![PTenigma's tweet photo. One of the most bizarre + complex talks at NeurIPS [1] was given by my fellow Yorkshireman, the inimitable Prof Karl Friston [2], explaining active inference to a room full of #AI people who are not really neuroscientists. This was interesting to me because
💡Karl Friston is a one-of-a-kind pioneer in brain mapping + neuroimaging analysis methods (co-developing SPM + DCM and to some extent Gaussian Random Field theory) but then he totally switched his research to study "active inference" - a theory of how brains work - which is somewhat related to Reinforcement Learning (RL) in #AI
💡He gave a talk at OHBM that was the most complex I have ever seen given at any conference, explaining the Free Energy Principle (FEP). which describes how systems (biological or artificial) minimize uncertainty about their environment by inferring the causes of sensory inputs + acting to fulfil their prior beliefs or reduce uncertainty. As far as I can tell, the main point is that agents (such as humans) try to minimize "variational free energy", actively measuring the difference between their brain's model of the world + its sensory experiences.
💡The AI people who do reinforcement learning [3] (e.g., programming self-driving cars) are doing a related thing, where an agent learns a "policy" to maximize a cumulative reward signal, given a state + actions; the agent balances trying new actions (exploration) with seeking known rewards, so the AI people in the room are using somewhat similar frameworks that focus on adaptive behavior to achieve the "best" outcomes.
💡Even so, these frameworks (active inference and reinforcement learning) aren't completely similar: RL maximises an external reward signal, but active Inference minimises a combination of "surprise "(discrepancy between observations + expectations) + uncertainty (lack of confidence in beliefs) but also (2) creates a generative model of the environment that predicts sensory inputs, given internal states.
💡 Presumably the AI people in the room are not incorporating intrinsic motivation into their algorithms but they may do now.
[1] I didn't go, but if you want to watch it, you can watch the whole thing for 200 USD and it's well worth it. The NeuroAI workshop was great, as was Arnaud Doucet's flow matching talk, which will give you a new perspective on Generative AI
[2] https://t.co/PpzRAOzSeE
[3] actually not all AI people are doing RL, and most AI books with predictive and generative AI have RL as the last chapter or the last 1/3, which can you can ignore if you are not designing autonomous agents (it is still interesting if you have time to read it).](https://pbs.twimg.com/media/GfYjWIVaUAAsNX6.jpg)
![PTenigma's tweet photo. One of the most bizarre + complex talks at NeurIPS [1] was given by my fellow Yorkshireman, the inimitable Prof Karl Friston [2], explaining active inference to a room full of #AI people who are not really neuroscientists. This was interesting to me because
💡Karl Friston is a one-of-a-kind pioneer in brain mapping + neuroimaging analysis methods (co-developing SPM + DCM and to some extent Gaussian Random Field theory) but then he totally switched his research to study "active inference" - a theory of how brains work - which is somewhat related to Reinforcement Learning (RL) in #AI
💡He gave a talk at OHBM that was the most complex I have ever seen given at any conference, explaining the Free Energy Principle (FEP). which describes how systems (biological or artificial) minimize uncertainty about their environment by inferring the causes of sensory inputs + acting to fulfil their prior beliefs or reduce uncertainty. As far as I can tell, the main point is that agents (such as humans) try to minimize "variational free energy", actively measuring the difference between their brain's model of the world + its sensory experiences.
💡The AI people who do reinforcement learning [3] (e.g., programming self-driving cars) are doing a related thing, where an agent learns a "policy" to maximize a cumulative reward signal, given a state + actions; the agent balances trying new actions (exploration) with seeking known rewards, so the AI people in the room are using somewhat similar frameworks that focus on adaptive behavior to achieve the "best" outcomes.
💡Even so, these frameworks (active inference and reinforcement learning) aren't completely similar: RL maximises an external reward signal, but active Inference minimises a combination of "surprise "(discrepancy between observations + expectations) + uncertainty (lack of confidence in beliefs) but also (2) creates a generative model of the environment that predicts sensory inputs, given internal states.
💡 Presumably the AI people in the room are not incorporating intrinsic motivation into their algorithms but they may do now.
