Podcasts by Towards Data Science

131. Jeremie Harris - TDS Podcast Finale: The future of AI, and the risks that come with it from 2022-10-19T16:17:56

On the last episode of the Towards Data Science Podcast, host Jeremie Harris offers his perspective on the last two years of AI progress, and what he thinks it means for everything, from AI safe...

130. Edouard Harris - New Research: Advanced AI may tend to seek power *by default* from 2022-10-12T13:31:22

Progress in AI has been accelerating dramatically in recent years, and even months. It seems like every other day, there’s a new, previously-believed-to-be-impossible feat of AI that’s achieved ...

129. Amber Teng - Building apps with a new generation of language models from 2022-10-05T15:27:05

It’s no secret that a new generation of powerful and highly scaled language models is taking the world by storm. Companies like OpenAI, AI21Labs, and Cohere have built models so versatile that t...

128. David Hirko - AI observability and data as a cybersecurity weakness from 2022-09-28T15:07:33

Imagine you’re a big hedge fund, and you want to go out and buy yourself some data. Data is really valuable for you — it’s literally going to shape your investment decisions and determine your o...

127. Matthew Stewart - The emerging world of ML sensors from 2022-09-21T13:58:52

Today, we live in the era of AI scaling. It seems like everywhere you look people are pushing to make large language models larger, or more multi-modal and leveraging ungodly amounts of processi...

126. JR King - Does the brain run on deep learning? from 2022-09-14T14:29:06

Deep learning models — transformers in particular — are defining the cutting edge of AI today. They’re based on an architecture called an artificial neural network, as you probably already know ...

125. Ryan Fedasiuk - Can the U.S. and China collaborate on AI safety? from 2022-09-07T16:40:41

It’s no secret that the US and China are geopolitical rivals. And it’s also no secret that that rivalry extends into AI — an area both countries consider to be strategically critical.

But...

124. Alex Watson - Synthetic data could change everything from 2022-05-18T15:07:14

There’s a website called thispersondoesnotexist.com. When you visit it, you’re confronted by a high-resolution, ph...

123. Ala Shaabana and Jacob Steeves - AI on the blockchain (it actually might just make sense) from 2022-05-12T14:08:48

Two ML researchers with world-class pedigrees who decided to build a company that puts AI on the blockchain. Now to most people — myself included — “AI on the blockchain” sounds like a winning e...

122. Sadie St. Lawrence - Trends in data science from 2022-05-04T14:22:51

As you might know if you follow the podcast, we usually talk about the world of cutting-edge AI capabilities, and some of the emerging safety risks and other challenges that the future of AI mig...

121. Alexei Baevski - data2vec and the future of multimodal learning from 2022-04-27T14:13:51

If the name data2vec sounds familiar, that’s probably because it made Listen

120. Liam Fedus and Barrett Zoph - AI scaling with mixture of expert models from 2022-04-20T14:51:49

AI scaling has really taken off. Ever since GPT-3 came out, it’s become clear that one of the things we’ll need to do to move beyond narrow AI and towards more generally intelligent systems is g...

119. Jaime Sevilla - Projecting AI progress from compute trends from 2022-04-13T14:47:09

There’s an idea in machine learning that most of the progress we see in AI doesn’t come from new algorithms of model architectures. instead, some argue, progress almost entirely comes from scali...

118. Angela Fan - Generating Wikipedia articles with AI from 2022-04-06T14:59:53

Generating well-referenced and accurate Wikipedia articles has always been an important problem: Wikipedia has essentially become the Internet's encyclopedia of record, and hundreds of millions ...

117. Beena Ammanath - Defining trustworthy AI from 2022-03-30T15:39:18

Trustworthy AI is one of today’s most popular buzzwords. But although everyone seems to agree that we want AI to be trustworthy, definitions of trustworthiness are often fuzzy or inadequate. May...

116. Katya Sedova - AI-powered disinformation, present and future from 2022-03-23T14:34:53

Until recently, very few people were paying attention to the potential malicious applications of AI. And that made some sense: in an era where AIs were narrow and had to be purpose-built for eve...

115. Irina Rish - Out-of-distribution generalization from 2022-03-09T16:03:19

Imagine, for example, an AI that’s trained to identify cows in images. Ideally, we’d want it to learn to detect cows based on their shape and colour. But what if the cow pictures we put in the t...

