Collaborating Authors

Australian grains corporation looking to help crop growers collect soil data in 3D


The Australian Grains Research and Development Corporation (GRDC) is looking to explore and identify possible tools and technologies that can help growers create data and identify soil constraints in 3D, and can interpret the collected data on the go. In a request for tender, the GRDC explained by investing in this evaluation process, Australian growers would be able to accurately and timely make improvements to the soil in their paddocks, reduce cost through the accurate management of soil constraints, and reduce the risk of soil structural damage and yield reduction through incorrect application. At the same time, the GDRC said the assessment could be used to determine targeted management approaches to help Australian grain growers maximise profitability from not only their paddocks but their entire farm. "Crop yields are currently constrained across 7 million hectares of duplex soils nationally. High spatial variability has currently limited the adoption of soil amelioration practices with the issue being identified as a high priority by national GRDC grower network meetings in 2018, 2019, and 2020," the GDRC explained. "Growers have limited access to information on the location in 3D of their constraints at the sub paddock level.

Machine Learning Market Size, Share, Statistics, Demand and Revenue, Forecast 2026 – IAM Network


The Machine Learning report provides independent information about the Machine Learning industry supported by extensive research on factors such as industry segments size & trends, inhibitors, dynamics, drivers, opportunities & challenges, environment & policy, cost overview, porter's five force analysis, and key companies profiles including business overview and recent development. The research report on Machine Learning market thoroughly investigates historical data of this business sphere to lay out the future roadmap of the industry. The study attempts to predict a long-term picture of the market scenario with respect to the various growth indicators, hindrances, and opportunities that determine the industry expansion. Moreover, the report provides an exhaustive synopsis of the industry at a global and regional level. In addition, it covers the impact of COVID-19 pandemic on the leading industry players and various market segmentations.

LI artificial intelligence startup predicts where COVID-19 will spike – IAM Network


A Long Island artificial intelligence startup has built software aimed at pinpointing U.S. counties where the COVID-19 outbreak is likely to be most deadly. In a June report, the data-mining company, Akai Kaeru LLC, forecast spiking COVID-19 mortality with the heaviest concentrations in counties of the Southeast, including Mississippi, Georgia and Louisiana, said co-founder and chief executive Klaus Mueller. Nationwide, the software found 985 out of all 3,007 U.S. counties are at risk. "These patterns identify groups of counties that have a steeper increase in the death-rate trajectory," he said. Closer to home, the software found Nassau and Suffolk counties are likely to be relatively stable, but Westchester and Rockland counties are potential tinderboxes that could tip into crisis, said Mueller, a computer science professor on leave from Stony Brook University.

AI in the (Increasingly Virtual) Workplace -- Machina Ventures


The advent of the pandemic has resulted in the virtualization of many jobs, perhaps permanently. The associated increased quantification of the workplace will enable the accelerated adoption of AI. As our private and professional lives inevitably blur as we work more from home, we need ethical rules more than ever about how companies can use employee-generated data and deploy AI in the workplace. One lasting change from COVID-19 is likely to be that we all will be working from home (or anywhere) more often. Many companies have announced they will allow work from home as a permanent policy.

JADC2 tops Pentagon's artificial intelligence efforts -- FCW


The Pentagon's Joint Artificial Intelligence Center is focused on overlaying artificial intelligence tools on the military's mega information-sharing platform effort, called Joint All Domain Command and Control. Nand Mulchandani, JAIC's acting director, told reporters during a July 8 news briefing the center is "spending a lot of time and resources focused on building the AI components on top of JADC2," which is a patchwork quilt of platforms to improve coordination and information sharing. This involves figuring out how to build AI components, such as data, AI modeling, training and deployment, across all domains including cyber, he said. Mulchandani said JAIC is also investing in cognitive assistance technologies, helping human operators make better decisions, using "predictive analytics or picking out particular things of interest, and those types of information overload cleanup." Working through objections to the Defense Department's use of AI in weapons systems is still a chief concern, however.

Holistic AI-Driven Quantification, Staging and Prognosis of COVID-19 Pneumonia


Improving screening, discovering therapies, developing a vaccine and performing staging and prognosis are decisive steps in addressing the COVID-19 pandemic. Staging and prognosis are especially crucial for organizational anticipation (intensive-care bed availability, patient management planning) and accelerating drug development; through rapid, reproducible and quantified response-to-treatment assessment. In this letter, we report on an artificial intelligence solution for performing automatic staging and prognosis based on imaging, clinical, comorbidities and biological data. This approach relies on automatic computed tomography (CT)-based disease quantification using deep learning, robust data-driven identification of physiologically-inspired COVID-19 holistic patient profiling, and strong, reproducible staging/outcome prediction with good generalization properties using an ensemble of consensus methods. Highly promising results on multiple independent external evaluation cohorts along with comparisons with expert human readers demonstrate the potentials of our approach.

When Genetic Algorithms Meet Artificial Intelligence


I just heard from those clever chaps and chapesses at Algolux, who tell me they are using an evolutionary algorithm approach in their Atlas Camera Optimization Suite, which -- they say -- is the industry's first set of machine-learning tools and workflows that can automatically optimize camera architectures intended for computer vision applications. As we will see, this is exciting on many levels, not the least that it prompted me to start cogitating, ruminating, and musing on the possibilities that might ensue from combining evolutionary algorithms (EAs) and genetic algorithms (GAs) with artificial intelligence (AI). But before we plunge headfirst into the fray with gusto and abandon (and aplomb, of course), let's remind ourselves that not everyone may be as familiar with things like genetic algorithms as you and yours truly, so let's take a slight diversion to bring everyone up to speed. Personally, I find the entire concept of genetic algorithms to be tremendously exciting. John Henry Holland (1929 – 2015) was an American scientist and Professor of psychology and Professor of electrical engineering and computer science at the University of Michigan, Ann Arbor.

Data-Intensive Text Processing with MapReduce - Programmer Books


Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

HackerNest Mega Tech Social


You must use this link to sign up: Don't miss out, join us as we explore different ways of connecting our tech nerd communities in the coming months! "HackerNest feels like coming home" - attendees First timer? While this Mega Tech Social - North America Edition features our (surprise, surprise!) North American communities, all are welcome, regardless of geography.

Theory of Mind and Artificial Intelligence


Our brain is a complex and not yet fully understood system. Trying to figure out how our mind functions is something that has been pursued by philosophers for as long as written evidence can prove. In this context, some of the most intriguing questions that can arise are related to the possibility to find out what is happening inside someone else's (or even one's self) mind. Is human behavior predictable given enough variables are observable? Such reflections stimulated psychologists to develop what is so-called "Theory of Mind" (ToM), as described by AI Goldman [1]: 'Theory of Mind' refers to the cognitive capacity to attribute mental states to self and others.