Ning Shi (Shining)

PhD @AmiiThinks @UAlbertaCS; Volunteer @ReviewAcl; Mentor UR2PhD & AI4Good; Formerly @BAAIBeijing, @AlibabaGroup, @GTOMSCS, @iSchoolSU, @NYUSPS; #NLPer

[ACLRollingReview @ReviewAcl]

📢 Attending #EMNLP2026?

Join the ARR Editors-in-Chief Meet’n’Greet (BoF) to learn how ARR works and discuss the challenges the EiC team faces.

Date: Oct 26, 2026
Time: 09:00–10:30 (Session 5)
Room: G11
#ARR #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR Oct authors

At least one author must have an OpenReview account by the ddl. Others have 1 week to complete profiles. ARR cannot resolve account issues.

FAQ on lottery exemptions:
https://t.co/K2oGZw1LHA

Only EMNLP/AACL26 can be verified before indexing.

#ARR #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR Reviewer Qualifications Checker is Live!
For the Oct cycle, each submission must name a qualified service contributor to be guaranteed review.
Before submission, you can quickly verify your status: https://t.co/3bC2v1Drnu
Policy: https://t.co/K2oGZw1LHA

#ARR #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR October 2026 authors

You do not need to wait until the deadline. Once you make a submission, even a partial one, the designated service contributor form and Responsible Checklist become available.
Please complete them within 48h.
https://t.co/K2oGZw1LHA

#ARR #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR October 2026 authors

Please review the new ACL Sustainable Reviewing Policy and FAQ to make sure your submissions follow the new requirements.

Policy: https://t.co/7WbmQl5MmL
FAQ: https://t.co/K2oGZw1LHA

#ARR #NAACL2027 #COLING2027 #NLProc

[ACLRollingReview @ReviewAcl]

📢 EMNLP 2026 authors: ORCID is required for camera-ready submission. All authors must have an ORCID linked to their OpenReview profile before the paper can be submitted. Please check with your co-authors early:
https://t.co/tRTw8Dc9rs

#ACL #EMNLP #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR May 2026 update

Metareviews will be released by the end of July 30 (AoE). We have received 99.6% and are working hard to collect the final 0.4%. Please bear with us!

#ARR #ACL #AACL #EMNLP #NLProc

[Amii @AmiiThinks]

Amii is pleased to receive $1M Claude API credits from @Anthropic, as part of their $10 million CAD investment in Canadian AI research.

This comes at a pivotal moment as Amii continues to build momentum and drive Canada’s global AI leadership. Read: https://t.co/XGjb00KsgT

[Ning Shi (Shining) @MrShininnnnn]

@xiye_nlp @UAlberta Welcome and Congrats! See you soon.

[EMNLP 2026 @emnlpmeeting]

📢 ARR May 2026 Update! To give everyone more time, the Author-Reviewer Discussion is extended one day to July 15 (AoE).

#EMNLP2026

[ACLRollingReview @ReviewAcl]

📢 ARR May 2026 update!

The Author-Reviewer Discussion period has been extended through July 15 (AoE).

#ARR #ACL #EMNLP2026 #NLProc

[Ning Shi (Shining) @MrShininnnnn]

TIL claude desktop @claudeai @ClaudeDevs and vs code keep separate chat lists, so your vs code chats look lost. they're not. just feed this link to your desktop session and let it sort it out 👇
https://t.co/tQB1yaetyz

#claudecode

[ACLRollingReview @ReviewAcl]

🚨 ARR is recruiting extra emergency reviewers and emergency Area Chairs (ACs) for this cycle.

Emergency Reviewer Registration:
https://t.co/DtVZqCHm1o

Emergency AC Registration:
https://t.co/jKxtqzvYbX

Thank you for helping support the ARR review process.
#ARR #ACL #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR May 2026 cycle update

We have published a blog post about the increased review assignments in the May cycle, what happened, and what comes next.

Please read the full post here:
https://t.co/55D2Og9mz3
#ARR #ACL #NLProc

[Ning Shi (Shining) @MrShininnnnn]

Random thought after using multiple coding agents:
Switching agents often means repeating the same context.
Maybe they should share it, so tokens are spent well.
A tiny plugin around this idea.
Feedback welcome.
https://t.co/5JS9k8xyGH

[Richard Sutton @RichardSSutton]

A new and possibly controversial perspective:
In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries.
https://t.co/zin5QbbT9N

The text of the speech:

AI Creativity and Discovery

Good day ladies and gentlemen. I regret that I am unable to be with you all today to engage in a back-and-forth discussion, but I am nevertheless pleased to be able to share with you, via this recording, some high-level thoughts about the current and future state of artificial intelligence, and in particular about AI’s relationship to science and mathematics, which is, as I understand it, the central focus of this meeting and of the SAIR Foundation.

