#probabilistic graphical models

[damilola-doodles]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[dammyanimation]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[damilola-ai-automation]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[damilola-warrior-mindset]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[damilola-moyo]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[govindhtech]

MMaDA: Open-Source Multimodal Large Diffusion Models

Multimodal Large Diffusion Language Models

Hugging Face introduces MMaDA, a new class of multimodal diffusion basis models, to achieve excellent performance in several areas. It strives for excellence in text-to-image generation, multimodal understanding, and textual reasoning.

Three key innovations distinguish this method:

In its unified diffusion architecture, MMaDA uses a modality-agnostic design and a shared probabilistic formulation. By eliminating modality-specific components, this ensures data integration and processing across types.

Mixed lengthy CoT fine-tuning: This strategy aims to curate a CoT format across modalities. This strategy allows cold-start training for the last reinforcement learning (RL) stage by coordinating reasoning processes between the textual and visual domains, improving the model's ability to solve difficult issues immediately.

The unified policy-gradient-based RL algorithm (UniGRPO) is a method created for diffusion foundation models. To unify post-training performance gains across reasoning and generating tasks, it uses diversified reward models.

Experimental data shows that MMaDA-8B is a unified multimodal foundation model with good generalisation. It outperforms other powerful models in several areas. MMaDA-8B outperforms LLaMA-3-7B and Qwen2-7B in textual reasoning, SDXL and Janus in text-to-image generation, and Show-o and SEED-X in multimodal understanding. These results show how successfully MMaDA links pre- and post-training in unified diffusion systems.

MMaDA has several training stage checkpoints:

After instruction tuning and pretraining, MMaDA-8B-Base is available. It generates simple text, photos, captions, and thoughts. Huggingface open-sourced MMaDA-8B-Base. Specifications are 8.08B. This process involves pre-training on ImageNet (step 1.1), an Image-Text Dataset (Stage 1.2), and text instruction following (Stage 1.3).

MixCoT (shortly to arrive): This edition uses mixed extended chain-of-thought (CoT) fine-tuning. It should have advanced multimodal, textual, and image-generation reasoning. After Mix-CoT finetuning with text reasoning (Stage 2.1), training adds multimodal reasoning (Stage 2.2). Release was expected two weeks following the 2025-05-22 update.

MMaDA-8B-Max (coming soon): UniGRPO reinforcement learning trains it. It should excel at complex logic and stunning graphics. Stage 3 of UniGRPO RL training is slated for release after the code move to OpenRLHF. It was expected one month following the 2025-05-22 upgrade.

For inference, MMaDA supports semi-autoregressive sample text production. Multimodal generation uses non-autoregressive diffusion denoising. We offer inference programs for text, multimodal, and text-to-image generation. The training method includes pre-training and Mix-CoT fine-tuning.

Research paper introducing MMaDA was submitted to arXiv on May 21, 2025. Also available through Hugging Face Papers. Huggingface and Gen-Verse/MMaDA's GitHub account hosts the training models and code. Huggingface Spaces offers a demo online. The project repository on GitHub has 11 forks and 460 stars, according to data. Python dominates the repository. Authors include Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang.

[krmangalam1011]

Study B.Tech. in Artificial Intelligence and Machine Learning in 2025

Are you extremely fascinated with the latest advancements in technology? Do you wish to pursue a spectacular career which elevates your career graph in one go? If Yes, then it’s time to pursue a B.Tech. in Artificial Intelligence and Machine Learning from K.R. Mangalam University which is one of the most sought-after programmes in today’s generation. 

Designed in collaboration with top experts from IBM, this course also offers constant mentorship to the students. Moving forward, in this blog we will talk about the major aspects related to this course which include its core highlights, eligibility criteria, fees and the overall programme structure. 

B.Tech. in Artificial Intelligence and Machine Learning Course Highlights 

A highly intellectual course which is curated in collaboration with leading professionals from IBM. Upon enrolling for this course, you will learn to develop advanced computer applications arising in the field of AI & ML. Moreover, students also get hands-on experience through internships, paid international visits, conferences and seminars. Eventually, all these aspects prepare the students for an impactful career in the data-driven industries. Here’s a quick snapshot of the course.  

Course Name 

B.Tech. CSE (AI & ML) with academic support of IBM & powered by Microsoft Certifications

Course Type: 

Undergraduate 

Duration: 

4 Years 

Study Mode:

Full-Time 

Programme Fee Per Year:

Rs 2,65,000/-  (as of 25th March 2025) 

Admission Process:

Written Test + Personal Interview 

Top Recruiters: 

Amazon, Flipkart, Google, OLA, KPMG, Cure Fit 

B.Tech. in Artificial Intelligence and Machine Learning Eligibility Criteria

To enrol for this course at KRMU, you must meet the necessary eligibility requirements asserted by the university. The general criteria are as follows: 

A candidate must have cleared the 10+2 examination with Physics and Mathematics as compulsory subjects.

