Discover Top Posts Tagged with #bayesian inference

You know you’re allowed to change your mind right?

As new information comes in your perspective and stance may change and that’s literally okay. It’s called updating your beliefs. That’s what growth is.

Bayes theorem.

#cauchyfeelings #mathblr #codeblr #bayes theorem #bayesian inference

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024

#artificial intelligence #active learning #bayesian mechanics #machine learning #deep learning #robotics #computer vision #natural language processing #uncertainty quantification #decision making #probabilistic modeling #bayesian inference #active interference #ai research #intelligent systems #interview #ai assisted writing #machine art #Youtube

Title: "Non-linear regression in a blurry cloud of (un-) certainty"

Date: 2023/06/12 - Size: DIN A4

Collage made with torn pieces of paper, printed background paper (top is rather dark night sky, bottom is mererly clouds in pastel-colors)

I resized and printed the non-linear regression visualisation/illustration and put it on top of the watercolour background paper.

I included a scrap piece of paper with the title of the picture and have torn it with a spiral-shaped jag at the bottom, which I bent around the top part of the non-linear regression illustration.

When it comes to my really unexact approach of thinking and non-linear reasoning, I came to understand that I used the concept of Bayesian Inference since I can remember my own thinking processes. To validate or falsify assumptions in a probabilistic, yet logically consistent manner. - The difference between non-linear logic and irrational illogic is that non-linear logic's assumptions follow a logically consistent route when further investigation of thought is made. Irrational illogic is rather fallacy-based, statements are arbitrarily defined as true or wrong, no sufficient logical consistency can be found. - In non-linear logical reasoning many parallel paths of reasoning not just occur, but also interfere, hence, only a probabilstic description of truthness is possible, which changes with new data/measured evidence. It's neat how such statistical methods can help with some of my ADHD-induced fallacies and wrong application of generalizations upon other special cases, using some sort of metacognition.

[I think the autism side may be the part that helps me with sufficient pedantry in linearizing/concretizing and de-tangling the parallel reasoning processes into more logically consistent-appearing chunks of more linear thought paths. - making me return to certain details and dissecting the details as if they were big pictures themselves - to find logical consistencies inside that information clot...)}

#random walk in my biological calabi-yau-manifold #adhd #non-linear thinking #divergent thinking #actually adhd #bayesian inference #actually neurodivergent #metacognition #autism #asd #math #cog sci #non-linear logic

Nobody knows who she was, just that she was different: a teenage girl from over 50,000 years ago of such strange uniqueness she looked to be

#Bayesian inference #DNA #artificial intelligence

Education and Bayesian Inference.

The Differentiated Model of Giftedness and Talent. Bayesian Inference. Links to Ted Talk by Daniel Wolpert: The real reason for brains. https://www.ted.com/talks/daniel_wolpert_the_real_reason_for_brains?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare Respect All

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#bayesian inference #daniel wolpert #david jarvis #mathematics #panlove #peacewise #probability #protection #spirit #ted #ted talks #the real reason for brains

Quantum Reasoning, human cognition and Artificial Intelligence

Quantum reasoning and the formalism of Lie Algebras are fascinating topics in Quantum Mechanics. By quantum reasoning we are referring to the way the human brain constructs its thoughts and cognitions in an ordered fashion, that is, in the way mathematical psychology has researched the implication of quantum reality on the brain cognitive processes. Quantum Physics is a field of physical sciences…