Collaborating Authors


Predictive Maintenance Using Deep Learning


Predictive maintenance allows equipment operators and manufacturers to assess the condition of machines, diagnose faults, and estimate time to failure. Because machines are increasingly complex and generate large amounts of data, many engineers are exploring deep learning approaches to achieve the best predictive results. You'll also see demonstrations of: How much do you know about power conversion control? Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

How Zoox vehicles "find themselves" in an ever-changing world


For a human to drive successfully around an urban environment, they must be able to trust their eyes and other senses, know where they are, understand the permissible ways to move their vehicle safely, and of course know how to reach their destination. Building these abilities, and so many more, into an autonomous electric vehicle designed to transport customers smoothly and safely around densely populated cities takes an astonishing amount of technological innovation. Since its founding in 2014, Zoox has been developing autonomous ride-hailing vehicles, and the systems that support them, from the ground up. The company, which is based in Foster City, California, became an independent subsidiary of Amazon in 2020. The Zoox purpose-built robot is an autonomous, pod-like electric vehicle that can carry four passengers in comfort.

Adversarial Artificial Intelligence Is Real


A panel of artificial intelligence (AI) experts from industry discussed some of the technology's promise and perils and predicted its future during an AFCEA TechNet Cyber Conference panel April 26 in Baltimore. The panelists were all members of AFCEA's Emerging Leaders Committee who have achieved expertise in their given fields before the age of 40. The group discussed AI in the cyber realm. Asked about "anti-AI" or "counter-AI," Brian Behe, lead data scientist, CyberPoint International, reported a recent case in which his team used a method called reinforcement learning to change the signature of malware files without altering the malware's functionality. "We use this as a way to do some security testing on other machine learning classifiers that had been built to detect malware. Sure enough, we were able to beat those classifiers," Behe explained.

In 2022, the most important trends in AI and Machine Learning will alter the timeline


With the Covid-19 outbreak, companies from all walks of life are leveraging advanced technology to revolutionize the way we work and live. Over the past few years, technology has undoubtedly become an important feature and guideline in times of crisis. Artificial intelligence, machine learning and other related technologies have the potential to transform traditional business models from the most basic to the simplest, efficient and inexpensive. The "smart" component of smart digital solutions refers to artificial intelligence and machine learning. These two fundamental elements are called the "brains" of intelligent machines, which are used to provide efficient and efficient business solutions.

How Electric Vehicle Manufacturers Employ AI Strategically


Machine learning models help with battery life cycle management. Blending advanced electronics with IoT, data science and digital twins, ML models with predictive intelligence can anticipate battery life, identify degradation breakdowns and their causes. Akhil Aryan, the cofounder of ION Energy, a company that applies intelligent battery analytics to improve the performance of lithium-ion batteries, says data on battery life includes performance, state of charge, stress from rapid acceleration and deceleration, temperature and the number of charge cycles.

Hyundai chooses IonQ's quantum tech to improve its vehicles' object recognition capabilities


IonQ announced an expansion of its relationship with automaker Hyundai that will see it applying its quantum computing technology to the task of allowing Hyundai vehicles to better recognize real-world objects. This new collaboration builds on an existing relationship that began earlier this year which saw IonQ's quantum tech being used to improve the efficiency and cost-effectiveness of Hyundai's electric vehicle (EV) batteries. The companies hope that the application of quantum machine learning to in-vehicle computer vision systems will allow both automated and human-controlled vehicles to better recognize objects on the road and beside it for safety and autonomous driving purposes. The duo claims they have already classified 43 different types of road signs for recognition using quantum machine learning tech. More: What is machine learning?

Artificial Intelligence and Advanced Machine Learning Market Surveying Report, Drivers, Scope, Regional Analysis by 2028


The report also provides the analysis of import/export, production and consumption ratio, supply and demand, cost, price, estimated revenue, and gross margins. The global Artificial Intelligence (AI) & advanced Machine Learning (ML) market size is expected to reach USD 471.39 Billion at a steady CAGR of 35.2% in 2028, according to latest analysis by Emergen Research. Artificial Intelligence (AI) and advanced Machine Learning (ML) technologies are witnessing increasing demand and deployment across various fields, such as in leading-edge medical diagnostics, advanced quantum computer systems, consumer electronics, and smart personal assistants. Machine Learning is a type of AI, which enables computers to learn without being initially programmed. Rising focus on development of computer programs that can teach themselves and change and evolve when exposed to new data, is a factor driving demand for these technologies.

Mimicking the Five Senses, On Chip


Machine Learning at the edge is gaining steam. BrainChip is accelerating this with their Akida architecture, which is mimicking the human brain by incorporating the 5 human senses on a machine learning-enabled chip. Their chips will let roboticists and IoT developers run ML on device for low latency, low power, and low-cost machine learning-enabled products. This opens up a new product category where everyday devices can affordably become smart devices. Rob is an AI thought-leader and Vice President of Worldwide Sales at BrainChip, a global tech company that has developed artificial intelligence that learns like a brain, whilst prioritizing efficiency, ultra-low power consumption, and continuous learning. Rob has over 20 years of sales expertise in licensing intellectual property and selling EDA technology and attended Harvard Business School. This is your host Abate, founder of fluid dev a platform that helps robotics and machine learning companies scale their teams up as they grow. So welcome Rob and honor to have you on here. Rob: Abate it's great to be here and thank you for having me on your podcast.

How AI Accelerates Chemical and Pharmaceutical Research


What if there's a quick way to screen molecules and predict their reactivity and other properties? Certainly this will make drug and material design much faster because chemists could then focus more on the most promising compounds instead of trying them all. This is what the Merck Molecular Activity Challenge somehow illustrates. Here, the goal is to predict biological activities of different molecules, both on- and off-target, given numerical descriptors generated from their chemical structures. In other words, we have to predict whether a certain molecule will become highly active towards the intended target and "inert" to others (thereby minimal or zero side effects).

Global warming is a hoax


Global warming (aka "climate change") is based upon a single hypothesis -- that increased levels of CO2 in the atmosphere cause higher surface temperatures. According to global warming adherents, burning fuel to warm your house, cook your food and charge your electric vehicle increases the earth's temperature and damages the environment -- because of the CO2 you generate. Let's use Data Science and Machine Learning to prove or disprove the hypothesis that increased levels of CO2 cause increased surface temperature. CO2 data is sourced from the US Government's Earth System Research Laboratory, Global Monitoring Division, Mauna Loa observations. Climate and sun data is taken from NASA/POWER CERES/MERRA2 Native Resolution Annual data for the Mauna Loa location.