114. Sam Bowman - Are we *under-hyping* AI? from 2022-03-02T15:02:47

Google the phrase “AI over-hyped”, and you’ll find literally dozens of articles from the likes of Forbes, Wired, and Scientific American, all arguing that “AI isn’t re...

113. Yaron Singer - Catching edge cases in AI from 2022-02-09T15:46:52

It’s no secret that AI systems are being used in more and more high-stakes applications. As AI eats the world, it’s becoming critical to ensure that AI systems behave robustly — that they don’t ...

112. Tali Raveh - AI, single cell genomics, and the new era of computational biology from 2022-02-02T16:22:09

Until very recently, the study of human disease involved looking at big things — like organs or macroscopic systems — and figuring out when and how they can stop working properly. But that’s all...

111. Mo Gawdat - Scary Smart: A former Google exec’s perspective on AI risk from 2022-01-26T14:57:26

If you were scrolling through your newsfeed in late September 2021, you may have caught this splashy headline from The Times of London that read, “Listen

110. Alex Turner - Will powerful AIs tend to seek power? from 2022-01-19T14:25:40

Today’s episode is somewhat special, because we’re going to be talking about what might be the first solid quantitative study of the power-seeking tendencies that we can expect advanced AI syste...

109. Danijar Hafner - Gaming our way to AGI from 2022-01-12T14:52:59

Until recently, AI systems have been narrow — they’ve only been able to perform the specific tasks that they were explicitly trained for. And while narrow systems are clearly useful, the holy gr...

108. Last Week In AI — 2021: The (full) year in review from 2022-01-05T15:21:52

2021 has been a wild ride in many ways, but its wildest features might actually be AI-related. We’ve seen major advances in everything from language modeling to multi-modal learning, open-ended ...

107. Kevin Hu - Data observability and why it matters from 2021-12-15T16:30:30

Imagine for a minute that you’re running a profitable business, and that part of your sales strategy is to send the occasional mass email to people who’ve signed up to be on your mailing list. F...

106. Yang Gao - Sample-efficient AI from 2021-12-08T15:26:47

Historically, AI systems have been slow learners. For example, a computer vision model often needs to see tens of thousands of hand-written digits before it can tell a 1 apart from a 3. Even gam...

105. Yannic Kilcher - A 10,000-foot view of AI from 2021-12-01T15:56:59

There once was a time when AI researchers could expect to read every new paper published in the field on the arXiv, but today, that’s no longer the case. The recent explosion of research activit...

104. Ken Stanley - AI without objectives from 2021-11-24T15:35:30

Today, most machine learning algorithms use the same paradigm: set an objective, and train an agent, a neural net, or a classical model to perform well against that objective. That approach has ...

103. Gillian Hadfield - How to create explainable AI regulations that actually make sense from 2021-11-17T16:34:02

It’s no secret that governments around the world are struggling to come up with effective policies to address the risks and opportunities that AI presents. And there are many reasons why that’s ...

102. Wendy Foster - AI ethics as a user experience challenge from 2021-11-10T15:33:15

AI ethics is often treated as a dry, abstract academic subject. It doesn’t have the kinds of consistent, unifying principles that you might expect from a quantitative discipline like computer sc...

101. Ayanna Howard - AI and the trust problem from 2021-11-03T14:51:23

Over the last two years, the capabilities of AI systems have exploded. AlphaFold2, MuZero, CLIP, DALLE, GPT-3 and many other models have extended the reach of AI to new problem classes....

100. Max Jaderberg - Open-ended learning at DeepMind from 2021-10-27T14:49:16

On the face of it, there’s no obvious limit to the reinforcement learning paradigm: you put an agent in an environment and reward it for taking good actions until it masters a task.

And b...

99. Margaret Mitchell - (Practical) AI ethics from 2021-10-20T14:47:26

Bias gets a bad rap in machine learning. And yet, the whole point of a machine learning model is that it biases certain inputs to certain outputs — a picture of a cat to a label that says “cat”,...

98. Mike Tung - Are knowledge graphs AI’s next big thing? from 2021-10-13T14:24:43

As impressive as they are, language models like GPT-3 and BERT all have the same problem: they’re trained on reams of internet data to imitate human writing. And human writing is often wrong, bi...

97. Anthony Habayeb - The present and future of AI regulation from 2021-10-06T15:01:25

Corporate governance of AI doesn’t sound like a sexy topic, but it’s rapidly becoming one of the most important challenges for big companies that rely on machine learning models to deliver value...

