Thomas G. Dietterich @tdietterich Twitter profile

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Thomas G. Dietterich

4 years

I've been trying to imagine how the ML research and publication enterprise could be re-organized. Here are some initial thoughts. Feedback welcome! 1/

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Thomas G. Dietterich

@tdietterich

6 years

Several machine learning researchers have signed a statement regarding the upcoming launch of Nature Machine Intelligence. If you agree, I encourage you to sign this as well.

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Thomas G. Dietterich

@tdietterich

2 years

"Sentient" is being misapplied by many ML folks. It means "the ability to perceive or feel things" or "capable of experiencing things through its senses". Like many other categories, it is a matter of degree. 1/

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Thomas G. Dietterich

@tdietterich

5 years

Dear academic colleagues. It is not appropriate for class projects to be submitted to @arxiv_org unless they are also being submitted for publication. I and my fellow moderators are seeing more and more of this behavior. It wastes our time and the time of your colleagues

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Thomas G. Dietterich

@tdietterich

2 years

The term "ablation" is widely misused lately in ML papers. An ablation is a removal: you REMOVE some component of the system (e.g., remove batchnorm). A "sensitivity analysis" is where you VARY some component (e.g., network width). #pedantic

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Thomas G. Dietterich

@tdietterich

5 years

I just published What does it mean for a machine to ‘understand’? 1/

What does it mean for a machine to “understand”?

Critics of recent advances in artificial intelligence complain that although these advances have produced remarkable improvements in AI…

link.medium.com

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Thomas G. Dietterich

@tdietterich

2 years

@GaryMarcus @davidchalmers42 @ylecun We just don’t care.

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Thomas G. Dietterich

@tdietterich

3 years

Reminder to my ML colleagues. @arxiv is not @github . Don't submit drafts to arXiv; you are wasting your colleagues' time. Submit when you have a final version ready for the research community to read and cite. Please enforce this rule on your students, too.

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Thomas G. Dietterich

@tdietterich

3 years

Every once in a while, something amazing comes out on @arxiv . Check out this cool new ML book by @mrtz and @beenwrekt .

Patterns, predictions, and actions: A story about machine learning

This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover...

arxiv.org

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Thomas G. Dietterich

@tdietterich

3 years

Here is a treat: Dmitri Bertsekas is teaching RL at ASU and posting the lectures on youtube. A great way to prep for doing reinforcement learning research!

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Thomas G. Dietterich

@tdietterich

2 years

I propose that we adopt the term "Large Self-Supervised Models (LSSMs)" as a replacement for "Foundation Models" and "LLMs". "LLMs" don't capture non-linguistic data and "Foundation Models" is too grandiose. Thoughts? @percyliang

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Thomas G. Dietterich

@tdietterich

3 years

Thoughts upon reading : In this paper, the authors compare highly-cited papers from 2008-2009 with papers from 2018-2019 published in NeurIPS and ICML and summarize the values (i.e., desirable aspects) highlighted in those papers. 1/

The Values Encoded in Machine Learning Research

Machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague...

arxiv.org

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Thomas G. Dietterich

@tdietterich

3 years

@_Kitty_Wampus_ @TshepoLethea @jasonintrator Sprinkler systems are a hard decision for libraries. They fail surprisingly often and soak the books. This can destroy them almost as easily as fire.

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Thomas G. Dietterich

@tdietterich

2 years

@ChadNotChud Shouldn't there be two periods: the one you are quoting and the one ending your own sentence? As in: Nixon was lying when he said "I am not a crook.".

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Thomas G. Dietterich

@tdietterich

6 years

Disappointing article by @GaryMarcus . He barely addresses the accomplishments of deep learning (eg NL translation) and minimizes others (eg ImageNet with 1000 categories is small ("very finite") ?). 1/

Gary Marcus

@GaryMarcus

6 years

Top 10 reasons #deeplearning isn’t getting us to artificial general intelligence. A critique of deep learning, 5 years into its resurgence, by @garymarcus

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Thomas G. Dietterich

@tdietterich

2 years

Wow: Next level equation explanations!

