Ai black box
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The document discusses the "black box problem" in artificial intelligence and neural networks. Specifically, it notes that while these systems can perform complex tasks, the inner workings and decision-making processes are not fully understood. It argues that developing theoretical frameworks grounded in other domains, like physics, could help increase transparency and interpretability of these technologies. More work is needed to better understand and explain how artificial intelligence systems learn and operate.
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Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
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Abstract:
Machine learning is at the forefront of many recent advances in science and technology, enabled in part by the sophisticated models and algorithms that have been recently introduced. However, as a consequence of this complexity, machine learning essentially acts as a black-box as far as users are concerned, making it incredibly difficult to understand, predict, or "trust" their behavior. In this talk, I will describe our research on approaches that explain the predictions of ANY classifier in an interpretable and faithful manner.
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Dr. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on large-scale and interpretable machine learning applied to natural language processing. Sameer was a Postdoctoral Research Associate at the University of Washington and received his PhD from the University of Massachusetts, Amherst, during which he also worked at Microsoft Research, Google Research, and Yahoo! Labs on massive-scale machine learning. He was awarded the Adobe Research Data Science Faculty Award, was selected as a DARPA Riser, won the grand prize in the Yelp dataset challenge, and received the Yahoo! Key Scientific Challenges fellowship. Sameer has published extensively at top-tier machine learning and natural language processing conferences. (http://sameersingh.org)
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A Comprehensive Review of Large Language Models for.pptx
The document presents a review of large language models (LLMs) for code generation. It discusses different types of LLMs including left-to-right, masked, and encoder-decoder models. Existing models for code generation like Codex, GPT-Neo, GPT-J, and CodeParrot are compared. A new model called PolyCoder with 2.7 billion parameters trained on 12 programming languages is introduced. Evaluation results show PolyCoder performs less well than comparably sized models but outperforms others on C language tasks. In general, performance improves with larger models and longer training, but training solely on code can be sufficient or advantageous for some languages.
Introduction to LLMs
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
Interpretable machine learning : Methods for understanding complex models
1. Interpretability helps understand complex machine learning models by explaining their outcomes based on inputs. Higher predictive accuracy often reduces interpretability.
2. Methods like LIME and SHAP attribute model outcomes to input features through local surrogate models and game theory.
3. Recourse analysis identifies actions individuals could take to improve outcomes from automated decisions.
Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data...
This was presented at the London Artificial Intelligence & Deep Learning Meetup.
https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/events/245251725/
Enjoy the recording: https://youtu.be/CY3t11vuuOM.
- - -
Kasia discussed complexities of interpreting black-box algorithms and how these may affect some industries. She presented the most popular methods of interpreting Machine Learning classifiers, for example, feature importance or partial dependence plots and Bayesian networks. Finally, she introduced Local Interpretable Model-Agnostic Explanations (LIME) framework for explaining predictions of black-box learners – including text- and image-based models - using breast cancer data as a specific case scenario.
Kasia Kulma is a Data Scientist at Aviva with a soft spot for R. She obtained a PhD (Uppsala University, Sweden) in evolutionary biology in 2013 and has been working on all things data ever since. For example, she has built recommender systems, customer segmentations, predictive models and now she is leading an NLP project at the UK’s leading insurer. In spare time she tries to relax by hiking & camping, but if that doesn’t work ;) she co-organizes R-Ladies meetups and writes a data science blog R-tastic (https://kkulma.github.io/).
https://www.linkedin.com/in/kasia-kulma-phd-7695b923/
Explainable AI
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Explainable AI
Introductory presentation to Explainable AI, defending its main motivations and importance. We describe briefly the main techniques available in March 2020 and share many references to allow the reader to continue his/her studies.
1 Introduction to AI.pptx
This document provides an overview of artificial intelligence including:
- It defines four approaches to AI: acting humanly through the Turing test, thinking humanly through cognitive modeling, thinking rationally through symbolic logic, and acting rationally as intelligent agents.
- It then summarizes the history of AI from its origins in the 1940s through developments in knowledge-based systems, connectionism, and the emergence of intelligent agents using large datasets.
- Finally, it briefly discusses recent applications of AI such as algorithms used by Facebook, TikTok, and noise cancellation in Zoom calls.
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of Machine Learning
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modelling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
Machine Learning Basics
This document provides an overview of machine learning basics including:
- A brief history of machine learning and definitions of machine learning and artificial intelligence.