96. Jan Leike - AI alignment at OpenAI from 2021-09-29T14:17:40

The more powerful our AIs become, the more we’ll have to ensure that they’re doing exactly what we want. If we don’t, we risk building AIs that use dangerously creative solutions that have side-...

95. Francesca Rossi - Thinking, fast and slow: AI edition from 2021-09-22T15:16:27

The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinforcement learning and other prosaic AI techniq...

94. Divya Siddarth - Are we thinking about AI wrong? from 2021-07-28T14:50:38

AI research is often framed as a kind of human-versus-machine rivalry that will inevitably lead to the defeat — and even wholesale replacement of — human beings by artificial superintelligences ...

93. 2021: A year in AI (so far) - Reviewing the biggest AI stories of 2021 with our friends at the Let’s Talk AI podcast from 2021-07-21T13:34:27

2020 was an incredible year for AI. We saw powerful hints of the potential of large language models for the first time thanks to Listen

92. Daniel Filan - Peering into neural nets for AI safety from 2021-07-14T13:15:02

Many AI researchers think it’s going to be hard to design AI systems that continue to remain safe as AI capabilities increase. We’ve seen already on the podcast that the field of AI alignment ha...

91. Peter Gao - Self-driving cars: Past, present and future from 2021-07-07T13:47:39

Cruise is a self-driving car startup founded in 2013 — at a time when most people thought of self-driving cars as the stuff of science fiction. And yet, just three years later, the company was a...

90. Jeffrey Ding - China’s AI ambitions and why they matter from 2021-06-30T14:31:22

There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to...

89. Pointing AI in the right direction - A cross-over episode with the Banana Data podcast! from 2021-06-23T13:15:02

This special episode of the Towards Data Science podcast is a cross-over with our friends over at the Banana Data podcast. We’ll be zooming out and talking about some of the most important curre...

88. Oren Etzioni - The case against (worrying about) existential risk from AI from 2021-06-16T13:26:32

Few would disagree that AI is set to become one of the most important economic and social forces in human history.

But along with its transformative potential has come concern about a str...

87. Evan Hubinger - The Inner Alignment Problem from 2021-06-09T14:14:16

How can you know that a super-intelligent AI is trying to do what you asked it to do?

The answer, it turns out, is: not easily. And unfortunately, an increasing number of AI safety resear...

86. Andy Jones - AI Safety and the Scaling Hypothesis from 2021-06-02T13:56:29

When OpenAI announced the release of their  GPT-3 API last year, the tech world was shocked. Here was a language model, trained only to perform a simple autocomplete task, which turned out ...

85. Brian Christian - The Alignment Problem from 2021-05-26T15:09:59

In 2016, OpenAI published a blog describing the results of one of their AI safety experiments. In it, they ...

84. Eliano Marques - The (evolving) world of AI privacy and data security from 2021-05-19T19:36:55

We all value privacy, but most of us would struggle to define it. And there’s a good reason for that: the way we think about privacy is shaped by the technology we use. As new technologies emerg...

83. Rosie Campbell - Should all AI research be published? from 2021-05-12T13:25:50

When OpenAI developed its GPT-2 language model in early 2019, they initially chose not to publish the algorithm, owing to concerns over its potential for malicious use, as well as the need for t...

82. Jakob Foerster - The high cost of automated weapons from 2021-05-05T15:17:52

Automated weapons mean fewer casualties, faster reaction times, and more precise strikes. They’re a clear win for any country that deploys them. You can see the appeal.

But they’re also a...

81. Nicolas Miailhe - AI risk is a global problem from 2021-04-28T15:47:48

In December 1938, a frustrated nuclear physicist named Leo Szilard wrote a letter to the British Admiralty telling them that he had given up on his greatest invention — the nuclear chain reactio...

80. Yan Li - The Surprising Challenges of Global AI Philanthropy from 2021-04-21T14:48:09

We’ve recorded quite a few podcasts recently about the problems AI does and may create, now and in the future. We’ve talked about AI safety, alignment, bias and fairness.

These are import...

79. Ryan Carey - What does your AI want? from 2021-04-14T17:44:04

AI safety researchers are increasingly focused on understanding what AI systems want. That may sound like an odd thing to care about: after all, aren’t we just programming AIs to want certain th...

