 5/34

6 AI and Business Value: Experiences from Industry Advanced Development Phase Advanced Development Phase Product Development Phase Market Application Phase - Beginning Application Phase - Mature Product Established non-ai Company Collaboration B2B AI-Type Startup Hard to evaluate from outside Access to Market AI Know How Usable Data Value of Company Access to Market AI Know How Usable Data Value of Company 6/34

7 AI and Business Value: Experiences from Industry Product Development Phase Advanced Development Phase Product Development Phase Market Application Phase - Beginning Application Phase - Mature Product Established non-ai Company Our partner is doing a very good job. They invest a lot into AI. AI is not our core business, so we ourselves don t invest into AI. Collaboration B2B AI-Type Startup Gained AI Know How Data from Testing, not Training Learned something on Market Got Data Have Customer Access to Market AI Know How Usable Data Value of Company Access to Market AI Know How Usable Data Value of Company 7/34

8 AI and Business Value: Experiences from Industry Market Application Phase - Beginning Advanced Development Phase Product Development Phase Market Application Phase - Beginning Application Phase - Mature Product Established non-ai Company Collaboration B2B AI-Type Startup Test Data not Managed as Business Value Learned something on Market Gained AI Know How Huge Increase in Data Amount Have more Customers Access to Market AI Know How Usable Data Value of Company Access to Market AI Know How Usable Data Value of Company 8/34

9 AI and Business Value: Experiences from Industry Market Application Phase Mature Product Advanced Development Phase Product Development Phase Market Application Phase - Beginning Application Phase - Mature Product Established non-ai Company Collaboration B2B AI-Type Startup Decrease in Market Value Gained AI Known as Know How Reliable Supplier of Critical Components Huge Increase in Data Amount Market Value Access to Market AI Know How Usable Data Value of Company Access to Market AI Know How Usable Data Value of Company The observed emergence of business value indicators is not good or bad. Rather it is a specific feature of AI related business cases. 9/34

10 AI and Business Value: Experiences from Industry Effect of Commercial Tool Suites from Major AI Heavy Weights Collaboration Collaboration Established non-ai Company B2B AI-Type Startup (using Commercial AI Suites) B2B Providers of Powerful Commercial AI Suites Decrease in Market Value Known as Reliable Supplier of Critical Components Gained Know How in Application of Tools Suite Market Value Known as AI Heavy Weight Gained AI Know How Market Value Access to Market AI Know How Usable Data Value of Company Access to Market AI Know How Usable Data Value of Company Access to Market AI Know How Usable Data Value of Company 10/34

11 AI and Business Value: Experiences from Industry Conclusions Business Value B2B collaboration in AI offers great opportunities on both sides. Data access is highly valuable. Gained AI know-how is highly valuable. Make a strategic decision for your company on your contribution, your assets and collaboration model. 11/34

12 Content AI and Business Value AI and Data Embedded Approach Summary 12/34

13 AI and Data Of Course, We Have Data! Companies usually have lots of data For AI purposes, data must be relevant for business case: Several sources of data need to be linked Label* Link sources together Data needs to be labeled Example from Home Credit Group: Home Credit Default Risk Can you predict how capable each applicant is of repaying a loan? *0 (will repay loan on time), 1 (will have difficulty repaying loan) Source: Home Credit Group, Kaggle competition Several sources 13/34

14 AI and Data Your data is one significant corporate business asset! Specify data requirements carefully. Plan and organize data collection. This is not priority B, it is priority A. Ensure rigid configuration management. 14/34

15 AI and Data Data and Generalization Generalization and its effect on company s business case is complex and hard to understand for management. Simple pictures help, but the difficulty of understanding remains. Methods to improve generalization applicable to all types of architectures: Data augmentation Weight decay Dropout Recent analyses, e.g. Google: Necessary methods, but not the game changer. Examples of image classification task - CIFAR-10 ( images): Multi Layer Perceptron (MLP): Test accuracy w/o weight decay: Test accuracy 52% Test accuracy with weight decay: Test accuracy 53% AlexNet in 2012: Test accuracy 81% -> much better than MLP, with or w/o decay Very few data points Learned model is overfitting to single data points 15/34