For the remaining course, choose from Chemistry/ Computer Science/ Electronics/ Information Technology/ Biology/ Informatics Practices/ Biotechnology/ Technical Vocational subject/ Agriculture/ Engineering Graphics/ Business Studies/ Entrepreneurship from a recognised board/university with a minimum 50% aggregate overall. 

B.Tech. in Machine Learning and Artificial Intelligence Subjects

At KRMU, we majorly focus on teaching the students about the basics of computational mathematics, and fundamental aspects of computer science along with modern developments taking place in AI and machine learning. In clear terms, the B.Tech. in AI and ML course is a highly informative programme which consists of 8 different semesters and is taught by expert professionals. Here’s a general overview of the artificial intelligence course syllabus for your reference. 

Linear Algebra and Ordinary Differential Equations

Object Oriented Programming using C++

Engineering Calculus

Clean Coding with Python

Engineering Drawing & Workshop Lab

Data Visualization using PowerBI

Discrete Mathematics

Data Structures

Java Programming

Probabilistic Modelling and Reasoning with Python Lab

Theory of Computation

Operating Systems

Natural Language Processing

Arithmetic and Reasoning Skills

Computer Organization & Architecture

Neural Networks and Deep Learning

Career Scope After B.Tech. in Artificial Intelligence & Machine Learning

The foremost benefit of pursuing a B.Tech. in Artificial Intelligence and Machine Learning course is that you have a plethora of career options available in different industries ranging from e-commerce, food, travel, automation etc. Top career options are: 

Machine Learning Engineer/Developer

AI Research Scientist

Data Scientist

Machine Learning Operations (MLOps) Engineer

AI/ML Software Developer

AI Product Manager

AI Ethics Consultant

Data Engineer

AI/ML Consultant

Research Analyst

Conclusion

B.Tech. in Artificial Intelligence and Machine Learning is a perfect programme for you if you’re keen on experimenting and developing unique computer applications. Pursue this course from K.R. Mangalam University and get access to highly sophisticated laboratories with the latest technologies. So what are you waiting for? Choose to enrol today and drive high towards the in-demand career opportunities. 

Frequently Asked Questions 

What is the average salary after B.Tech. in Artificial Intelligence and Machine Learning programme?

After completing this popular programme, students are expected to secure a whopping package ranging in between 6-10 LPA. 

What is the future scope of B.Tech. in AI & ML?

This programme holds an impactful future. Students are loaded with diversified career opportunities in multiple sectors. 

What can I pursue after B.Tech. in Artificial Intelligence?

You can pursue an M.Tech in AI & ML or an MBA after completing your graduation. 

[stuartfrost]

How Causal AI Is Transforming Industrial Digitization: Insights From Stuart Frost

In today’s fast-evolving industrial landscape, the drive for efficient, data-driven decision-making has never been more crucial. Enter Causal AI, a revolutionary force reshaping how industries embrace digitization.

At its core, causal AI enables companies to understand correlations and the actual cause-and-effect relationships within their data. This deeper insight empowers businesses to make more informed decisions, optimize processes, and predict outcomes with unprecedented accuracy. Stuart Frost, CEO of Causal AI company, Geminos, explores how Causal AI will benefit industries as they compete in an increasingly data-centric world.

Understanding Causal AI

Causal AI is quickly becoming a cornerstone in the way industries approach data analysis. While traditional data analytics often focus on identifying patterns and correlations, Causal AI digs deeper, aiming to uncover the root causes behind these patterns. This understanding enables industries to make smarter decisions, pinpointing the true drivers of performance and change. But to grasp what makes Causal AI so revolutionary, it’s essential to differentiate between mere correlations and genuine causation and to explore the mechanisms that enable Causal AI to function effectively.

In the industrial sector, the distinction between causation and correlation is critical. Correlation indicates a relationship between two variables, meaning they often move together. However, correlation does not imply that one variable causes the other to change. This is where many businesses fall into traps; they make decisions based on assumed causes, only to find that they’ve addressed symptoms rather than root problems.

Causal AI helps identify these cause-and-effect relationships, going beyond the surface to drive more precise and effective industrial strategies. It’s like having a map that shows not just the roads but also which ones actually lead to your destination.

