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Bringing AI to your company (Innovation Pioneers 2018)
- 1. Bringing AI to your company Tales from the other side Galina Esther Shubina Director of Software Engineering and AI Northvolt
- 2. 2 Silicon Valley: 4 years Moscow: 15 years Sweden: 12 years US East Coast: 12 years
- 5. To Amazing
- 10. ? ? ?
- 11. Hype vortex...
- 13. The important things are the stories we tell with our data and tools
- 14. Step 1
- 15. OK, what about that AI thing? A. That thing that might kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 16. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. Artificial cognition G. All of the above A. That thing that might kill us
- 17. AI is A. That thing that will kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. Artificial cognition G. All of the above B. A subfield of computer science
- 18. Machine learning - algorithms that allow computers to learn without being explicitly programmed. Classification Clustering Predictive Algorithms Neural Networks Reinforcement Learning Supervised Algorithms Unsupervised Algorithms C. Machine Learning Algorithms
- 19. D. Deep Neural Networkshms
- 20. D. Automation and roboticsms
- 21. OK, what about that AI thing? A. That thing that might kill us B. A subfield of computer science C. Machine learning algorithms D. Deep neural networks E. Automation, robotics F. All of the above
- 22. Step 2
- 23. Automated analytics for everyone Data-informed product development Data- and ML- driven products and services What does a modern business need?
- 24. Automated analytics for everyone Data-informed product development Data- and ML- driven products and services What does a modern business need?
- 25. Automated analytics for everyone Data-informed product development Data- and ML- driven products and services What does a modern business need?
- 26. Automated analytics for everyone Data-informed product development Data- and ML- driven products and services What does a modern business need?
- 27. What does a modern business need? DataInfrastructure Automated analytics for everyone Data-informed product development Data- and ML- driven products and services
- 28. Step 3
- 29. ? ? ?
- 37. Step 4
- 38. Data ≠ Results The Year is 2018 and search still sucks on most websites. Forbes: “Why Investments In Big Data And Analytics Are Not Yet Paying Off” Getting results from data requires ● New roles ● New collaborative mindsets ● New organizational structures
- 41. Jobs you will be hiring for... Backend Engineer Distributed SystemsEngineer StatisticianData Engineer Product Manager (Tech) Project Manager Frontend Engineer Mobile Engineer Data Scientist Machine Learning Engineer Product Analyst UX Researcher Business Analyst Embedded Engineer
- 42. Build a small central multidisciplinary group...
- 43. ...keep it a small central multidisciplinary group
- 44. Infuse data and data thinking throughout the company!
- 45. DataInfrastructure Automated analytics for everyone Data-informed product development Data- and ML- driven products and services Two projects Both alike In dignity…
- 47. Step 5
- 49. Design, implement, maintain data platform consisting of many parts Ask questions Extract data Clean and join big data Analyse, and visualize data Build data-based feature prototypes Statistics, ML, AI Communicate results Ask questions Extract data Analyse and visualize data Communicate results Data Engineer Data Infrastructure Data Scientist Product Manager Data Analyst Machine Learning Engineer Hire Humble People who Can
- 50. NOT your first Data Science hires ● Statisticians ● People who don’t want to program ● Business Intelligence folks ● Other types of data analysts ● Data engineers ● Other engineers who took a few online courses in data science
- 51. Best data science people come from domain fields (aeronautics, physics, biology, etc) or Already have at least 1-2 jobs under their belt with practical domain experience.
- 52. NOT your first Data Engineers ● Backend engineers ● Frontend engineers ● Other engineers ● Data scientists (unless they are unicorns)
- 53. People are not resources Hire for: ● Common sense ● Curiosity and interest ● Communication skills ● Ethics ● Basic technical skills Develop: ● Technical skills ● Business skills ● Story-telling skills ● Management skills
- 54. Evaluating Hires ● Create great job descriptions (by borrowing) ● Get help (and not just any help) ● Evaluate resumes (carefully) ● Conduct technical interviews ● Remember the data
- 55. Upskill your other developers! ● Need to both hire and upskill ● Great for you and for them ● Online courses (Coursera, Udacity) ● Day courses like Google Cloud OnBoard ● Innovation training to build a learning mindset and build a sense of ownership ● Visibility
- 57. Strong Executive Buy- in Does your board have a technologist?