[1] I didn't go, but if you want to watch it, you can watch the whole thing for 200 USD and it's well worth it. The NeuroAI workshop was great, as was Arnaud Doucet's flow matching talk, which will give you a new perspective on Generative AI
[2] https://t.co/PpzRAOzSeE
[3] actually not all AI people are doing RL, and most AI books with predictive and generative AI have RL as the last chapter or the last 1/3, which can you can ignore if you are not designing autonomous agents (it is still interesting if you have time to read it).](https://pbs.twimg.com/media/GfYjSV8bIAAdtg7.jpg)
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One of the most bizarre + complex talks at NeurIPS [1] was given by my fellow Yorkshireman, the inimitable Prof Karl Friston [2], explaining active inference to a room full of #AI people who are not really neuroscientists. This was interesting to me because
💡Karl Friston is a one-of-a-kind pioneer in brain mapping + neuroimaging analysis methods (co-developing SPM + DCM and to some extent Gaussian Random Field theory) but then he totally switched his research to study "active inference" - a theory of how brains work - which is somewhat related to Reinforcement Learning (RL) in #AI
💡He gave a talk at OHBM that was the most complex I have ever seen given at any conference, explaining the Free Energy Principle (FEP). which describes how systems (biological or artificial) minimize uncertainty about their environment by inferring the causes of sensory inputs + acting to fulfil their prior beliefs or reduce uncertainty. As far as I can tell, the main point is that agents (such as humans) try to minimize "variational free energy", actively measuring the difference between their brain's model of the world + its sensory experiences.
💡The AI people who do reinforcement learning [3] (e.g., programming self-driving cars) are doing a related thing, where an agent learns a "policy" to maximize a cumulative reward signal, given a state + actions; the agent balances trying new actions (exploration) with seeking known rewards, so the AI people in the room are using somewhat similar frameworks that focus on adaptive behavior to achieve the "best" outcomes.
💡Even so, these frameworks (active inference and reinforcement learning) aren't completely similar: RL maximises an external reward signal, but active Inference minimises a combination of "surprise "(discrepancy between observations + expectations) + uncertainty (lack of confidence in beliefs) but also (2) creates a generative model of the environment that predicts sensory inputs, given internal states.
💡 Presumably the AI people in the room are not incorporating intrinsic motivation into their algorithms but they may do now.
[1] I didn't go, but if you want to watch it, you can watch the whole thing for 200 USD and it's well worth it. The NeuroAI workshop was great, as was Arnaud Doucet's flow matching talk, which will give you a new perspective on Generative AI
[2] https://t.co/PpzRAOzSeE
[3] actually not all AI people are doing RL, and most AI books with predictive and generative AI have RL as the last chapter or the last 1/3, which can you can ignore if you are not designing autonomous agents (it is still interesting if you have time to read it).
![PTenigma's tweet photo. One of the most bizarre + complex talks at NeurIPS [1] was given by my fellow Yorkshireman, the inimitable Prof Karl Friston [2], explaining active inference to a room full of #AI people who are not really neuroscientists. This was interesting to me because
💡Karl Friston is a one-of-a-kind pioneer in brain mapping + neuroimaging analysis methods (co-developing SPM + DCM and to some extent Gaussian Random Field theory) but then he totally switched his research to study "active inference" - a theory of how brains work - which is somewhat related to Reinforcement Learning (RL) in #AI
💡He gave a talk at OHBM that was the most complex I have ever seen given at any conference, explaining the Free Energy Principle (FEP). which describes how systems (biological or artificial) minimize uncertainty about their environment by inferring the causes of sensory inputs + acting to fulfil their prior beliefs or reduce uncertainty. As far as I can tell, the main point is that agents (such as humans) try to minimize "variational free energy", actively measuring the difference between their brain's model of the world + its sensory experiences.
💡The AI people who do reinforcement learning [3] (e.g., programming self-driving cars) are doing a related thing, where an agent learns a "policy" to maximize a cumulative reward signal, given a state + actions; the agent balances trying new actions (exploration) with seeking known rewards, so the AI people in the room are using somewhat similar frameworks that focus on adaptive behavior to achieve the "best" outcomes.