78. Melanie Mitchell - Existential risk from AI: A skeptical perspective from 2021-04-07T15:49:40

As AI systems have become more powerful, an increasing number of people have been raising the alarm about its potential long-term risks. As we’ve covered on the podcast before, many now argue th...

77. Josh Fairfield - AI advances, but can the law keep up? from 2021-03-31T14:53:12

Powered by Moore’s law, and a cluster of related trends, technology has been improving at an exponential pace across many sectors. AI capabilities in particular have been growing at a dizzying p...

76. Stuart Armstrong - AI: Humanity's Endgame? from 2021-03-24T15:31:44

Paradoxically, it may be easier to predict the far future of humanity than to predict our near future.

The next fad, the next Netflix special, the next President — all are nearly impossib...

75. Georg Northoff - Consciousness and AI from 2021-03-17T14:11:50

For the past decade, progress in AI has mostly been driven by deep learning — a field of research that draws inspiration directly from the structure and function of the human brain. By drawing a...

74. Ethan Perez - Making AI safe through debate from 2021-03-10T14:46:16

Most AI researchers are confident that we will one day create superintelligent systems — machines that can significantly outperform humans across a wide variety of tasks.

If this ends up ...

73. David Roodman - Economic history and the road to the singularity from 2021-03-03T15:20:43

There’s a minor mystery in economics that may suggest that things are about to get really, really weird for humanity.

And that mystery is this: many economic models predict that, at some ...

72. Margot Gerritsen - Does AI have to be understandable to be ethical? from 2021-02-24T21:24:18

As AI systems have become more ubiquitous, people have begun to pay more attention to their ethical implications. Those implications are potentially enormous: Google’s search algorithm and Twitt...

71. Ben Garfinkel - Superhuman AI and the future of democracy and government from 2021-02-17T16:12:40

As we continue to develop more and more sophisticated AI systems, an increasing number of economists, technologists and futurists have been trying to predict what the likely end point of all thi...

70. Sarah Williams - What does ethical AI even mean? from 2021-02-10T15:28:36

There’s no question that AI ethics has received a lot of well-deserved attention lately. But ask the average person what ethics AI means, and you’re as likely as not to get a blank stare. I thin...

69. Anders Sandberg - Answering the Fermi Question: Is AI our Great Filter? from 2021-02-03T15:16:18

The apparent absence of alien life in our universe has been a source of speculation and controversy in scientific circles for decades. If we assume that there’s even a tiny chance that ...

68. Silvia Milano - Ethical problems with recommender systems from 2021-01-27T16:32:22

One of the consequences of living in a world where we have every kind of data we could possible want at our fingertips, is that we have far more data available to us than we could possibly revie...

67. Joaquin Quiñonero-Candela - Responsible AI at Facebook from 2021-01-20T16:50:48

Facebook routinely deploys recommendation systems and predictive models that affect the lives of billions of people everyday. That kind of reach comes with great responsibility — among other thi...

66. Owain Evans - Predicting the future of AI from 2021-01-13T14:53:13

Most researchers agree we’ll eventually reach a point where our AI systems begin to exceed human performance at virtually every economically valuable task, including the ability to generalize fr...

65. Helen Toner - The strategic and security implications of AI from 2021-01-06T16:06:47

With every new technology comes the potential for abuse. And while AI is clearly starting to deliver an awful lot of value, it’s also creating new systemic vulnerabilities that governments now h...

64. David Krueger - Managing the incentives of AI from 2020-12-30T15:02:55

What does a neural network system want to do?

That might seem like a straightforward question. You might imagine that the answer is “whatever the loss function says it should do....

63. Geordie Rose - Will AGI need to be embodied? from 2020-12-23T15:50:12

The leap from today’s narrow AI to a more general kind of intelligence seems likely to happen at some point in the next century. But no one knows exactly how: at the moment, AGI remains a signif...

62. Nicolai Baldin - AI meets the law: Bias, fairness, privacy and regulation from 2020-12-16T16:05:04

The fields of AI bias and AI fairness are still very young. And just like most young technical fields, they’re dominated by theoretical discussions: researchers argue over what words like “priva...

61. Ben Goertzel - The unorthodox path to AGI from 2020-12-09T17:16:44

No one knows for sure what it’s going to take to make artificial general intelligence work. But that doesn’t mean that there aren’t prominent research teams placing big bets on different theorie...

