Канал: Cassie Kozyrkov

Machine Unlearning, Model Collapse, and the LinkedIn AI Scandal

Machine Unlearning, Model Collapse, and the LinkedIn AI Scandal

ChatGPT Demo of Strawberry (model o1-preview) -- Hallucination Killer?

ChatGPT Demo of Strawberry (model o1-preview) -- Hallucination Killer?

IBM Think 2024: The nerve center of your AI operations

IBM Think 2024: The nerve center of your AI operations

AI, quantum computing, and... Davos travel tips at the World Economic Forum

AI, quantum computing, and... Davos travel tips at the World Economic Forum

Is prompt engineering a basic skill? Is it even... engineering?

Is prompt engineering a basic skill? Is it even... engineering?

How to work with inherited datasets

How to work with inherited datasets

The importance of domain expertise in data science

The importance of domain expertise in data science

Why it's important to split your data

Why it's important to split your data

Where does math impostor syndrome come from?

Where does math impostor syndrome come from?

What makes you an excellent data scientist?

What makes you an excellent data scientist?

Judgment calls in data science

Judgment calls in data science

The data scientist's guide to data documentation

The data scientist's guide to data documentation

The hidden labor cost of inherited data

The hidden labor cost of inherited data

How to set the complexity of your decision

How to set the complexity of your decision

How to make better decisions

How to make better decisions

Decision-making: How to discover your options

Decision-making: How to discover your options

GenAI is a raw material, not a finished product

GenAI is a raw material, not a finished product

Optimize your life with decision science

Optimize your life with decision science

The leader's role in data science

The leader's role in data science

Step-by-step guide to AI projects

Step-by-step guide to AI projects

Reactive and proactive decision-making

Reactive and proactive decision-making

Will there be anything left for humans to do after AI?

Will there be anything left for humans to do after AI?

How to avoid AI disasters

How to avoid AI disasters

GenAI is a UX revolution, not an AI revolution

GenAI is a UX revolution, not an AI revolution

What is thunking?

What is thunking?

Why AI needs us all to be better decision-makers

Why AI needs us all to be better decision-makers

AI does not automate thinking!

AI does not automate thinking!

A perfect analogy for AI

A perfect analogy for AI

Traditional programming VS AI

Traditional programming VS AI

All of AI boils down to just 2 lines

All of AI boils down to just 2 lines

We should not allow technology to be faceless

We should not allow technology to be faceless

Can AI make art?

Can AI make art?

Are you a "Secret Cyborg"?

Are you a "Secret Cyborg"?

Thinking vs Thunking

Thinking vs Thunking

What if we let AI do the thinking?

What if we let AI do the thinking?

What if we let AI do the thinking?

What if we let AI do the thinking?

The promise and peril of AI

The promise and peril of AI

Decision Intelligence Q&A

Decision Intelligence Q&A

How to do simulation with R (Accidental Statistics ASMR)

How to do simulation with R (Accidental Statistics ASMR)

How to do simulation with Google Sheets and MS Excel (Accidental Statistics ASMR)

How to do simulation with Google Sheets and MS Excel (Accidental Statistics ASMR)

2 Minute Intro to Generative AI Art (Midjourney & Firefly demo)

2 Minute Intro to Generative AI Art (Midjourney & Firefly demo)

What do most AI disasters have in common?

What do most AI disasters have in common?

Whose job does AI automate?

Whose job does AI automate?

Unboxing brand new Google Bard and GPT-4

The value of "soft" skills in data science

The value of "soft" skills in data science

The leader's guide to data science

The leader's guide to data science

The 12 Steps to Machine Learning and AI

The 12 Steps to Machine Learning and AI

Thank you for 50k subscribers!

Thank you for 50k subscribers!

The top 4 ingredients of a good AI use case

The top 4 ingredients of a good AI use case

Making Friends with Machine Learning: The Entire Course

Making Friends with Machine Learning: The Entire Course

The 3 biggest problems with data science

The 3 biggest problems with data science

Do data scientists need to read the documentation?

Do data scientists need to read the documentation?