I would like to start with an old joke; I am sure you have heard it before. It is the one about the researcher whose work is being evaluated, and the review comes back, and says “This work is both novel and good. Unfortunately, the parts that are good are not novel, and the parts that are novel are not good.”

My first point about AI is that this assessment applies exactly to large parts of AI as we know it today. Not all of today’s AI, but a large part of it. Pretty much all of what we mean by “Generative AI”---which includes large language models, and the images and video models, and even the new methods for learning world models. All of these AIs take large numbers of examples and produce a “model” which behaves similar to the examples, that is, which generates text like people, or images like artists or nature, and videos like we find on the internet. Don’t get me wrong, Generative AI can be extremely useful. No doubt about that. But the assessment of the joke still applies. These systems can produce output that is both novel and good, but not at the same time.

In many ways this is just absolutely not a problem. When we ask an AI for an answer from the internet, or to summarize a document, we don’t want it to be novel. We are happy if the quality of the answer, the goodness, comes from the source material—from the people who wrote the document or the articles on the internet. If the AI’s answer is novel it means it is going beyond the source material, adding something beyond it. This is what we call “hallucinations”. In most cases, we don’t like it when the AI makes something up, when it adds something novel.

One exception, of course, is when we are looking not for facts or reality, but for fiction and entertainment. We might ask for a bedtime story for a child, or an image based on existing images on the internet but which is nevertheless different and distinct from them. In these cases, it is never easy for us to know how creative the AI is actually being, as we do not know how close the AI’s story, poem, or image is to the source material. In a real practical sense we can not know this because the internet is too big, the possible sources that the AI may draw upon are too numerous.

When we ask for a fiction or novelty, the AI can give it to us because its processing is in part stochastic. Every decision can go multiple ways and will go different ways and produce a different trajectory every time. The trajectory can be random—and thus novel—or it can be based on the training data—and thus “good” because the training data is good, sourced from people or reality. Thus, the trajectory is either novel or good—based on randomness or based on data—but never both at the same time.

Really, I think it is okay if the output of Generative AI is never good and novel at the same time. For the researcher in the joke this is a devastating criticism, but for most things it is not, and for Generative AI it is not. Generative AI is meant to be a mimic. This is what supervised learning is for. Generative AI can be extremely useful, even when it just mimics, if it is faster, or cheaper, or smaller, or more customizable, or more copy-able, than the thing being mimicked. It is okay if Generative AI cannot be both novel and good at the same time. It is still a transformative technology.

But it is a limitation. And remember we are here to use AI for science and mathematics, and for these areas the assessment of the reviewer in the joke is devastating. For these areas we need true creativity and discovery. Generative AI—or Mimicking AI—will never get where us there. For these we need something more, and indeed we have something more in other parts of AI. We have many AI systems which can give us more. We have AlphaGo with its world-changing move 37, or AlphaZero with its brilliant original chess-playing style. We have GT-Sophy that drives simulated racecars better than any human. We have AlphaFold and AlphaProof and Claude-Code, which have brought true advances in science, mathematics, and programming. We have RL-Lyft which optimizes the assignment of cars to passengers in the ride-hailing business. All these systems have found things that are both novel and good. And, truth be told, some language models have been augmented in ways that make them more than Generative AI based on supervised learning.

All these systems have some additional features that make them capable of true creativity and true discovery. It is important for us to recognize what this is—and that it is not present in ordinary, garden-variety Generative AI. It is something that can not come from just supervised learning, from learning from examples. What is it? Well, it is a simple thing, a commonsense thing. It is not new. We have many names for it, but unfortunately none of them are very good names. I will call it Discovery. Basically, Discovery is just the idea of trying many things and seeing which of them work, then keeping those that worked the best. Evolution by natural selection works this way. The scientific method works this way. And just ordinary life and learning works this way. We try things and remember what works. What could be more obvious? In this behavioral case, psychology has two names for it— “instrumental learning” and “operant conditioning”—and in machine learning it is what we mean by “reinforcement learning”. We also see the idea of Discovery in planning and combinatorial search—anything that involves the idea of “generate and test”.