60. Rob Miles - Why should I care about AI safety? from 2020-12-02T13:46:42

Progress in AI capabilities has consistently surprised just about everyone, including the very developers and engineers who build today’s most advanced AI systems. AI can now match or exceed hum...

59. Matthew Stewart - Tiny ML and the future of on-device AI from 2020-11-25T15:15:05

When it comes to machine learning, we’re often led to believe that bigger is better. It’s now pretty clear that all else being equal, more data, more compute, and larger models add up to give mo...

58. David Duvenaud - Using generative models for explainable AI from 2020-11-18T14:43:23

In the early 1900s, all of our predictions were the direct product of human brains. Scientists, analysts, climatologists, mathematicians, bankers, lawyers and politicians did their best to antic...

57. Dylan Hadfield-Menell - Humans in the loop from 2020-11-11T15:04:09

Human beings are collaborating with artificial intelligences on an increasing number of high-stakes tasks. I’m not just talking about robot-assisted surgery or self-driving cars here — every day...

56. Annette Zimmermann - The ethics of AI from 2020-11-04T15:37:34

As AI systems have become more powerful, they’ve been deployed to tackle an increasing number of problems.

Take computer vision. Less than a decade ago, one of the most advanced applicati...

55. Rohin Shah - Effective altruism, AI safety, and learning human preferences from the state of the world from 2020-10-28T15:23:35

If you walked into a room filled with objects that were scattered around somewhat randomly, how important or expensive would you assume those objects were?

What if you walked into the sam...

54. Tim Rocktäschel - Deep reinforcement learning, symbolic learning and the road to AGI from 2020-10-15T14:58:32

Reinforcement learning can do some pretty impressive things. It can optimize ad targeting, help run self-driving cars, and even win StarCraft games. But current RL systems are still highly task-...

53. Edouard Harris - Emerging problems in machine learning: making AI "good" from 2020-10-08T15:37:49

Where do we want our technology to lead us? How are we falling short of that target? What risks might advanced AI systems pose to us in the future, and what potential do they hold? And what does...

52. Sanyam Bhutani - Networking like a pro in data science from 2020-09-23T15:39:10

Networking is the most valuable career advancement skill in data science. And yet, almost paradoxically, most data scientists don’t spend any time on it at all. In some ways, that’s not terribly...

51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need from 2020-09-16T13:43:11

We’ve talked a lot about “full stack” data science on the podcast. To many, going full-stack is one of those long-term goals that we never get to. There are just too many algorithms and data str...

50. Ken Jee - Building your brand in data science from 2020-09-09T15:00:29

It’s no secret that data science is an area where brand matters a lot.

In fact, if there’s one thing I’ve learned from A/B testing ways to help job-seekers get hired at Listen

49. Catherine Zhou - The data science of learning from 2020-09-02T14:28:30

If you’re interested in upping your coding game, or your data science game in general, then it’s worth taking some time to understand the process of learning itself.

And if there’s one co...

48. Emmanuel Ameisen - Beyond the jupyter notebook: how to build data science products from 2020-08-26T14:57:49

Data science is about much more than jupyter notebooks, because data science problems are about more than machine learning.

What data should I collect? How good does my model need to be t...

47. Goku Mohandas - Industry research and how to show off your projects from 2020-08-19T16:16:56

Project-building is the single most important activity that you can get up to if you’re trying to keep your machine learning skills sharp or break into data science. But a project won’t do you m...

46. Ihab Ilyas - Data cleaning is finally being automated from 2020-08-12T14:06:43

It’s cliché to say that data cleaning accounts for 80% of a data scientist’s job, but it’s directionally true.

That’s too bad, because fun things like data exploration, visualization and ...

45. Kenny Ning - Is data science merging with data engineering? from 2020-08-05T13:40:50

There’s been a lot of talk about the future direction of data science, and for good reason. The space is finally coming into its own, and as the Wild West phase of the mid-2010s well and truly c...

44. Jakob Foerster - Multi-agent reinforcement learning and the future of AI from 2020-07-29T16:18:37

Reinforcement learning has gotten a lot of attention recently, thanks in large part to systems like AlphaGo and AlphaZero, which have highlighted its immense potential in dramatic ways. And whil...

43. Ian Scott - Data science at Deloitte from 2020-07-22T15:19:05

Data science can look very different from one company to the next, and it’s generally difficult to get a consistent opinion on the question of what a data scientist really is.

That’s why ...