MFML 120 - When to use neural networks

MFML 120 - When to use neural networks

MFML 119 - Neural network architecture

MFML 119 - Neural network architecture

DALL·E 2 Demo 2

DALL·E 2 Demo 2

DALL·E 2 Demo

DALL·E 2 Demo

Is good decision-making a skill or a talent?

Is good decision-making a skill or a talent?

MFML 118 - Gotchas, pros, and cons of deep learning

MFML 118 - Gotchas, pros, and cons of deep learning

Why your machine learning project will fail without primary data

Why your machine learning project will fail without primary data

MFML 117 - Backpropagation

MFML 117 - Backpropagation

MFML 116 - Components of a neural network

MFML 116 - Components of a neural network

MFML 115 - Using AI for automatic feature extraction

MFML 115 - Using AI for automatic feature extraction

MFML 114 - What's inside a neural network?

MFML 114 - What's inside a neural network?

MFML 113 - Introduction to deep learning

MFML 113 - Introduction to deep learning

MFML 112 - How to do ranking at scale

MFML 112 - How to do ranking at scale

When is it okay to let the universe decide for you?

When is it okay to let the universe decide for you?

MFML 111 - What are all those sigmoid functions for?

MFML 111 - What are all those sigmoid functions for?

MFML 109 - When should you use linear regression?

MFML 109 - When should you use linear regression?

MFML 108 - What do all regression models have in common?

MFML 108 - What do all regression models have in common?

MFML 107 - What's so naive about Naive Bayes?

MFML 107 - What's so naive about Naive Bayes?

MFML 105 - Ensemble models

MFML 105 - Ensemble models

Three types of error in statistics explained

Three types of error in statistics explained

MFML 104 - What is a random forest?

MFML 104 - What is a random forest?

MFML 103 - Bootstrap Aggregation a.k.a. Bagging

MFML 103 - Bootstrap Aggregation a.k.a. Bagging

MFML 102 - Decision trees and SVMs compared

MFML 102 - Decision trees and SVMs compared

MFML 101 - XAI and the interpretability debate

MFML 101 - XAI and the interpretability debate

MFML 100 - What is a decision tree?

MFML 100 - What is a decision tree?

How to interpret p-values

How to interpret p-values

MFML 099 - SVMs and the kernel trick

MFML 099 - SVMs and the kernel trick

MFML 098 - What are support vectors?

MFML 098 - What are support vectors?

MFML 097 - Maximal margin classifiers

MFML 097 - Maximal margin classifiers

MFML 096 - What are perceptrons?

MFML 096 - What are perceptrons?

MFML 095 - The curse of dimensionality

MFML 095 - The curse of dimensionality

MFML 094 - Lazy learning and k-NN

MFML 094 - Lazy learning and k-NN

MFML 093 - Clustering and k-means

MFML 093 - Clustering and k-means

MFML 092 - Opening the black box

MFML 092 - Opening the black box

MFML 091 - AI system maintenance

MFML 091 - AI system maintenance

MFML 090 - Monitoring your AI system

MFML 090 - Monitoring your AI system

MFML 089 - Live traffic experiments

MFML 089 - Live traffic experiments

MFML 088 - AI safety and policy layers

MFML 088 - AI safety and policy layers

MFML 087 - How to catch outliers and AI failures

MFML 087 - How to catch outliers and AI failures

MFML 086 - The danger of the long tail in AI

MFML 086 - The danger of the long tail in AI

MFML 085 - When your AI model fails retesting

MFML 085 - When your AI model fails retesting

MFML 084 - Making tiny changes to AI code

MFML 084 - Making tiny changes to AI code

MFML 083 - Be careful with chained AI systems

MFML 083 - Be careful with chained AI systems

MFML 082 - The training-serving skew

MFML 082 - The training-serving skew

MFML 081 - How often should you retrain your AI system?

MFML 081 - How often should you retrain your AI system?

MFML 080 - Solving AI latency problems

MFML 080 - Solving AI latency problems

MFML 106 - Introduction to Bayes' Rule

MFML 106 - Introduction to Bayes' Rule

MFML 110 - Logistic regression

MFML 110 - Logistic regression

MFML 079 - Repurposing data safely in AI

MFML 079 - Repurposing data safely in AI

MFML 078 - Productionization

MFML 078 - Productionization

MFML 077 - The importance of testing your AI solution

MFML 077 - The importance of testing your AI solution

MFML 076 - What should you do if model testing fails?

