Will LLMs always hallucinate?
As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.
A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.
While the idea of hallucinations as features isn't new, the researchers' explanation is.
They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.
In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.
This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.
So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.
It also suggests that research into making models more robust and understanding their failure modes is crucial.
No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases
LLM & Generative AI Resources: https://t.iss.one/generativeai_gpt
As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.
A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.
While the idea of hallucinations as features isn't new, the researchers' explanation is.
They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.
In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.
This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.
So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.
It also suggests that research into making models more robust and understanding their failure modes is crucial.
No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases
LLM & Generative AI Resources: https://t.iss.one/generativeai_gpt
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AI Prediction in 2025
1. Major Acquisitions: Anthropic (Amazon), Mistral (Meta), and Cohere (Google).
2. Mistral will be absorbed into Meta
3. Cohere will be bought by Google
4. Rest like SSI (from Ilya) etc will simply fold like Inflection AI etc.
5. Only OpenAI and XAI may remain as independent companies.
6. Mainstream Adoption of AI Agents.
7. Proliferation of Specialized Large Language Models.
1. Major Acquisitions: Anthropic (Amazon), Mistral (Meta), and Cohere (Google).
2. Mistral will be absorbed into Meta
3. Cohere will be bought by Google
4. Rest like SSI (from Ilya) etc will simply fold like Inflection AI etc.
5. Only OpenAI and XAI may remain as independent companies.
6. Mainstream Adoption of AI Agents.
7. Proliferation of Specialized Large Language Models.
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Artificial_Intelligence,_Game_Theory_and_Mechanism_Design_in_Politics.pdf
2.8 MB
Artificial Intelligence, Game Theory and Mechanism Design in Politics
Tshilidzi Marwala, 2023
Tshilidzi Marwala, 2023
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Fundamentals_of_Deep_Learning_Designing_Next_Generation.pdf
15.9 MB
Fundamentals of Deep Learning
Nithin Buduma, 2022
Nithin Buduma, 2022
👍2
Modern_Deep_Learning_for_Tabular_Data_Novel_Approaches.pdf
51.8 MB
Modern Deep Learning for Tabular Data
Andre Ye, 2023
Andre Ye, 2023
👍2
12 Fundamental Math Theories Needed to Understand AI
1. Curse of Dimensionality
This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data.
2. Law of Large Numbers
A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods.
3. Central Limit Theorem
This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning.
4. Bayes’ Theorem
A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI.
5. Overfitting and Underfitting
Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance.
6. Gradient Descent
This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models.
7. Information Theory
Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency.
8. Markov Decision Processes (MDP)
MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents.
9. Game Theory
Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments.
10. Statistical Learning Theory
This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions.
11. Hebbian Theory
This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory.
12. Convolution (Kernel)
Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap.
1. Curse of Dimensionality
This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data.
2. Law of Large Numbers
A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods.
3. Central Limit Theorem
This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning.
4. Bayes’ Theorem
A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI.
5. Overfitting and Underfitting
Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance.
6. Gradient Descent
This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models.
7. Information Theory
Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency.
8. Markov Decision Processes (MDP)
MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents.
9. Game Theory
Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments.
10. Statistical Learning Theory
This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions.
11. Hebbian Theory
This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory.
12. Convolution (Kernel)
Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap.
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🚨 IIT Ropar AI Entrance Test – This Sunday!
📅 Date: 12th Jan
🌐 Mode: Online
💡 Who Can Apply? Anyone with logical thinking—no specific background required!
Learn from IIT Professors like Prof. Sudarshan Iyengar and master the most in-demand skill: AI.
⚡ Limited slots! Register now: 👇
https://masaischool.com/iit-ropar-ai-cse?utm_source=U10&utm_medium=T
📅 Date: 12th Jan
🌐 Mode: Online
💡 Who Can Apply? Anyone with logical thinking—no specific background required!
Learn from IIT Professors like Prof. Sudarshan Iyengar and master the most in-demand skill: AI.
