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How to get on the AI journey?
1) The document discusses reasons to invest in AI now, how companies can adopt AI, and the future of AI. 2) Key reasons to invest now include increased computing power, large datasets, and major companies investing billions in AI. AI could contribute $15 trillion to global GDP by 2030. 3) Companies should start with small automation projects to reduce costs and errors, then expand use of AI. They should also create cross-functional product teams to identify opportunities. 4) The future of AI includes more advanced voice recognition, predictive platforms, expanded use in industries like manufacturing and education, and applications in healthcare like disease diagnosis and drug development. AI is already used widely in applications like digital assistants,
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How to get on the AI journey?
- 1. Who & Whydo I care ? 1 Hidden Figures Marvin Laucher, part time coal miner, part time website builder. C++ CNN Convolution Neural NetworkRef – Nasa.com, Cnn Money,youtube.com
- 2. BUILDING AI PRODUCTS AarthiSrinivasan Director of Product Management Target Inc. 2
- 3. Successful if wediscuss 1. Why should I invest in AI now ? 2. How can I use AI research to help my company? 3. What should I expect in the future ? 3 Goal: Provide a perspective on AI adoption
- 4. WHY SHOULD IINVEST IN AI NOW ? 4
- 5. We adopt technologyfaster than grandma 5
- 6. AI is notnew - Why now? 6 Ref – McKinsey Co, MIT Lex Fridman, HBR, Indeed.com 1. Computing scale: CPU, GPU, ASICs 2. Datasets and infrastructure to handle big data 3. Amazon, Google, FB, MSFT investing in platforms
- 7. $37B market by2025 7 * - 2012 – 2017 ; Ref: Venture beat, $15 B* AI investments with $15Trillion impact on GDP by 2030 Images: Intershala Start ups ~$8B (2012-2016) AutoTech BI Analytics (Training data) Healthcare 2 31 Big Corporations ~$6B (2013 - 2016) Voice is the newText AI Platform Cloud 1 2 Vision & Image recognition 3 Ford invested $1B in Argo self-drivingAI tech
- 8. Talent in highdemand & ROI still too early Reference - McKinsey & co HBR Survey, Engagednet 8 • AI can boost your top and bottom line 30% of the users scaling AI solutions are achieving revenue increase Google launched “Auto Ads” which automatically places and selects ads / formats (revenue lift of 10 percent with revenue increases ranging from five to 15 percent) • # of open jobs in AI / ML / Data Science – 150K • Digital capabilities come before AI Odds of generating profit from using AI are 50% higher for companies that have strong experience in digitization.
- 9. HOW CAN IGET ON THE AI TRAIN ? 9
- 10. Academia meets businesswith AI 10 Researchers Training Data Prep Machine Learning Deep Learning Reading papers Implement results Businesses Customer Pain Corporate Strategy Growth Key Results such as Profit, Revenue, Basket size • Google’s Kaggle - open source competitions • Product Managers collaborate on use cases Examine the end to end operations of the company and look for areas that can be automated
- 11. 11 Start Small -Save Cost / Automation with error reduction Try new solutions in few channels to showcase results Expand to other channels & problem sets Resulting web of trees Create a product organization with agile practices & transparency with key objectives Where can I use AI? 1. “Use Deep learning to automate anything a human can do without thinking much in less than 1 second e.g. Identify cat pictures or smile / frown 2. Use for Predictive analysis “ – Andrew Ng with caveats
- 12. Sample product purchaseevolution 12 Door to door sales or In-Store Online & Mobile platform Crowd sourced Marketplace (Amazon, Ebay, Etsy, Nextdoor, Facebook) Predictive Marketplaces Predictive Voice Alexa Google Home iOT Dash (Human in the loop) Vending Machines Robots Inventory Robots Pepper Robot
- 13. When should Istop the algorithm? Ref – Andrew Ng 13Time Human Level Performance Human level accuracy Optimal Error Rate / Bayes Error Rate
- 14. WHAT IS THEFUTURE ? 14
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- 16. AI is alreadyin our everyday lives 16 Health & WellnessBasics • DNA sampling & diagnosis • Lifestyle predictive Management • ER & Hospital management • Remote Doctors • Mental Health • Drug Discovery • Shopping Recommendations • FB image recognition • Siri Voice recognition • Google Home / Alexa assistants • Optimized path maps • Spam filter on email • News aggregators / Personalized news
