My name is Jack Etheredge. I’m currently a senior machine learning engineer at Twosense.ai.

I was previously a senior machine learning engineer at American Express, where I performed deep learning research and developed and deployed machine learning powered software while mentoring junior team members.

I particularly enjoy machine learning research addressing human health and environmental conservation problems as well as “pure” machine learning research pursuing the creation of artificial general intelligence.

I earned a PhD from the University of Cambridge in neuroscience for work describing gene expression patterns in developing neuronal stem cells lineages.

Areas of interest:

I have several broad and disparate research interests:

  • Machine learning for the creation of artificial general intelligence (AGI)
  • Human health, particularly regenerative medicine
  • Environmental conservation and sustainability

I think that artificial general intelligence is achievable, and at this point inevitable. I think that humanity could leverage such an intelligence to help solve a vast array of problems, including but not limited to human health and environmental sustainability issues. If we assume for a moment that AGI will be created eventually, I think it’s important that we keep human and environmental health top of mind throughout its development. Social bias and the environmental footprint of model training are two research areas that immediately come to mind as being important today and even more important once a hypothetical artificial general intelligence could start improving itself with or without human oversight.

Technical skills relating to machine learning:

  • Python
  • Statistical machine learning models (applied)
  • Deep learning (applied and research)
    • Natural language processing (NLP)
    • Computer vision
    • Generative models
    • Reinforcement learning

Purpose of this blog:

The purpose of this blog is to showcase some of the projects I’ve done, teach machine learning through examples and tutorials, share machine learning paper implementations, and to discuss foundational machine learning papers.