The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior.

code: https://github.com/salesforce/ctrl

article: https://einstein.ai/presentations/ctrl.pdf

https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/

E.g., the new challenge from Lyft has a top prize of $12,000.

A model that outperforms their current implementation would presumably bring in a huge amount of value, so why are they and the other companies who sponsor these challenges so stingy on the payouts? https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles

CIHR Postdoctoral Research - Machine Learning / Health Informatics
Hello everyone,

There is an opportunity to support one application for a prospective postdoctoral fellow for a multi-disciplinary project tentatively titled “Development of an at-home multi-modal sensor system to detect social isolation, functional and cognitive decline among post discharge rehabilitation population”. The selected candidate will be co-supervised by Dr. Shehroz Khan and Dr. Charlene Chu. Dr. Khan is a Scientist at KITE, Toronto Rehabilitation Institute, University Health Network and Assistant Professor at Institute of Biomaterials and Biomedical Engineering, University of Toronto. Dr. Chu is an Assistant Professor at the Lawrence S. Bloomberg Faculty of Nursing at the University of Toronto, and an Affiliate Scientist at KITE-Toronto Rehab at the University Health Network.

The selected candidate for this project is expected to work on their research proposal with advice from Drs. Khan and Chu and take responsibility to fill in their Canadian Common CV and submit the application to the CIHR portal before the deadline. The candidates must first check their eligibility using the Researchnet webpage before applying (see below the link). The deadline to submit final CIHR application is 1st October’2019.

Stipend - Trainees with a PhD degree is $40,000 per annum.
Research Allowance - $5,000 per annum

Duration of Support:
The maximum duration of support, taking into account all federal funding held, will depend on the degree(s) held by the applicant. For holders of a PhD degree, or PhD and health professional degrees, the maximum period of support is three (3) years.

Qualifications:
The applicant must hold or be completing their PhD degree in Computer Science, Electrical & Communication or Biomedical Engineering. Prior experience in working in a clinical setting is an asset. Strong background in sensor technology, signal processing, statistics and machine learning is required for this role. This position requires strong programming skills, especially Android code development and Python.

Application
The interested candidates should send their application to [email protected] with the following information:
- The subject of the email should be "Postdoc CIHR"
- Two page CV + Publications List as one single PDF
Only selected candidates will be contacted for interviews.

Brief Description
CIHR’s Health Research Training Strategy aims to equip research trainees so that they emerge from their training as scientific, professional, or organizational leaders within and beyond the health research enterprise. Generating Research Leaders of tomorrow is a key objective for CIHR. Fellowships provide support for highly qualified applicants in all areas of health research at the post-PhD degree or post-health professional degree stages to add to their experience by engaging in health research either in Canada or abroad.

*** A PDF version of the advertisement with links is available here - https://individual.utoronto.ca/shehroz/files/CIHR-Fellowship-Advertisement.pdf ***

regards
Dr. Shehroz Khan
Scientist, TRI-UHN,
Asst. Professor, IBBME, U. of Toronto.

Post-doc position in Deep learning and NLP at EMORY School of Medicine (Atlanta, USA)
Department of Biomedical Informatics at Emory School of Medicine is searching for a postdoctoral scholar. The Laboratory is led by Dr. Imon Banerjee (website), who is also affiliated with the Departments of Radiology and Biomedical Informatics at Emory University. The lab focuses on cutting‐edge research at the intersection of imaging science and biomedical informatics, developing and applying AI methods to large amounts of medical data for biomedical discovery, precision medicine, and precision health (early detection and prediction of future disease).



The postdoctoral scholar will be working on two core research topics: (1) develop foundational AI methods for analyzing and extracting information from clinical texts; (2) develop clinical prediction models using multi-modal and longitudinal electronic medical records (EMR) data. The scholar will deploy and evaluate these methods as clinical applications to transform medical care.



