MLretreat-2019.pdf

Transcript

1. The places we'll go Tania Allard, PhD Developer Advocate @ Microsoft Google Developer Expert ML/Tensorflow Industrial fellow Alan Turing Institute
2. 2019... And we still see these headlines 2
4. Have deeply thought on how AI is embedded in your daily lives? How many of you
6. Let’s make AI boring
7. Better call centres
8. The vast amounts of data available The plethora of open source tools And even the number of open access journals and open data sets It’s an exciting time to be doing Machine Learning 8
10. Need to make a distinction 10 Machine automation - Previously observed outcomes Machine discovery - Search for novel or unknown outcomes Increased data and algorithmic complexity
11. ML for scientific discovery
12. Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma 12 What Researchers applied network analysis and hierarchical clustering (HC) to explore the connectivity structure of component-specific IgEs and identified seven clusters of component-specific sensitisation. Cluster membership mapped closely to the structural homology of proteins and/or their biological source. https://doi.org/10.1371/journal.pmed.1002691
13. Discovering new planets 13
15. Integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease 15 Segler, M., Preuss M., Waller M. Nature 2018 v555
16. Moving from discovery 16
17. Transitioning from lab to industry
19. Right framework Right production Right strategy
21. (ML) Research starts with good data
22. What does this even mean? As the widespread of ML increases so does the evident lack of high-quality data
24. You are then living in the dumpster
25. A quick glance Build models easily Scarce data and incomplete data Deploy to production Can only predict from what has learned before Scale Also sparsity scales
26. https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007
27. Issues we can tackle now Data unification Bringing data together into one unified data context Error detection While data cleaning has long been a research topic in academia, it often has been looked at as a theoretical logic problem
28. Wonder where you can make the most impact? • In industry: ◦ Machine automation • In academia* • Both ◦ Auto ML ◦ Interpretable ML ◦ Machine discovery ◦ High-quality data quest
29. Thank you tania.allard@microsoft.com