Up to Speed on AI and Deep Learning: December 2 to December 9
Nvidia Moves Clara Healthcare AI To The Edge (The Next Platform) At the Radiological Society of North America (RSNA) conference last weekend, Nvidia unveiled its Clara Federated Learning (FL) technique that will enable organizations to leverage training while keeping the data housed within the healthcare facility systems.
Kaolin: The first comprehensive library for 3-D deep learning research (Techxplore) 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.
3 questions to ask before investing in machine learning for pop health (Healthcare IT News) Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions. But fundamental issues related to executive support, staff buy-in and patient risk stratification need to be understood and addressed before machine learning applications can help with population health goals.
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 (arXiv) 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 (arXiv) 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? (arXiv) 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.
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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Financial services businesses can use DLP to eliminate the risk of data exfiltration and boost their overall security strategy. Learn what the different types of PII are, what’s really at stake when this data is at risk, and how laws only do some of the work needed to keep data safe.
Maynard Webb, a Nightfall investor, tech veteran, and industry thought leader recently joined us for a discussion. We talk about how his early career shaped his values and perspective, what motivated him to write his New York Times bestseller, Rebooting Work, as well as how the coronavirus will reboot the tech industry.
It's impossible to understand cloud security without first understanding the shared responsibility model. First touted by AWS, the shared responsibility model is now a staple of many services and the best way of understanding on which parties specific security obigations lie.
Ryan Nece followed in his father Ronnie Lott’s footsteps to become a Super Bowl champion, philanthropist, and venture capital leader. As the co-founder and managing partner of Next Play Capital, he's investing in rising startups like Caliva, hims, and Nightfall. Ryan shares what it takes to achieve in Silicon Valley, why giving back is essential for personal and professional growth, and how he manages a demanding, multi-faceted career.
Stuart McClure, CEO and founder of Cylance as well a Nightfall Investor talks about his what sparked his interest in information security as well as how he built prominent security companies like Foundstone and Cylance, the latter of which was acquired by Blackberry for $1.4 billion last year. Stuart shared his thoughts on the importance of AI in cloud security as well as what it takes to innovate in the infosec space.