DeepSummary
In this episode, Michael Kennedy interviews Keelan Cooper, a neuroscientist at the University of California, Irvine. Keelan discusses how he uses Python in his neuroscience lab, where they record electrical signals from neurons in the brain using tiny wires and silicon probes. He explains how they preprocess and analyze this data using Python and various data science libraries.
Keelan talks about the history of programming in neuroscience, starting with early mechanical devices and Fortran, and how Python has become increasingly popular in recent years due to its ease of use and vast ecosystem of libraries. He also discusses the varying levels of software engineering practices in academic labs, from those that prioritize robust code and testing to those that prioritize getting results quickly.
The conversation then turns to the use of deep learning in neuroscience, as well as the broader implications of AI and language models like GPT. Keelan shares his thoughts on the responsible development and deployment of AI, and how it can be used to accelerate scientific discoveries and solve real-world problems.
Key Episodes Takeaways
- Python and data science tools are increasingly being used in neuroscience research for tasks such as preprocessing and analyzing brain data.
- There is a wide range of software engineering practices in academic labs, from those that prioritize robust code and testing to those that prioritize getting results quickly.
- Deep learning and AI are being used in neuroscience for tasks such as neural decoding and protein structure prediction, significantly accelerating research.
- The development of AI systems like language models is helping scientists better understand the components of intelligence and challenging preconceptions about what is easy or difficult for machines.
- While AI has the potential to automate many tasks, it should be viewed as a tool to enhance human capabilities, similar to historical labor-saving technologies.
- Responsible development and deployment of AI is crucial, with a focus on managing the transition well and retraining workers whose jobs may be impacted.
- Open-source software and collaboration are essential in neuroscience and other scientific fields, facilitating reproducibility and accelerating progress.
- Programming skills, particularly in Python, are becoming increasingly important in academia, as they enable researchers to automate tasks and more efficiently analyze data.
Top Episodes Quotes
- “My favorite definition of AI is it's whatever computers can't do yet, because, like, you know, 30 years ago, if we had this conversation, it'd be like, so what do you think of deep blue? Do you think deep blue that the AI that becomes profit chess can, you know, think and is it going to take all our jobs?“ by Keelan Cooper
- “We're kind of peeling at this onion, and we're kind of segmenting intelligence into its different categories to really kind of break it apart just from this vague word of intelligence into actually what are the parts that make something intelligent?“ by Keelan Cooper
- “Like you said a long time ago when you were in your eye track. The same reason that none of us want to sit around and churn butter and go teal the fields and walk to work.“ by Keelan Cooper
Entities
Person
Product
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Episode Information
Talk Python To Me
Michael Kennedy (@mkennedy)
5/9/24
Episode sponsors
Neo4j
Posit
Talk Python Courses
Links from the show
Keiland on Twitter: @kw_cooper
Keiland on Mastodon: @kwcooper@fediscience.org
Journal of Open Source Software: joss.readthedocs.io
Avalanche project: avalanche.continualai.org
ContinualAI: continualai.org
Executable Books Project: executablebooks.org
eLife Journal: elifesciences.org
Watch this episode on YouTube: youtube.com
Episode transcripts: talkpython.fm
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