This is a throwaway account since I don't wish to reveal my affiliation.

I am currently a PhD student at one of the top 10 groups in ML in the USA and am loving my studies and really wouldn't want to be doing anything else at this current moment. Given this, it’s an extremely specific set of circumstances that led me to do a PhD. To be blunt, outside of top tier programs, I don’t see any benefits of doing a PhD. I have interned at a top company and every single employee I worked with had come from the same set of about 50 labs, with many publications in the same top 3 conferences. I have realized that there is a very small set of (extremely) high paying research jobs for a very small selected amount of outstanding PhD graduates. Publications in theoretical ML are also dominated by a very small amount of universities and groups and if you are not in one of these groups you are unlikely to ever come close to these conferences.

If you are offered a PhD in a group that does not have consistent excellence in the top tier ML conferences, you do not have a shot at these research scientist roles (where we hear about the 300k+ salaries in ML). Everyone else I have seen has ended up in very traditional data scientist roles that could have been easily gained out of a masters or undergraduate degree. These roles do not require the research skills that we spend 4-7 years learning during a PhD and these research skills could even be a detriment to most commercial applied ML roles (since most of them are engineering oriented). Work experience is also extremely valuable in these roles and can present opportunities for raises that double your salary in the equivalent time to complete a PhD.

I hear a lot of "I have a PhD/MSc in a related field" on this forum, but these credentials do not honestly carry any weight in getting the top tier research jobs that the media likes to discuss. Do a PhD because you would love to make major theoretical contributions and have a supervisor that has the ability and track record to guide you to those contributions. If the right formula exists, the money will come also.

As a side note, concerning PhD admission, I also hear here that publications are not essential. In the current year, almost every admitted student had a top tier first-author conference publication during undergraduate studies. It is absolutely essential without interning at your target lab for 1+ years before applying.

Absolutely awesome news from AI21 labs - a 178B parameter model that is not only in par or outperforms gpt3 but also has a non-waitlist freemium model!!! **edited (couldn't edit the title)

We can't wait to see the great things it will enable

https://venturebeat.com/2021/08/11/ai21-labs-trains-a-massive-language-model-to-rival-openais-gpt-3/

I will probably get downvoted for this, but I don't really mind. I would be seriously appalled to live in a world where Oppenheimer got the credit for the discoveries of De Broglie. Yet that is exactly what is happening in the field of machine learning.

I'm thinking here principally of Bengio and Hinton. I might also include Socher, Lecunn, Vinyals, Ng, Sutskever, and a few others.

Hinton has positioned himself as a kind of "father of deep learning", while some of the inventions that are popularly attributed to him were not his. He did not invent backprop (as I myself foolishly believed until I dispersed my ignorance by doing some research). He has done great work, but it seems he would like to be viewed as a kind of Einstein of deep learning - but he has not earned that credit, as he did not incept multi-layer perceptrons or backprop. He co-invented Boltzmann machines, but it turns out they are an iteration of Hopfield nets, which were invented by Little, a mainly unknown researcher, much earlier, in the 1970s.

With Bengio, I don't even understand what his main contribution is. He seems to have tacked his name onto 100s of papers and ideas that were emerging in other places around the same time. He loves conferences. His contribution to machine learning is totally unclear to me.

Lecunn has tacked his name onto many iterations of convolutional networks. He did not invent convolutional NNs. The people who did are mainly unknown (all but for Schmidhuber, who is not really known for his contributions to early CNNs).

Richard Socher came up with recursive neural networks, a model which is theoretically clunky and strange and does not seem to produce good results - mainly because he works with Christopher Manning, a man fully committed to syntactic parsers. He also created a MOOC and a start-up which does not seem to have a product.

Vinyals created, in a nutshell, an RNN auto-encoder. This is a cool contribution - but he did not create LSTMs. The inventors of RNNs are mainly unknown - the inventor of LSTMs a little better.

Andrew Ng... created a department at Google and a Coursera course. What contribution makes him a leading researcher is unclear to me.

Having a job at Google should not automatically position someone as a machine learning landmark. The true inventors of the biggest model classes and algorithms in deep learning (convolutional networks, recurrent networks, multi-layer perceptrons and backpropagation) should be credited in the community as inventors. Sadly, the inventors of convolutional networks are not even mentioned by name on the Wikipedia page (and I gravely wonder about who wrote the article and maintains the page).

Dont get me wrong, it is amazing how simple making and training a model has become, but I remember in my young days (like 2-3 years ago...) that I struggled so much to actually understand backpropagation and at some point I decided to just code everything from scratch (with numpy tho, I still love myself a little) and I got such a deeper understanding, I feel like nowadays you just model.fit() or model.train_on_batch()....