16 AI and Data Methods to Improve Generalization: Optimization of Architecture Architectural optimization in case of Deep Learning NN means moving from full connectivity of several layers to tailored connectivity and feature extraction Each node ( Neuron ) connected to every node of next layer Example AlexNet*: Build-in structure to enforce extraction of features Generic Deep Learning NN architecture: Each node connected to each node of next layer Big data leads to really high number of parameters Poor generalization. Example: 3 hidden layer MLP on test data achieves approx. 50% test accuracy on difficult image classification task (CIFAR-10) * Krizhevsky et al: ImageNet Classification with Deep Convolutional Neural Networks, 2012 Tailored architecture, in case of big image classification task, is still resulting in very high number of parameters: 5 convolutional layers and 3 fully connected layers 62.3 million parameters Convolution layers account for 6% of all the parameters, consume 95% of the computation Achieved > 80% test accuracy on CIFAR-10 (2012 results) 16/34

17 AI and Data Automatic Optimization of Architectures 17/34 Algorithms for finding hyperparameters of Deep learning architectures to maximize expected accuracy. Examples: Early work: Scott Fahlman, CMU: Cascade Correlation Algorithm 1991 Recent work: Zoph et al, Google: NASNet using recurrent neural network as controller to search for NN architectures. Best architecture results in parameters for CIFAR-10 Uses 500 Nvidia P100 GPUs* across 4 days resulting in 2,000 GPU-hours. High intensity of work by Google. * > more powerful than GTX1080 Cascade Correlation Algorithm, 1991 Neural Architecture Search NASNet, 2017 > 25 y Example of constructed block

18 Content AI and Business Value AI and Data Embedded Approach Summary 18/34

19 Embedded Approach for Quick Start With AI 1st Step: Training 1 st Step: Training core team in your organization In the view of your employees, even in the view of your key people, there may be a misconception on the effect of AI and ML for our live, your company and their work. Spread technical understanding about AI and ML in your company. Organize training, remove the misconception, make people look forward and see chances, not risks. Include management to ensure AI acceptance starts at the top. 19/34

20 Embedded Approach for Quick Start With AI 2nd Step: Idea Exploration Understand the status of AI/ML in your business context: Do benchmarking with other companies. Observe Kaggle Competitions to see what is going on and learn Do not host Kaggle competiton by yourself at this stage of idea exploration. Option A: Involve small core team ( persons) in idea exploration: Define team of explorative, innovative person Mix people from product management, marketing, sales, product development, management Do workshops, events to bring people together and condensate ideas. Option B: Involve >> 15 persons in idea exploration: Run idea competition. Reserve and plan Innovation Days for whole departments to develop ideas. Give them time to explore. 20/34

21 Embedded Approach for Quick Start With AI Case Study: Innovation Days Company: Home Security Company Established products: Connected Home Security Products Approach: One Innovation Day per month with whole company (except production) to develop ideas (approx. 80 persons). Continued for half year -> approx. 500 person days Many new ideas: Using connected home security technology for Intelligent Home Care using Noise Recognition Outdoor Home Care Give your people time to explore. 21/34

22 Embedded Approach for Quick Start With AI 3rd Step: Selection of Business Cases for Advanced Development Goals of selection: Decide on budget and allocated ressources Select ideas to be explored in advanced development mode, collect and label data. Use your core team to select. Do not plan too far ahead. AI/ML includes surprises. Selection criteria: User acceptance Technical feasibility, scaling of the approach Potential start of production Revenue Activities to be asap started for selected business cases: Data collection and data preparation (must not be done by ôwn AI experts/data scientists; some guidance necessary) 22/34

23 Embedded Approach for Quick Start With AI 4th Step: Advanced Development of Selected Business Cases Use agile working mode: Sprints of weeks duration. Sprint n+1 Sprint n+2 Start with real small toy-sized applications, using own data. Grow architecture with data amount. Data Collection weeks weeks Transfer to Developm. Training and Optimization Integration & Analysis Increase data amount in following sprints. Apply architectural optimization as needed. Automate what can be automated. This is first time, real AI/ML expertise is needed: Hire experienced people (industry, PhDs) Get external support Hire master students Use public, non- commercial libraries to get started. Run on own HW, not in cloud. Get implementation and optimization ideas from Kaggle, but do not participate in public. 23/34

24 Embedded Approach for Quick Start With AI 4th Step: Advanced Development of Selected Business Cases Apply agile working mode with homogeneous drum beat to synchronize different activities: Sprint n Sprint n+1 Sprint n+2 Sprint n+i weeks weeks weeks weeks Business Case Refinement Epics and Data Requirements Business Case Refinement Epics and Data Requirements Business Case Refinement Epics and Data Requirements Data Collection Transfer to Developm. Data Collection Transfer to Developm. Data Collection Transfer to Developm. Training and Optimization Integration & Analysis Training and Optimization Integration & Analysis Business Case Evaluation (see next slide) 24/34