Requirements:



Post-graduate degree (PhD or MD, completed or near completion) in biomedical data science, informatics, computer science, engineering, statistics, computational biology, or a related field, with a background or interest in imaging



· Experience in machine learning and AI, particularly in computer vision and image analysis



· Strong record of distinguished scholarly achievement



· Outstanding communication and presentation skills with fluency in spoken and written English

https://t.iss.one/ArtificialIntelligenceArticles

· Established record of distinguished scholarly achievement



Interested applicants should submit a Curriculum Vitae, a brief statement of research interests using this link: https://faculty-emory.icims.com/jobs/42390/job

Postdoc at Monash University (Melbourne) for probabilistic & deep learning
An exciting opportunity has opened up within the Faculty of Information Technology for an exceptional Research Fellow to conduct research into Machine Learning in collaboration with world-class academics; Professor Geoff Webb, Dr François Petitjean and Professor Wray Buntine, as part of a prestigious Discovery Project funded by the Australian Research Council. The aim is for the Fellow to take ownership of the project and progressively become a driver of our research team.

The aim of the project is to tackle what we call target-agnostic learning: How can we create machine learning models that are able to predict the value of any variable in the dataset?

The job advertisement is here:
https://careers.pageuppeople.com/513/cw/en/job/592138/research-fellow-senior-research-fellow

Basic details:
Melbourne is rated as one of the world's most livable cities.
Monash's ML group is very strong with excellent international funding and world reknown researchers.
Exciting innovative research with probabilistic and/or deep learning approaches.
For questions or applications, follow the link.

Multiple positions in ML/causality/deep learning for biomed in Bonn, Germany

Over the coming months, we will have multiple openings for ML/causality/deep learning projects at the DZNE in Bonn, Germany. We work on both methodology per se (recent papers in JMLR, JRSSB etc) and on cutting-edge applications in biomedicine. High-dimensional data and causality are relevant themes, but we welcome enquiries from candidates with a strong ML or statistical background, regardless of specific focus. The DZNE is a flagship federal institute offering outstanding conditions for research. Bonn is a beautiful city on the banks of the Rhine, offering a high quality of life and a fantastic location in the heart of Europe.

Salaries for postdoc-level positions would typically be roughly EUR50k per year (depending on the candidate). Positions are typically for 2-3 years, but in some cases can be extended for longer periods.

If interested, please email me with a CV and list of publications (as a single pdf please) asap.

Sach.

Data Engineer
MongoDB is growing rapidly and seeking a Data Engineer to be a key contributor to the overall internal data platform at MongoDB. You will build data driven solutions to help drive MongoDBs growth as a product and as a company. You will take on complex data-related problems using very diverse data sets.

Who?

You have experience with:

several programming languages (Python, Scala, Java, etc..)
data processing frameworks like Spark
streaming data processing frameworks like Kafka, KSQ, and Spark Streaming
a diverse set of databases like MongoDB, Cassandra, Redshift, Postgres, etc.
different storage format like Parquet, Avro, Arrow, and JSON
AWS services such as EMR, Lambda, S3, Athena, Glue, IAM, RDS, etc.
orchestration tools such as Airflow, Luiji, Azkaban, Cask, etc.
Git and Github
CI/CD Pipelines
Also

Enjoy wrangling huge amounts of data and exploring new data sets
Value code simplicity and performance
Obsess over data: everything needs to be accounted for and be thoroughly tested
Plan effective data storage, security, sharing and publishing within the organization
Are constantly thinking of ways to squeeze better performance out of the pipelines
Bonus Points

You are deeply familiar with Spark and/or Hive
You have expert experience with Airflow
Understand the differences between different storage format like Parquet, Avro, Arrow, and JSON
Understand the tradeoffs between different schema designs like normalization vs denormalization
In addition to data pipelines, you’re also quite good with Kubernetes, Drone, and Terraform
You’ve built end to end production grade data solutions that run on AWS
Have experience building ML pipelines using tools likeSparkML, Tensorflow, Scikit-Learn, etc.
What?

As a Data Engineer, you will:

Build large-scale batch and real-time data pipelines with data processing frameworks like Spark on AWS
Help drive best practices in continuous integration and delivery
Help drive optimization, testing and tooling to improve data quality
Collaborate with other software engineers, ML experts and stakeholders, taking learning and leadership opportunities that will arise every single day

https://ai-jobs.net/job/data-engineer-40/

Software Engineer – Machine Learning (Search Engine)
Twitter Search is the search engine for Twitter: it’s the place to find the most relevant and engaging content for any topic or interest. We build products on top of a super realtime pipeline that processes nearly one trillion tweets from the whole of Twitter’s history, organizes the world’s conversation as it happens, and personalizes it to each individual user’s needs and context We connect users to the most relevant people and conversations around their interests. We need your help building this exciting product!