Sibin

@sibinmohan

2 years

I’ve been using #AnnotatedEquations in my recent papers. I think it really adds to the readability and understanding of the math. Here are some examples. It uses #tikz in #latex . Let me know if you like it. Happy for any feedback. [GitHub link: next tweet] #AcademicChatter +

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Thomas G. Dietterich

@tdietterich

2 months

This looks cool: Mathieu Blondel ( @mblondel_ml ) and Vincent Roulet have posted a first draft of their book on arXiv:

The Elements of Differentiable Programming

Artificial intelligence has recently experienced remarkable advances, fueled by large models, vast datasets, accelerated hardware, and, last but not least, the transformative power of...

arxiv.org

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Thomas G. Dietterich

@tdietterich

1 year

@MelMitchell1 I also did not sign. The letter is such a mess of scary rhetoric and ineffective/non-existent policy prescriptions. There are important technical and policy issues, and many of us are working on them.

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Thomas G. Dietterich

@tdietterich

5 years

Interesting paper by Vapnik and Izmailov. "Rethinking statistical learning theory: learning using statistical invariants" shows how to impose invariants on SVM-style learning. 1/

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Thomas G. Dietterich

@tdietterich

23 days

The concept of "AGI" (a system that can match or exceed human performance across all tasks) shares all of the defects of the Turing Test. It defines "intelligence" entirely in terms of human performance. 1/

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Thomas G. Dietterich

@tdietterich

6 years

Important essay by Michael Jordan: “Artificial Intelligence — The Revolution Hasn’t Happened Yet” by Michael Jordan

Artificial Intelligence — The Revolution Hasn’t Happened Yet

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists and…

medium.com

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Thomas G. Dietterich

@tdietterich

5 years

Wonderful profile of Yoshua Bengio: He is both an intellectual leader and a moral leader of the field

He Helped Create A.I. Now, He Worries About ‘Killer Robots.’ (Published 2019)

Still, Yoshua Bengio, an artificial intelligence pioneer and co-winner of this year’s prestigious A.M. Turing prize, says A.I. will prove to be a boon, if regulated.

www.nytimes.com

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Thomas G. Dietterich

@tdietterich

2 years

The research community needs to find a new name for AGI now that AGI is appearing in venture capital pitch decks

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Thomas G. Dietterich

@tdietterich

4 years

I see many papers that begin with a sentence equivalent to "Topic X is popular". Popularity is not a sound scientific reason for studying a topic, so such opening sentences strike me as lame. How about "This paper shows how to solve issue Y with method M for X"? 1/2

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Thomas G. Dietterich

@tdietterich

4 years

One advantage of attending a conference from home: the ratio of toilets to attendees is much better

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Thomas G. Dietterich

@tdietterich

5 years

Nice article summarizing recent progress in deep learning. I would have titled it "Recent progress in deep learning leaves DL critics searching for new things to criticize"

Bob E. Hayes

@bobehayes

5 years

What are the limits of #deeplearning ? @garymarcus #artificialintelligence #MachineLearning

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Thomas G. Dietterich

@tdietterich

4 years

I'm delighted to share the happiness of people with papers accepted to NeurIPS. But I'd be even more delighted if you write a short thread introducing your paper and telling me why I should read it. Love links to arXiv (or wherever)

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Thomas G. Dietterich

@tdietterich

2 years

Anomaly detection methods compute an anomaly score A(x), and in research, we measure their effectiveness using AUC for the binary decision "anomaly" vs. "not anomaly". But in applications, we need to choose a threshold tau. How can we set tau without having labeled anomalies? 1/

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Thomas G. Dietterich

@tdietterich

3 years

We are recruiting new people to help moderate the cs.LG (machine learning) section of @arxiv . If you are interested, please DM me. Reasons to be a moderator: 1. Help promote open science and rapid communication of new results in machine learning 1/

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Thomas G. Dietterich

@tdietterich

6 years

. @katecrawford makes many very important points. Here is my attempt to translate some of them into engineering terminology. 1/

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Thomas G. Dietterich

@tdietterich

2 years

ML Twitter: What is the current best practice for the following setting? Problem: Image classification. Setting: I'm given an initial supervised training set of labeled images drawn from P(x,y), and I train a net. Then I'm given a second set of labeled images also from P(x,y). 1/

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Thomas G. Dietterich

@tdietterich

6 years

This is a classic case of "truly-ism". It turns out every problem we solve was solvable. One day, someone will answer the core questions of intelligence and someone will say, "we thought it was difficult but it was in fact not."

