Канал: Towards Data Science

Edouard Harris - New Research: Advanced AI may tend to seek power *by default*

Edouard Harris - New Research: Advanced AI may tend to seek power by default

Amber Teng - Building apps with a new generation of language models

Amber Teng - Building apps with a new generation of language models

David Hirko - AI observability and data as a cybersecurity weakness

David Hirko - AI observability and data as a cybersecurity weakness

Matthew Stewart - The emerging world of ML sensors

Matthew Stewart - The emerging world of ML sensors

JR King - Does the brain run on deep learning?

JR King - Does the brain run on deep learning?

Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?

Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety?

Alex Watson - Synthetic data could change everything

Alex Watson - Synthetic data could change everything

Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense)

Sadie St. Lawrence - Trends in data science

Sadie St. Lawrence - Trends in data science

Alexei Baevski - data2vec and the future of multimodal learning

Alexei Baevski - data2vec and the future of multimodal learning

Liam Fedus & Barret Zoph - AI scaling with mixture of expert models

Liam Fedus & Barret Zoph - AI scaling with mixture of expert models

Jaime Sevilla - Projecting AI progress from compute trends

Jaime Sevilla - Projecting AI progress from compute trends

Angela Fan - Generating Wikipedia articles with AI

Angela Fan - Generating Wikipedia articles with AI

Beena Ammanath - Defining trustworthy AI

Beena Ammanath - Defining trustworthy AI

Katya Sedova - AI-powered disinformation, present and future

Katya Sedova - AI-powered disinformation, present and future

Irina Rish - Out-of-distribution generalization

Irina Rish - Out-of-distribution generalization

Sam Bowman - Are we *under-hyping* AI?

Sam Bowman - Are we under-hyping AI?

Yaron Singer - Catching edge cases in AI

Yaron Singer - Catching edge cases in AI

Tali Raveh - AI, single cell genomics, and the new era of computational biology

Tali Raveh - AI, single cell genomics, and the new era of computational biology

Mo Gawdat - Scary Smart: A former Google exec's perspective on AI risk

Mo Gawdat - Scary Smart: A former Google exec's perspective on AI risk

Alex Turner - Will powerful AIs tend to seek power?

Alex Turner - Will powerful AIs tend to seek power?

Danijar Hafner - Gaming our way to AGI

Danijar Hafner - Gaming our way to AGI

Last Week In AI — 2021: The (full) year in review

Last Week In AI — 2021: The (full) year in review

Kevin Hu - Data observability and why it matters

Kevin Hu - Data observability and why it matters

Yang Gao - Sample-efficient AI

Yang Gao - Sample-efficient AI

Yannic Kilcher - A 10,000-foot view of AI

Yannic Kilcher - A 10,000-foot view of AI

Ken Stanley - AI without objectives

Ken Stanley - AI without objectives

Gillian Hadfield - How to create explainable AI regulations that actually make sense

Gillian Hadfield - How to create explainable AI regulations that actually make sense

Wendy Foster - AI ethics as a user experience challenge

Wendy Foster - AI ethics as a user experience challenge

Ayanna Howard - AI and the trust problem

Ayanna Howard - AI and the trust problem

Max Jaderberg - Open-ended learning at DeepMind

Max Jaderberg - Open-ended learning at DeepMind

Margaret Mitchell - (Practical) AI ethics

Margaret Mitchell - (Practical) AI ethics

Mike Tung - Are knowledge graphs AI's next big thing?

Mike Tung - Are knowledge graphs AI's next big thing?

Anthony Habayeb - The present and future of AI regulation

Anthony Habayeb - The present and future of AI regulation

Jan Leike - AI alignment at OpenAI

Jan Leike - AI alignment at OpenAI

Francesca Rossi - Thinking, fast and slow: AI edition

Francesca Rossi - Thinking, fast and slow: AI edition

Divya Siddarth - Are we thinking about AI wrong?

Divya Siddarth - Are we thinking about AI wrong?