Twitter Search is responsible for producing content timelines for keywords, trends, hashtags, topics, realtime events, and even places and emojis. We are not only surfacing tweets, but also users, images, videos, as well as live events. What’s more, we provide features like spelling correction and query suggestions as you type, bringing you even closer to what you need. Twitter Search is also a powerful generic information retrieval system that drives many other products and internal applications at Twitter.



Who We Are

We are a distributed and collaborative team building the real-time Twitter search engine and working across areas such as machine learning, applied data science, recommendation systems, information retrieval systems, natural language processing, large graph analysis, anti-spam and anti-abuse. We put these skills to use finding, personalizing and organizing relevant content for users. We create algorithms by solving a wide range of problems in IR, NLP, and ML with the goal of understanding users’ intent through query and context, ranking and organizing content, and extracting insights to make suggestions for better navigation of content on Twitter.



What You Will Do

You will participate in the engineering life-cycle at Twitter, designing and implementing components, pipelines and algorithms related to machine learning.
Collect, clean up, analyze production and user data to draw insights and produce ideas, working alongside the data scientist in the team.
Collaborating in an engineering team, conducting code reviews and design reviews.
Writing code and tests for production services, offline jobs, and internal tools
Conducting offline and online experiments.
Deploying and maintaining production services, participating in on-call rotations.
Collaborating across teams, working alongside our platform engineers and SREs.


Who You Are

You have a passion for machine learning and improving the ways people communicate and get informed about the world, live. You would like to solve problems in machine learning, information retrieval, text understanding, recommendation, user behavior understanding, and more. You have experience dealing with large data sets in a distributed environment. Also it would be great if you



Have a good grasp of CS fundamentals, data structure, common algorithms.
Comfortable working with at least one OOP or functional language and one interpretive/script language, experience with Java, Scala, and Python a plus.
Have knowledge in one or more of the following fields: machine-learning, information retrieval, recommendation systems, NLP
Have knowledge of distributed systems and parallel computing.
A plus to have experience in collaborating across multiple teams including analytics, product management, and operations.

https://ai-jobs.net/job/software-engineer-machine-learning-search-engine-2/

Learning Symbolic Physics with Graph Networks

Cranmer et al.: https://arxiv.org/abs/1909.05862

#MachineLearning #GraphNetworks #Physics

Chinese Face Recognition vending machine.

No cash, card, or phone needed.

Read: https://www.businessinsider.com/a-tiktok-from-china-shows-facial-recognition-equipped-vending-machine-2019-8

Supervised Machine Learning Lecture Notes - Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön

Download: https://www.it.uu.se/edu/course/homepage/sml/literature/lecture_notes.pdf

Find The Most Updated and Free Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, R Programming Resources.: https://www.marktechpost.com/free-resources/

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan : https://arxiv.org/abs/1909.04711
#GenerativeAdversarialNetworks #MachineLearning #Physics

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Yu et al.: https://arxiv.org/abs/1909.05378
#ArtificialIntelligence #Database #MachineLearning

What do Deep Networks Like to Read?
Jonas Pfeiffer, Aishwarya Kamath, Iryna Gurevych, Sebastian Ruder : https://arxiv.org/abs/1909.04547
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Perceptual Image Anomaly Detection. https://arxiv.org/abs/1909.05904


@ArtificialIntelligenceArticles

The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
Elena Voita, Rico Sennrich, Ivan Titov
Blog: https://lena-voita.github.io/posts/emnlp19_evolution.html
Paper: https://arxiv.org/abs/1909.01380
#ArtificialIntelligence #MachineLearning #Transformers

Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning. https://arxiv.org/abs/1909.05477

Evolution of Representations in the Transformer

The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives
https://lena-voita.github.io/posts/emnlp19_evolution.html

paper https://arxiv.org/pdf/1909.01380.pdf

Couldn't make it to our Pie & AI meetup in Medellín? Watch the full video of Andrew Ng and Helmuth Trefftz's conversation to learn why we believe in Latin America as a new global AI hub:

https://youtu.be/wlQvPJHxfOE