Paul Zikopolous, IBM - Into the Mysterious World of a Thinking Business - H2O World SF
This session was recorded in San Francisco on February 5th, 2019 and can be viewed at: https://youtu.be/l1d9he0ARPQ Bio: Paul C. Zikopoulos, is the VP of Cognitive BigData Systems at IBM. He’s an award winning writer and speaker who has been consulted on the topic of BigData by the popular TV show “60 Minutes,” advises various universities on their graduate analytics programs, and named to over a dozen “Experts to Follow” lists in social media. You’ll also find Paul taking a very active role around Women in Technology (including a seated board member for Women 2.0 - a global brand for women in tech and entrepreneurship that works to close the gender gaps of tech companies). Paul has written 19 books and over 360 articles on data. He doesn’t think NoSQL is something you put on a resume if you don’t have SQL skills and he knows JSON isn’t a person in his department. Ultimately, Paul is trying to figure out the world according to Chloë—his daughter, whom he notes didn’t come with a handbook and is more complex than the topic of BigData itself, but more fun too. The rest of the bio? It would be BLAH BLAH, BLAH, so find him on Twitter @BigData_paulz
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Paul Zikopolous, IBM - Into the Mysterious World of a Thinking Business - H2O World SF
- 1. IA needs an AI your paul zikopoulos @BigData_paulz | IBMCanada
- 2. agricultural revolution lasted about 8000 years industrial revolution then ... 120 years later this was pretty cool 90 years after this we put this dude here and 22 years later changed things forever 9 years later
- 3. high visible value investment is obvious well understood gold (high value per byte data) can be near invisible within the dirt (low value per byte data) Schema First Schema {Need, Read, Never}
- 4. your data is like a gym membership... it has no value unless you use it. NETWORK EFFECTS 80% OF THE WORLD’S DATA CAN’T BE GOOGLEDECONOMIES OF SCALE
- 5. © 2016 IBM Corporation5 Technology can enhance human connections and experiences
- 7. watching | feeling | listening | understanding annotating | categorizing | transcribing sensing | translating | composing
- 9. 1st Epoch at a cand outsors, whele havise took i with holdiss, that he has, that, intener. Her arathishess of has seated. ”it, as teen, a seremest as inspant at vind. it wolks.
- 11. today AI is like this car’s owner… those that participate are the privileged few
- 12. make AI a team sport, inviting everyone to participate and democratize it for the many…
- 13. requires different sets of skills FINE-TUNE & DEPLOY “rinse & repeat” MAINTAIN ACCURACY & EXPLAIN THYSELF iterate faster and do it againassisted parameter selection and tuning ~80% of an AI project’s is time spent here DATA PREPARATION up and running over a quick lunch time spent drops from 80% to 30% extremely long training times curtailing broader proliferation BUILD, TRAIN, OPTIMIZE 9 days to train a model becomes 4 hours weeks to months UP & RUNNING imagine if everyday users could contribute business domain expertise, help with data preparation, and even build initial models so data science teams could focus on fine tuning the models data science skill needed as the hard stuff happens here … help with quick detection of sub-optimal hyper parameter or feature selection, ‘what if’ exploration ...this is where you want data science teams to spend their time
- 14. © 2016 IBM Corporation14
- 15. Titanic Kaggle Competition 34 min of data analysis 33 min of automated feature engineering & model buildings 95.7% 1hr 7min 4 hours data preparation 4 hours feature engineering 1hr code dev and data exploration 1hr creating 5 models to find best solution 95.5% 10 hours
- 16. “Double the propensity for our banking customers to accept an offer pinpoint credit and default risks with greater accuracy than
- 17. that in turn requires much higher computation to train them as models go deeper or get boosted, they provide a dramatic increase in accuracy.
- 18. talent | time | trust 9days Shape Attenuation Boundary morphology 54x What will you do? Create more models? More accurate models? BOTH?? 4hours 4hours 4hours faster data ingest 2.x faster feature engineering 1.5x faster machine learning 30x train more | build more | know more
- 19. talent | time | trust train more | build more | know more forethought open architecture afterthought closed architecture