Gigasquid

@gigasquid

6 years

"Deep learning has succeeded primarily by showing that certain questions or tasks we thought were difficult are in fact not. It has not addressed the truly difficult questions that continue to prevent us from achieving human like AI" - Judea Pearl - The Book of Why

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Thomas G. Dietterich

@tdietterich

4 years

I was hoping that the quality of COVID-19 papers submitted to cs.LG @arxiv would improve over time. But I think it is getting worse. Now I'm seeing more random curve fitting papers. Would anyone make life and death policy decisions based on an LSTM without any medical basis?

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Thomas G. Dietterich

@tdietterich

6 years

If this is due to machine learning, it is the most clear-cut case yet of optimizing the wrong objective. I'll bet it becomes a case study in future AI Ethics classes. Great article by @zeynep

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Thomas G. Dietterich

@tdietterich

4 years

Gee @garymarcus , the goal of DL (and of the AI community) is to advance the science and engineering of intelligent systems, not to win debates or claim credit. The contributions of DL will stand or fall on their own merits. 1/

Gary Marcus

@GaryMarcus

4 years

. @rodneyabrooks is right: the deep learning community is currently positioning itself to take credit for any future technique that anyone might come up with, without really committing to much of anything. It's a neat rhetorical trick. I will have more to say about this, soon.

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Thomas G. Dietterich

@tdietterich

6 years

DL is essentially a new style of programming--"differentiable programming"--and the field is trying to work out the reusable constructs in this style. We have some: convolution, pooling, LSTM, GAN, VAE, memory units, routing units, etc. 8/

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Thomas G. Dietterich

@tdietterich

4 years

Very thought-provoking talk by Justin Gilmer at the #ICML2020 UDL workshop. Adversarial examples are just a case of out-of-distribution error. There is no particular reason to defend against the nearest OOD error (i.e., L-infty adversarial example) 1/

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Thomas G. Dietterich

@tdietterich

4 years

Having a great time teaching in #DatSciAfrica

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Thomas G. Dietterich

@tdietterich

4 years

@hardmaru @slashML Third, don’t try to hit a home run. Breakthroughs and insights come from surprising places, usually after you are deeply familiar with a problem. The desire to “do something big” can prevent you from seeing the insights revealed by a small, clean case

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Thomas G. Dietterich

@tdietterich

6 years

The NIPS Foundation has posted a "Statement on inappropriate behavior" at #NIPS2017 . See

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Thomas G. Dietterich

@tdietterich

5 years

I liked this paper from NeurIPS: … They put a deep learning wrapper around a differentiable physics engine and then can rapidly learn to fix the errors of the physics engine. They learn breakout in a few thousand steps.

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Thomas G. Dietterich

@tdietterich

9 months

@SierraClubIL @GovPritzker @ilenviro I used to be a Sierra Club member. Your opposition to nuclear power is bad for the environment. Yes, there is no permanent solution, but if we don't stop CO2 emissions we know what "permanent solution" is coming.

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Thomas G. Dietterich

@tdietterich

11 months

@Meaningness I think there are still good uses for OOP in implementing user interfaces and agent-based simulations. In such cases, it models the problem very well.

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Thomas G. Dietterich

@tdietterich

5 months

@katherine1ee @sharongoldman I disagree. Memorizing is the ability to correctly answer questions/info it was trained on. Generalizing is correctly answering questions it was not trained on. And hallucinating is incorrectly answering questions it was not trained on 1/

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Thomas G. Dietterich

@tdietterich

2 years

@thegautamkamath I like to think about research as a relay race. Each paper (and its authors) seeks to hand off something useful to the next paper (and its authors). The total progress achieved is the sum of these individual contributions even if some turn out to be wrong/irrelevant

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Thomas G. Dietterich

@tdietterich

4 years

I think @ACM_president and @esa are on the wrong side of this issue. It is time to re-imagine publication models and break the stranglehold of for-profit publishers on the dissemination of scientific research. Time to rethink how research is done!

Association of American Publishers

@AmericanPublish

4 years

Proud to join 125+ other organizations to oppose a costly proposed Administration policy that would undermine scientific discovery, American jobs, & our global competitiveness. Read the coalition letter: @americanpublish @AmericanCancer @globalIPcenter

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Thomas G. Dietterich

@tdietterich

10 months

@oneunderscore__ @emilymbender Closing quote "If you want to run a company whose entire endeavor is to trick people into accidentally clicking on [content], then [AI] might be worth your time," she said. "But if you want to run a media company, maybe trust your editorial staff to understand what readers want."