Let's Talk AI - 2021: A year in AI (so far)

Let's Talk AI - 2021: A year in AI (so far)

Daniel Filan - Peering into neural nets for AI safety

Daniel Filan - Peering into neural nets for AI safety

Peter Gao - Self-driving cars: Past, present and future

Peter Gao - Self-driving cars: Past, present and future

Jeffrey Ding - China's AI ambitions and why they matter

Jeffrey Ding - China's AI ambitions and why they matter

Towards Data Science & The Banana Data Podcast - Pointing AI in the right direction

Towards Data Science & The Banana Data Podcast - Pointing AI in the right direction

Oren Etzioni - The case against (worrying about) existential risk from AI

Oren Etzioni - The case against (worrying about) existential risk from AI

Evan Hubinger - The Inner Alignment Problem

Evan Hubinger - The Inner Alignment Problem

Andy Jones - AI Safety and the Scaling Hypothesis

Andy Jones - AI Safety and the Scaling Hypothesis

Brian Christian - The alignment problem

Brian Christian - The alignment problem

Rosie Campbell - Should all AI research be published?

Rosie Campbell - Should all AI research be published?

Eliano Marques - The (evolving) world of AI privacy and data security

Eliano Marques - The (evolving) world of AI privacy and data security

Jakob Foerster - The high cost of automated weapons

Jakob Foerster - The high cost of automated weapons

Nicolas Miailhe - AI risk is a global problem

Nicolas Miailhe - AI risk is a global problem

Yan Li - The surprising challenges of global AI philanthropy

Yan Li - The surprising challenges of global AI philanthropy

Ryan Carey - What does your AI want?

Ryan Carey - What does your AI want?

Melanie Mitchell - Existential risk from AI: A skeptical perspective

Melanie Mitchell - Existential risk from AI: A skeptical perspective

Josh Fairfield - AI advances, but can the law keep up?

Josh Fairfield - AI advances, but can the law keep up?

Stuart Armstrong - AI: Humanity's Endgame?

Stuart Armstrong - AI: Humanity's Endgame?

Ethan Perez - Making AI safe through debate

Ethan Perez - Making AI safe through debate

David Roodman - Economic history and the road to the singularity

David Roodman - Economic history and the road to the singularity

Margot Gerritsen - Does AI have to be understandable to be ethical?

Margot Gerritsen - Does AI have to be understandable to be ethical?

Ben Garfinkel - Superhuman AI and the future of democracy and government

Ben Garfinkel - Superhuman AI and the future of democracy and government

Sarah Williams - What does ethical AI even mean?

Sarah Williams - What does ethical AI even mean?

Anders Sandberg - Answering the Fermi Question: Is AI our Great Filter?

Anders Sandberg - Answering the Fermi Question: Is AI our Great Filter?

Silvia Milano - Ethical problems with recommender systems

Silvia Milano - Ethical problems with recommender systems

Joaquin Quinioñero-Candela - Responsible AI at Facebook

Joaquin Quinioñero-Candela - Responsible AI at Facebook

Owain Evans - Predicting the future of AI

Owain Evans - Predicting the future of AI

Helen Toner - The strategic and security implications of AI

Helen Toner - The strategic and security implications of AI

David Scott Krueger - Managing the incentives of AI

David Scott Krueger - Managing the incentives of AI

A Classification Model For Source Code Languages From Scratch

A Classification Model For Source Code Languages From Scratch

Geordie Rose - Will AGI need to be embodied?

Geordie Rose - Will AGI need to be embodied?

Nicolai Baldin - AI meets the law: Bias, fairness, privacy and regulation

Nicolai Baldin - AI meets the law: Bias, fairness, privacy and regulation

Ben Goertzel - The unorthodox path to AGI

Ben Goertzel - The unorthodox path to AGI

Spatial Data Analysis: Cell Declustering | Fouad Faraj

Spatial Data Analysis: Cell Declustering | Fouad Faraj

Evaluating linear relationships | Emily A. Halford

Evaluating linear relationships | Emily A. Halford

Rob Miles - Why should I care about AI safety?