- 17. 17 Next 5 and10 years • Voice recognition & ease of use • Platforms to prepTraining data & Analyze • AppliedAI in Finance, Security, Education (Block chain will aid this) • Industry 4.0 (Manufacturing / Weaponry…) • Autonomous vehicles • AR/VR in projection spaces (don’t need to wear devices) • Vision & Emotion precision: E-commerce (Electronic malls), Intelligent individual • Healthcare Disease diagnosis • Distant Doctor / Drug development • Disease free longevity (cancer cure) • Space exploration • Environment protection (if we live on Earth) 5+ 10+
- 18. 18 Unite using technologyto solve big problems without dehumanizing Ref – http://blog.crisman.com image with edits
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- 21. HBR survey summaryof 3000 AI executives Reference - McKinsey & co 21 • Don’t believe the hype: Not every business is using AI despite a 26B investment in AI 20% are using at least 1 solution at scale • Believe the hype that AI can potentially boost your top and bottom line 30% of the users are achieving revenue increase • Without support from leadership, your AI transformation might not succeed • You don’t have to go it alone on AI — partner for capability and capacity. Machine learning is a powerful tool, but it’s not right for everything. • Resist the temptation to put technology teams solely in charge of AI initiatives • Take a portfolio approach to accelerate your AI journey Short-term: Focus on use cases where there are proven technology solutions today and scale Medium-term: Experiment with technology that’s emerging but still relatively immature Long-term: Partner to solve a high-impact use case with bleeding-edge AI technology • Digital capabilities come before AI Odds of generating profit from using AI are 50% higher for companies that have strong experience in digitization.
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- 24. Identify data needs& create a service mentality Data Hygienists Clean incoming data for accuracy E.g. Calendar days vs. working days to count # of days Data Explorers Sift through data to discover the data we actually need E.g.Training data Solution Architects Organize the explored data for analysis & querying Data Scientists Model the organized data for predictive analytics Experience Experts Turn the models into experiences that get results E.g. e-mail, Interactive Reference – HBR 24
- 25. Auto-tech, BI &Healthcare are major investment areas 25 Investments By Sectors in USD MM
- 26. 26 Investments By Sectorsin USD MM Early prevention & diagnosis will increase life span
- 27. Robotics, Auto, Facial& Voice recognition attract huge investments 27 Investments Size in USD MM Investments Size in USD MM Number of companies by investment size
- 28. Corporate acquisitions primarilyin Core AI 28 Top Acquisitions by Big Companies1
- 29. Voice will bethe new Text 29 Google • 11+ acquisitions • ML Platform creation • Vision / Image and Speech recognition • Business Process improvements Apple • 7+ acquisitions • Vision / Image and Speech recognition • Catch up with Google on platform creation (Turi) Facebook • Vision / Image and Speech recognition • Voice activation SDKs Microsoft • Voice enabled assistant • Type ahead predictor • Voice activation SDKs (AI Fund)
- 30. 30 These companies marketcap surpass the GDP of India (previously Russia and Canada) Reference – Scott Galloway Ted talk
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Editor's Notes
- #2 Dorothy Vaugh Hidden figures Fortran Coal Miners Computer C++ CNN Corporate Social Responsibility to learn and let others learn.
- #6 Google gets 1 new question for every 6 questions – Scott Galloway. 1.3 to 1.4 Billion per second Google and Facebook have made ¼ of India’s GDP in market cap due to our love of these products 3.5 B searches per day Now we have the 700,000 best and brightest, and these are the best and brightest from the four corners of the earth. They are literally playing with lasers relative to slingshots, relative to the squirt gun. They have the GDP of India to work at. And after studying these companies for 10 years, I know what their mission is. Is it to organize the world's information? Is it to connect us? Is it to create greater comity of man? It isn't. I know why we have brought together -- I know that the greatest collection of IQ capital and creativity, that their sole mission is: to sell another fucking Nissan.
