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.
- Deep Learning Chip Market Expected to Reach $29,368.0 million, Globally, by 2025, at 39.9% CAGR
The global deep learning chip market was valued at $1,975 million in 2017, and is projected to reach $29,368.0 million by 2025, growing at a compound annual growth rate of 39.9% from 2018 to 2025.
- 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
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.
- Companies could be fined if they fail to explain decisions made by AI
Businesses and other organizations could face multimillion-pound fines if they are unable to explain decisions made by artificial intelligence, under plans put forward by the UK’s Information Commissioner’s Office.
- 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.
- AWS’ CodeGuru uses machine learning to automate code reviews
AWS announced CodeGuru, a new machine learning-based service that automates code reviews based on the data the company has gathered from doing code reviews internally. It supports GitHub and CodeCommit, for the time being.
- Facebook’s Head of AI Says the Field Will Soon ‘Hit the Wall’
Jerome Pesenti, Head of AI at Facebook shares insights into how Facebook plans on using AI and how he thinks AI will progress as a technology.
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
- Why you need to pay more attention to combatting AI bias
According to a DataRobot report, nearly half of tech pros are concerned about AI bias, yet many organizations still use untrustworthy AI systems.
- 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
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?
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.