MFML 076 - What should you do if model testing fails?

MFML 075 - Statistical significance

MFML 075 - Statistical significance

MFML 074 - Understanding p-values

MFML 074 - Understanding p-values

MFML 073 - Interpreting AI test output

MFML 073 - Interpreting AI test output

MFML 072 - The 12 steps of statistics

MFML 072 - The 12 steps of statistics

MFML 071 - What's the difference between testing and validation?

MFML 071 - What's the difference between testing and validation?

MFML 070 - Validation roulette

MFML 070 - Validation roulette

MFML 069 - Model validation done right

MFML 069 - Model validation done right

MFML 068 - What to do when model validation fails

MFML 068 - What to do when model validation fails

MFML 067 - Can you skip the tuning phase in AI?

MFML 067 - Can you skip the tuning phase in AI?

MFML 066 - Advanced AI debugging with cross-validation

MFML 066 - Advanced AI debugging with cross-validation

MFML 065 - Understanding k-fold cross-validation

MFML 065 - Understanding k-fold cross-validation

Meet 3 kinds of data worker... which one are you?

Meet 3 kinds of data worker... which one are you?

MFML 064 - What is a holdout set and how do you use it?

MFML 064 - What is a holdout set and how do you use it?

Why do businesses fail at machine learning?

Why do businesses fail at machine learning?

MFML 063 - Hyperparameter tuning

MFML 063 - Hyperparameter tuning

MFML 062 - Debugging your machine learning model

MFML 062 - Debugging your machine learning model

MFML 061 - Can you skip the training phase in AI?

MFML 061 - Can you skip the training phase in AI?

MFML 060 - How to avoid target leakage

MFML 060 - How to avoid target leakage

MFML 059 - Regularization

MFML 059 - Regularization

The most important rule of decision-making

The most important rule of decision-making

MFML 058 - When your machine learning project takes forever

MFML 058 - When your machine learning project takes forever

Summary: Bayesian vs Frequentist statistics

Summary: Bayesian vs Frequentist statistics

MFML 057 - Statistics versus "statistics"

MFML 057 - Statistics versus "statistics"

MFML 056 - How to speed up your ML/AI training phase

MFML 056 - How to speed up your ML/AI training phase

MFML 055 - A dataset's ideal shape

MFML 055 - A dataset's ideal shape

MFML 054 - Is training an AI system easy?

MFML 054 - Is training an AI system easy?

MFML 053 - How to select an AI algorithm

MFML 053 - How to select an AI algorithm

MFML 052 - Exploratory data analysis (EDA)

MFML 052 - Exploratory data analysis (EDA)

MFML 051 - The importance of data splitting

MFML 051 - The importance of data splitting

Perfect timing: When should you make your decision?

Perfect timing: When should you make your decision?

Guide to AI algorithms - MFML Part 4

Guide to AI algorithms - MFML Part 4

MFML 050 - Should you care about underfitting?

MFML 050 - Should you care about underfitting?

MFML 049 - The danger of overfitting

MFML 049 - The danger of overfitting

MFML 048 - Data engineering

MFML 048 - Data engineering

A quick guide to inherited data

A quick guide to inherited data

MFML 047 - Setting performance criteria for AI

MFML 047 - Setting performance criteria for AI

MFML 046 - Loss functions

MFML 046 - Loss functions

MFML 045 - What is optimization?

MFML 045 - What is optimization?

MFML 044 - Precision vs recall

MFML 044 - Precision vs recall

MFML 043 - Ground truth

MFML 043 - Ground truth

Introduction to ML and AI - MFML Part 1

Introduction to ML and AI - MFML Part 1

MFML 042 - Performance metrics

MFML 042 - Performance metrics

MFML 041 - Confusion matrix

MFML 041 - Confusion matrix

MFML 040 - False positives and true negatives

MFML 040 - False positives and true negatives

MFML 039 - What is "good behavior" for AI?

MFML 039 - What is "good behavior" for AI?

MFML 038 - Don't forget data!