42. Will Grathwohl - Energy-based models and the future of generative algorithms from 2020-07-15T15:22:24

Machine learning in grad school and machine learning in industry are very different beasts. In industry, deployment and data collection become key, and the only thing that matters is whether you...

41. Solmaz Shahalizadeh - Data science in high-growth companies from 2020-07-08T15:28:05

One of the themes that I’ve seen come up increasingly in the past few months is the critical importance of product thinking in data science. As new and aspiring data scientists deepen their tech...

40. David Meza - Data science at NASA from 2020-07-01T14:42:11

Machine learning isn’t rocket science, unless you’re doing it at NASA. And if you happen to be doing data science at NASA, you have something in common with David Meza, my guest for today’s epis...

39. Nick Pogrebnyakov - Data science at Reuters, and the remote work after the coronavirus from 2020-06-24T16:15:02

Nick Pogrebnyakov is a Senior Data Scientist at Thomson Reuters, an Associate Professor at Copenhagen Business School, and the founder of Leverness, a market...

38. Matthew Stewart - Data privacy and machine learning in environmental science from 2020-06-17T14:19:54

One Thursday afternoon in 2015, I got a spontaneous notification on my phone telling me how long it would take to drive to my favourite restaurant under current traffic conditions. This was alar...

37. Sean Knapp - The brave new world of data engineering from 2020-06-10T14:57:01

There’s been a lot of talk in data science circles about techniques like AutoML, which are dramatically reducing the time it takes for data scientists to train and tune models, and create reliab...

36. Max Welling - The future of machine learning from 2020-06-03T13:59:32

For the last decade, advances in machine learning have come from two things: improved compute power and better algorithms. These two areas have become somewhat siloed in most people’s thinking: ...

35. Rubén Harris - Learning and looking for jobs in quarantine from 2020-05-27T14:41:52

Coronavirus quarantines fundamentally change the dynamics of learning, and the dynamics of the job search. Just a few months ago, in-person bootcamps and college programs, live networking events...

34. Denise Gosnell and Matthias Broecheler - You should really learn about graph databases. Here’s why. from 2020-05-20T14:57:55

One great way to get ahead in your career is to make good bets on what technologies are going to become important in the future, and to invest time in learning them. If that sounds like somethin...

33. Roland Memisevic - Machines that can see and hear from 2020-05-13T13:00

One of the most interesting recent trends in machine learning has been the combination of different types of data in order to be able to unlock new use cases for deep learning. If the 2010s were...

32. Bahador Khaleghi - Explainable AI and AI interpretability from 2020-05-06T15:14:14

If I were to ask you to explain why you’re reading this blog post, you could answer in many different ways.

For example, you could tell me “it’s because I felt like it”, or “because my ne...

31. Russell Pollari - Building habits and breaking into data science from 2020-04-29T14:47:24

Most of us want to change our identities. And we usually have an idealized version of ourselves that we aspire to become — one who’s fitter, smarter, healthier, more famous, wealthier, more cent...

30. Interviewing the Medium data science team from 2020-04-22T14:39:59

Revenues drop unexpectedly, and management pulls aside the data science team into a room. The team is given its marching orders: “your job,” they’re told, “is to find out what the hell is going ...

29. Cameron Davidson-Pillon - Data science at Shopify from 2020-04-15T16:08:30

If you want to know where data science is heading, it helps to know where it’s been. Very few people have that kind of historical perspective, and even fewer combine it with an understanding of ...

28. Emily Robinson - Building a Career in Data Science from 2020-04-07T16:36:14

It’s easy to think of data science as a purely technical discipline: after all, it exists at the intersection of a number of genuinely technical topics, from statistics to programming to machine...

27. Alayna Kennedy - AI safety, AI ethics and the AGI debate from 2020-03-30T16:07:50

Most of us believe that decisions that affect us should be made rationally: they should be reached by following a reasoning process that combines data we trust with a logic that we find acceptab...

26. Jeremy Howard - Coronavirus: the data behind the disease from 2020-03-20T16:32:54

In mid-January, China launched an official investigation into a string of unusual pneumonia cases in Hubei province. Within two months, that cluster of cases would snowball into a full-blown pan...

25. Chris Parmer - Plotly founder on what data science is, and where it's going from 2020-03-18T15:06:19

It’s easy to think of data scientists as “people who explore and model data”. Bur in reality, the job description is much more flexible: your job as a data scientist is to solve problems...