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Thomas G. Dietterich

@tdietterich

4 years

Prediction: In a couple of months, the US will need to do a much harsher total shutdown, which combined with testing and tracing, will do a much better job of controlling the virus. Our first try has failed to get R0 significantly below 1. We'll need a second attempt

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Thomas G. Dietterich

@tdietterich

8 years

Without care, #MachineLearning is confirmation bias on steroids

caroaragon

@caroaragon

8 years

" #MachineLearning is the scientific method on steroids." @pmddomingos

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Thomas G. Dietterich

@tdietterich

2 years

Summary: Simple sentience (responding to sensor input) is easy to achieve, and every interactive system (including LLMs) exhibits this. LLMs mimic some behaviors that are associated with more complex forms of sentience, but there is no basis for saying they have "feelings". end/

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Thomas G. Dietterich

@tdietterich

6 months

Hey authors, when prompting ChatGPT to write your abstract, tell it you are a serious academic that does not include hype in the abstracts. I'm seeing @arxivorg submissions with fluent abstracts containing business-hype words. 1/

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Thomas G. Dietterich

@tdietterich

10 months

@sharongoldman @CohereAI @aidangomezzz Training on synthetic data cannot lead to new knowledge discoveries. Training on synthetic data is a process of transforming one representation of knowledge into another. Any knowledge discovered by the second system must be implicit in the data generator. 1/

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Thomas G. Dietterich

@tdietterich

3 years

@ylecun @RichardDawkins But Facebook and Twitter accelerate the spread. You deploy algorithms that optimize for spread!

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Thomas G. Dietterich

@tdietterich

6 years

I'm excited to read this new (draft) book: An Introduction to Probabilistic Programming by Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood

An Introduction to Probabilistic Programming

This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also...

arxiv.org

Dan Roy

@roydanroy

6 years

New book on probabilistic programming on arXiv. I’m sure the authors @hyang144 @jwvdm @frankdonaldwood will welcome feedback.

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Thomas G. Dietterich

@tdietterich

1 year

Yoshua Bengio makes many good points in his latest blog post. I would like to share a few thoughts

How Rogue AIs may Arise - Yoshua Bengio

yoshuabengio.org

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Thomas G. Dietterich

@tdietterich

4 years

I've always said that Trump was intent on unilateral disarmament in international competition. Ending immigration is unconditional surrender to the rest of the world. Absolutely crazy for the US to throw away our best weapon: immigration of the best and brightest

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Thomas G. Dietterich

@tdietterich

5 years

I recently gave a short course on Trustable Machine Learning at Tsinghua University. Slides are available at 1/

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Thomas G. Dietterich

@tdietterich

5 months

#ICML will have a Position Paper track. "The goal of this track is to highlight papers that stimulate (productive, civil) discussion on timely topics that need our community’s input" #AI Read more here:

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Thomas G. Dietterich

@tdietterich

3 years

A review of “Underspecification Presents Challenges for Credibility in Modern Machine Learning” by D’Amour et al. 0/

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Thomas G. Dietterich

@tdietterich

5 years

@ReadyReporting @mackenzief @ndiakopoulos @JetBlue Perhaps even more troubling is that if you have a face that doesn't reliably match, you will be hassled every time you board. This is the repeated harm caused by poor computer vision technology.

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Thomas G. Dietterich

@tdietterich

5 years

This is a terrible idea. Machine learning is good for modeling frequent events with low stakes consequences, because it is never perfect. Nuclear launch is--we hope--an event of probability zero that requires perfect decision making.

Bulletin of the Atomic Scientists

@BulletinAtomic

5 years

Two US military experts have proposed giving artificial intelligence control over the nuclear launch button. @mchorowitz weighs in on the risks: "...training an algorithm for early warning means that you’re relying entirely on simulated data.”

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Thomas G. Dietterich

@tdietterich

5 years

Excellent post by @AnimaAnandkumar on requiring code release for published papers. I have not been very good about this myself, so I will make this my resolution for the New Year: The research is not done until the paper and the code are published.

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Thomas G. Dietterich

@tdietterich

6 years

Machine learning has been at the forefront of the movement for free and open access to research. For example, in 2001 the Editorial Board of the Machine Learning Journal resigned en masse to form a new zero-cost open access journal, Journal of Machine Learning Research (JMLR).

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Thomas G. Dietterich

@tdietterich

6 years

The RL community is rediscovering what folks in operations research have long known: Writing objective functions is a difficult kind of programming, and we need lots of tools and careful testing to get it right.