- 58. Step 0
- 59. How do you know that you succeeded?
- 61. Constant Change
- 63. Bringing AI to your company Q & A
- 64. References and copyrights https://thenounproject.com/term/data-scientist/38238/ https://upload.wikimedia.org/wikipedia/commons/9/96/Vintage Mixer.jpg Big data landscape: http://mattturck.com/bigdata2018/ Slide 16: Terminator (1984) Slide 17: Accenture 2016 Slide 19: variety of CC Slide 20: Labyrint, SVT Slide 30: https://hackernoon.com/the-ai-hierarchy-of-needs- 18f111fcc007 Slide 31-36: Sesame Street
Editor's Notes
- Today we’re going to take a ride to the other side. Opinionated practical advice.
- https://thenounproject.com/term/data-scientist/38238/ We’ll run a quick project to deliver some value out of this.
- https://thenounproject.com/term/data-scientist/38238/ We’ll run a quick project to deliver some value out of this.
- https://thenounproject.com/term/data-scientist/38238/ We’ll run a quick project to deliver some value out of this.
- https://thenounproject.com/term/data-scientist/38238/ We’ll run a quick project to deliver some value out of this.
- https://upload.wikimedia.org/wikipedia/commons/9/96/VintageMixer.jpg https://thenounproject.com/term/data-scientist/38238/ Hear and understand that we need to do this AI thing We have some data and we heard AI is a thing - let’s hire a data scientist Somehow, hire a data scientist (ideally a PhD) Provide data scientist with all the data we have Discover that your data is not great Data scientist spins his wheels while asking for better data... … annoying the engineers. They say your data is just fine! A few data bugs are fixed (either cludgy data set or over-engineered data lake is created in process). This takes months if not years. A rather underwhelming set of products are created a long time later In the meantime, the business-oriented managers wonders why we don’t buy an off-the-shelf 3rd party software (and sometimes, they do) Unclear amount of value is created for everything that’s produced (esp. Given amounts spent) Classic stories: from start to recommendations about 6 months, despite simplicity
- Now, a few years ago, big data was the sexy thing, and everything related to data analysis, data science and machine learning would get rolled under that. Now AI is at apex. Many conferences speak to aspects of AI, if hype is to be believed, everyone talks more to their Alexas than to their children (or was it their children talking mainly to their Alexas)? Google has been talking about going AI first for a while. Fewer people talk about advanced analytics and more talk about AI. And it seems like every startup is an AI startup. Or, are they? As someone who came from depths of technology and toward management, to me, it’s clearly a continuous technology wave. The key is that there were certain things and ways of working with digitalisation and technology that companies could get away with before, when the IT department that dealt with your printers could deal with your other technology needs. Now in order to be competitive, innovative and future looking we need to have end-to-end big data and AI products - this project-based or silo-based or, shall we call it, IT mindset is no longer an option. And this is part of what I’d like to talk to you about today.
- Add everything AI snapshots. Big data is like a healthy diet, everyone talks about it, nobody really knows how to do it, everybody thinks everyone else is doing it, so everyone claims they are doing it. (Rephrasing a quote from Dan Ariely) A few years ago, when big data was the hotness, I’d show a slide like this, and ask the audience - who here feels they know what big data is?
- Understand what AI is and isn’t.
- SPREAD THIS OUT INTO 5 SLIDES.
- Cognition and turing test
- Now artificial intelligence is a well defined field, but in my time it was a bit of a kitchen sink. The joke that it was every failed field.
- Labyrint
- What this isn’t? Anything actionable
- Understanding what what you’re trying to do. Your domain is the thing.