Rob Miles - Why should I care about AI safety?

Matthew Stewart - Tiny ML and the future of on-device AI

Matthew Stewart - Tiny ML and the future of on-device AI

Dylan Hadfield-Menell - Humans in the Loop

Dylan Hadfield-Menell - Humans in the Loop

Annette Zimmermann - The ethics of AI

Annette Zimmermann - The ethics of AI

Rohin Shah - Effective altruism, AI safety, and learning human preferences from the world's state

Rohin Shah - Effective altruism, AI safety, and learning human preferences from the world's state

Edouard Harris - Emerging problems in machine learning: Making AI “good”

Edouard Harris - Emerging problems in machine learning: Making AI “good”

Using regression with correlated data | Emily A. Halford

Using regression with correlated data | Emily A. Halford

Sanyam Bhutani - Networking like a pro in data science

Sanyam Bhutani - Networking like a pro in data science

A Comparison of DNN, CNN and LSTM using TF/Keras | Prasanna Sethuraman

A Comparison of DNN, CNN and LSTM using TF/Keras | Prasanna Sethuraman

Adrien Treuille and Tim Conkling - Streamlit Is All You Need

Adrien Treuille and Tim Conkling - Streamlit Is All You Need

Panel: Creative Ways to Collect & Use Data for AI | H. Ngo, S. Sun, H. Kontozopoulos, and R. Tabrizi

Panel: Creative Ways to Collect & Use Data for AI | H. Ngo, S. Sun, H. Kontozopoulos, and R. Tabrizi

Determining Which ML Technologies To Act On | T. Pillai, S. Gandrabur, O. Shai & T. Poutanen

Determining Which ML Technologies To Act On | T. Pillai, S. Gandrabur, O. Shai & T. Poutanen

The ultimate guide to building maintainable Machine Learning pipelines using DVC | Déborah Mesquita

The ultimate guide to building maintainable Machine Learning pipelines using DVC | Déborah Mesquita

AI in Clinical Decision Support: Roadblocks & Opportunities | Niki Athanasiadou

AI in Clinical Decision Support: Roadblocks & Opportunities | Niki Athanasiadou

Explain Yourself! Leveraging Language Models for Common Sense Reasoning | Nazneen Rajani

Explain Yourself! Leveraging Language Models for Common Sense Reasoning | Nazneen Rajani

DevOps for ML and other Half-Truths: Processes and Tools for the ML Life Cycle | Kenny Daniel

DevOps for ML and other Half-Truths: Processes and Tools for the ML Life Cycle | Kenny Daniel

A Fresh Look at Clustering Algorithms | Dmitry Selemir

A Fresh Look at Clustering Algorithms | Dmitry Selemir

A Bayesian Approach to Linear Mixed Models (LMM) in R | Eduardo Coronado Sroka

A Bayesian Approach to Linear Mixed Models (LMM) in R | Eduardo Coronado Sroka

A Bayesian Approach to Linear Mixed Models (LMM) in Python | Eduardo Coronado Sroka

A Bayesian Approach to Linear Mixed Models (LMM) in Python | Eduardo Coronado Sroka

A Flexible Framework for Entity Resolution | Hoyoung Jang & Cheng Lin

A Flexible Framework for Entity Resolution | Hoyoung Jang & Cheng Lin

Deep Reinforcement Learning in Production at Zynga | Patrick Halina & Mehdi Ben Ayed

Deep Reinforcement Learning in Production at Zynga | Patrick Halina & Mehdi Ben Ayed

