Zulqarnain Khan

Research Scientist at @Experiential_AI

[Zulqarnain Khan @zq90]

The "AI" deployment inside @turbotax is plain awful. The chatbot is useless and don't even get me started on how bad the software is at extracting tables from PDFs (which they now require instead of CSVs which used to work like a charm). Don't "AI" what's not broken.

[Zulqarnain Khan @zq90]

Hard to not feel proud of the role Pakistan is playing currently. May sanity and peace prevail today.

[Zulqarnain Khan @zq90]

Got the answer

https://t.co/6DYSH9uKbs

[Jürgen Schmidhuber @SchmidhuberAI]

The #NobelPrizeinPhysics2024 for Hopfield & Hinton rewards plagiarism and incorrect attribution in computer science. It's mostly about Amari's "Hopfield network" and the "Boltzmann Machine."

1. The Lenz-Ising recurrent architecture with neuron-like elements was published in 1925 [L20][I24][I25]. In 1972, Shun-Ichi Amari made it adaptive such that it could learn to associate input patterns with output patterns by changing its connection weights [AMH1]. However, Amari is only briefly cited in the "Scientific Background to the Nobel Prize in Physics 2024." Unfortunately, Amari's net was later called the "Hopfield network." Hopfield republished it 10 years later [AMH2], without citing Amari, not even in later papers.

2. The related Boltzmann Machine paper by Ackley, Hinton, and Sejnowski (1985) [BM] was about learning internal representations in hidden units of neural networks (NNs) [S20]. It didn't cite the first working algorithm for deep learning of internal representations by Ivakhnenko & Lapa (Ukraine, 1965)[DEEP1-2][HIN]. It didn't cite Amari's separate work (1967-68)[GD1-2] on learning internal representations in deep NNs end-to-end through stochastic gradient descent (SGD). Not even the later surveys by the authors [S20][DL3][DLP] nor the "Scientific Background to the Nobel Prize in Physics 2024" mention these origins of deep learning. ([BM] also did not cite relevant prior work by Sherrington & Kirkpatrick [SK75] & Glauber [G63].)

3. The Nobel Committee also lauds Hinton et al.'s 2006 method for layer-wise pretraining of deep NNs (2006) [UN4]. However, this work neither cited the original layer-wise training of deep NNs by Ivakhnenko & Lapa (1965)[DEEP1-2] nor the original work on unsupervised pretraining of deep NNs (1991) [UN0-1][DLP].

4. The "Popular information" says: “At the end of the 1960s, some discouraging theoretical results caused many researchers to suspect that these neural networks would never be of any real use." However, deep learning research was obviously alive and kicking in the 1960s-70s, especially outside of the Anglosphere [DEEP1-2][GD1-3][CNN1][DL1-2][DLP][DLH].

5. Many additional cases of plagiarism and incorrect attribution can be found in the following reference [DLP], which also contains the other references above. One can start with Sec. 3:

[DLP] J. Schmidhuber (2023). How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Swiss AI Lab IDSIA, 14 Dec 2023. https://t.co/Nz0fjc6kyx

See also the following reference [DLH] for a history of the field:

[DLH] J. Schmidhuber (2022). Annotated History of Modern AI and Deep Learning. Technical Report IDSIA-22-22, IDSIA, Lugano, Switzerland, 2022. Preprint arXiv:2212.11279. https://t.co/Ys0dw5hkF4 (This extends the 2015 award-winning survey https://t.co/7goTtI5Uwv)

[Zulqarnain Khan @zq90]

Surprising! But a win for the field none the less.

(also, wonder what schmidhuber thinks)

[The Nobel Prize @NobelPrize]

BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”

[The Olympic Games™ @Olympics]

Believe it, Nadeem.
You just became Pakistan's first ever athletics Olympic champion. 🇵🇰🥹

[Institute for Experiential AI at Northeastern @Experiential_AI]

Find out what's next at the intersection of #AI, #drugdiscovery and #networkscience in our FREE webinar with Director of AI + Life Sciences, @svscarpino, and @harvardmed and @BrighamWomens faculty, @menicgiulia on Friday, March 1.

