The places we'll go
Tania Allard, PhD
Developer Advocate @ Microsoft
Google Developer Expert ML/Tensorflow
Industrial fellow Alan Turing Institute
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2019...
And we still see these
headlines
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Have deeply thought on how
AI is embedded in your daily
lives?
How many of you
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Let’s make AI
boring
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Better call centres
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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
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Need to make a
distinction
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Machine automation
- Previously observed outcomes
Machine discovery
- Search for novel or unknown
outcomes
Increased data and algorithmic complexity
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ML for scientific
discovery
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Machine learning to identify pairwise interactions between
specific IgE antibodies and their association with asthma
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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
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Discovering new planets
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Integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease
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Segler, M., Preuss M., Waller M. Nature 2018 v555
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Moving from discovery
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Transitioning from lab
to industry
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Right framework
Right production
Right strategy
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(ML) Research starts with
good data
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What does this even mean?
As the widespread of ML increases so
does the evident lack of high-quality data
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You are then living in the dumpster
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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
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
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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