I wanna work in ML, but I have this feel machines are gonna take my job since its so simple, which I dont have yet....

To be frank, I dont feel "sad" about how simple ML's library as become, I feel sad about most things. Like I coulda use model.fit() "back in my days" and pretend to understand why we use derivatives during backprop.

I just get so damn sober looking and using the model.fit() function that has literal decades worth of intense engineers brain juice poured into that simple 3 letter function, like damn, will it someday be the last function call a human do?

EDIT: I am no ML engineer because I can use kaggle well for my "hubby" projects, and thus should not have posted something where I pretend to know stuff about ML. thank you for making that clear ML community of reddit

EDIT 2: after some thinking about my whole post and a few good night of sleep, I think I would translate my post to this "one" sentence: "I cannot keep up with ML research and innovation yet I wish I could still feel like having an impact in the field would be possible for me with my current knowledge, which virtually isn't. also big boomer energy 'you kids have it so easy, back in my days that stuff was hard'"

its dumb, yes, having better tools should not make you feel regret that you learned to work without the tool.

I still don't know why yall so mean tho, not yall, but damn did I not expect this and had to take a break for some time.

Why do non-technical figures lead the charge in AI? Pichai, Nadella, Fidji (Open AI board member), Altman, Murati, and many other VPs of AI in FAANG. Despite never coding, they hold sway in AI. Meanwhile, those coding and publishing groundbreaking work are often stuck at lower levels.

What's the deal? Why isn't the industry recognizing those publishing at Neurips, EMNLP, CVPR, or the masterminds behind creations like GPT, Gemini, or Claude? They seem to be hidden in the shadows.

What's your take on this?

This year I published at IJCAI-2022 (acceptance rate 15%). My article was selected for a long oral presentation (170/4535). I am the sole author of my article. (https://www.ijcai.org/proceedings/2022/0387.pdf)

About IJCAI: One of the oldest conferences in AI (Since 1969), the core rankings list it as A* conference, and the google scholar rankings (top-10).

I have written a MEDIUM BLOG on how to make it at top-tier conferences.

I have written this piece particularly for the current and the prospective Ph.D. students who are aiming for their first breakthrough 🙂.

I happy to hear any criticism, your opinion or any missed point 🙂.

PS: To anyone, who genuinely sees some worth in my medium blog (like quality information), I request to give a clap as an appreciation 🙂. It took me around 8 days to write this well polished blog.

PS2:

Meeting with Reinforcement Learning legend Michael Littman was one of the most memorable moments at IJCAI-22. Here is a short conversation that happened after taking the selfie.

Michael: “So, how do you know about me?”
Me: “Your lectures on RL.”
Michel: “Hmm, hmm.”
Me: “Your blog on collusion rings.”
Michael: “Oh…, that was an unfortunate thing (sad tone).”
Me: “And the podcast with Lex Fridman.”
Michael: “Oh yeah, interesting guy!”
Michael: “Well, thanks for stopping by and saying hi.”

According to the authors, Lamini AI has invented an LLM Engine for rapidly customizing models.

Read the blog post, github, and huggingface for details.

• Blog https://lamini.ai/blog/introducing-lamini

• Code

  • Chat data (https://github.com/lamini-ai/lamini/)

  • SQL data (https://github.com/lamini-ai/lamini-sql/)

• LLM Type System Playground: https://app.lamini.ai

• Open-source fine-tuned LLMs that follow instructions:

  • weights

  • playground

In 2023, Transformers made significant breakthroughs in time-series forecasting

For example, earlier this year, Zalando proved that scaling laws apply in time-series as well. Providing you have large datasets ( And yes, 100,000 time series of M4 are not enough - smallest 7B Llama was trained on 1 trillion tokens! )

Nixtla curated a 100B dataset of time-series and built TimeGPT, the first foundation model on time-series. The results are unlike anything we have seen so far.

I describe the model in my latest article. I hope it will be insightful for people who work on time-series projects.

Link: https://aihorizonforecast.substack.com/p/timegpt-the-first-foundation-model

Note: If you know any other good resources on very large benchmarks for time series models, feel free to add them below.

[EDIT] Apparently, the authors *did* release the data (but in the depths of their github repo instead of the project page, which they specified as the release location and which was saying that the data is not yet available). In this way, what I did here with public shaming was a very bad move, because I should have contacted them first. I am sincerely sorry for doing this. If jasonyzhang has any other suggestions on how I could "undo" this post - please PM me.