Register ASAP: https://t.co/1gMsK9WksJ

[Institute for Experiential AI at Northeastern @Experiential_AI]

"Unless it’s done at the individual level, it’s really difficult to drive any kind of data-related transformation."

Thank you to @magazine_cdo for covering "Data Literacy at Scale," our two-day event with @harvard_data on #dataliteracy across industries! https://t.co/ZbHjK0384O

[Zulqarnain Khan @zq90]

We won't respond to your rebuttal.

We will just repeat our concerns word for word the next time you submit.

Rinse. Repeat.

[Zulqarnain Khan @zq90]

And then @byron_c_wallace said "regression is still pretty cool"

[Zulqarnain Khan @zq90]

Join me at @Experiential_AI's annual #business conference, Leading with #AI Responsibly, in Boston.

🗓️ Wednesday, October 18, 2023
⏰ 9:00 AM - 5:00 PM EDT
📍 @Northeastern University, East Village, Boston
💻 Or attend online

Register now: https://t.co/hhqkcTGrBH

#LeadWithAI23

[Northeastern Global News @NUGlobalNews]

To prepare the U.S. for the next #outbreak, the @CDCgov is giving @Northeastern $17.5 million to launch and lead an #infectiousdisease prediction center.
https://t.co/YmkTjpKpEE

[Dan Roy @roydanroy]

This just crossed my desk and seems quite interesting https://t.co/7i4Iw12zxN

[Institute for Experiential AI at Northeastern @Experiential_AI]

How did a game of cricket bring @zq90, a research scientist on our AI + Health team, into the world of #machinelearning?

Read more about Zulqarnain's work and how #AI can help #scientists discover what they don't know: https://t.co/D7rX2qYtKD

#AIhealth #health #science #ML

[Zulqarnain Khan @zq90]

Hi @emilymbender, read this piece of yours and had a Q:

Would this analogy imply that systems that are simultaneously trained on language+other data e.g. text + video (e.g. this https://t.co/4PML5dwJqv from Meta) might be able to access meaning?

https://t.co/5GozapZaku

[AI at Meta @AIatMeta]

Introducing ImageBind by Meta AI: the first AI model capable of binding data from six modalities at once. This breakthrough brings machines one step closer to the human ability to bind together information from many different senses.

More on this new open source work ⬇️

[Lisa Feldman Barrett @LFeldmanBarrett]

Kindness is good for your brain & body — and the brains & bodies of those around you. I'll share a list of the best novels I've read in which kindness plays a major role. They're complex, sometimes funny, and engaging stories that kept me thinking and left me optimistic. 1/7

[Institute for Experiential AI at Northeastern @Experiential_AI]

Have you registered to join us for lunch at @Northeastern on Wednesday, Nov. 16, 2022? @menicgiulia will discuss tackling the complexity of #foodsystems. Free to attend! Registration is required. https://t.co/L05ByhJWuA

#AISeminar #AIlecture #AIevent

[Zulqarnain Khan @zq90]

AI is no where near being able to "model out bias" or being an adjudicator of accuracy/truth.

[Mark Cuban @mcuban]

You might consider proactively offering your $8 verification program for free to those you deem the most accurate Notes contributors. Won't be easy to confirm accuracy. But if AI can try to model out bias, maybe it can model in accuracy ?

[Zulqarnain Khan @zq90]

I’m excited to share that I have joined Northeastern’s Institute for @Experiential_AI. Be sure to follow their social media to learn more about our faculty, events, news, and more as we deploy AI to create solutions in the real world.

[Institute for Experiential AI at Northeastern @Experiential_AI]

AI SEMINAR 1PM ET TODAY!!
Best-selling author @GaryMarcus presents "Toward a Foundation for AGI"👉 https://t.co/DEbtwZuSot
#AGI #AIseminar