“Causal AI employs several methodologies to identify and analyze causal relationships,” says Stuart Frost. “One of the primary methods is causal inference, which uses statistical models to determine cause-and-effect links. This method goes beyond traditional statistical techniques by focusing on how variables interact in their natural settings.”

Graphical models called DAGs (Directed Acyclic Graphs) are a cornerstone of Causal AI. They represent the probabilistic relationships among variables. These models help in mapping out potential scenarios and understanding how changes in one variable affect others and they are a great communication tool for business analysts, data scientists and subject matter experts. Then there’s structural equation modeling, which combines statistical data with causal assumptions to model complex relationships. This approach allows industries to build comprehensive models that reflect real-world complexities.

Together, these methods equip industries with tools to not only identify causation but also to simulate the outcomes of various decisions, leading to optimized processes and forward-thinking strategies.

Impact of Causal AI on Industrial Processes

Causal AI is driving a paradigm shift in industrial processes, empowering businesses with actionable insights that direct their operational strategies. As organizations strive to enhance efficiency and effectiveness, Causal AI stands out with its focus on cause-and-effect, enabling industries to not just react to changes but anticipate and influence them. Let’s explore how it’s reshaping key areas like maintenance, supply chain, and quality control.

Notes Frost, “Imagine a factory floor where machines can predict when they need repairs. Causal AI makes this possible by offering insights that conventional analytics can miss.”

By understanding the causal links between machine usage and potential failures, industries can switch from reactive maintenance to predictive strategies. This ability to foresee and address issues before they escalate reduces costly downtime and boosts overall efficiency. Maintenance schedules become more dynamic, adapting to real-time conditions rather than routine checks, ensuring machinery operates at peak performance with minimal interruptions.

The supply chain is the heartbeat of manufacturing operations, yet it is often vulnerable to disruptions. Causal AI helps untangle the complexities of supply chain dynamics by pinpointing the causal factors that influence production and logistics. It sifts through vast datasets to identify hidden patterns, providing businesses with a blueprint for optimizing their supply chains.

Maintaining high-quality standards is crucial in any industry. Causal AI strengthens quality control by moving beyond superficial data patterns to reveal the underlying causes of defects. By identifying and addressing these root causes, businesses can implement improvements that prevent recurring issues. This proactive approach not only enhances product quality but also reduces wastage and recalls, leading to substantial cost savings. It is like having a digital detective on hand, ready to solve the mystery of defects before they affect the final product.

Challenges and Considerations

As industries embrace Causal AI to drive digitization, they face numerous challenges. From overcoming data obstacles to navigating organizational culture shifts, understanding and addressing these issues is crucial. This section explores these key challenges and offers insights into tackling them effectively.

“Handling data in the context of Causal AI is no small task. Data must be high-quality, unbiased, and free from noise to produce accurate outcomes,” says Frost.

Noise in data can act like static on a radio, interfering with the clear signal you’re trying to capture, which in AI terms translates to misleading insights. To combat this, industries are employing rigorous data-cleaning methods. Pre-processing data with tools that detect and filter out noise ensures that only relevant, clean data is used in modeling.

Bias in data is another formidable hurdle. Bias can skew results and lead to faulty conclusions, much like a biased umpire skewing the outcome of a game. To mitigate this, Causal AI’s graphical models can be used to identify and eliminate potential sources of bias.

Integrating Causal AI into existing frameworks requires more than just technical adjustments. It demands a cultural shift within organizations. Resistance to change is common, much like how a ship resists a change in course despite needing to head in a new direction. Overcoming this inertia requires strong leadership and a clear vision of the potential of Causal AI.

Education is a key component in driving this cultural change. By investing in training and development, organizations can build a workforce well-versed in AI technologies. When employees understand the benefits and workings of Causal AI, they are more likely to embrace it. Additionally, creating cross-functional teams encourages collaboration, fostering a shared sense of purpose and breaking down silos that might resist new technology.

Organizational structures may also need to evolve. Decision-making can no longer rely solely on intuition but should be data-driven. This shift can be likened to a transition from gut-feel navigation to compass-guided travel. Companies that adapt by fostering a culture of data-driven decision-making often find themselves more agile and competitive.

Causal AI is revolutionizing industrial digitization, offering a profound shift in how industries operate. By unveiling the cause-and-effect dynamics entrenched in vast datasets, it facilitates intelligent decision-making and strategic planning. This technology pushes past traditional analytics, allowingImpact of Causal AI on Industrial Processes industries not only to react but to anticipate changes, streamlining processes across maintenance, supply chain, and quality control.

