AI Scholar Weekly - Issue #47

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AI Scholar Weekly - Issue #47
By Educate AI  • Issue #47 • View online
Fujitsu’s Technology Successfully Achieved the World’s Highest Accuracy Level in Behavior Recognition; Google AI Published over 800 Papers in 2020; Framework for Researching, Implementing, and Operating Data science Projects; Open Source Dataset for Research in High Precision Text Generation; The Best ML Courses and Certifications (and more).

Top AI Research Papers This Week
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Google AI Research: Looking Back at 2020, and Forward to 2021
In a recently published blog post, Google Research say they published more than 800 research publications in the past year.
In 2020, as the world has been reshaped by COVID-19, Google says they saw the ways research-developed technologies could help billions of people better communicate, understand the world, and get things done. Proud of what they’ve accomplished, and excited about new possibilities on the horizon, they have published long posts covering numerous categories including research work done in robotics, COVID-19, AutoML, Open Datasets, Reinforcement Learning, Natural Language Understanding, and many more.
The goal of Google Research is to work on long-term, ambitious problems across a wide range of important topics — from predicting the spread of COVID-19 to designing algorithms, to learning to translate more and more languages automatically, to mitigating bias in ML models.
Google is also dedicated to ensuring that research is done responsibly and has a positive impact, using their AI Principles as a guiding framework and applying particular scrutiny to topics that can have broad societal impacts. 
Check out their post for a more comprehensive look, there’s something for everyone!
A Python Library Providing Fast Interpretable Matching for Causal Inference
As human beings, we think in terms of cause and effect. When we understand why something happens, we can change our behavior and improve future outcomes. Causal inference enables AI and machine learning algorithms to reason in a similar way.
Understanding cause and effect in AI and ML systems can make them smarter and more efficient. The ability to understand causality can help us, ML engineers, to create models that better understand their data.
In this paper, researchers introduce dame-flame, a Python package providing two algorithms, DAME and FLAME, for fast and interpretable treatment-control matches of categorical data.
The dame-flame package offers efficient, easy-to-use implementations of the DAME and FLAME algorithms, allowing users to perform fast, and interpretable matching for causal inference for observational data with discrete covariates. The package is easily accessible and accompanied by detailed documentation, with concrete examples. The package is written in a highly modular manner, facilitating the introduction of new features and variations of the DAME and FLAME algorithms. 
The package provides several adjustable parameters to adapt the algorithms to specific applications, and can calculate treatment effects after matching. Descriptions of these parameters, details on estimating treatment effects, and further examples are available here. Access on GitHub 
A Proven Systems Engineering Approach for Machine Learning Development and Deployment
The development and deployment of machine learning (ML) systems can be executed easily with modern tools. However, but the process can get rushed and a lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences.
To address the challenge, this research unveils Machine Learning Technology Readiness Levels (MLTRL), an industry-hardened systems engineering framework for robust, reliable, and responsible machine learning. MLTRL is derived from the processes and testing standards of spacecraft development, yet lean and efficient for ML and software workflows.
Examples from several organizations across industries demonstrate the effectiveness of MLTRL for AI and ML technologies, from research and development through productization and deployment, in important domains such as healthcare and physics.
Moreover, MLTRL establishes a much-needed jargon for the AI ecosystem. In the future, researchers aim to provide open-source tools for more teams and organizations to adopt MLTRL. Their hope is that the framework is adopted broadly in AI and ML organizations, and that “technology readiness levels” become common nomenclature across AI stakeholders – from researchers and engineers to sales-people and executive decision-makers.
Researchers Show That Progress Can Be Made On Machine Ethics
The demand for ethical machine learning has led researchers to study and propose various ethical principles for applications.
Recently, a collaboration of researchers with Microsoft, UC Berkeley, UChicago, and Columbia University has introduced an ETHICS dataset to assess a machine learning system’s ability to predict necessary human ethical judgments in open-world settings.
The dataset is based on natural language scenarios, enabling engineers to construct diverse situations involving interpersonal relationships, everyday events, and thousands of objects. This means models must connect various facts about the world to their ethical consequences. For instance, taking a penny on the street is usually acceptable, whereas taking cash from a wallet lying on the street is not.
The new benchmark spans concepts in justice, well-being, duties, virtues, and commonsense morality.
Researchers found that with the ETHICS dataset, current language models have a promising but incomplete ability to predict basic human ethical judgments. Their work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values.
ETHICS dataset is available here.
A New Reinforcement Learning Approach for Providing Quality of Service
Providing guarantees on the end-to-end delay of a service system is of great interest to both customers and service providers.
Current state of the art methods do not directly consider the delay of the system in their problem formulation and therefore, cannot provide any delay guarantees.
This paper introduces an RL-based framework that takes a QoS constraint as input and provides a dynamic service-rate control algorithm that satisfies the constraint without overuse of the service resources. This makes our method distinct from the existing service-rate control algorithms that quantify their performance by the achieved overall reward, which is highly dependent on the reward definition and might have no practical interpretations.
The proposed method is capable of guaranteeing probabilistic upper-bounds on the end-to-end delay of the system, only using the queue length information of the network and without any knowledge of the system model. Since a general queueing model has been used in this study, our method can provide insights into various applications, such as VNF chain auto-scaling in the cloud computing context.
Other Great AI Papers
Google AI has released ToTTo, a dataset for research in high precision text generation. Specifically, ToTTo is a large, English table-to-text dataset that presents both a controlled generation task and a data annotation process based on iterative sentence revision. ToTTo can be helpful for tasks such as table understanding and sentence revision. The dataset and code are open-sourced on their Github Repo
The BERT model has arisen as a popular state-of-the-art machine learning model in recent years that is able to cope with multiple NLP tasks such as supervised text classification without human supervision. This interesting study introduces four different NLP scenarios where researchers show how BERT has outperformed the traditional NLP approach, adding empirical evidence of its superiority in average NLP problems with regard to classical methodologies. Read more 
Content analysis is a well-established social scientific methodology that can provide high-quality labels, especially for subjective features. Recently, social scientists have begun to investigate the applicability of crowdsourcing for generating quality labels for subjective features. The results in this paper demonstrate that it is possible to collect high-quality answers of subjective semantic features at a large scale via crowdsourcing. Read more
Is it possible to measure the effect of a recommender system under different types of user behavior? In this paper, Google AI propose a simulation framework that goes beyond one-step recommendation and incorporates the interaction between user preferences and system effects, to better understand a recommender system biases over time. Read more
Being able to accurately predict the future location and/or route of a vehicle has some obvious advantages for individual vehicles, e.g. avoiding traffic congestion, estimating travel time and electric range, and more. This research introduces a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. Read more
Top AI Resources
ForML is a framework for researching, implementing, and operating data science projects that’s naturally easy to reuse, extend, reproduce, or share and collaborate on. GitHub Link
Top 9 best machine learning courses and certifications. See here
ML Visuals is a new collaborative effort to help the machine learning community in improving science communication by providing free professional, compelling and adequate visuals and figures. Get Access
Top AI News
Fujitsu Laboratories Ltd. recently announced the development of a technology that utilizes deep learning to recognize the positions and connections of adjacent joints in complex movements or behavior in which multiple joints move in tandem. This technology successfully achieved the world’s highest accuracy against the world standard benchmark in the field of behavior recognition. Read story
Deep Learning outperforms standard Machine Learning in biomedical research applications, research shows
Forget coding, you can now solve your AI problems with Excel. Read more
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