When I was entering into graduate school in nanoscale science, I did not have very many clear career goals besides, “I’d like to perform cutting-edge research in computational physics”, “I’d like to work on green energy technology”, and maybe “I’d like to use my talents to make the world a better place.” Perhaps if I had been a little more considerate in some of my education choices and jumped on the AI/machine intelligence bandwagon earlier I would be more established in a career path right now, but hindsight is 20/20.
I don’t think such a mentality has really led me astray, however, and I do not have any regrets. My career path, despite in transition, has allowed me access to a lot more parts of the information technology spectrum than just physics and nanoscale science (and simulations thereof). Furthermore, my curiosities are piqued upon consideration and synthesis of the longitudinal aspects of various technologies and how they will impact society. I think communication of science and technology to laypeople and stakeholders is extremely important, and as the information-centered infrastructure of our world becomes more and more complex,
The intent of this post is to establish a framework of current problems that I would like to invest time and energy into in my career, whether they are part of a day job, solo side-projects or collaborative efforts. I almost certainly won’t have the time and energy to investigate them all, but as it stands in September 2017, this is a loose list of the things that I think the most often about:
- The plight of farmed animals for food; how to create more environmentally friendly and reduced-emissions protein sources that minimize cruelty* in the supply chain (*defined herein as the taking of life, forced confinement and/or loss of agency in animal and humans)
- The plight of animal models for toxicology, illness and psychological studies; how to use computational toxicology and other scientific approaches to reduce cruelty inherent in animal model research and potentially make it obsolete
- Force multipliers in machine learning for mobilizing collective power, rather than operating hierarchically ‘downwards’ originating from analysts (see here)
- Legislative approaches towards algorithmic transparency
- The encoding of emotional information in environment-percept-agent-action models for general artificial intelligence, and how to structure considerations of agents before actions are taken to recursively consider effects on other agents (to reduce suffering of human agents or maximize the number of liberties of other agents that themselves maximize liberties, and so on)
- Natural language processing (NLP) approaches to de-escalation of personal crises
- How to avert a coming catastrophe in workforce automation: work-centered identities, e.g: truck-drivers still viewing themselves as truck drivers post-automation but without reliable employment, and what that means in the context of culture war, re-training for other jobs, etc.
- The use of video games and/or virtual reality as storytelling and educational devices, to share information in a way that allows, for example, students to learn in an optimal manner for their learning styles (with obvious applications to learning-disabled or autism-spectrum individuals)
- “Big data” approaches to centrally-planned economies
- Blockchain approaches to (decentralized) credit monitoring
- The intersection between category theory, decision-making in general AI, cellular automata, and theorem proving (hopefully combined with (5) and hence, a general model that may operate in a falsifiable ‘least-suffering’ praxis)
- Collectively-owned ad-hoc data analytics enterprises
- Abolition of prisons and the police force; what would be necessary and how could technology compliment such aims while respecting peoples privacy, agency, and humanity
- Mapping and understanding the role of music subculture in codifying a person’s beliefs and choices using computational approaches
- Understanding product management and business analytics (or, making progress towards general competency in the area)
- Staying on the forefront of developments in neural networks, machine/deep learning, data science, data engineering, interactive visualizations and advanced statistical and analytical tools
I’m sure I’ll have more to update in the future.