AI 101

Artificial intelligence (AI) is rapidly transforming science and laboratory automation. With all the development (and hype) surrounding AI, many scientists and managers have fundamental questions about the technology:

What is AI?
How does it work?
Can it solve my problem?

This workshop offers a gentle introduction to the field of AI.The course will cover the three main branches of AI and machine learning: supervised, unsupervised, and reinforcement learning. Each topic will include a case study to illustrate how AI is used to solve data-intensive problems.

Who Should Attend
The workshop is designed for scientists, technicians and managers and requires only a background in introductory statistics and basic biology/chemistry.

Workshop Benefits
By the end of this workshop, participants will be able to:
Describe the capabilities and limitations of common AI techniques.
Understand the types of data and problems amenable to AI algorithms.
Partner with AI practitioners to solve problems.

Workshop Topics
The workshop will cover the three main branches of artificial intelligence and machine learning (AI/ML):
 Supervised learning
Unsupervised learning
Reinforcement learning

Each section will include a case study selected from presentations at the previous year’s SLAS Annual Meeting.
Examples include:
Supervised learning: Predicting chemical reactivity with artificial neural networks
Unsupervised learning: Identifying biomarkers with Principal Component Analysis
Reinforcement learning: Optimizing chemical synthesis through automation
The case study model aims to: Introduce the most common AI methods for each type of learning connect the AI methods to a real-world example of interest to the SLAS community

Paul Jensen, Ph.D.

Assistant Professor

University of Illinois at Urbana-Champaign

Paul Jensen is an assistant professor in the Department of Bioengineering and the Carl R. Woese Institute for Genomic Biology at the University of Illinois. Paul’s research group applies artificial intelligence and laboratory automation to solve combinatorial problems in biology. He is the PI of an NSF grant to study career expectations in AI and the recipient of an NIBIB Trailblazer Award and an NIGMS Maximizing Investigators’ Research Award. Paul teaches classes on data science, machine learning and the design of experiments.

Mark Hendricks, Ph.D.

Assistand Professor

Whitman College

Mark Hendricks is an assistant professor in the Department of Chemistry at Whitman College. His research is devoted to understanding the chemistry underlying the synthesis of nanomaterials, with a project devoted to developing automation tools and artificial intelligence to gain insights into the synthesis of metallic nanocrystals. Mark teaches physical, materials and general chemistry at Whitman and has developed lab automation modules for the undergraduate chemistry curriculum.

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AI 1010
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Open to view video. Artificial intelligence (AI) is rapidly transforming science and laboratory automation. With all the development (and hype) surrounding AI, many scientists and managers have fundamental questions about the technology: What is AI? How does it work? Can it solve my problem? This workshop offers a gentle introduction to the field of AI. The course will cover the three main branches of AI and machine learning: supervised, unsupervised, and reinforcement learning. Each topic will include a case study to illustrate how AI is used to solve data-intensive problems. Who Should Attend The workshop is designed for scientists, technicians and managers and requires only a background in introductory statistics and basic biology/chemistry. Workshop Benefits By the end of this workshop, participants will be able to: Describe the capabilities and limitations of common AI techniques. Understand the types of data and problems amenable to AI algorithms. Partner with AI practitioners to solve problems. Workshop Topics The workshop will cover the three main branches of artificial intelligence and machine learning (AI/ML): Supervised learning Unsupervised learning Reinforcement learning Each section will include a case study selected from presentations at the previous year’s SLAS Annual Meeting. Examples include: Supervised learning: Predicting chemical reactivity with artificial neural networks Unsupervised learning: Identifying biomarkers with Principal Component Analysis Reinforcement learning: Optimizing chemical synthesis through automation The case study model aims to: Introduce the most common AI methods for each type of learning Connect the AI methods to a real-world example of interest to the SLAS community
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