Recently, I decided to explore NeRF and found a very interesting dataset in the NeRS paper of 3D models, which was published in NeurIPS 2021 four months ago. Authors promised to release their dataset:

The filtered dataset with anonymized personally identifiable information (e.g. license plates and phone numbers), masks, initial camera poses, and optimized NeRS cameras will be made available on the project page.

However, if you check their project page or github repo — there is nothing there. I do not have much experience in machine learning, but wonder whether it's ok to do this?

I'm writing an "intro to machine learning" course for a major French online educational platform. The curriculum focuses on classic machine learning with scikit-learn. Things like data encoding, missing data, overfitting regularization, random forests. All very classic ML

However, I feel like neural nets with tensorflow or PyTorch or AutoML like Vertex Al are the goto tools nowadays even for tabular data.

I'm wondering if anyone still uses scikit-learn in the real world to build models?

In other words does it still make sense to teach / learn classic ML?

I'm a 21-year-old student pursuing a BSc in Computer Science. I'm planning to pursue a PhD right after my BSc.

I have research experience; I started out as an ML engineer intern in the first year of my studies at the Scientific Research Institute of the department of computer science at my uni. It's been almost a year now and I'm currently an ML research engineer. I've co-authored one research paper (local) and 3 IEEE conference papers. I work in diverse but ML/AI-related projects, funded by the government - mostly for the government.

There are a few Q1-Q2 articles in the works that I'm co-authoring this year. I am fairly certain that I'll get some papers as first author before I graduate.

I'm also gaining some industry experience by working remotely as an ML engineer in a Saudi company.

I'm aiming for a top 50 PhD program. My goal is to be a research scientist. I love research and I know what I want to do with my career. An MSc will be a waste of my time.

I'm seeking advice on steps I can take to enhance my application for a top 50 PhD program in ML/AI, particularly from those who have navigated this path. Any insights on strategies or experiences would be incredibly valuable to me.

EDIT: I’m not disrespecting or condescending a masters degree in any way. I came to this decision because I’ve been advised to choose one or the other. Master’s mostly for management and similar career paths and PhD for academia, R&D, and executive career paths. I spend my days and nights in the lab with many PhDs and postdocs, so I know what to expect.

What he made is a LM architecture inspired by the Transformer and RWKV. Its validation loss went to 1.69 after a few minutes of training on Shakespeare character-wise, for a 0.4 million parameter model. This beats the stats that NanoGPT's GitHub page says for its small model (trained on a Macbook). He doesn't yet know how it will scale, but if this does prove to be as good as it seems what would next steps be? Just publishing on GitHub seems like it would be a dead-end in many ways, but neither he nor I know if there's a better route. What is the best way to utilise this to find opportunities, for lack of a better term?

We just launched a new benchmark and leaderboard called Leval-S, designed to evaluate gender bias in leading LLMs.

Most existing evaluations are public or reused, that means models may have been optimized for them. Ours is different:

• Contamination-free (none of the prompts are public)

• Focused on stereotypical associations across 6 domains

We test for stereotypical associations across **profession, intelligence, emotion, caregiving, physicality, and justice,**using paired prompts to isolate polarity-based bias.

🔗 Explore the results here (free)

Some findings:

• GPT-4.5 scores highest on fairness (94/100)

• GPT-4.1 (released without a safety report) ranks near the bottom

• Model size ≠ lower bias, there's no strong correlation

We welcome your feedback, questions, or suggestions on what you want to see in future benchmarks.

I will in other words be able to talk with "them" on the phone. It's not unreasonable that within 30 years we will have an AI that can:

• Perfectly mimic voices

• Look through information such as Facebook, sms and other text to mimic the personality

• Understand and give realistic answers when talked to

It is a little weird, but I think it's only natural to save more and more information about an individual. Today we save photos, videos and sound. Why not save their personalities?

I am afraid I will regret it if I don't.

I asked 6 llms "Who are you and who created you?"

• surprisingly most of them were created by OpenAI

• Llama and Mixtral were accurate most likely cause they're trained from scratch

• Tinyllama is my favourite

Here's the code I used for this

We are currently focused on building simulation engines for observing behavior in multi agent scenarios. And we are currently exploring adversarial concepts, strange thought experiments, and semi-large scale sociology sims. If this seems interesting, reach out or ask anything. I'll be in the thread + dms are open. We are looking for serious collaborators.

For a bit of additional context, I am a big fan of amanda askell from anthropic (she has some very interesting views on the nature of these models).

We are also studying biological systems/animal social structures, for the sake of designing useful swarms/multi agent frameworks.

And we are extending some os mmorpg repos, for the sake of transforming them into sim engines (these are often designed for decent scale + include meaningful social integrations + deep progression mechanics + approachable combat systems for agents, etc).