- CLOSER integration with all departments AI is all of these things, which helps us none at all. Reality is practical and less exciting. The truth of the matter is that we want our companies to be long-term viable businesses, which means that they have to be on top of latest technologies to one degree of another and have capacity for innovation. Technology companies change a lot through their lifecycle. This will be the case with most companies from now on. Data and AI are essential parts of this strategy. And this has three key components. Raise hand if your company passes about excel spreadsheets with monthly reports? We need to be able to look at data in an easy way, consume it and get actionable results at every level. A CEO might look at a single KPI, a product manager will see performance of his product sliced by geography and multiple other parameters, a data analyst should be able to dig around in rawer data with some SQL. Innovative products - we don’t know what we are going to get and we need to be able to test the results. Having an easy setup to test the results of your experiments, and difference you make, is a driver for more and better product development. “Productivity growth is a key economic indicator of innovation.” All these three things have components of what we deem to be AI. They are all in different places. Let me talk about how an average company or startup goes about this, especially if it’s not a core competency.
- Point 1. You need all three things, and they need to be tight together. It’s difficult to punt on one. If the focus is in analytics (and you put everyone there), tends to go with third party tooling and it doesn’t work. If the focus is in product development, ML/data science lands in some kind of product insights function and nobody is happy. If ML-driven products is the focus, tend to end up without good analytics. All of these are important. So is data platform. Data and different skill sets should be EVERYWHERE! Point 2. Don’t separate the three things out and build it on a different basis. Keep them as close together as possible. What happens if you don’t? Everyone owns your data, except for you and you’re paying extra all the time. Hard to find tails and ends and no one source of truth. Tech changes every two years, hard to change things properly. GDPR is easier. All of these should be tightly knit or we would be creating a large number of different ways of working with data. Point 3: Pitfalls of data infrastructure COMMIT, IDENTIFY TWO PROJECTS IDEALLY FROM TWO AREAS
- Understanding what what you’re trying to do. Your domain is the thing.
- https://upload.wikimedia.org/wikipedia/commons/9/96/VintageMixer.jpg https://thenounproject.com/term/data-scientist/38238/ Hear and understand that we need to do this AI thing We have some data and we heard AI is a thing - let’s hire a data scientist Somehow, hire a data scientist (ideally a PhD) Provide data scientist with all the data we have Discover that your data is not great Data scientist spins his wheels while asking for better data... … annoying the engineers. They say your data is just fine! A few data bugs are fixed (either cludgy data set or over-engineered data lake is created in process). This takes months if not years. A rather underwhelming set of products are created a long time later In the meantime, the business-oriented managers wonders why we don’t buy an off-the-shelf 3rd party software (and sometimes, they do) Unclear amount of value is created for everything that’s produced (esp. Given amounts spent) Classic stories: from start to recommendations about 6 months, despite simplicity
- The original by Monica Rogati Maslow - phisiological - safety- love/belonging - esteem - self-actualization
- There’s an expression in data science - garbage in/garbage out. Or as my former manager and amazing economist used to say: a clear picture of fuzzy data is a fuzzy picture. It doesn’t matter how great your data scientist is and your infrastructure is. Surprisingly getting _good_ data is actually a problem. SERIOUS PROBLEM. Sounds simple - not simple. There’s no agreement what good data is. Data tends to be collected/instrumented by operational types who care about a different set of applications (less concerned about ids, more recent data, incomplete data). They can be surprisingly difficult to convince. “Nobody cares about this data.” Identifying all users/things in the same way can be difficult. Washed over Schibsted story. Timestamps are a difficult problems. We want things at higher resolution because we aim to make marginal improvements and marginal improvements require more precision. Naming things well is difficult Debugging data problem has sa cost. If your data is bad now, it means you can’t use it for historic training of machine learning models, so “fix it later” is a lot more fraught. Engineers are very good at making sure their systems run, but no so good at verifying that they are getting high quality data “in”. This is still not a solved problem. Solution: recognize this is a simple sounding but difficult problem. Break any silos you need to make sure data doesn’t get tossed over the wall for people to “deal” with. You want some data science people/application people involved in data generation end to end, or be prepared for garbage in. You can’t build AI problems unless you can solve your analytics problems. Recommend creating visualizations with open source tools 1st party and 3rd party data. Confusing and unavoidable. You deposited your data.