💡Even so, these frameworks (active inference and reinforcement learning) aren't completely similar: RL maximises an external reward signal, but active Inference minimises a combination of "surprise "(discrepancy between observations + expectations) + uncertainty (lack of confidence in beliefs) but also (2) creates a generative model of the environment that predicts sensory inputs, given internal states.
💡 Presumably the AI people in the room are not incorporating intrinsic motivation into their algorithms but they may do now.
[1] I didn't go, but if you want to watch it, you can watch the whole thing for 200 USD and it's well worth it. The NeuroAI workshop was great, as was Arnaud Doucet's flow matching talk, which will give you a new perspective on Generative AI
[2] https://t.co/PpzRAOzSeE
[3] actually not all AI people are doing RL, and most AI books with predictive and generative AI have RL as the last chapter or the last 1/3, which can you can ignore if you are not designing autonomous agents (it is still interesting if you have time to read it).](https://pbs.twimg.com/media/GfYjXusbAAA2rRT.jpg)
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New deadline approaching! Submit by TODAY, Dec 18, 11:59 PM EST, USA! The "PAY LATER" option is the only way to submit your abstract. Your submission will still count as on-time. We will reach out regarding payment information in the coming days. Thank you!
For those struggling with the #OHBM2025 abstract submission and payment, check this 👇.
There is a "pay later" option such that the abstract IS submitted (confirmation email received) and I'll pay later the fee when the "heat" is off...
Pfouuuu. ☺️
🚨 Due to an ongoing technical issue with our payment processor, the submissions will be allowed to be finalized with a "Pay Later" option. Your submission will be considered on-time, and the abstract fee will be settled later. We apologize for the inconvenience.
Haaaa, my abstract is ready to be submitted and "finalized" but the web page seems overloaded!
Payment did not seem to go through, abstract not finalized but new payment denied as considered as "duplicate transaction"! 😱😬🤔
🚨 FINAL DEADLINE! 🚨This is it! Today is your LAST CHANCE to submit your content for #OHBM2025 in Brisbane, Australia! 🌏🗒️ The submission window CLOSES at 11:59 PM EST, USA on December 17, 2024. 🔗 in comments. Submit now before it’s too late! #Neuroscience #CallforContent
Opportunity to submit your manuscript on Open Datasets to @ApertureOHBM - Special Issue -collaboration between Aperture Neuro and the OHBM Open Science SIG @OHBM @OhbmOpen
🚨 ONLY 4 DAYS LEFT! Submit your content NOW! Be part of #OHBM2025 and connect with global leaders in neuroscience.🗒️ Deadline: 11:59 PM EST, USA on December 17, 2024. NO extensions under ANY circumstances! Late submissions will NOT be considered. 🔗in comments. #Neuroscience
Be aware 👇!
Yes, @Christi99886417 (Christine Bastin) is organizing it again this year !
Check out all the details in the link. 😀
They are not kidding...
🚨ONLY 5 DAYS LEFT! Time is running out!⏰ Don’t miss your chance to share your research at #OHBM2025.🗒️ Deadline: 11:59 PM EST, USA on December 17, 2024. NO extensions under ANY circumstances! Late submissions will NOT be considered. 🔗 in comments. #Neuroscience #CallforContent
4th edition of the RFN conference: Cognitive Neuroscience of Memory: The Recollection, Familiarity, and Novelty Detection conference (RFN2025) on 15 and 16 April 2025 in Liège, Belgium.
Call for abstracts: deadline: January 17, 2025
https://t.co/oEITHzk4oD
Yeah, my @frsFNRS research project, aka PDR, on "Metabolite-based CEST MR imaging for the characterization of healthy ageing and Alzheimer's disease risks." has been funded for the next 4 years at @Giga_CRCivi ! 😄
Poke @GIGA_ULiege @ULiegeResearch @ULiegeFSA
📢 Résultats de l'appel Crédits et Projets 2024 !
➡️ Ce 10 décembre 2024, le Conseil d'administration du FNRS a octroyé les Projets de Recherche (dont PDR avec des partenaires Weave) suivants :
👉👉 https://t.co/RWOauET13Q
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