As the industrial landscape continues to evolve, the integration of Causal AI with emerging technologies like IoT and big data remains crucial. This convergence promises enhanced operational efficiencies and innovative pathways. Industries that embrace Causal AI now secure a competitive edge, paving the way for future advancements.

Originally Published At: https://techbullion.com/ On November 26, 2024

[slacourses]

What are the top Python libraries for data science in 2025? Get Best Data Analyst Certification Course by SLA Consultants India

Python's extensive ecosystem of libraries has been instrumental in advancing data science, offering tools for data manipulation, visualization, machine learning, and more. As of 2025, several Python libraries have emerged as top choices for data scientists:

1. NumPy

NumPy remains foundational for numerical computations in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on them. Its efficiency and performance make it indispensable for data analysis tasks. Data Analyst Course in Delhi

2. Pandas

Pandas is essential for data manipulation and analysis. It offers data structures like DataFrames, which allow for efficient handling and analysis of structured data. With tools for reading and writing data between in-memory structures and various formats, Pandas simplifies data preprocessing and cleaning.

3. Matplotlib

For data visualization, Matplotlib is a versatile library that enables the creation of static, animated, and interactive plots. It supports various plot types, including line plots, scatter plots, and histograms, making it a staple for presenting data insights.

4. Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics. It simplifies complex visualization tasks and integrates seamlessly with Pandas data structures, enhancing the aesthetic appeal and interpretability of plots. Data Analyst Training Course in Delhi

5. Plotly

Plotly is renowned for creating interactive and web-ready plots. It offers a wide range of chart types, including 3D plots and contour plots, and is particularly useful for dashboards and interactive data applications.

6. Scikit-Learn

Scikit-Learn is a comprehensive library for machine learning, providing simple and efficient tools for data mining and data analysis. It supports various machine learning tasks, including classification, regression, clustering, and dimensionality reduction, and is built on NumPy, SciPy, and Matplotlib. Data Analyst Training Institute in Delhi

7. Dask

Dask is a parallel computing library that scales Python code from multi-core local machines to large distributed clusters. It integrates seamlessly with libraries like NumPy and Pandas, enabling scalable and efficient computation on large datasets.

8. PyMC

PyMC is a probabilistic programming library for Bayesian statistical modeling and probabilistic machine learning. It utilizes advanced Markov chain Monte Carlo and variational fitting algorithms, making it suitable for complex statistical modeling.

9. TensorFlow and PyTorch

Both TensorFlow and PyTorch are leading libraries for deep learning. They offer robust tools for building and training neural networks and have extensive communities supporting their development and application in various domains, from image recognition to natural language processing. Online Data Analyst Course in Delhi

10. NLTK and SpaCy

For natural language processing (NLP), NLTK and SpaCy are prominent libraries. NLTK provides a wide range of tools for text processing, while SpaCy is designed for industrial-strength NLP, offering fast and efficient tools for tasks like tokenization, parsing, and entity recognition.

These libraries collectively empower data scientists to efficiently process, analyze, and visualize data, facilitating the extraction of meaningful insights and the development of predictive models.

Data Analyst Training Course Modules Module 1 - Basic and Advanced Excel With Dashboard and Excel Analytics Module 2 - VBA / Macros - Automation Reporting, User Form and Dashboard Module 3 - SQL and MS Access - Data Manipulation, Queries, Scripts and Server Connection - MIS and Data Analytics Module 4 - MS Power BI | Tableau Both BI & Data Visualization Module 5 - Free Python Data Science | Alteryx/ R Programing Module 6 - Python Data Science and Machine Learning - 100% Free in Offer - by IIT/NIT Alumni Trainer

Regarding the "Best Data Analyst Certification Course by SLA Consultants India," I couldn't find specific information on such a course in the provided search results. For the most accurate and up-to-date details, I recommend visiting SLA Consultants India's official website or contacting them directly to inquire about their data analyst certification offerings. For more details Call: +91-8700575874 or Email: hr@slaconsultantsindia.com

[codezup]

Optimizing Predictive Maintenance with Conditional Random Fields for Real-World Applications

Real-World Predictive Maintenance with Conditional Random Fields Introduction Predictive maintenance is a critical aspect of modern industrial operations, enabling companies to anticipate and prevent equipment failures, reducing downtime, and increasing overall efficiency. Conditional Random Fields (CRFs) are a type of probabilistic graphical model that has been successfully applied to…

[sunaleisocial]

A new approach to modeling complex biological systems

New Post has been published on https://sunalei.org/news/a-new-approach-to-modeling-complex-biological-systems/

A new approach to modeling complex biological systems

Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can produce enormous quantities of data from a given cellular or multicellular system.

