Date
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Material for the event is here
Thanks to all for coming to the workshop
Thanks to Mark Roberts for a great talk on an Intro to machine Learning and Artificial Intelligence
Key output plus the conversations of the conference is the Centre of Excellence
- The workshop audience felt there was a strong demand for Best practices sharing
- Avoiding bad practice
- Promoting good practice
- What does good quality look like?
- Reproducibility of AI
- What does a mature AI capability?
- Data challenges remain significant in creating quality datasets
- Key Data use cases & principles were described:
- Access to all data - complex and correct'
- Tidy and tabular
- Quality definition
- Avoid incompatible joining of data
- Incentives for data creators
- Skills and the race of key talent remains, much recruitment underway across the sector
- Beyond the existing data areas of interest, new ones introduced:
- ADHD and Neuroscience
- Rare Disease
- Translational models
- Wearables
Conference Breakout Material and Discussions
Material for the event is here
Outcomes
Questions posed to the groups in the breakout session:
- Thinking about Large and disparate data sets, and manually intensive data exploration?
- What manually intensive and data intensive tasks do you currently carry out?
- Where do you see the value of AI in the next period?
Key themes that we prioriised
- Equivalence of terms, data standards and enrichment
- Instrument data, Feature extraction
- Chem informatics, Molecular property prediction
- Distributed data environment - protecting and preserving privacy
- NLP
- Clinical trials
Next Steps
These ideas and examples from the workshop will be used to guide potential projects and data improvement activities
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