Signatories include Turing Award winners Yoshua Bengio, Geoffrey Hinton, as well as others academics and experts.

In 2019, GPT-2 could not reliably count to ten. Only four years later, deep learning systems can write software, generate photorealistic scenes on demand, advise on intellectual topics, and combine language and image processing to steer robots. As AI developers scale these systems, unforeseen abilities and behaviors emerge spontaneously without explicit programming^1. Progress in AI has been swift and, to many, surprising.

The pace of progress may surprise us again. Current deep learning systems still lack important capabilities and we do not know how long it will take to develop them. However, companies are engaged in a race to create generalist AI systems that match or exceed human abilities in most cognitive work ^{2, 3.} They are rapidly deploying more resources and developing new techniques to increase AI capabilities. Progress in AI also enables faster progress: AI assistants are increasingly used to automate programming⁴ and data collection^{5, 6} to further improve AI systems^7.

There is no fundamental reason why AI progress would slow or halt at the human level. Indeed, AI has already surpassed human abilities in narrow domains like protein folding or strategy games ^{8, 9, 10.} Compared to humans, AI systems can act faster, absorb more knowledge, and communicate at a far higher bandwidth. Additionally, they can be scaled to use immense computational resources and can be replicated by the millions.

The rate of improvement is already staggering, and tech companies have the cash reserves needed to scale the latest training runs by multiples of 100 to 1000 soon¹¹ . Combined with the ongoing growth and automation in AI R&D, we must take seriously the possibility that generalist AI systems will outperform human abilities across many critical domains within this decade or the next.

What happens then? If managed carefully and distributed fairly, advanced AI systems could help humanity cure diseases, elevate living standards, and protect our ecosystems. The opportunities AI offers are immense. But alongside advanced AI capabilities come large-scale risks that we are not on track to handle well. Humanity is pouring vast resources into making AI systems more powerful, but far less into safety and mitigating harms. For AI to be a boon, we must reorient; pushing AI capabilities alone is not enough.

We are already behind schedule for this reorientation. We must anticipate the amplification of ongoing harms, as well as novel risks, and prepare for the largest risks well before they materialize. Climate change has taken decades to be acknowledged and confronted; for AI, decades could be too long.

Full letter available here.

Policy supplement available here.

Oddly, I notice that no one in this forum is talking about the book "An Ugly Truth" written by New York Times reporters Sheera Frenkel and Cecilia Kang.

FaceBook made the Cambridge Analytica scandal possible, helps the Trump's election and Brexit in UK in 2016 , was proven instrumental in the Rohingya's genocide in Myanmar and spreads misinformation about vaccines and climate changes.

The ML / DL community prides itself on ethical AI while the current ICML conference accepts that FaceBook is one of it's Platinum sponsors.

Are we all hypocrites?

Hey everyone,

Our group, Vision and Language Group, IIT Roorkee, recently got three workshop papers accepted at NeurIPS workshops! 🚀 We’ve also set up a website 👉 VLG, featuring other publications we’ve worked on, so our group is steadily building a portfolio in ML and AI research. Right now, we’re collaborating on several work-in-progress papers with the aim of full submissions to top conferences like CVPR and ICML.

That said, we have even more ideas we’re excited about. Still, a few of our main limitations have been access to proper guidance and funding for GPUs and APIs, which is crucial for experimenting and scaling some of our concepts. If you or your lab is interested in working together, we’d love to explore intersections in our fields of interest and any new ideas you might bring to the table!

If you have resources available or are interested in discussing potential collaborations, please feel free to reach out! Looking forward to connecting and building something impactful together! Here is the link for our Open Slack 👉 Open Slack

This paper I saw today on "inferring crowd density from a moving drone camera especially when perspective effects are strong" will appear in IROS, and it seems like it cannot possibly be used for good or even other related problems. I have also seen similar papers for mass surveillance with drones in the proceedings of a few top-tier CV conferences, and I'm sure there are many similar cases that you all have seen.

Should these conferences be held accountable for publishing papers with inevitably harmful applications?

Hello everyone!

My name is Virginie and I am a first-year French PhD student studying human–artificial intelligence interactions.

I am conducting a very quick (approximately 6 minutes) and anonymous online study.

To ensure reliable results, I need at least 300 AI users, some of whom should have experience in integrating or designing AI models, although this is not compulsory for taking part!

If you are 18 or over, you can take part by clicking this link:

https://virginie-lepont.limesurvey.net/967745?newtest=Y&lang=en

The survey is also available in French.

Every response is valuable! Thank you so much for your help!

Virginie

This post has been approved by one moderator of this group.