However, making sense of this information is not always easy. This is especially true when trying to analyze complex systems such as the cascade of interactions that occur when the immune system encounters a foreign pathogen.

MIT biological engineers have now developed a new computational method for extracting useful information from these datasets. Using their new technique, they showed that they could unravel a series of interactions that determine how the immune system responds to tuberculosis vaccination and subsequent infection.

This strategy could be useful to vaccine developers and to researchers who study any kind of complex biological system, says Douglas Lauffenburger, the Ford Professor of Engineering in the departments of Biological Engineering, Biology, and Chemical Engineering.

“We’ve landed on a computational modeling framework that allows prediction of effects of perturbations in a highly complex system, including multiple scales and many different types of components,” says Lauffenburger, the senior author of the new study.

Shu Wang, a former MIT postdoc who is now an assistant professor at the University of Toronto, and Amy Myers, a research manager in the lab of University of Pittsburgh School of Medicine Professor JoAnne Flynn, are the lead authors of a new paper on the work, which appears today in the journal Cell Systems.

Modeling complex systems

When studying complex biological systems such as the immune system, scientists can extract many different types of data. Sequencing cell genomes tells them which gene variants a cell carries, while analyzing messenger RNA transcripts tells them which genes are being expressed in a given cell. Using proteomics, researchers can measure the proteins found in a cell or biological system, and cytometry allows them to quantify a myriad of cell types present.

Using computational approaches such as machine learning, scientists can use this data to train models to predict a specific output based on a given set of inputs — for example, whether a vaccine will generate a robust immune response. However, that type of modeling doesn’t reveal anything about the steps that happen in between the input and the output.

“That AI approach can be really useful for clinical medical purposes, but it’s not very useful for understanding biology, because usually you’re interested in everything that’s happening between the inputs and outputs,” Lauffenburger says. “What are the mechanisms that actually generate outputs from inputs?”

To create models that can identify the inner workings of complex biological systems, the researchers turned to a type of model known as a probabilistic graphical network. These models represent each measured variable as a node, generating maps of how each node is connected to the others.

Probabilistic graphical networks are often used for applications such as speech recognition and computer vision, but they have not been widely used in biology.

Lauffenburger’s lab has previously used this type of model to analyze intracellular signaling pathways, which required analyzing just one kind of data. To adapt this approach to analyze many datasets at once, the researchers applied a mathematical technique that can filter out any correlations between variables that are not directly affecting each other. This technique, known as graphical lasso, is an adaptation of the method often used in machine learning models to strip away results that are likely due to noise.

“With correlation-based network models generally, one of the problems that can arise is that everything seems to be influenced by everything else, so you have to figure out how to strip down to the most essential interactions,” Lauffenburger says. “Using probabilistic graphical network frameworks, one can really boil down to the things that are most likely to be direct and throw out the things that are most likely to be indirect.”

Mechanism of vaccination

To test their modeling approach, the researchers used data from studies of a tuberculosis vaccine. This vaccine, known as BCG, is an attenuated form of Mycobacterium bovis. It is used in many countries where TB is common but isn’t always effective, and its protection can weaken over time.

In hopes of developing more effective TB protection, researchers have been testing whether delivering the BCG vaccine intravenously or by inhalation might provoke a better immune response than injecting it. Those studies, performed in animals, found that the vaccine did work much better when given intravenously. In the MIT study, Lauffenburger and his colleagues attempted to discover the mechanism behind this success.

The data that the researchers examined in this study included measurements of about 200 variables, including levels of cytokines, antibodies, and different types of immune cells, from about 30 animals.

The measurements were taken before vaccination, after vaccination, and after TB infection. By analyzing the data using their new modeling approach, the MIT team was able to determine the steps needed to generate a strong immune response. They showed that the vaccine stimulates a subset of T cells, which produce a cytokine that activates a set of B cells that generate antibodies targeting the bacterium.

“Almost like a roadmap or a subway map, you could find what were really the most important paths. Even though a lot of other things in the immune system were changing one way or another, they were really off the critical path and didn’t matter so much,” Lauffenburger says.

The researchers then used the model to make predictions for how a specific disruption, such as suppressing a subset of immune cells, would affect the system. The model predicted that if B cells were nearly eliminated, there would be little impact on the vaccine response, and experiments showed that prediction was correct.

This modeling approach could be used by vaccine developers to predict the effect their vaccines may have, and to make tweaks that would improve them before testing them in humans. Lauffenburger’s lab is now using the model to study the mechanism of a malaria vaccine that has been given to children in Kenya, Ghana, and Malawi over the past few years.

