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Career Profile: Andrew E. Brereton - Computational Scientist

#artificialintelligence

I was born/grew up in: I was born in Nova Scotia, but grew up in Parry Sound, Ontario. I now live in: I now live in Barrie, Ontario, and work remotely for a company headquartered in Toronto. I work now at a company called Cyclica. We are a biotechnology company that uses Artificial Intelligence (AI) to help make medicines that are more effective for patients. I do research and develop methods for computational drug design.


Novel Systems Machine Learning Engineer

#artificialintelligence

STR's Analytics division researches and develops advanced analytics and machine learning-based solutions to solve challenging problems related to national security. Our team consists of passionate and motivated engineers with advanced degrees in engineering, computer science, mathematics, and data sciences, who are seeking opportunities to use their deep technical knowledge and creativity to tackle some of the hardest problems that our customers face. Our projects span multiple different data modalities and incorporate advanced algorithms, deep learning, and statistical techniques to uncover patterns in social media, structured and unstructured text, time series, geospatial, and imagery data, and must operate under challenging constraints not typically found in the commercial world. The tools and technologies we develop have real world impact and are used by analysts to extract and enrich intelligence information around the globe. In the Machine Learning Engineer – Algorithms Lead role, you will lead teams that develop and evaluate statistical and machine learning algorithms to uncover hidden information and patterns from a diverse collection of massive datasets.


Why business is booming for military AI startups

MIT Technology Review

Militaries are responding to the call. NATO announced on June 30 that it is creating a $1 billion innovation fund that will invest in early-stage startups and venture capital funds developing "priority" technologies such as artificial intelligence, big-data processing, and automation. Since the war started, the UK has launched a new AI strategy specifically for defense, and the Germans have earmarked just under half a billion for research and artificial intelligence within a $100 billion cash injection to the military. "War is a catalyst for change," says Kenneth Payne, who leads defense studies research at King's College London and is the author of the book I, Warbot: The Dawn of Artificially Intelligent Conflict. The war in Ukraine has added urgency to the drive to push more AI tools onto the battlefield.


Precision, Accuracy, Scale – And Experience – All Matter With AI

#artificialintelligence

When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. But more than anything else, particularly at the beginning of any data processing revolution, it is experience that matters most. Whether to gain it or buy it. With AI being such a hot commodity, many companies that want to figure out how to weave machine learning into their applications are going to have to buy their experience first and cultivate expertise later. This realization is what caused Christopher Ré, an associate professor of computer science at Stanford University and a member of its Stanford AI Lab, Kunle Olukotun, a professor of electrical engineer at Stanford, and Rodrigo Liang, a chip designer who worked at Hewlett-Packard, Sun Microsystems, and Oracle, to co-found SambaNova Systems, one of a handful of AI startups trying to sell complete platforms to customers looking to add AI to their application mix. The company has raised an enormous $1.1 billion in four rounds of venture funding since its founding in 2017, and counts Google Ventures, Intel Capital, BlackRock, Walden International, SoftBank, and others as backers as it attempts to commercialize its DataScale platform and, more importantly, its Dataflow subscription service, which rolls it all up and puts a monthly fee on the stack and the expertise to help use it. SambaNova's customers have been pretty quiet, but Lawrence Livermore National Laboratory and Argonne National Laboratory have installed DataScale platforms and are figuring out how to integrate its AI capabilities into the simulation and modeling applications. Timothy Prickett Morgan: I know we have talked many times before during the rise of the "Niagara" T series of many-threaded Sparc processors, and I had to remind myself of that because I am a dataflow engine, not a storage device, after writing so many stories over more than three decades. I thought it was time to have a chat about what SambaNova is seeing out there in the market, but I didn't immediately make the connection that it was you.


25 AI Insurance Companies You Should Know

#artificialintelligence

The insurance industry has always dealt in data, but it hasn't always been able to put that data to optimal use. With the rise of artificial intelligence, which analyzes and learns from massive sets of digital information culled from public and private sources, insurers are embracing the technology's many facets -- from machine learning and natural language processing to robotic process automation and audio/video analysis -- to provide better products. Customers, too, are benefitting from practices like comparative shopping, quick claims processing, around-the-clock service and improved decision management. To get a better sense of how AI impacts the insurance industry, check out these 25 AI insurance applications. Liberty Mutual explores AI through its initiative Solaria Labs, which experiments in areas like computer vision and natural language processing. Auto Damage Estimator is one result of these efforts.


Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks

#artificialintelligence

The study population were patients with dilated cardiomyopathy, in which an explainable pre-trained deep neural network (FactorECG) was trained for the outcome of life-threatening ventricular arrhythmias. This network encoded the median beat ECG into 21 factors to generate an ECG using only these factors, allowing to evaluate most characteristics that make up an ECG automatically, in a relatively small dataset. LVAD, left ventricular assist device.


Future of weather forecasting using IoT sensors and machine learning - Enterprise Podcast Network - EPN

#artificialintelligence

Carlos Gaitan, the CEO and Co-founder of Benchmark Labs a leading provider of AI & IoT-driven weather forecasting solutions for the agriculture, energy, and insurance sectors joins Enterprise Radio. Dr. Gaitan is the Co-founder and CEO of Benchmark Labs. He did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is an elected member of the American Meteorological Society's (AMS) Artificial Intelligence Committee.


Sparse Weight Activation Training- Reduce memory and training time in Machine Learning

#artificialintelligence

A little bit ago, I covered Google AI's pathways architecture, calling it a revolution in Machine Learning. One of the standouts in Google's novel approach was the implementation of sparse activation in their training architecture. I liked this idea so much that I decided to explore this in a lot more depth. That's where I came across Sparse Weight Activation Training (SWAT), by some researchers at the Department of Electrical And Computer Engineering, University of British Columbia. And the paper definitely has me excited.


The Download: Tweaking AI for energy efficiency, and China's leaked data

MIT Technology Review

What's the news?: Deep learning is behind machine learning's most high-profile successes. But this incredible performance comes at a cost: training deep-learning models requires huge amounts of energy. Now, new research shows how scientists who use cloud platforms to train algorithms can dramatically reduce the energy they use, and therefore the emissions they create. How can they do it?: Simple changes to cloud settings are the key. Researchers created a tool that measures the electricity usage of any machine-learning program that runs on Azure, Microsoft's cloud service, during every phase of their project.


Understanding Multilevel Models(Artficial Intelligence)

#artificialintelligence

Abstract: Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. However, in all but the simplest of cases, direct computation of these quantities is impossible. Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform in practice.