25 Embedded Approach for Quick Start With AI 5th Step: Evaluation of Results 5th Step: Evaluation of Results. Look carefully on what you achieved! Does the application fulfill the expectations of the business case? Does the application scale to a size that is needed for the business case? Is data collection and preparation manageable to fit the real world business case? Is the effort for tuning of architecture manageable for your company? Do the CPU/GPU/memory requirements fit to the business case (including consideration of online service scenarios) 25/34

26 Embedded Approach for Quick Start With AI 5th Step: Consideration of Scale of Application The more interesting/valuable the business case, the higher is the need for flexibility of system. Example - Speech input: Speaker independence Robustness against noisy environments Large vocabulary Several languages Combination of speech and gestures High flexibility will increase the complexity of the system. Large data will be required to train flexible system. Effort is dramatically increasing. Flexibility/Effort Dilemma Proposal: Do not try to find the one best combination of Flexibility and Effort Rather strive to get faster in growing from small to big Large Data Small Data Low Flexibility, Low Complexity Flexibility/Effort Dilemma High Flexibility, High Complexity 26/34 Focus on agile growth, not perfection* *Still need to be in line with functional safety requirements in case of safety critical products.

27 Embedded Approach for Quick Start With AI Case Study: Scale of Application Company: Very large company in transportation services. Item of interest: Corporate planning has two levels: Yearly planning using full cost structure Strategic planning for next years. Effects of strategic measures (e.g. new logistic centers) on cost structures of products not transparent. Approach: Prediction of effect of strategic measures on level of cost categories and cost types. Not enough data available to do predictive analysis on all cost types. Data amount not manageable at that time for all cost types and full horizon of strategic planning. Solution: Introduction of Mid Term planning clearly showing the cost categories, but not all cost types. Fit to what is possible right now. 27/34

28 Embedded Approach for Quick Start With AI 6th Step: Be prepared for a deep dive. Enforce activities for selected business case(s) Successful applications of AI/ML is not easygoing stuff! It is hard work! Expand data collection and preparation, application tuning, scope (example: speech input -> speech and gesture input). You will very likely need to expand the team significantly. Optimize the application, build in the required flexibility and robustness to drive your business case 28/34

29 Embedded Approach for Quick Start With AI Case Study: Expanding Team Company: Automotive supplier for Driver Assistance Systems Product: Remote Parking of cars, using sensor fusion of park sensors, front facing camera, surround view system, including Remote Parking. Issue: Decision making is very complex and too slow PM Overall Project SW Organization Approach: Expanding team to required size at one location was not feasible. R&D 3 5 Collaboration between two development sites System SystTest SW Issue: Decision making is too slow. Solution: Global project is dominant over local line structure. PM Vision Project SW Organization R&D System SystTest SW Project organization is dominant over line organization. 29/34

30 Embedded Approach for Quick Start With AI 7th Step: Early Preparation for Market Entry Don t wait for perfection. It will not be perfect! First Market Entry Rather: Stop market entry, if it is obviously lousy (not meeting customer expectations at all) are not in line with functional safety requirements in case of safety critical products. Have access to Market as Supplier of AI driven Products Gained AI Know How Huge Increase in Data Amount Have real Customers using your AI driven product Market entry will give you much more data and experience with the application. This is what will bring you forward Access to Market AI Know How Usable Data Value of AI activities 30/34

31 Embedded Approach for Quick Start With AI Case Study: Stepwise Improvement of Solution Company: Start Up growing to several thousand employees Product: Very high demand for clinical documentation in USA. To improve the physician-patient oriented documentation, the doctor must be supported when typing into computer. Solution: Speech Recognition. Approach: Several evolution steps of the service over many years: 1. Automatic speech recognition combined with manual correction by transcribers listening to complete recording -> You don t need to be perfect to enter market! 2. Manual correction by transcribers only listening to suspect parts. 3. In-workflow feedback to the clinician, combining speech recognition and natural language understanding. Data scientist would say: Mission NOT accomplished! But: You get tons of labelled data!! Start simple and grow the approach. 31/34

32 Content AI and Business Value AI and Data Embedded Approach Summary 32/34

33 Summary: 6 Recommendations For a quick start of an AI driven business it is important to Involve important people understanding your business, not only AI experts. Start with own ressources. Get AI expertise as you need it: People, coaching, consulting, services, or buy a company. From the AI-Technology perspective it is important to: Get good performance on small amount of data on your own business case to get started Install the ability to quickly increase the amount of data to become more flexible Develop the ability to adapt AI model to growing amount of data to grow with your customers expectations 33/34

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