His lab is also using this type of modeling to study the tumor microenvironment, which contains many types of immune cells and cancerous cells, in hopes of predicting how tumors might respond to different kinds of treatment.

The research was funded by the National Institute of Allergy and Infectious Diseases.

[aibyrdidini]

Bayesian Inference Algorithms.

A Unified Framework for Perception, Reasoning, and Decision-making.

I'll provide you with a simplified example of combining AI with perception (computer vision), reasoning (probabilistic graphical models), and decision-making (Markov decision processes) using Python.

First, let's install the required libraries:

```bash

pip install numpy opencv-python-headless matplotlib pgmpy gym

```

Now, let's create a generic example for each of the three tasks and then combine them.

1. Perception (Computer Vision):

```python

import cv2

import numpy as np

def detect_circles(image):

image = cv2.medianBlur(image, 5)

circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)

return circles[0] if circles is not None else []

image = cv2.imread("circles.png", cv2.IMREAD_GRAYSCALE)

circles = detect_circles(image)

for circle in circles:

x, y, r = circle

cv2.circle(image, (x, y), r, (0, 255, 0), 2)

cv2.imshow("Detected circles", image)

cv2.waitKey(0)

cv2.destroyAllWindows()

```

2. Reasoning (Probabilistic Graphical Models):

```python

from pgmpy.models import BayesianModel

from pgmpy.estimators import MaximumLikelihoodEstimator, BayesianEstimator

from pgmpy.inference import VariableElimination

model = BayesianModel([('A', 'B'), ('B', 'C'), ('C', 'D')])

model.add_evidence('A', 1)

model.add_cpds(

{'A': np.array([[0.7, 0.3]]),

'B': np.array([[0.9, 0.1], [0.5, 0.5]]),

'C': np.array([[0.8, 0.2], [0.4, 0.6]]),

'D': np.array([[0.6, 0.4], [0.3, 0.7]])})

infer = VariableElimination(model)

query = infer.query(variables=['D'], evidence={'A': 1})

print(query)

```

3. Decision-making (Markov Decision Processes):

```python

import gym

env = gym.make("FrozenLake-v0")

def policy_evaluation(policy, env, theta=0.0001, gamma=1.0):

rewards = np.zeros(env.nS)

while True:

delta = 0

for s in range(env.nS):

v = 0

for a, action_prob in enumerate(policy[s]):

for prob, next_state, reward, done in env.P[s][a]:

v += action_prob * prob * (reward + gamma * rewards[next_state])

delta = max(delta, np.abs(v - rewards[s]))

rewards[s] = v

if delta < theta:

break

return rewards

policy = np.array([[0.0, 1.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 1.0],

[1.0, 0.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 1.0]])

policy_evaluation(policy, env)

```

4. Combining AI, Perception, Reasoning, and Decision-making:

In a real-world application, these tasks would be combined based on the specific use case. For example, a self-driving car would use computer vision to perceive its surroundings, probabilistic graphical models to reason about the state of the world, and Markov decision processes to make decisions.

Due to the complexity of creating a complete example, I'll provide a high-level outline of how these tasks can be combined in a self-driving car scenario:

- Perception:

- Use computer vision techniques to detect and recognize objects, such as cars, pedestrians, and traffic signs.

- Reasoning:

- Use probabilistic graphical models to infer the state of the world, such as the positions and velocities of other vehicles and pedestrians.

- Decision-making:

- Use Markov decision processes to decide on the best actions, such as accelerating, braking, or steering.

This outline is not a complete solution, but it should give you an idea of how AI, perception, reasoning, and decision-making can be combined in a real-world scenario.

RDIDINI PROMPT ENGINEER

[evoldir]

