Lenore Blum

640 Followers
446 Following
49 Posts
Mathematician and Theoretical Computer Scientist (#mathematics, #TCS) interested in #Consciousness and #NeuroAI (#Neurodon, #Neuroscience, #AI). Distinguished Career Prof of CS at CMU, Emerita and Distinguished Prof-in-Residence of CS at UCBerkeley. President, AMCS (https://amcs-community.org/) For more info see, https://www.linkedin.com/in/lenore-blum-1a47224/

Updated Online Discussion:The Future of Consciousness Science

https://twitter.com/JohannesKleiner/status/1706411797916385382/photo/1

Tomorrow, Tues, Sept 26,
19:00Paris;1pmNY;2amTokyo,.

Join with zoom link: bit.ly/46thwCx

Johannes Kleiner on X

X (formerly Twitter)

Tomorrow, Tues, Sept 26,
19:00Paris;1pmNY;2amTokyo,.

Online Discussion:The Future of Consciousness Science

The following will kick-start the discussion: Claire Sergent,Anil Seth,Lenore Blum,Thomas Andrillon,David Chalmers,Ryota Kanai,Erik Hoel,Jakob Jolij

Join with zoom link: bit.ly/46thwCx

The Association for #Mathematical #Consciousness Science (AMCS) and the Oxford Mathematics of Consciousness&Applications Network are pleased to jointly sponsor an Online talk by

Simon Kochen on The Free Will Theorem (https://omcan.web.ox.ac.uk/event/free-will-theorem),
Wednesday, June 14, 16:00, Berlin Time.

Link to join: https://bit.ly/42HZRoV
Password: ask3cb31o1

If joining for the 1st time, read https://bit.ly/3MdmKtF. If having problems when joining, refer to the trouble shooting information on the same page.

On the Free Will Theorem

Early bird registration for the Models of #Consciousness 2023 conference at the #Mathematical Institute, University of Oxford, UK
4-8 September 2023 is this Friday. See:
https://amcs-community.org/events/moc-4-2023/ and
https://amcs-community.org/registration-moc-4/
Models of Consciousness 2023 – AMCS

Dear Friends,
Today I removed the twitter app from my iphone.

Twitter was a life-saver for me during lockdown. I met so many new friends and research articles and thoughtful discussions.

But starting last fall, these posts became fewer and far between; now I have to wade through so much debris before I see a welcome gem.

Today, with elon musk using his twitter handle to promote classic antisemitic tropes, I’ve had it.

Now, I plan to mainly use my sigmoid.social account. Keep in touch!

Excited that our Open Letter emphasizing the urgent need for accelerated research in #consciousness science in light of rapid advancements in #AI has been posted, see,
https://amcs-community.org/open-letters/ and
https://amcs-community.org/press/
We welcome your signatures.
AMCS

As AIs become more powerful, we expect AIs that construct world models to experience “feelings of consciousness”. If we want to avoid inflicting suffering on our planet’s co-inhabitants it is more important than ever to understand #consciousness. See our Viewpoint, https://arxiv.org/abs/2303.17075
Viewpoint: A Theoretical Computer Science Perspective on Consciousness and Artificial General Intelligence

We have defined the Conscious Turing Machine (CTM) for the purpose of investigating a Theoretical Computer Science (TCS) approach to consciousness. For this, we have hewn to the TCS demand for simplicity and understandability. The CTM is consequently and intentionally a simple machine. It is not a model of the brain, though its design has greatly benefited - and continues to benefit - from neuroscience and psychology. The CTM is a model of and for consciousness. Although it is developed to understand consciousness, the CTM offers a thoughtful and novel guide to the creation of an Artificial General Intelligence (AGI). For example, the CTM has an enormous number of powerful processors, some with specialized expertise, others unspecialized but poised to develop an expertise. For whatever problem must be dealt with, the CTM has an excellent way to utilize those processors that have the required knowledge, ability, and time to work on the problem, even if it is not aware of which ones these may be.

arXiv.org

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#Mastodon

1)    New paper is out! Very excited to share our findings in monkeys and humans playing Battleship. Joint work with @[email protected], @[email protected], and Daniel Salzman. Thread below… https://www.nature.com/articles/s41598-023-27662-9.pdf
2)    How do we learn and reason in the face of the messy complexity of the real world? Historically, research on learning regards simple conditional associations. More recent work has focused on learning latent features, but often only a small number at a time based on rewards from single choices.
3)    In the real world, however, latent features are often learned from gathering information over sequences of decisions. For example, searching on the internet, hunting down the best deals in the store, and (perhaps especially!) scientific research all involve gathering information over multiple decisions.
4)    We wanted a task that would contain all these features and that both humans and animals could perform. We landed on Battleship!
5)    In our task, humans and monkeys learn to reveal shapes during trials by turning over tiles on a grid. If they found a part of the shape (a hit), they received a reward; if they did not (a miss), then they advanced to the next choice. Trials ended once shapes were revealed.
6)    On each trial, there was a single shape, drawn from a small number, but the participants had to learn the shapes over multiple trials.
7)    Both species were excellent at the task, though (unsurprisingly) the humans learned the shapes much faster. So how did participants learn to reveal the shapes?
8)    In a shock to us all, participants foraged for information to learn the shapes! They tended to jump to different parts of the board when their recent searches dropped below the average information gathered about the shapes. This is a version of the well-known Marginal Value Theorem in optimal foraging theory. A similar explanation based on rewards did not describe their behavior as well.
9)    In addition, in a genuine fall-out-of-my-chair-in-the-lab moment, we discovered that how well their behavior could be described as following the marginal value rule predicted how quickly they learned the shapes. In short, better foragers learned shapes faster!
10) This work has been cultivated for nearly 7 years now. Many thanks to my co-authors for their help and to the NIH for my K99 for funding this work. I’m eager to begin building on these findings--I’m on the job market, so feel free to reach out to learn more!

Hi Mastodon! #introduction 👋
My name is John Butler.
I'm Maths/Stats Lecturer and Neuroscience researcher in the School of Mathematics and Statistics in TU Dublin since 2015.

My research uses computational neuroscience methods to investigate multisensory processing. My favourite sensory system is the vestibular system it is just great.

I am a member of @neuromatch I’ve contributed materials to the summer schools. I also help organise the Conference and I’m particularly proud of the introduction of Neuromatch for Kids which shows how maths and neuroscience work together.

I really enjoy teaching, I've taught mathematics, statistics and computational neuro to all levels from graduate, down to primary. Here are two modules:
• Numerical Analysis with Applications in Python (https://john-s-butler-dit.github.io/NumericalAnalysisBook/ )
• Short Introduction to Maths in Neuroscience with Python (https://github.com/john-s-butler-dit/Basic-Introduction-to-Python)
There are more on my my GitHub.

Lastly, I do a bit of#scicomm, so I give talks at events like; Maths week and Science Week, Pint of Science, Bright Club, I’m a Mathematician Get Me Out of Here, and I regularly give talks in primary and secondary schools about his work.

Numerical Analysis with Applications in Python — Numerical Analysis