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Thomas G. Dietterich

@tdietterich

5 years

@isbellHFh @Noahpinion I'd love to see Democratic candidates run on a "Law and Order" platform just for the pure irony. Anti-corruption, anti-white terrorism, pro-rule of law.

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Thomas G. Dietterich

@tdietterich

4 years

So many important scientists (Fisher, Newton) had views that are repulsive or strange in retrospect. It makes me wonder what my ancestors believed or did. I also wonder which of my views and actions will be judged as horrifying by subsequent generations

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Thomas G. Dietterich

@tdietterich

4 years

Recommended reading on industry needs for fairness in machine learning. Many interesting directions for future research

Improving fairness in machine learning systems: What do industry...

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the...

arxiv.org

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Thomas G. Dietterich

@tdietterich

3 years

The president should resign or be impeached

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Thomas G. Dietterich

@tdietterich

3 years

Highly recommended. I've already been referring to it in my own research

Kevin Patrick Murphy

@sirbayes

3 years

I am pleased to announce that the camera ready version of my new textbook, "Probabilistic Machine Learning: An Introduction", is finally available from . Hardcopies will be available from MIT Press in Feb 2022.

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Thomas G. Dietterich

@tdietterich

8 months

"Frontier model" is pure hype. I encourage reviewers to insist that authors remove the phrase from their papers. What is on the "frontier" today will not be tomorrow, so it is a guarantee that your title will be wrong (probably even before you get the reviews back).

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Thomas G. Dietterich

@tdietterich

1 year

Important post from @Noahpinion . LLM-based tools have the potential to make all of us more efficient. Don't let the fearmongers rob us of the benefits of good AI tools.

Noah Smith 🐇🇺🇸🇺🇦

@Noahpinion

1 year

This paper inspired a somewhat ranty blog post:

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Thomas G. Dietterich

@tdietterich

5 years

Yes @RealAAAI , this is not acceptable. Faculty and students have families and family obligations. Please do this better!

Jean Kossaifi

@JeanKossaifi

5 years

Despite all the talk about it, work-life balance in academia remains an elusive dream. #AAAI rebuttal window is Friday to Sunday. As an academic I routinely have to spend weekends an evening working. This weekend, #AAAI rebuttals and writing #ICLR reviews amongst the rest. 1/3

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Thomas G. Dietterich

@tdietterich

2 years

@sbmisi The purpose of these LLMs is not to explain jokes or to generate art but instead to learn representations and knowledge that can support many downstream tasks. The jokes and pictures are ways of assessing and demoing what has been learned.

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Thomas G. Dietterich

@tdietterich

1 year

@amanpour @NPCollapse Geoff @hinton has always been an unreliable spokesperson. When he was selling AI to Canada, he always over-sold it. He would claim we were replicating the brain. Now he has decided he doesn't like AI, so now we risk the apocalypse.

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Thomas G. Dietterich

@tdietterich

8 months

@TaliaRinger @emilymbender @mmitchell_ai @ErikWhiting4 Of course, as an @arXiv supporter, I prefer to view arXiv as the authoritative version, and journal publication as additional evidence that the paper is worth reading. No paywalls, uniform interface, long term accessibility! :-)

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Thomas G. Dietterich

@tdietterich

1 year

@mezaoptimizer Old time AI people like me have always been working on AGI. We just called it AI. To me AGI is just a marketing term. We old timers think we know how hard the problem is, and we are lolling as the younger generation discovers this

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Thomas G. Dietterich

@tdietterich

6 years

Every time we read a great paper, we should tweet or blog about it. This would be particularly valuable for researchers who don't have a PR department to trumpet their accomplishments. Much more meaningful than bibliometrics. 2/2

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Thomas G. Dietterich

@tdietterich

4 years

@KLdivergence I think the biggest difference is that ML people are trying to build software systems, whereas statisticians seek to support scientific inquiry. This is why ML folks don't typically care about estimation or statistical inference

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Thomas G. Dietterich

@tdietterich

7 years

Through bad typing, I accidentally coined a new word today: "entrepreneural" = launching a neural net company?

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Thomas G. Dietterich

@tdietterich

5 years

This is absolutely ridiculous and a violation of academic and intellectual freedom.

J Q

@JQinsight

5 years

Unbelievable, IEEE is forced to ban Huawei employees from peer-reviewing papers or handling papers as editors.