Fwd: Course: Como_Italy.BayesianPhylogenetics.Aug26-30

Begin forwarded message: > From: evoldir@evol.biology.mcmaster.ca > Subject: Course: Como_Italy.BayesianPhylogenetics.Aug26-30 > Date: 29 February 2024 at 06:45:50 GMT > To: rdmpage@gmail.com > > > > We are glad to inform you that the next Applied Bayesian Statistics > school - ABS24 will be held in the city of Como, along the Lake Como > shoreline, on August 26-30, 2024. > > The school is organised by CNR IMATI (Institute of Applied Mathematics > and Information Technologies at the National Research Council of Italy > in Milano), in cooperation with Fondazione Alessandro Volta. > > The topic will be BAYESIAN PHYLOGENETICS AND INFECTIOUS DISEASES. > > The lecturer will be Prof. MARC SUCHARD (Department of Biostatistics, > UCLA Fielding School of Public Health, USA), with the support of Filippo > Monti (PhD student in Biostatistics, UCLA, USA). > > If interested, you can register on the school website: > > https://ift.tt/kEg2rm5 > > If you are interested in the school but unwilling to register for the > moment, please send an email to abs24@mi.imati.cnr.it and we will send > you updates and reminders. > > As in the past (since 2004), there will be a combination of theoretical > and practical sessions, along with presentations by participants about > their work (past, current and future) related to the topic of the school. > > OUTLINE: The aim of this course is to explore the core challenges of > Bayesian inference of stochastic processes in modern biology in terms of > data-scale, model-dimensionality and compute-complexity. Challenging and > emerging statistical solutions will be illustrated through the analysis > of biological sequences, such as genes and genomes. Molecular > phylogenetics has become an essential analytical tool for understanding > the complex patterns in which rapidly evolving pathogens propagate > across and between countries, owing to the complex travel and > transportation patterns evinced by modern economies, along with growing > political factors such as increased global population and urbanisation. > As an accessible course for all, a brief introduction of the underlying > biology (for statistical researchers) and of modern Bayesian inference > (for practicing biologists) will be also provided. > > Topics will cover probabilistic modeling techniques using both discrete- > and continuous-valued stochastic processes including continuous-time > Markov chains and Gaussian processes; large-scale data-integration > approaches incorporating factors like human mobility and climate > measurements; recursive computing and other mathematical tricks to > evaluate seemingly intractable likelihoods; state-of-the-art sampling > methods for high-dimensional models including Hamiltonian Monte Carlo > and its more recent non-reversible extensions; delivering timely > inference on advancing computing hardware like graphics processing units > and (maybe even) quantum devices. > > We hope you will be interested in the school and we would like to meet > you in Como next year. > > We invite you also to share the information with people potentially > interested. > > Best regards > > Elisa Varini and Fabrizio Ruggeri > Executive Director and Director of ABS24 > > > Marc Suchard

[jhavelikes]

In a glance, we can perceive whether a stack of dishes will topple, a branch will support a child’s weight, a grocery bag is poorly packed and liable to tear or crush its contents, or a tool is firmly attached to a table or free to be lifted. Such rapid physical inferences are central to how people interact with the world and with each other, yet their computational underpinnings are poorly understood. We propose a model based on an “intuitive physics engine,” a cognitive mechanism similar to computer engines that simulate rich physics in video games and graphics, but that uses approximate, probabilistic simulations to make robust and fast inferences in complex natural scenes where crucial information is unobserved. This single model fits data from five distinct psychophysical tasks, captures several illusions and biases, and explains core aspects of human mental models and common-sense reasoning that are instrumental to how humans understand their everyday world.

Simulation as an engine of physical scene understanding | PNAS

[soha2222]

Master In Data Science

https://www.skillshiksha.com/master-in-data-science-course

MASTERING THE ART OF DATA SCIENCE:

Data science fundamentals encompass a broad range of concepts and skills that are essential for anyone aspiring to work in the field of data science. Here are some key fundamentals:

Programming Knowledge:- Programming is a fundamental skill in a data science,The two most widely used programming languages in the field of data science are Python and R. Here's an overview of their roles and significance in data science:

1. Python: 

Machine Learning: Python is the preferred language for implementing machine learning algorithms and building models. Libraries like TensorFlow and PyTorch are popular for deep learning applications.

Versatility: Python is a versatile and general-purpose programming language. Its syntax is clean and easy to read, making it suitable for both beginners and experienced programmers.

Extensive Libraries: Python has a rich ecosystem of libraries and frameworks that are widely used in data science, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

2. R:

Statistical Computing: R was specifically designed for statistical computing and data analysis. It has a comprehensive set of statistical and mathematical packages.

Data Visualization: R is known for its powerful data visualization capabilities, with packages like ggplot2 providing a flexible and expressive system for creating graphics.

Community of Statisticians: R has a strong user base in the statistical community, and it is often the language of choice for statisticians and researchers.

In practice, Python is often favored for its overall versatility and strong support in the machine learning community, while R is preferred for its statistical capabilities and visualization tools.

Statistics and Probability:

Statistics and probability plays a crucial role in understanding and interpreting data.

Descriptive Statistics: 

Mean, Mode, Median: These measures provide central tendencies and help summarize the central value of a dataset. 

Percentiles and Quartiles: Useful for understanding the distribution of data and identifying outliers.

