As the particle physics community needs higher and higher precisions in order to test our current model of the subatomic world, larger and larger datasets are necessary. With upgrades scheduled for the detectors of colliding-beam experiments around the world, and specifically at the Large Hadron Collider at CERN, more collisions and more complex interactions are expected. This directly implies an increase in data produced and consequently in the computational resources needed to process them. At CERN, the amount of data produced is gargantuan. This is why the data have to be heavily filtered and selected in real time before being permanently stored. This data can then be used to perform physics analyses, in order to expand our current understanding of the universe and improve the Standard Model of physics. This real-time filtering, known as triggering, involves complex processing happening often at frequencies as high as 40 MHz. This thesis contributes to understanding how machine learning models can be efficiently deployed in such environments, in order to maximize throughput and minimize energy consumption. Inevitably, modern hardware designed for such tasks and contemporary algorithms are needed in order to meet the challenges posed by the stringent, high-frequency data rates. In this work, I present our graph neural network-based pipeline, developed for charged particle track reconstruction at the LHCb experiment at CERN. The pipeline was implemented end-to-end inside LHCb's first-level trigger, entirely on GPUs. Its performance was compared against the classical tracking algorithms currently in production at LHCb. The pipeline was also accelerated on the FPGA architecture, and its performance in terms of power consumption and processing speed was compared against the GPU implementation.

Photo By Brian Feeney Members of the DEVCOM CBC Deep Green Challenge team, Dr. Thomas Ingersol, Edward Emm,...... read more read more Photo By Brian Feeney Members of the DEVCOM CBC Deep Green Challenge team, Dr. Thomas Ingersol, Edward Emm, Julie Jenner, Dr. Samir Deshpande and Matt Browe, gather to work on improving their AI perception model for unmanned ground vehicles to navigate across land. DEVCOM Chemical Biological Center Places Third in Machine Learning Challenge By Dr. Brian B. Feeney Aberdeen Proving Ground, MD -- "None of us is as smart as all of us," is an old saying in business management, and it held true for a team of six U.S. Army Combat Capabilities Development Command Chemical Biological Center (DEVCOM CBC) researchers, all with different technical backgrounds, when they placed third in a U.S. Army machine learning contest. The Army's Office of Business Transformation joined up with the DEVCOM Army Research Laboratory to create the Deep Green Challenge in 2021. Its purpose is to improve Army organizations' skill in applying artificial intelligence and machine learning (AI/ML) to their technology development programs. For 2022, the challenge was to build AI perception models to solve the real-world challenge of getting unmanned ground vehicles (UGV) to navigate over land. UGVs have to be able to distinguish between an obstacle that requires rerouting such as a lake or a fallen tree from non-obstacles such as a puddle or fallen branch.

The demand for machine learning is only going to increase, thus the need for engineers and data scientists will follow suit. No one wants to talk about the potential roadblocks you'll encounter when developing ML models. As you begin developing your ML models, here are the common challenges you might encounter during your project. We've worked with several companies, including Uber, and the biggest challenge with their machine learning team is building a model that's good enough that will provide business value. We hear that nearly 80% of ML models built, don't make it production because it doesn't provide value.

The growth of machine learning and its ability to provide deep insights using big data continues to be a hot topic. Many C-level executives are developing deliberate ML initiatives to see how their companies can benefit, and cybersecurity is no exception. Most information security vendors have adopted some form of ML, however it's clear that it isn't the silver bullet some have made it out to be. While ML solutions for cybersecurity can and will provide a significant return on investment, they do face some challenges today. Organizations should be aware of a few potential setbacks and set realistic goals to realize ML's full potential.

The 2020 LHC Olympics challenged teams to develop a machine learning code to find a hidden signal in particle-collision data. This image shows particle-collision data captured by the ATLAS detector at CERN's Large Hadron Collider. In searching for new particles, physicists can lean on theoretical predictions that suggest some good places to look and some good ways to find them: It's like being handed a rough sketch of a needle hidden in a haystack. But blind searches are a lot more complicated, like hunting in a haystack without knowing what you are looking for. To find what conventional computer algorithms and scientists may overlook in the huge volume of data collected in particle collider experiments, the particle physics community is turning to machine learning, an application of artificial intelligence that can teach itself to improve its searching skills as it sifts through a haystack of data.

We've always pictured the rise of artificial intelligence as being the end of civilization, at least from watching movies like'The Terminator – Judgement Day'. We could not have imagined that something as insignificant as misinformation, would lead to the collapse of organisations; beginning wars and even mass suicides. The definition of what we regard as "Fake" news has a broad spectrum. Consider an article published in 2001, which was true at the time. That same article being published now, excluding the date… giving it an appearance of recently occurring events.

The Radiological Society of North America (RSNA) is organizing a challenge intended to show the application of machine learning and artificial intelligence on medical imaging and the ways in which these emerging tools and methodologies may improve diagnostic care. The RSNA Pediatric Bone Age Machine Learning Challenge addresses a familiar image analysis activity for pediatric radiologists: assessment of bone age from hand radiographs of pediatric patients used to evaluate growth and diagnose developmental disorders. The Challenge uses a dataset of hand radiographs provided by a consortium of leading research institutions -- Stanford University, the University of California, Los Angeles and the University of Colorado -- that have associated bone age assessments provided by multiple expert observers. Participants in the challenge will be judged by how well the bone age evaluations produced by their algorithms accord with the expert observers' evaluations. Participants will have the opportunity to directly compare their algorithms in a structured way using this carefully curated dataset.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, Data Scientist on Oracle's Information Management Platform Team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that lies with using machine learning in a big data environment.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, data scientist on Oracle's Information Management Platform team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that comes with using machine learning in a big data environment.

We are living in the era of 4th Industrial revolution – the evolution which is based on extreme automation of machine to machine communication, not only just the communication but way beyond. Machines can understand each other, negotiate with each other, sign and execute the contracts with each other. They can predict each other's behavior, and able to sort of establish a social network of machines. This type of network is self-sustaining and can run without much intervention of humans. At the heart of this exciting 4th revolution are some of the advanced technologies at work.