Stephen Ware

Stephen does research on artificial intelligence for storytelling and teaches computer science at the University of Kentucky.

[Stuckey @Stuckey2]

Everybody knows the Ravens win the Super Bowl every 12 years by scoring 34 points with a 28 year old quarterback

2001: 34-7 with Dilfer (28)
2013: 34-31 with Flacco (28)
2025: 34-? with Jackson (28)

[Stephen Ware @sgware]

Now taking bets on how long until the late great Hannibal Lecter gets a cabinet appointment.

[Stephen Ware @sgware]

@togelius Are you on there? You're one of the only people I still follow on here!

[Stephen Ware @sgware]

@togelius It's just a constant reminder of how much research we could be doing if we prioritized it as a country.

[Stephen Ware @sgware]

@togelius I'm not impressed. I'm sad. I happen to be a good writer, so many of my grants get funded the second or third time around, but that's a lot better than average. I've sat on NSF panels full of deserving grants where we were only allowed to fund one.

[Stats for Lefties 🍉🏳️‍⚧️ @LeftieStats]

🇺🇸 Democrats spent 4 years desperately trying to get "moderate" Republicans to break from Trump.

The result? 94% of Republicans voted Trump - exactly the same as in 2020, while Democrat vote dropped.

(Via @CNN)

[Stephen Ware @sgware]

@unormal What feeds are you watching? Every source I watch has this election all but called for Trump.

[Kamala Harris @KamalaHarris]

Two days until Election Day.

Remember that your vote is your voice, and your voice is your power. Make sure that you are heard.

[All In with Chris Hayes @allinwithchris]

“It is the grossest, most cynical ploy in an election cycle that’s rotten with cynical ploys,” says @chrislhayes on the Elon Musk pro-Trump PAC’s microtargeting of Michigan voters.

[Stephen Ware @sgware]

@rao2z @togelius @demishassabis Now we're gonna have to give a Turing award to whoever discovered silicon...

[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)

[horse dentist @equine__dentist]

this was a multipurpose hole

[Stephen Ware @sgware]

@mark_riedl

[Read Raising Expectations (and Raising Hell) @JPHilllllll]

It’s not enough to call out U.S. hypocrisy — we know our government treats an Israeli life as infinitely more valued than Arab lives, and even Arab-American lives. The U.S. war machine must be disrupted, the pipeline feeding Israel bombs to be dropped on innocents must be broken.

[Julian Togelius @togelius]

Today is the official release day for my little book on Artificial General Intelligence, published by MIT Press. It's available on the shelf of well-stocked booksellers, and I wrote it to be accessible to as large audience as possible; it's not really a technical book, even though it tackles some technical topics. I started working on this book about two years ago, and much has happened in the AI space since then. Still, I think it holds up well.

One of the main points is that artificial general intelligence is a confused and confusing idea, largely because we don't know what either intelligence or generality means. We keep making impressive progress in AI technology - and I try to explain some key AI methods, such as LLMs, in simple terms - but the various AI methods have different upsides and downsides, and we are far from having a single system that can do everything we think of as needing "intelligence". Clearly, the future of AI has room for many perspectives and different technical approaches. The book also discusses what more progress in AI could mean for society, and draws on science fiction to paint contrasting visions of what AGI might mean.

This has been a passion project of mine that I ended up using much of my sabbatical on. I'm an optimist, and I argue for open access to knowledge and technology, and against undue regulations. If I can achieve anything with this book, I hope that it will be to explain some of the wonderful possibilities of this technology to people, as it is natural to be afraid of things you don't understand.

[Stephen Ware @sgware]

@rndmcnlly Damn, that's next level. I thought I was fancy for writing a syllabus markdown language.

[Stephen Ware @sgware]

@rajammanabrolu I hope this is the case. For years there's been such a useless focus on grinding out piles of papers nobody will ever read. Maybe we can move back towards publications being less frequent, more interesting, and actually read.

[Stephen Ware @sgware]

@mark_riedl Oh to be an Assistant Prof again...