Standard Deviation: Indicate the spread or dispersion of data points around the mean.

Inferential Statistics:

Hypothesis Testing: Statistical tests, such as t-tests and chi-square tests, are used to make inferences about population parameters based on sample data.

Confidence Intervals: Provide a range of values within which the true population parameter is likely to fall, along with an associated level of confidence.

Regression Analysis: Helps understand relationships between variables and make predictions based on statistical models.

Probability: 

Foundation of Uncertainty: Probability theory provides a mathematical framework for dealing with uncertainty, which is inherent in real-world data.

Data Sampling: Probability is essential in designing and understanding random sampling methods, which are crucial for obtaining representative datasets. 

Advanced Mathematics: Advanced mathematical concepts that form a backbone of a data science: 

Linear Algebra: Essential for tasks like dimensionality reduction (e.g., PCA), solving systems of linear equations, and understanding neural network operations.

Calculus: Optimization algorithms, such as gradient descent, heavily rely on calculus. Calculus is also used in understanding the slope of curves, which is important in various statistical and machine learning techniques.

Statistics: Probability distributions, hypothesis testing, and statistical inference.Forms the basis for making inferences from data, assessing model performance, and dealing with uncertainty.

Probability: Deals with uncertainty and randomness. Central to Bayesian statistics, probability distributions, and probabilistic models in machine learning.

Different Equations: Describes the rate of change of a authority. Used in modeling dynamic systems, particularly in time series. 

Optimization: Finding the minimum or maximum of a function. 

Information Theory: Measures the efficiency the encoding information. 

Numerical Analysis: It focuses on algorithms for numerical solutions. Data Science Application Essential for developing algorithms that can handle large datasets efficiently. 

Fundamental Analysis: Studies vector spaces of functions. 

These fundamental concepts provide the mathematical underpinnings necessary for data scientists to understand, develop.

Big Data Processing: Big data processing refers to the techniques and technologies used to analyze and complex datasets. Data science is  a field that uses mathematical and statistical models and algorithms.

Big data processing is a crucial component of data science, as it enables the processing and analysis. Some common big data processing techniques include map- reduce, hadoop and spark. Let’s talk about technologies involved in big data science:

1.Map-reduce: A programming model for processing and generating large datasets that can be parallelized across a distributed cluster. It enables parallel processing and scalability. 

2.Hadoop: An open- source framework for distributed storage and processing of large data sets. It splits data into smaller chunks and distributes them across a cluster for parallel processing.

3.Apache Spark: An open-source, distributed computing system that can process large datasets quickly. It provides in-memory processing, making it faster than traditional Mapreduce. 

4. Steam Processing: Analyzing and processing data in real time as it is created. 

5. Data Lakes: A centralized repository that allows you to store all structured and unstructured data at any scale.

Deep Learning: Deep learning is a fundamental aspect of data science that revolves around the use of neural networks to extract pattern and insights from large datasets. 

Neural Networks- Neural networks are the foundation of deep learning. They are composed of interconnected neurons organized into layers. Input layer receives the data, hidden layers process it, and the output layer produces the final layer result. 

Deep learning involves neural networks with many hidden layers, known as deep neural networks. 

Activation Functions:  Neurons use activation functions to introduce non-linearity into the network enabling it to learn complex patterns. 

Common activation functions include sigmoid, hyperbolic tangent.

Backpropagation: Backpropagation is a training algorithm used to minimize the error by adjusting the weights of the neural network. 

Loss Functions: Loss functions measure the difference between the predicted output and the actual target. 

Common loss functions include mean squared error for regression tasks.

Optimization Algorithm: Optimization algorithms, such as stochastic gradient descent (SGD) and variants like Adam, are used to minimize the loss function during training.

Understanding these fundamental concepts is crucial for anyone delving into the field of data science are focusing on deep learning. Continuous learning and staying updated with advancements are essential in this rapidly evolving domain. 

Conclusion: “Mastering the art of data science”

In reaching this conclusion, it is imperative to acknowledge that mastering data science is not merely about proficiency in coding languages, statistical models, or machine learning algorithms. It expands to the art of asking the right questions, framing problems strategically and deriving actionable insights from complex datasets. 

Furthermore, the journey to mastering data science underscores the importance of continuous learning and adaptability. The field is dynamic, with emerging technologies and methodologies requiring practitioners to stay abreast of the latest developments. 

[ai-news]

In the domain of reasoning under uncertainty, probabilistic graphical models (PGMs) have long been a prominent tool for data analysis. These models provide a structured framework for representing relationships between various features in a dataset a #AI #ML #Automation