Daniel

Mental Health. Sciences. Liberal.

[Sheenah🌼 @DShenah]

I want someone without bias and abuses to explain to me like a child, what is wrong with being gay. And I don’t want “it’s not our culture” I want a genuine reason. Thank you 😊

[Daniel @rolex_guy]

@qataharraymond M23 laundry

[Daniel @rolex_guy]

@whiteheadcomm 💯

[Daniel @rolex_guy]

@akorising @TheMutaD You think they are not paying….

[Daniel @rolex_guy]

@SmilesTitus @charlesgava_256 Not wholly true. Ask how much of that money goes to airports and tax collectors. The answer is somewhere there.

[Daniel @rolex_guy]

@jamesonen Same way US army has chaplains in battle as they kill children and women

[Daniel @rolex_guy]

@Raycheal43 Wait….

[Daniel @rolex_guy]

@SamMugumya Nairobi. Be careful. Operatives all over!

[Daniel @rolex_guy]

@jamesonen You should read about the Science experiments on prisoners. Dissecting people alive and removing their organs for ‘science’, hacking prisoners and letting them bleed to time how long it takes to die etc. Japanese are too brutal!
Somehow that history was sanitized.

[Daniel @rolex_guy]

@KakandeAlex @TheMutaD Now go back 15 years

[TOKO @GodwinTOKO]

Today alone @MaleMabiriziHKK went to:

1. Civil Division of the High Court for the habeas corpus hearing,
2. To @Makerere Senate for the papers of Judge Ssekanna,
3. Then main building for Nawangwe’s papers,
4. To UNEB over some school in Seeta & their national results,
5. To @UCC_Official over Radio Simba,
6. Did an physical interview with Kasuku,
7. Finished at the Anti-Corruption court for a ruling involving @AnitahAmong.

Please send him money for the trip to Rwanda.

The Ngolo Kante of RULE OF LAW! 👑

[Daniel @rolex_guy]

@KakandeAlex Talk in a more stable currency like US$. 15 years ago the dollar was like 2k. Now it’s almost UGX 4k.

So if the dollar in 15 years is UGX 8k….how much actual value he actually have gained in $?

[Daniel @rolex_guy]

@Brianw256 @Herbert_MugK AI

[Daniel @rolex_guy]

@Stephen23179104 @emeka_ug @slinemotorsltd Uh. I have a story for you…

[David Challen @David_Challen]

A soldier, a nurse, a lorry driver, a builder, a baker… these are just some of the men accused of the rape and assault of Gisèle Pelicot. By labelling rapists as “monsters,” we actively distance them from ordinary men, ignoring the reality that sexual violence against women is a deliberate choice made by countless men who walk all parts of our society.

Such labels prevent us from confronting the truth this trial now holds up to the world: that many men walk freely in our communities, appearing trusted and safe, yet actively choose to commit acts of violence against women and girls…

Many of the men accused in this trial have attempted to normalise their acts of sexual violence by framing their behaviour as a misunderstanding of consent, and even a reaction to personal struggles. These excuses do not mitigate their actions. Some of the men conceded they ‘should have left’ when they saw Gisèle Pelicot was unconscious, whilst others have been more explicit in their views towards women earlier in life, “I had a hatred towards women.”

This normalisation by these men of their violent behaviours creates a disturbing disparity, minimising the seriousness of their crimes and shifting focus away from the harm of the victim. One of the men accused went as far to state he was ��was simply following her husband’s instructions,’ going as far to directly tell Gisèle Pelicot in court:

“It’s not me you should be angry with, it’s your husband,”

This is an abhorrent case. Yet, it is an incredible survivor, Gisèle Pelicot, who in choosing to waive her anonymity now holds these ‘ordinary’ men up to the world, to show the full extent of the crimes. To stare down that the men who committed these acts were characters who occupied the community in her life, even ones she unwittingly greeted in her everyday life:

“I saw him now and then in the bakery; I would say hello. I never thought he’d come and rape me,”
- Gisèle Pelicot

➡️ https://t.co/bJkQkU1voG

[The Nobel Prize @NobelPrize]

This year’s chemistry laureates Demis Hassabis and John Jumper have developed an AI model, AlphaFold2, to solve a 50-year-old problem: predicting proteins’ complex structures.

Check out two examples of protein structures determined using AlphaFold2. First up, a bacterial enzyme that causes antibiotic resistance. The structure is important for discovering ways of preventing antibiotic resistance.

Animation: ©Terezia Kovalova/The Royal Swedish Academy of Sciences

#NobelPrize

[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)

[Tom Silver @tomssilver]

I am hiring multiple PhD students this year to launch the Princeton Robot Planning and Learning lab:

https://t.co/iUMYvpBZ5S

Thanks for helping me spread the word!

[Guolan Lu @GuolanLu]

Please RT. I am currently hiring graduate students and postdoc fellows. Details can be found here: https://t.co/eU2Xk0g07u My lab is dedicated to developing and integrating imaging, AI, and spatial omics technologies to uncover mechanisms driving disease initiation, progression, and therapeutic response, with the ultimate goal of advancing next-generation theragnostics to cure diseases. We are excited to be equipped with cutting-edge spatial omics technologies, including Xenium spatial transcriptomics @10xGenomics and Phenocycler-fusion @AkoyaBio .

[Dr. Lauren Guillette (she/her) @laurenguillette]

I am accepting applications for fully-funded PhD & MSc students (Sept 2025 start)

International applicants welcome to apply!

Application open now, closes 01 Dec 2024.

Apply https://t.co/m30ZY9nNuz

My team https://t.co/mLAwMIBSaV

Eligibility https://t.co/EcLAOPHVuk