MFML 038 - Don't forget data!

MFML 037 - Data science flowchart

MFML 037 - Data science flowchart

MFML 000 - Welcome to machine learning!

MFML 000 - Welcome to machine learning!

MFML 036 - What on earth is data science?

MFML 036 - What on earth is data science?

MFML 035 - Reinforcement learning

MFML 035 - Reinforcement learning

MFML 034 - Semi-supervised learning

MFML 034 - Semi-supervised learning

All of machine learning and AI in just 2 sentences

All of machine learning and AI in just 2 sentences

MFML 033 - Unsupervised learning

MFML 033 - Unsupervised learning

MFML 032 - Supervised learning

MFML 032 - Supervised learning

MFML 031 - Instances, features, and targets

MFML 031 - Instances, features, and targets

MFML 030 - Classification vs regression

MFML 030 - Classification vs regression

Taking AI from prototype to production - MFML Part 3

Taking AI from prototype to production - MFML Part 3

MFML 029 - Where to start with applied AI?

MFML 029 - Where to start with applied AI?

MFML 028 - The 12 steps of AI

MFML 028 - The 12 steps of AI

MFML 027 - Our AI future

MFML 027 - Our AI future

MFML 026 - AI is a team sport!

MFML 026 - AI is a team sport!

MFML 025 - Wish responsibly

MFML 025 - Wish responsibly

MFML 024 - Good intentions vs bad metrics

MFML 024 - Good intentions vs bad metrics

Life of an AI project - MFML Part 2

Life of an AI project - MFML Part 2

MFML 023 - Reliable or unreliable?

MFML 023 - Reliable or unreliable?

MFML 022 - Skilled decision-makers

MFML 022 - Skilled decision-makers

MFML 021 - Why do businesses fail at machine learning?

MFML 021 - Why do businesses fail at machine learning?

MFML 020 - Decision Intelligence

MFML 020 - Decision Intelligence

MFML 019 - How to avoid machine learning pitfalls

MFML 019 - How to avoid machine learning pitfalls

MFML 018 - AI takes an exam (Intro to training, validation, and testing)

MFML 018 - AI takes an exam (Intro to training, validation, and testing)

MFML 017 - Explainability and AI

MFML 017 - Explainability and AI

MFML 016 - Why trust AI?

MFML 016 - Why trust AI?

MFML 015 - How Do GANs work?

MFML 015 - How Do GANs work?

MFML 014 - Human creativity in AI

MFML 014 - Human creativity in AI

MFML 013 - How to find good AI use cases

MFML 013 - How to find good AI use cases

MFML 012 - Real applications

MFML 012 - Real applications

MFML 011 - Algorithms, data, and platforms

MFML 011 - Algorithms, data, and platforms

MFML 010 - Why did we wait so long for AI?

MFML 010 - Why did we wait so long for AI?

MFML 009 - What is AI?

MFML 009 - What is AI?

MFML 008 - Feature engineering

MFML 008 - Feature engineering

MFML 007 - Multiple linear regression

MFML 007 - Multiple linear regression

MFML 006 - Simple linear regression

MFML 006 - Simple linear regression

MFML 005 - What's inside the black box?

MFML 005 - What's inside the black box?

MFML 004 - How to test machine learning

MFML 004 - How to test machine learning

MFML 003 - How does machine learning work?

MFML 003 - How does machine learning work?

MFML 002 - Why use machine learning?

MFML 002 - Why use machine learning?

MFML 001 - What is machine learning?

MFML 001 - What is machine learning?

Should you hire an analyst or a statistician?

Should you hire an analyst or a statistician?

Why your machine learning project needs analysts

Why your machine learning project needs analysts

Data + Assumptions = Prediction

Data + Assumptions = Prediction

Soft skills in data science aren't "soft"

Soft skills in data science aren't "soft"

How is analytics different from data journalism?

How is analytics different from data journalism?

What should you do with outliers?

What should you do with outliers?

What every decision-maker should know about analytics

What every decision-maker should know about analytics

Inheriting data-driven disappointment

Inheriting data-driven disappointment

Will AI kill analytics and statistics?

Will AI kill analytics and statistics?