- When machine learning is needed and its relationships to statistics, data mining, and other fields.
- The main types of learning problems - supervised, unsupervised, reinforcement learning.
- Common machine learning algorithms and examples of classification, regression, clustering, and dimensionality reduction.
- Popular programming languages for machine learning like Python and R.
- An introduction to simple linear regression and how it is implemented in scikit-learn.
Explainable AI in Industry (FAT* 2020 Tutorial)
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
Explainable AI (XAI)
Explainable Artificial Intelligence (XAI)
Presented at Lightning Talk session at ICACCI'18 on 20th September 208
An Explainable AI (XAI) or Transparent AI is an artificial intelligence (AI) whose actions can be easily understood by humans. It contrasts with the concept of the "black box" in machine learning, meaning the "interpretability" of the workings of complex algorithms, where even their designers cannot explain why the AI arrived at a specific decision.
https://en.wikipedia.org/wiki/Explainable_Artificial_Intelligence
Rule Based System
Rule based systems are specialized software that encapsulate human intelligence and knowledge to make intelligent decisions quickly and repeatedly. They represent knowledge using if-then rules and work memory. There are two types of rules - forward chaining which is data-driven and deductive, and backward chaining which is goal-driven and inductive. While rule engines are the core of applications, fully utilizing them requires additional components for interfacing, data exchange, data storage, and version management. Examples of companies using rule engines include Dell, Cisco, Vodafone, and Blue Cross Blue Shield.
Artificial intelligence and Fuzzy Logic
Introduction, Basic Techniques, History of Artificial Intelligence, Application of Areas, Fuzzy Logic
Transformers, LLMs, and the Possibility of AGI
The document provides an overview of transformers, large language models (LLMs), and artificial general intelligence (AGI). It discusses the architecture and applications of transformers in natural language processing. It describes how LLMs have evolved from earlier statistical models and now perform state-of-the-art results on NLP tasks through pre-training and fine-tuning. The document outlines the capabilities of GPT-3, the largest LLM to date, as well as its limitations and ethical concerns. It introduces AGI and the potential for such systems to revolutionize AI, while also noting the technical, ethical and societal challenges to developing AGI.
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Lecture1 AI1 Introduction to artificial intelligence
Lecture1 AI1 Introduction to artificial intelligence
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Interpretable machine learning : Methods for understanding complex models
Interpretable machine learning : Methods for understanding complex models
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Interpretable Machine Learning Using LIME Framework - Kasia Kulma (PhD), Data...
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Dr. Wojciech Samek of the Fraunhofer Heinrich Hertz Institute delivers the presentation "Methods for Understanding How Deep Neural Networks Work" at the Embedded Vision Alliance's September 2017 Vision Industry and Technology Forum. In his presentation, Dr. Samek covers the following topics:
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Avoiding Machine Learning Pitfalls 2-10-18
talk to Bellevue ML meetup
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AI history (Epita International Masters)
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DWX 2018 Session about Artificial Intelligence, Machine and Deep Learning
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AI Fables, Facts and Futures: Threat, Promise or Saviour
Man’s dreams of ‘intelligences and robots’ go back thousands of years to the worship of gods and statues; mythologies: talisman and puppets; people, places and objects with supposed magical and (often) judgemental/punitive abilities. But it wasn’t until the electronic revolution in 1915, accelerated by WWII that we saw the realisation of two game changing-machines: Colossus (Decoding Machine of Bletchley Park) 1943 and ENIAC (Artillery Computation Engine and Nuclear Bomb Design @ The University of Pennsylvania) 1946.
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In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
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Ai black box
- 1. Dr. Marcel Blattner - The Black Box Problem The Black Box Problem 1 Dr. Marcel Blattner Chief Data Scientist TX Group marcel.blattner@tx.group @blattnerma
- 2. Dr. Marcel Blattner - The Black Box Problem The Black Box Problem 2 5 Ways Artificial Intelligence Will Change the World by 2050 https://news.usc.edu/trojan-family/five-ways-ai-will-change-the-world-by-2050/ How artificial intelligence will change every aspect of our lives https://nypost.com/2018/09/07/how-artificial-intelligence-will-change-every-aspect-of-our-lives/ How Artificial Intelligence Is Totally Changing Everything https://science.howstuffworks.com/artificial-intelligence.htm AI will write a best-seller by 2049, experts predict https://www.weforum.org/agenda/2018/03/timeline-of-creative-ai/ - What have opinion leaders to offer? X → YWe can summarise these statements: Such statements are useful only if X is understood! Sadly, X is not enough understood in most cases Hypeitup!OMG!!