Measuring Statistical Dispersion with the Gini Coefficient | Kimberly Fessel

Measuring Statistical Dispersion with the Gini Coefficient | Kimberly Fessel

Creating Effective Industry-Academic AI Partnerships | Darin Graham

Creating Effective Industry-Academic AI Partnerships | Darin Graham

3 Strategies to Switch (Successfully) to a Thriving Data Science Career

3 Strategies to Switch (Successfully) to a Thriving Data Science Career

Introducing 3D ggplots with Rayshader (R) l Carrie Lo

Introducing 3D ggplots with Rayshader (R) l Carrie Lo

Lookahead Optimizer: k steps forward, 1 step back | Michael Zhang

Lookahead Optimizer: k steps forward, 1 step back | Michael Zhang

Don’t be Afraid of Nonparametric Topic Models | Eduardo Coronado

Don’t be Afraid of Nonparametric Topic Models | Eduardo Coronado

Dialogues with Databases | Kathryn Hume

Dialogues with Databases | Kathryn Hume

Certifiable Robustness to adversarial Attacks; What is the Point? | Nick Frosst

Certifiable Robustness to adversarial Attacks; What is the Point? | Nick Frosst

Productize AI | Daniel Weimer (No slides)

Productize AI | Daniel Weimer (No slides)

Explaining With Impact | Sheldon Fernandez and Michael St. Jules

Explaining With Impact | Sheldon Fernandez and Michael St. Jules

Why Sigmoid: A Probabilistic Perspective | Logan Yang

Why Sigmoid: A Probabilistic Perspective | Logan Yang

Temporal Concept Localization on Videos | Satya Krishna Gorti

Temporal Concept Localization on Videos | Satya Krishna Gorti

Interviewing the Medium data science team

Interviewing the Medium data science team

Sequential Decision Making in Recommendations | Jaya Kawale

Sequential Decision Making in Recommendations | Jaya Kawale

New to Data Visualization? Start with New York City | Thomas Hikaru Clark

New to Data Visualization? Start with New York City | Thomas Hikaru Clark

Cameron Davidson-Pillon - Data science at Shopify

Cameron Davidson-Pillon - Data science at Shopify

Walkthrough: Mapping GIS Data in Python | Nicole Janeway Bills

Walkthrough: Mapping GIS Data in Python | Nicole Janeway Bills

Latent Stochastic Differential Equations | David Duvenaud

Latent Stochastic Differential Equations | David Duvenaud

Emily Robinson - Building a Career in Data Science

Emily Robinson - Building a Career in Data Science

Rearchitecting Legacy Machine Learning Systems | Amit Jain & Ronaldo Felipe

Rearchitecting Legacy Machine Learning Systems | Amit Jain & Ronaldo Felipe

Scaling Machine Learning | Razvan Peteanu

Scaling Machine Learning | Razvan Peteanu

Real Talk with AI Leader at Google

Real Talk with AI Leader at Google

How He Became a Kaggle Grandmaster and Got a Computer Vision Engineer Job at Lyft

How He Became a Kaggle Grandmaster and Got a Computer Vision Engineer Job at Lyft

Building Differentially private Machine Learning Models Using TensorFlow Privacy | Chang Liu

Building Differentially private Machine Learning Models Using TensorFlow Privacy | Chang Liu

Jeremy Howard - Coronavirus: the data behind the disease

Jeremy Howard - Coronavirus: the data behind the disease

Chris Parmer - Plotly founder on what data science is, and where it’s going

Chris Parmer - Plotly founder on what data science is, and where it’s going

Nvidia gave me a $15K Data Science Workstation — here’s what I did with it

Nvidia gave me a $15K Data Science Workstation — here’s what I did with it

Data Science @ The New York Times | Christopher Wiggins

Data Science @ The New York Times | Christopher Wiggins

Fail to Scale | Ian Scott

Fail to Scale | Ian Scott

Xander Steenbrugge - ML as a creative tool, and the quest for artificial general intelligence

Xander Steenbrugge - ML as a creative tool, and the quest for artificial general intelligence

Crack SQL Interviews

Crack SQL Interviews

Iain Harlow - Leaving academia for industry and optimizing how you learn

Iain Harlow - Leaving academia for industry and optimizing how you learn

An Explanation of What, Why, and how of Explainable AI (XAI) | Bahador Khaleghi

An Explanation of What, Why, and how of Explainable AI (XAI) | Bahador Khaleghi

A Scalable Prediction Engine for Automating Structured Data Prep | Ihab Ilyas

A Scalable Prediction Engine for Automating Structured Data Prep | Ihab Ilyas

Adam Waksman - Data science is becoming software engineering

Adam Waksman - Data science is becoming software engineering

Self-Learning Data Science and Sharing the Knowledge on Medium (w/ Will Koehrsen)