- #7 Large data set & processing tools Modern algorithms: Backprop, CNN, LSTM Infrastructure / Software
- #8 With 15 Trillion impact by 2030 and 15 B in Investments (8 B in startups and 6 – 7B in large companies), we can determine how the funding is distributed. Top 3 areas will be: Auto tech – self-driving cars Training data prep and prediction on open source data using block chain for security (Ayushnet) Healthcare – Early prevention and diagnosis
- #9 The adoption is slow because of the digital proficiency (legacy platforms) Non unified data sources learning curve and talent Platforms are not standardized Leadership support missing Gartner Says More Than 40 Percent of Data Science Tasks Will Be Automated by 2020 Approximately 32058K By 2018 alone we could be short of about 140K to 190K people with deep analytical skills according to McKinsey Global Institute Don’t believe the hype: Not every business is using AI despite a 26B investment in AI 20% are using at least 1 solution at scale Without support from leadership, your AI transformation might not succeed You don’t have to go it alone on AI — partner for capability and capacity. Machine learning is a powerful tool, but it’s not right for everything. Resist the temptation to put technology teams solely in charge of AI initiatives
- #11 Andrew Ng Use Deep learning to automate anything a human can do without much thinking less than 1 second e.g. Identify cat pictures or colors Predictive analysis Retailers use both physical automation such as robots in warehouses and algorithms to predict what users will purchase Where can you automate by augmenting human wisdom?
- #12 Big data initiatives fail because the internal customers don’t have confidence in the team and don’t trust the models. Trust starts with transparency - open about who is working on what. Align incentives of the Data team with business teams. Are your models being used by business? WORRY LESS ABOUT CRUNCHING IT BUT MORE FOCUSSED ON SERVING YOUR MODEL.
- #13 WALK HORSE CARRAIGE CARS RENTAL Services/ Taxis Uber Marketplaces Autonomous cars
- #14 In statistical classification, Bayes error rate is the lowest possible error rate for any classifier of a random outcome (into, for example, one of two categories) and is analogous to the irreducible error. A number of approaches to the estimation of theBayes error rate exist. = Optimal Error Rate Theoretical limit of performance e.g. blurry images or sound that is not recognizable and that level is call optimal error rate (the line above the human error rate)
- #18 Intelligent Individuals – The computer is analyzing sentiment, emotion and recommending right action or learning function (read ABC)
- #19 Unite to solve
- #22 Where are the opportunities?
- #25 What are the types of data roles? Big data initiatives fail because the internal customers don’t have confidence in the team and don’t trust the models. Trust starts with transparency - open about who is working on what. Align incentives of the Data team with business teams. Are your models being used by business? WORRY LESS ABOUT CRUNCHING IT BUT MORE FOCUSSED ON SERVING YOUR MODEL.
- #26 BI & Analytics – Training data preparation and prediction on open source data Top 3 areas will be: Auto tech – self-driving cards Training data prep and prediction on open source data using block chain for security (Ayushnet) Healthcare
- #27 Drill down to healthcare - Disease diagnosis Predictive Analytics Remote monitoring and data analysis Drug development
- #28 1. Predicted that there are more number of startups in early stages and some move on to multiple series or just get huge investments because they require heavy Capex like auto / robotics or Industry 4.0 manufacturing robots. 2. I wish there were more investment in Education consumer and health tech AI products which help everyday people reduce the education gap / health gap. 3. Where do you want to see the future AI - Space exploration
- #30 Brain is Google’s automated algorithm learning system. RankBrain is used for search queries Business process improvements with Deepmind acquisition e.g. energy savings with data Vision / Speech / Image recognition AI acquisitions Google 11+ acquisitions ML Platform creation Vision / Image and Speech recognition Business Process improvements Apple 7+ acquisitions Vision / Image and Speech recognition Catch up with Google on platform creation (Turi) Facebook Vision / Image and Speech recognition Voice activation SDKs Microsoft Voice enabled assistant Type ahead predictor Voice activation SDKs (AI Fund)