- 3. Dr. Marcel Blattner - The Black Box Problem The Black Box Problem 3 - Who are the opinion leaders? • Nearly 60 percent of news articles across industries are indexed to industry products, initiatives, or announcements. 33 percent of unique sources across all articles are affiliated with industry, almost twice as many as those from academia, and six times as many as those from government. Nearly 12 percent of all articles reference Elon Musk. You can safely count me in here! https://reutersinstitute.politics.ox.ac.uk/our-research/industry-led-debate-how-uk-media-cover-artificial-intelligence That’s bad news. Opinion in public is driven mainly by completely biased industry players
- 4. Dr. Marcel Blattner - The Black Box Problem The Black Box Problem 4 “…..the dynamics of learning observed in deep neural networks remain much of a mystery to this day.” - What does a giant (Yoshua Bengio) say about AI and ANN? On the Learning Dynamics of Deep Neural Networks (2019) I have some questions!
- 5. Dr. Marcel Blattner - The Black Box Problem 5 Input Output You don’t know the inner working. Usually we do not care about using black boxes! Why? The Black Box Problem - What we mean by Black Box
- 6. Dr. Marcel Blattner - The Black Box Problem What is a black box? 6 - How to make a computer smart? Data New Data Deductive Algorithms Model Abstraction Inductive Training Application Connectionists • Rules are based on abstractions created by machines • Rules are mostly not interpretable by humans • Expressive advantages • System stability and general behaviour is not fully predictable Algorithms Experten- Knowledge Application Rules New Data Deductive if/then else Symbolic • Rules are human knowledge based • Rules are transparent • Expressive disadvantage
- 7. Dr. Marcel Blattner - The Black Box Problem 7 The Black Box Problem - Object-Detector (CNN) - Trained on 1.8 Mio. images - Ported to iOS for real time object recognition - How to confuse a Artificial Neural Network
- 8. Dr. Marcel Blattner - The Black Box Problem 8 Source: Adversarial Patch: https://arxiv.org/pdf/1712.09665.pdf Adversarial Attack - example The Black Box Problem - How to confuse a Artificial Neural Network
- 9. Dr. Marcel Blattner - The Black Box Problem 9 The Black Box Problem Making a Tesla 50mph faster than allowed https://electrek.co/2020/02/19/tesla-autopilot-tricked-accelerate-speed-limit-sign/ - How to confuse a Artificial Neural Network
- 10. Dr. Marcel Blattner - The Black Box Problem 10 The Black Box Problem - What is needed to make a black box interpretable? InterpretabilityTransparency Based on meta theories, e.g. real understanding of the learning dynamics Explainability Based on post-hoc experiments, e.g. increase experience of input-output relation Operator Executor Decision subject Creator According Tomsett et al. (2018)
- 11. Dr. Marcel Blattner - The Black Box Problem 11 The Black Box Problem - Explainability Explainability Based on post-hoc experiments, e.g. increase experience of input-output relation Layer-Wise Relevance Propagation pug, pug-dog (254): 18.939 French bulldog (245): 11.256 bull mastiff (243): 10.136 doormat, welc ome mat (539): 9.809 Norwegian elk hound, (174): 9.591
- 12. Dr. Marcel Blattner - The Black Box Problem 12 Theoretical framework ANN (specific architecture) Mapping to a framework (possibly outside CS) Predictions / new insights Guided experiments Inverse mapping Hypothesis Proposal Testing by experiments with ANN The Black Box Problem - Transparency
- 13. Dr. Marcel Blattner - The Black Box Problem Theoretical framework ANN (specific architecture) Mapping to a framework (possibly outside CS) Metastable configurations (mini/max Landscape Guided experiments Inverse mapping Hypothesis Loss Surface Testing by experiments with ANN H = − ∑ i1>⋯>ip=1 Ji1...ip σi1 ⋯σip Y = q n0 ∑ i=1 γ ∑ j=1 Xi, j Ai, j K ∏ k=1 w(k) i, j 13 - What could be a good candidate for a theory? The Black Box Problem TheorySimulations
- 14. Dr. Marcel Blattner - The Black Box Problem Summary 14 • The Black Box Problem is important because of the technology adoption in industry, government and private sectors • Try harder!! We need better models (theories) to understand these technologies • Theoretical physics could make valuable contributions