The essence of Discovery is to combine three steps:
1. Variation,
2. Evaluation, and
3. Selective retention.

Of course, I am not the first to say this. I am not the first to point out that this combination of steps is key to science, to evolution by natural selection, and to animal behavior. I think particularly of papers by Donald Campbell, by Daniel Dennett, and by Gary Cziko. What is new in my remarks is to directly relate the idea of Discovery to modern AI to help us see that it is not present in supervised learning or Generative AI—in particular, that Discovery is not present in backpropagation or gradient descent.

Let me say explicitly what is missing from Generative AI. As we have remarked, these systems do have a stochastic aspect, so they do generate a variety of trajectories and behavior. What is missing is the Evaluation step. The generator was pre-trained by supervised learning, leaving no way at runtime to Evaluate what it generates. And of course without Evaluation there can be no Selective retention, and thus no Discovery. The variation can bring novelty, but without evaluation there is no Discovery, and arguably, no creativity. That is, I would say that creativity requires that the new things generated be Evaluated. Without evaluation, and retention of the best, there is nothing created. The novelty flickers into existence but, if its value is unrecognized, it flickers away and is lost.

In many cases, Evaluation is done by people to make a discovery. As when we have Generative AI make many pictures for us, and then we pick the one that we like the best. The human+AI system completes the discovery.
In many other cases, the Evaluation comes from a clear objective. Some moves lead to checkmate, some steps lead to a proof, some actions result in high reward, some genotypes make more copies, some theories explain the data better.

Some prefer the Variation step to be called Blind variation, where “blind” here means that it is uninformed, a shot in the dark. It does not need to be completely uninformed; a good scientist does not select theories to test at random. But neither can it be completely informed and determined. There must be some uncertainty about where the answer lies in order for there to be a discovery. In practice, the variation is partly informed and partly blind, but it is the blind part that corresponds to the discovery.

Now let us briefly go all the way to modern deep learning, to the backpropagation algorithm. At first it might seem that backpropagation is incapable of discovery because it is deterministic and thus incapable of variation. But this is not correct. The weight updates of backprop are deterministic, but the weights are initialized to small random values. The random initialization is often downplayed, but in fact it is a necessary form of variation; it must be done properly to get good performance. In backprop this Variation is done once, at network initialization, so its effect is temporary, and later the network may lose its ability to learn. This is the weakness of deep learning that is alleviated with a new algorithm that my group presented in Nature a couple of years ago. Our “continual backpropagation” made one small change: every so often a less-used neuron would be re-initialized to small random weights. This allows the variation to continue and plasticity to be retained.

Although there is much more to be said about Creativity and Discovery, this is the key point: they are more than supervised learning, more than pattern recognition, more than prediction, and more than world modeling. Those things are important, but they alone will not bring us to discovery. Discovery requires Evaluation from a person or from an explicit goal, and only in the latter case will we attain full autonomy.

So that is my call to arms. If we want the full power of AI scientists, then we should share the goals with them so they can create, evaluate, discover, and in these ways fully participate in achieving the goals. Let’s be bold! Let’s fully automate Creativity and Discovery!

[ACLRollingReview @ReviewAcl]

📢 ARR-May reviewers can now try REVAS, an experimental review support tool.
REVAS gives feedback on review quality criteria and ARR reviewer heuristics, but does not suggest review content or scores.
🔗 https://t.co/MOysgnz4UQ
#ARR #EMNLP #ACL #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR Data Collection license agreements have been updated starting from the ARR May cycle.

The updated protocol clarifies what peer review data may be collected and when donated data may be publicly released.

Full protocol:
https://t.co/T6MMyxb3hu

#ARR #ACL #EMNLP #NLProc

[ACLRollingReview @ReviewAcl]

📢 ARR CFP Update
Authors with concurrent submissions must discuss them in related work and share anonymized versions. Check the “Originality requirement and overlap with other work by the same authors” section before the ARR May deadline.
https://t.co/wSE9cYVm3p
#EMNLP #ACL #ARR

[ACL 2027 @aclmeeting]

Metareviews are due on the 4th of March. Should an area chair fail to provide their checklists and metareviews, they may be prevented from committing their own papers. Please read the incentives 2026: https://t.co/q1ecW170Ny @ReviewAcl #ACL2026NLP #NLProc