Self-Learning Data Science and Sharing the Knowledge on Medium (w/ Will Koehrsen)

This is How You Build Deep AI Systems (feat. Azalia Mirhoseini of Google Brain)

This is How You Build Deep AI Systems (feat. Azalia Mirhoseini of Google Brain)

The Most Undervalued Standard Python Library

The Most Undervalued Standard Python Library

Edouard Harris - Mastering the data science job hunt

Edouard Harris - Mastering the data science job hunt

Nate Nichols - Product instinct and data storytelling (TDS Podcast - Clip)

Nate Nichols - Product instinct and data storytelling (TDS Podcast - Clip)

Real Talk with Machine Learning Engineer (w/ CS Dojo)

Real Talk with Machine Learning Engineer (w/ CS Dojo)

Introduction to Machine Learning

Introduction to Machine Learning

Why Machine Learning Is More Boring Than You May Think

Why Machine Learning Is More Boring Than You May Think

Building a Data Science Startup & Getting Into Data Science (w/ Jeremie Harris from SharpestMinds)

Building a Data Science Startup & Getting Into Data Science (w/ Jeremie Harris from SharpestMinds)

Want to Truly Master Scikit-Learn? 2 Essential Tips from Core Developer Himself

Want to Truly Master Scikit-Learn? 2 Essential Tips from Core Developer Himself

Real Talk with the Director of Data Science at Columbia University

Real Talk with the Director of Data Science at Columbia University

How to avoid the worst mistake every Data Scientist can make - using these 2 crucial steps

How to avoid the worst mistake every Data Scientist can make - using these 2 crucial steps

Ben Lorica - Trends in data science with O'Reilly Media's Chief Data Scientist (TDS Podcast - Clip)

Ben Lorica - Trends in data science with O'Reilly Media's Chief Data Scientist (TDS Podcast - Clip)

George Hayward: comedian, lawyer and data scientist (TDS Podcast - Clip)

George Hayward: comedian, lawyer and data scientist (TDS Podcast - Clip)

Serkan Piantino - Data science is becoming an engineering problem (TDS Podcast - Clip)

Serkan Piantino - Data science is becoming an engineering problem (TDS Podcast - Clip)

Rocio Ng - Data science and product management at LinkedIn (TDS Podcast - Clip)

Rocio Ng - Data science and product management at LinkedIn (TDS Podcast - Clip)

Akshay Singh - The thin line between data science and data engineering (TDS Podcast - Clip)

Akshay Singh - The thin line between data science and data engineering (TDS Podcast - Clip)

Susan Holcomb - Nontechnical career skills for data scientists (TDS Podcast - Clip)

Susan Holcomb - Nontechnical career skills for data scientists (TDS Podcast - Clip)

Tan Vachiramon - Choosing the right algorithm for your real-world problem (TDS Podcast - Clip)

Tan Vachiramon - Choosing the right algorithm for your real-world problem (TDS Podcast - Clip)

Joel Grus - The case against the jupyter notebook (TDS Podcast - CLIP)

Joel Grus - The case against the jupyter notebook (TDS Podcast - CLIP)

3 Strategies to Guarantee a Data Science Job with No Experience

3 Strategies to Guarantee a Data Science Job with No Experience

GPU Accelerated Data Analytics & Machine Learning [Tutorial]

GPU Accelerated Data Analytics & Machine Learning [Tutorial]

Professor turned Data Scientist: Why Dr. Guido Maretto left academia for the startup life

Professor turned Data Scientist: Why Dr. Guido Maretto left academia for the startup life

MBA to IBM Data Scientist: Exclusive Interview with Greg Rafferty

MBA to IBM Data Scientist: Exclusive Interview with Greg Rafferty