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Thomas G. Dietterich

@tdietterich

2 years

@MPhillipsWSJ @WSJ “Opposite the US East Coast”?

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Thomas G. Dietterich

@tdietterich

5 months

@erikbryn The technique in this paper is more suited to algorithm discovery rather than scientific discovery. It relies on having a method for verifying a proposed solution. The LLM doesn't know the answer, but it can generate good proposals. 1/

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Thomas G. Dietterich

@tdietterich

5 years

@sapinker The Board wasn’t “forced” to change the name. We welcome the new name and the name change is only one of many steps we are taking to make our community more welcoming to everyone.

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Thomas G. Dietterich

@tdietterich

1 year

Wonderful blog post by @yoavgo on "Reinforcement Learning for Language Models". I especially like the insight that training for "truthfulness" depends on the teacher knowing what the LLM believes.

rl-for-llms.md

GitHub Gist: instantly share code, notes, and snippets.

gist.github.com

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Thomas G. Dietterich

@tdietterich

3 years

Google has lost its way. @google

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Thomas G. Dietterich

@tdietterich

2 years

@thegautamkamath One paper with fundamental insights is worth more than dozens of incremental contributions

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Thomas G. Dietterich

@tdietterich

4 years

To researchers submitted Covid-19 lung x-ray papers to @arxiv_org : If you test on a small test set (e.g., 100-200 images), you can't possibly measure error rates, AUC, etc. to 4 significant digits. Nobody will take you seriously if you don't compute confidence intervals!

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Thomas G. Dietterich

@tdietterich

5 years

Can you spot the outlier? What a disaster for computing!

Moshe Vardi

@vardi

5 years

Female share of Bachelor's degrees

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Thomas G. Dietterich

@tdietterich

23 days

I think we should be building systems that complement people; systems that do well the things that people do poorly; systems that make individuals and organizations more effective and more humane. 3/

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Thomas G. Dietterich

@tdietterich

4 years

Anecdotally, I find that if I run speedtest at the same time as a file download, the file downloads much faster. Is @comcast @xfinity detecting speedtest and adapting?

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Thomas G. Dietterich

@tdietterich

5 years

I am recruiting PhD students interested in how AI systems can detect and respond to surprise. Methods include anomaly detection, uncertainty assessment, calibration, and model-based RL. If you are at @icmlconf , DM me and we can chat

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Thomas G. Dietterich

@tdietterich

6 years

I agree that existing ML/AI systems focus on closed worlds. This is the fundamental reason that these systems are not safe to deploy in high-stakes open-world applications. But the idea that knowledge engineering will avoid these problems is puzzling. 1/

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Thomas G. Dietterich

@tdietterich

5 months

I will be at @NeurIPSConf and would be eager to meet with people. I'm not recruiting or offering big signing bonuses, I'm just interested in learning about what people are working on and what difficulties they are encountering. DM me and we can schedule a time to chat

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139

Thomas G. Dietterich

@tdietterich

4 years

@rao2z @ylecun But stepping back a bit, are we formulating the problem correctly? Our algorithms are measuring expected error, but we want to minimize something like worst-case error over the entire input space. Relying on expected error inevitably produces bias

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Thomas G. Dietterich

@tdietterich

3 years

It's a lot of fun watching the automated transcription of these @icmlconf talks. My favorite one: "Mushroom learning community"

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Thomas G. Dietterich

@tdietterich

6 years

@MuslimIQ "Tear gas is a chemical weapon banned in war. But Ferguson police shoot it at protesters." The US is banned from using tear gas on the battlefield, but we use it on our own citizens routinely

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Thomas G. Dietterich

@tdietterich

6 years

Looking forward to debating the future of deep learning with @GaryMarcus at @icmlconf (suggestions for talking points welcome!)

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Thomas G. Dietterich

@tdietterich

1 year

The pre-training of LLMs ignores "factuality". Has anyone developed loss functions for optimizing factuality? I'd be interested in a philosophical analysis, too. A related question: What is the best place to read about automated fact checking? 1/

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Thomas G. Dietterich

@tdietterich

5 years

Let me add my congratulations to @ylecun @geoffreyhinton and Yoshua Bengio for this well-earned recognition!

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Thomas G. Dietterich

@tdietterich

2 years

It is also strange to speak of LLMs as being sentient when the only sensor they have is the incoming text stream. Certainly humans can experience social pain when exchanging text message (as twitter proves every day), but LLMs are not social agents 11/

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