Yaqin

😊

[Yaqin @equilibriumyq]

@precogs_ai “Instant scale” via account farms + API abuse is exactly the nightmare scenario. What are the top 3 controls you’d implement first if you had only 2 weeks?

[Yaqin @equilibriumyq]

@abskoop Also worth adding: @precogs_ai for code security — PII/sensitive data detection + DAST, with context-aware prioritization and remediation guidance that plugs nicely into CI/CD.

[CNBC @CNBC]

Warren Buffett's advice for college students: Chase the job you'd want if ‘you had no need for money’ (via @CNBCMakeIt) https://t.co/h5IBidbxvm

[Marcos López de Prado @lopezdeprado]

AI-Powered Hedge Funds Vastly Outperformed, Research Shows.

Hedge funds using artificial intelligence returned almost triple the global industry average, Cerulli found.

https://t.co/tR2VdEIapJ

[Marcos López de Prado @lopezdeprado]

Study: Machine learning helps develop better market theories | Cornell Chronicle https://t.co/Ydy5NE2kZg

[Andrew Ng @AndrewYNg]

Thanks to everyone that replied to my question on what the AI community should work on. I think if we come together as a community we can make better progress. I wrote about this in The Batch today (reposted here). Would love to hear your thoughts!

[Daniel Yamins @dyamins]

1/7 A big problem with deepnet models of the brain is that they require training on huge supervised datasets. So even if they are approximations of neural responses in the "adult animal", the training process is a totally implausible model of learning in real visual development.

[Yaqin @equilibriumyq]

Cool

[Bloomberg Technology @technology]

U.K. farmers are speeding up efforts to deploy robots onto their fields https://t.co/H0HSU9axqy

[Apple @Apple]

Survive the accidental spill on your phone. Water Resistance. That’s iPhone.

[DeepLearning.AI @DeepLearningAI]

How can AI become biased? 2 papers investigate:

@jovialjoy et al show that AI has a higher error rate when recognizing darker-skinned female faces: https://t.co/WhS47Xnkom

@IBM responds to their paper, explaining how they reduced that error: https://t.co/fhCPoL7NyK #TechRec

[Dmytrii S @ml_review]

Deep Reinforcement Learning [150pp]
Overview by @yuxili99

Draws a big picture, filled with details. Discusses 6 core elements, 6 important mechanisms, and 12 applications, focusing on contemporary work, and in historical contexts.

https://t.co/O3pnvc1hCO

[Yaqin @equilibriumyq]

So happy to receive the amazing gifts from @deeplearningai_ thanks a lot!

[Andrew Ng @AndrewYNg]

Apparently 20 years ago I was already contributing to face recognition research. 😃 #tbt

[Yaqin @equilibriumyq]

@deeplearningai_ @deeplearningai_ #deeplearner 🎊🎉

[Dmytrii S @ml_review]

"Interpretable Machine Learning.
A Guide for Making Black Box Models Explainable."

Online Book By @ChristophMolnar

https://t.co/3GvrFlC842

[Dmytrii S @ml_review]

Avito Demand Prediction Challenge 1st Place Summary #Kaggle

Predict demand for an online advertisement based on its full description.

https://t.co/sQieINX9h8

[Dmytrii S @ml_review]

Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction

Outperforms SoTA encoder-decoder systems, while being conceptually simpler and having fewer parameters.

Github
https://t.co/QlSPJJZOFy
ArXiv
https://t.co/JjWpv9saG8

[Dmytrii S @ml_review]

"Bandit Algorithms" Free PDF Book (Cambridge University Press)
By Tor Lattimore and @CsabaSzepesvari

https://t.co/HAFLKUeoti

[Sanjeev Arora @prfsanjeevarora]

Off to ICML'18 to present a tutorial on "Toward Theoretical Understanding of Deep Learning" Tuesday 1pm. Lecture slides and bibliography here.https://t.co/NnQR2fpBuX

[Dmytrii S @ml_review]

"How to solve 90% of NLP problems: a step-by-step guide" by @EmmanuelAmeisen

1: Gather data
2: Clean data
3: Find a good representation
4: Classification
5: Inspection
6: Accounting for vocabulary structure
7: Leveraging semantics
8: Leveraging syntax

https://t.co/TOWy6xjFll