Pre-Debate Material

“Yoshua Bengio, Revered Architect of AI, Has Some Ideas About What to Build Next”

The Turing Award winner wants AI systems that can reason, plan, and imagine

https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/yoshua-bengio-revered-architect-of-ai-has-some-ideas-about-what-to-build-next

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Machine Learning in a company is 10% Data Science & 90% other challenges It's VERY hard. Everything in this guide is ON POINT, and it's stuff you won't learn in an ML book "Best Practices of ML Engineering" This is a lifesaver.
project:
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

Very interesting use of #AI to tackle bias in the written text by substituting words automatically to more neutral wording. However, one must also consider the challenges and ramifications such technology could mean to the written language as it can not only accidentally change the meaning of what was written, it can also change the tone and expression of the author and neutralize the point-of-view and remove emotion from language.
#NLP
https://arxiv.org/pdf/1911.09709.pdf

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Named Entity Recognition Benchmark: spaCy, Flair, m-BERT and camemBERT on anonymizing French commercial legal cases

http://bit.ly/2rq1I5H
#DataScience #MachineLearning #ArtificialIntelligence #NLP

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"If the future can be different from the past and you don't have deep understanding, you should not rely on AI." - a rule from Ray Dalio for when to leverage machine learning for decision-making.

Full conversation:

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Evolutionary Powell's method is a discrete optimization algorithm I've found useful for hyperparameter tuning.

It makes weaker assumptions than Bayesian methods (and so is more robust), but stronger than random exploration (and so has better performance). It fills in the gap between then a bit.

Here's the full post on how Evolutionary Powell's method works:

We develop it as part of End-to-End Machine Learning Course 314:

The open source Ponderosa optimization package where it lives:

The line-by-line code walkthrough:

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Usa Metabase


https://github.com/g-adamante/metabase_csv_export

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Deep Speech, a good #Persian podcsts about #AI

We will talk about #ArtificialIntelligence, #MachineLearning and #DeepLearning news.

https://castbox.fm/channel/Deep-Speech-id2420707?country=us

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Machine Learning Tutorial Suite - 90+ Free Tutorials

https://data-flair.training/blogs/machine-learning-tutorials-home/

NASA: Neural Articulated Shape Approximation.

Timothy Jeruzalski, Boyang Deng, Mohammad Norouzi, JP Lewis, Geoffrey Hinton, and Andrea Tagliasacchi

arxiv.org/abs/1912.03207

What is My Data Worth? – The Berkeley Artificial Intelligence

https://bair.berkeley.edu/blog/2019/12/16/data-worth/

The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Deviance Information Criterion (DIC) are perhaps the most widely-used information criteria (IC) in model building and selection. A fourth, Minimum Description Length (MDL), is closely related to the BIC. In a nutshell, they provide guidance as which alternative model provides the most "bang for buck," i.e., the best fit after penalizing for model complexity. Penalizing for complexity is important since, given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice. In line with Occam's razor, complex models sometimes perform poorly on data not used in the model building. There are several others, including AIC3, SABIC, and CAIC, and no clear consensus among authorities as far as I am aware as to which is "best" overall. IC will not necessarily agree on which model should be chosen. Cross-validation, Predicted Residual Error Sum of Squares (PRESS) statistic, a kind of cross-validation, and Mallows’ Cp are also used instead of IC. Information criteria are covered in varying levels in detail in most statistics textbooks and are the subject of numerous academic papers. I know of no single go-to source on this topic.

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best to summarize the key trends I saw at NeurIPS 2019. The themes covered are:

1. Deconstructing the deep learning black box: many papers aim to understand the theories behind deep learning including convergence and generalization. It also analyzes exciting and contentious approach is neural tangent kernel.

2. New approaches: Bayesian deep learning & uncertainty estimation, graph neural networks, and convex optimization.
3. Neuroscience in machine learning: consciousness and attention And more.

https://huyenchip.com/2019/12/18/key-trends-neurips-2019.html

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Best of arXiv.org for #AI, #MachineLearning, and #DeepLearning – November 2019

https://bit.ly/36OWsaD

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Pandora/SiriusXM is currently looking for Summer 2020 interns on our Radio & Music Informatics team. We're looking for candidates with a strong background in recommender systems, machine learning, music generation, or natural language processing.

If you're looking to work on Audio Entertainment (including Music, Podcasts, News, etc) at scale we might have just what you're looking for. Our service spins music to over 100 million monthly active listeners and we have collected over 90 billion thumbs. On the content-based side we're working with millions of songs labeled by expert musicologists on up to 450 musical characteristics, and access to dozens of millions of music tracks from our catalog.

Selected candidates will be working with members of our research team, led by Òscar Celma. Interns are well compensated and our office is located in Oakland, California in the beautiful San Francisco Bay Area.

You can find more about our internship program and apply here:
http://www.pandora.com/careers/roadcrew
https://recruiting.adp.com/srccar/public/nghome.guid?c=1147611&d=ExternalCareerSite&prc=RMPOD3&r=5000541409506

Requirements:
- PhD candidate in CS/EE/Statistics/Math or other technical field

Pluses:
- Strong publication record in machine learning, recommender systems, natural language processing, and/or music information retrieval
- Ability to prototype and develop custom algorithms
- Knowledge of recommender systems
- Proficiency in Python/Scala/Java
- Experience in Spark/Hadoop/GCP

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I am forming a new research group in Turkey and have multiple open positions for PhD students (you can keep living in Istanbul or in Ankara, Turkey and can work with me).

Students will receive TUBITAK’s scholarship to work on projects funded by TUBITAK and / or by EU. TUBITAK scholarship pays 4,500 TL per month which is equivalent to an amount around $800. Depending on the students’ performance, the amount that the students receive may increase over time. Also keep in mind that living costs for students in Turkey are cheaper than many big US cities and many other European cities.

I am looking for students with background in:

·      machine learning algorithm design and /or deep learning experience,

·      Python and C/C++ experience in programming,

·      Experience or interest in autonomous systems.

You should hold a M.Sc. (or an equivalent) degree and be excited to work on autonomous vehicles at various levels.

Where: Students can get into the PhD program at Istanbul Technical University at Istanbul Turkey or at Bilkent University at Ankara, Turkey. (You can work with me, if you get accepted into the PhD program at either university). Each institution has its own PhD acceptance requirement and interested students are encouraged to check those requirements.

Contact: If you are interested, then you are encouraged to send a CV and a brief statement listing your interests and relevant experiences. If you have published papers or a Github page, please include them in your email as well.

My web: http://cs.bilkent.edu.tr/~sedat/

Sedat Ozer, Ph.D.,
www.SedatOzer.com

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Top 14 Machine Learning Research Papers Of 2019


https://bit.ly/2ExLslR

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XGBoost: An Intuitive Explanation
Ashutosh Nayak :
https://towardsdatascience.com/xgboost-an-intuitive-explanation-88eb32a48eff
#MachineLearning #DataScience