- REliable data flow, infrastructure, pipelines, ETL, structured and unstructured data storage Remember that frustrated data scientist? One of the thing he did is arrive… 1st party and 3rd party data Single source of truth Standard data sets are business multipliers, but the ability to do this sits with the data infastructure - in-between area Engineers who understand TDD but don’t necessarily understand how a data scientist operates. Engineer shows up in the morning and writes code. Data scientists shows up in the morning and pokes at data sets. The two don’t speak the same language Thought must be given to anonymisation of data lest it bites you later ALL data, collect all data if possible in great formats and store them raw Solution: Understand that this is an area you need to invest in and do so continuously. Have senior people here, who will avoid one-off solutions. Focus on trying to understand what would standard data sets be? Google - a few standard expandable high quality data sets are business multipliers. People come and create products nobody thought of (essense of innovation). Standard data sets also mean that the pack to a product/analysis is much shorter, and you can rebuild the infrastructure a year or two later when times come (rather than having to chase down everybody’s exponential data pipelines). Balance this area with communicative engineers who will neither over-engineer nor do a one-off. Bonus if you can have initial design done by a machine learning engineer.
- The benefit of getting the other steps right is technology improvement is going to be easier.
- Now we arrived at a place where we can do visible useful things! We can count things, we can create descriptive metrics, and visualizations. We can start replacing your weekly Excel spreadsheets with something else.
- Now at this point the whole thing seems rather complex. Is it worth doing? The whole innovation process is important. In modern companies, we’re looking to solve relatively open ended problems in innovative ways with the newest technology often by producing fairly complex systems, whose shape is unknown at the start. Producing unknown complex outputs requires complex inputs and complex processing systems. Now we discussed data and tech some, but people and process are who makes this happen. Understand that part of the benefit is in the future and can’t be quantified now. How do we go about this?
- All this is rather complex. Do we still want this? Isn’t this all just hype? Future of your business lies in a cross-functional data-aware organization
- Now at this point the whole thing seems rather complex. Is it worth doing? The whole innovation process is important. In modern companies, we’re looking to solve relatively open ended problems in innovative ways with the newest technology often by producing fairly complex systems, whose shape is unknown at the start. Producing unknown complex outputs requires complex inputs and complex processing systems. Now we discussed data and tech some, but people and process are who makes this happen. Understand that part of the benefit is in the future and can’t be quantified now. How do we go about this? X3 and x2
- Point 1: Engineering departments - not IT departments. Software not printers. Point 2: KEEP THEM TIGHT. Same building, same floor, same set of desks ideally. Got them on different floors? Merge them. Point 3: T-shaped people. Grow Skills and make people spread out Point 4: Don’t treat people like resources. The domain knowledge your people develop are part of the premium of your company. Point 5: Hire for common sense, curiosity and interest, communication skills - keep developing this and other skills Point 6: If you have no competency in your organization, you won’t hit a slam dunk on your hires, unless you’re very lucky. Everyone is a data scientist these days. Be honest, and get help. The best people will give you a sense they know what they don’t know. From hierarchies to networks Companies as knowledge propagation mechanisms Multi-disciplinary approach is often a must Diversity on multiple dimensions Continuous deployment for systems - continuous development for people Executives and board who have walked the walk earlier
- How you will start and how you will end --- one small team with a lot of interaction to one small central team responsible for data infrastructure, data sets and consulting and data thinking across the entire company…. Reporting into… Don’t make it super special, we’re better than everyone else. Hire humble people
- How you will start and how you will end --- one small team with a lot of interaction to one small central team responsible for data infrastructure, data sets and consulting and data thinking across the entire company….
- COMMIT, IDENTIFY TWO PROJECTS IDEALLY FROM TWO AREAS
- Non-trivial complexity to them.
- All this is rather complex. Do we still want this? Isn’t this all just hype? Future of your business lies in a cross-functional data-aware organization
- All this is rather complex. Do we still want this? Isn’t this all just hype? Future of your business lies in a cross-functional data-aware organization
- Today we’re going to take a ride to the other side. Opinionated practical advice.