Sushma Anand Akoju. she/her.

This is the account of Sushma Anand Akoju. My account has been moved to

[Elon Musk @elonmusk]

A small number of repeat offenders are responsible for the vast majority of violent crime

[Fei-Fei Li @drfeifei]

Scientific research is fundamental to advancing civilization and helping people globally to solve the most critical problems, from medicine to materials, from brain science to physics, and much beyond. This is only possible when scientists have access to the best tools of the time to conduct scientific research, including having access to AI-based tools.

[David Sirota @davidsirota]

Destroying the @InternetArchive's @WayBackMachine would be the equivalent of the burning of the Library of Alexandria - one of the worst losses of knowledge in history.

Media giants are now threatening to do this.

We can't let this happen.

Pass it on.

[Sushma Anand Akoju. She/Her @sushmanandakoju]

Prof. Tom Mitchell beautifully highlights that questioning conventional wisdom — and having the resilience to follow through — is not just admirable, it is a required ingredient for researchers in the making. A timeless reminder for all of us. 🙏

This also connects to my earlier thoughts on symbolic AI: https://t.co/b82Xje5Z1y

His key work "The Roles of Symbols in Neural-based AI: They are Not What You Think!" remains inspiration for my recent paper accepted at AAAI 2026 Bridge Program "Finding Consensus from AI Alignment Studies: A Short Survey". A preprint version: https://t.co/BmCtzqCmCQ

[Sushma Anand Akoju. She/Her @sushmanandakoju]

The first episode of the podcast by my mentor Prof. Tom Mitchell @tommitchell — "Machine Learning: How did we get here?" — nearly teleported me back to his 2018 seminar 10-812 "Seminar on Never Ending Learning Architectures" at CMU.

My students at CU Boulder, University of Arizona, and UNH always asked: "Did you really work with Prof. Tom Mitchell who wrote the Machine Learning textbook?" Yes. 🙏

This video is a gift for all learners — covering foundational research papers that shaped my work in knowledge representation, AI and logic.

His key work "The Roles of Symbols in Neural-based AI: They are Not What You Think!" is equally inspiring and essential reading for anyone serious about the future of AI. This work remains inspiration for my recent paper accepted at AAAI 2026 Bridge Program "Finding Consensus from AI Alignment Studies: A Short Survey".

Grateful for the enriching mentorship and experiences. Highly recommend for anyone curious about the history and future of ML. https://t.co/OQGUf48LFa

[Sushma Anand Akoju. She/Her @sushmanandakoju]

Human‑generated short survey “Finding Consensus from AI Alignment Studies: A Short Survey” is now accepted at the AAAI 2026 Bridge program Logical and Symbolic Reasoning in Language Models (LMReasoning).​​
It maps research on alignment and divergence in human–AI and brain–AI representational studies—no new technical results, but a structured snapshot of the field up to 2025, based on independent work with no external funding.​
Peer‑reviewed at CogInterp (NeurIPS 2025) and LMReasoning (AAAI 2026). OpenReview “Accept” and reviews: https://t.co/A5JynA6Gao​

Early preprint version (record of progress since June 2025): https://t.co/BmCtzqCmCQ