⚡ Limited slots! Register now: 👇
https://masaischool.com/iit-ropar-ai-cse?utm_source=U10&utm_medium=T
👍1
Famous programming languages and their frameworks
1. Python:
Frameworks:
Django
Flask
Pyramid
Tornado
2. JavaScript:
Frameworks (Front-End):
React
Angular
Vue.js
Ember.js
Frameworks (Back-End):
Node.js (Runtime)
Express.js
Nest.js
Meteor
3. Java:
Frameworks:
Spring Framework
Hibernate
Apache Struts
Play Framework
4. Ruby:
Frameworks:
Ruby on Rails (Rails)
Sinatra
Hanami
5. PHP:
Frameworks:
Laravel
Symfony
CodeIgniter
Yii
Zend Framework
6. C#:
Frameworks:
.NET Framework
ASP.NET
ASP.NET Core
7. Go (Golang):
Frameworks:
Gin
Echo
Revel
8. Rust:
Frameworks:
Rocket
Actix
Warp
9. Swift:
Frameworks (iOS/macOS):
SwiftUI
UIKit
Cocoa Touch
10. Kotlin:
- Frameworks (Android):
- Android Jetpack
- Ktor
11. TypeScript:
- Frameworks (Front-End):
- Angular
- Vue.js (with TypeScript)
- React (with TypeScript)
12. Scala:
- Frameworks:
- Play Framework
- Akka
13. Perl:
- Frameworks:
- Dancer
- Catalyst
14. Lua:
- Frameworks:
- OpenResty (for web development)
15. Dart:
- Frameworks:
- Flutter (for mobile app development)
16. R:
- Frameworks (for data science and statistics):
- Shiny
- ggplot2
17. Julia:
- Frameworks (for scientific computing):
- Pluto.jl
- Genie.jl
18. MATLAB:
- Frameworks (for scientific and engineering applications):
- Simulink
19. COBOL:
- Frameworks:
- COBOL-IT
20. Erlang:
- Frameworks:
- Phoenix (for web applications)
21. Groovy:
- Frameworks:
- Grails (for web applications)
1. Python:
Frameworks:
Django
Flask
Pyramid
Tornado
2. JavaScript:
Frameworks (Front-End):
React
Angular
Vue.js
Ember.js
Frameworks (Back-End):
Node.js (Runtime)
Express.js
Nest.js
Meteor
3. Java:
Frameworks:
Spring Framework
Hibernate
Apache Struts
Play Framework
4. Ruby:
Frameworks:
Ruby on Rails (Rails)
Sinatra
Hanami
5. PHP:
Frameworks:
Laravel
Symfony
CodeIgniter
Yii
Zend Framework
6. C#:
Frameworks:
.NET Framework
ASP.NET
ASP.NET Core
7. Go (Golang):
Frameworks:
Gin
Echo
Revel
8. Rust:
Frameworks:
Rocket
Actix
Warp
9. Swift:
Frameworks (iOS/macOS):
SwiftUI
UIKit
Cocoa Touch
10. Kotlin:
- Frameworks (Android):
- Android Jetpack
- Ktor
11. TypeScript:
- Frameworks (Front-End):
- Angular
- Vue.js (with TypeScript)
- React (with TypeScript)
12. Scala:
- Frameworks:
- Play Framework
- Akka
13. Perl:
- Frameworks:
- Dancer
- Catalyst
14. Lua:
- Frameworks:
- OpenResty (for web development)
15. Dart:
- Frameworks:
- Flutter (for mobile app development)
16. R:
- Frameworks (for data science and statistics):
- Shiny
- ggplot2
17. Julia:
- Frameworks (for scientific computing):
- Pluto.jl
- Genie.jl
18. MATLAB:
- Frameworks (for scientific and engineering applications):
- Simulink
19. COBOL:
- Frameworks:
- COBOL-IT
20. Erlang:
- Frameworks:
- Phoenix (for web applications)
21. Groovy:
- Frameworks:
- Grails (for web applications)
👍6
Coding Interview Preparation
Here are some highly recommended tools and materials to help you succeed in your tech interviews.
AlgoMonster:
Learn coding interview patterns which can be used to solve variety of coding problems on LeetCode and NeetCode
Educative-99:
a curated set of 99 coding interview questions designed to help candidates master 26 essential problem-solving patterns. It provides a hands-on, setup-free coding environment where users can efficiently practice and internalize coding patterns crucial for tech interviews, making it easier to tackle various coding challenges in a structured manner
LeetCode:
Practice coding problems of varying difficulty levels.
NeetCode:
Get access to a structured plan for mastering coding problems.
Cracking the Coding Interview: A comprehensive guidebook with 189 programming questions and solutions.
Here are some highly recommended tools and materials to help you succeed in your tech interviews.
AlgoMonster:
Learn coding interview patterns which can be used to solve variety of coding problems on LeetCode and NeetCode
Educative-99:
a curated set of 99 coding interview questions designed to help candidates master 26 essential problem-solving patterns. It provides a hands-on, setup-free coding environment where users can efficiently practice and internalize coding patterns crucial for tech interviews, making it easier to tackle various coding challenges in a structured manner
LeetCode:
Practice coding problems of varying difficulty levels.
NeetCode:
Get access to a structured plan for mastering coding problems.
Cracking the Coding Interview: A comprehensive guidebook with 189 programming questions and solutions.