Meeting #3
Friday, 2/25/2022
Table of Contents
Meeting recording available here.
Agenda
- Housekeeping
5:10-5:20
- Agenda
- Journal Club Announcement
- Social Media
- Discord Tutorial - Joining as a Member
- Discussion Questions
5:20-5:50
- Key components of thought
- How much do you trust your brain?
- Perception and truth
- What do trees feel? - Is Siri intelligent?
- Card Games - resolving the spoons tournament
5:50-6:00
- Presentation Groups and Work Time
6:00-6:30
- Ideas for Journal Club
6:30-6:50
Deliverables
Have a presentation ready with your group for next week (Meeting 4)!
Based on the form interest, the following research groups have been created:
Topic | People |
---|---|
Biologically Informed/Plausible Networks | Drew, Chanh |
Reinforcement Learning | Varun, Aditya, Timothy |
Neuromorphic Computing Hardware | Yegor, Chris |
Swarm Intelligence/Robots/Coded Agents | Ary, Jason, Chaytan |
Meta-Modeling and Self-Aware Networks | Sabrina, Marlene, Andre |
Neuroscience of Predictive Coding & Memory | Janna, Jay, Alec |
Next meeting, each group will give an informal presentation sharing their research. If you want to be involved in another topic, let us know and we’ll add you!
Presentation Guidelines & Objectives
You will research a given topic in small interdisciplinary groups, then give a small presentation on what you learned. The idea is to discover what experts are doing in your topic/field, and where our team may be able to add value through our own project.
Use whatever form of creating slides works best for you - Canva, Google Slides, PowerPoint - your choice. |
Touch on the following questions, not necessarily in this order:
- How does this topic relate to the idea of intelligence
- Does it have neuroscience ties? Computer science ties? Other related fields in which it is relevant? (These ties are not requirements for the topic, just an open question.)
- What are some interesting recent papers, projects, or advances in this topic?
- What are at least 2 project ideas or directions that would build upon the progress of this field?
- Any libraries/technologies required/could help with implementation?
- What do we need to know more about to better understand this topic?
Notes
- Discord tutorial - make sure to go to
#role-react
and click the brain emoji to be given the Member role.- Clicking the
Member
role button unlocks presentation group channels where you can collaborate with your group members. - Generally, make sure to stay on top of
#announcements
channel. - All meeting notes, zoom recordings, slides, and more are on the website:
interactive-intelligence.github.io
- Clicking the
- Journal Club - scheduled for Monday, 5-6 PM, Sieg Hall 332 (see schedule page for most up-to-date information).
- Interested in helping us out with social media? Message us!
Discussion
Should we trust our brain?
- Shepard’s Tone - the perception of continuously increasing/decreasing pitch.
- Optical (and audio) illusions as examples that we do not perceive reality or objective truth
- Do you trust your brain?
- Success rate of information being given.
- Whenever possible - try to offload everything from the brain - memory, etc.
- Complementary relationship between human and machine properties
- Rely on data and things other than our brain - Hume and induction
- Is maximum likelihood estimation the best we can do? If we have only ever seen white swans then must we logically assume all swans are white?
- Preciseness of measurement.
- Abstract and precise truths. Humans are generally good at abstract truths (e.g. “the United States is a country”); machines are generally good at precise truths (e.g. “it is 23.4837… degrees Celsius”, “12345 + 98374 = 110719”).
- Metaphorical truth - something that is not necessarily true but is useful.
- Attaching emotions to events - instinctive judgements.
- Outliers
- Is our idea of reality constructed by majority? What if the outlier is ‘correct’? How do we choose whether to include outliers or not? What about the gradient of outlier-ness - i.e. which data points are ‘more typical’ than others?
- Out of Distribution Detection - what is the distribution of very high-dimensional datasets? See Balestriero, Pesenti, LeCun: “Learning in High Dimension Always Amounts to Extrapolation”
- Idea that the more independent observations of the same phenomena occur the more likely to be true?
- Example of DNA and transcribing proteins being analogous to computing and executing programs, these phenomena independently emerged, so does that evidence an objective reality
- What about all the people that report seeing elves when they take DMT (independently reporting same hallucinations)? What is their ‘truth’/’experience’?
- Standpoint epistemology - see Harding, “Rethinking Standpoint Epistemology: What is ‘Strong Objectivity’?”
- Is our idea of reality constructed by majority? What if the outlier is ‘correct’? How do we choose whether to include outliers or not? What about the gradient of outlier-ness - i.e. which data points are ‘more typical’ than others?
- Should we trust AI? Can we trust AI?
- Court cases - how emotions can bias the truth, skewing events, etc.
- Voices in your head - who is write? Who knows about what we think?
- Both cells and CPUs share similar features that have been independently developed.
- Godel’s incompleteness theorem
- If math - perhaps the most ‘objective’ system known to humanity - is incomplete, what is objective? Can anything beyond the complete calculation of the universe be objective (and no model capable of ‘pure’ objectivity)?
- Objectivity is still based in general consensus, math founded on the idea that we can all come to a common representation of what ‘one’ object is / we can all count the same
- Aristotelian models - we like discrete representations and dislike fuzzy understandings.
- Legal and symbolic systems - Not a mistake that legal systems have been mentioned as examples for truths (e.g. abstract truths), they are designed to classify the world into good / bad
- Can we measure brain waves and neural activity during computation of something “objective” like “1+1” - how similar are the computations as a metric for ground truth?
- Hierarchical model of perception in both neuroscience and neural networks: as information is processed by different modules of the brain, higher order abstraction and meaning emerges.
- If we can’t perceive something, that doesn’t make it wrong/nonexistent/incorrect.
- Cats can see wavelengths of light we don’t have perception for
- How can you compare intelligence when the sensory inputs aren’t even the same
- Survival as a rough metric for how accurately our perception matches some underlying reality?
Groups
- Everyone has been assigned a group. The group will conduct independent research and present on their research.
- Goal: understand where the field is, how it relates to AGI, and how we can contribute with possible projects
- Initial documents and communications are happening in the drive and in the discord.
- See
#announcements
for further details
- See
Journal Club Possibilities
- Again, see you all Monday Feb. 28th, Sieg Hall 332 5-6pm for a lowkey first journal club!
- Chaytan’s ideas for possible journal club topics, non-exhaustive list, share any ideas wherever –
#papers-and-ideas
or#general
or any other channel works- Information theory textbook, Jason recommends certain chapters
- Interview w Ben Goertzel on AGI + accompanying podcast
- Classic neural network papers or neuroscience papers, RL theory papers?
- Neuromorphic hardware Shih-Chii Liu: Neuromorphic electronics, A historical perspective (Telluride Neuromorphic 2020)
- Intel Loihi chip design Asynchronous Circuits
- Recorded lecture: Towards Conscious AI
- Less technical video about AGI: PBS, Can We Build a Brain?
- Awesome merging of neuroscience and ML: Predictive Coding Models of Perception
- Nature paper on Predictive Coding: Neurons learn by predicting future activity
- Cell paper on DL from neuroscience: Informing deep neural networks by multiscale principles of neuromodulatory systems
- Taking suggestions for any subjects you’re interested in or passionate about!
- Have one person present or take charge of a paper that is shared beforehand and present during the Journal Club.
- Will incorporate a mix of Youtube videos, publicly available recorded lectures, etc (tend to be broader in scope) with deeper dives into papers.
- People that want to present what they have learned or know about given subjects can do that as well on any given week.