Up to Speed on AI and Deep Learning: December 2 to December 9


In Nightfall’s Up to Speed on AI and Deep Learning, we summarize the latest news, research, technology, and applications of AI and Deep Learning. In this week's edition:

  • Learn about Nvidia’s big move into healthcare with a method to security apply machine learning in healthcare settings.

  • Read about research that seeks to improve IoT energy optimization while conforming devices to user preferences.

  • See how artificial intelligence & machine learning will combine to bring smart cities to life.

Read these stories and other timely AI and Deep Learning news below.


  • Kaolin: The first comprehensive library for 3-D deep learning research
    Kaolin, the PyTorch library, contains a variety of tools for constructing deep learning architectures that can analyze 3-D data, which are both efficient and easy to use. It also allows researchers to load, preprocess, and manipulate 3-D data before it is used to train deep learning algorithms.

  • AI is already disrupting sales, today
    (CIO Magazine)
    With the assistance of AI, sales managers can also focus on the bottom line to produce better results. Myriad new approaches and software tools for CRM bear this out.

Research and Tutorials 

  • Open Access Evaluation of colorectal cancer subtypes and cell lines using deep learning
    (Life Science Alliance)
    Molecular profiling techniques have been used to better understand the variability between tumors and disease models such as cell lines. The method used efficiently leverages multiomics data sets containing copy number alterations, gene expression, and point mutations and learns latent factors that describe data in lower dimensions.

  • Making Smart Homes Smarter: Optimizing Energy Consumption with Human in the Loop
    This paper presents a novel approach to accurately configure IoT devices while achieving the twin objectives of energy optimization along with conforming to user preferences. The study comprises of unsupervised clustering of devices' data to find the states of operation for each device, followed by probabilistically analyzing user behavior to determine their preferred states.

  • Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
    Explaining the output of a complex machine learning (ML) model often requires approximation using a simpler model. To construct interpretable explanations that are also consistent with the original ML model, counterfactual examples --- showing how the model's output changes with small perturbations to the input --- have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples.

  • A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
    In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. The research presents current problems and disturbance patterns for four different state of the art lidar systems. Moreover, the research investigates how tuning internal parameters can improve their performance in bad weather situations.

AI and ML in Society

  • Artificial intelligence & machine learning: The brain of a smart city
    See how a combination of artificial intelligence and machine learning can act as the brains of a smart city while simultaneously considering how a smart city experience can become more personalized without compromising the privacy of its residents.

  • How organizations can develop a pool of ‘machine learning masters’ from within
    How does an organization find the right talent to build artificial intelligence and machine learning-driven applications to steer into the future? The answer lies in getting the right blend of upskilling and reskilling of their existing talent pool.

  • How neural networks work—and why they’ve become a big business
    (Ars Technica)
    This feature offers a primer on neural networks. We'll explain what neural networks are, how they work, and where they came from. And we'll explore why—despite many decades of previous research—neural networks have only really come into their own since 2012.

  • Will AI liberate the IoT's potential?
    (Smart Industry)
    When deployed in tandem, artificial intelligence (AI) and the Internet of Things (IoT) can bring powerful new capabilities and competitive advantages—a net effect that is greater than the sum of its constituent parts.

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