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Welcome! You are invited to join a webinar: Autonomous robot inspections in the energy sector. After registering, you will receive a confirmation email about joining the webinar.

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Given the expansive scope of inspections in different environments, industries are in need of a mixed fleet of specialist robots that are tailored to these conditions. Our robot-agnostic solution enables industries to manage a mixed fleet of robots in different environments (incl. ATEX/IECEx Zone 1 areas) through one single interface. In this session, we will present a live-demo of autonomous inspection and delve into how these robots can be equipped with extensible sensors and skills that match your inspection needs. Join our webinar and watch robots perform inspection missions autonomously. What to look forward to in the webinar? 1. Live demo of inspection missions by our robot fleet including Spot, from Boston Dynamics, and ExR-2 from ExRobotics 2. The need for a mixed fleet of specialist robots and how you can manage them through a single interface 3. Learn about industrial use-cases and problems solved through autonomous robots 4. Best practices derived from 2 years of our experience in deploying 50+ robots in the brownfield industry with over 50,000 hours of deployment 5. Value added by autonomous inspections in terms of operational efficiency, workplace safety and cost effectiveness Be a part of the Webinar and learn how we push the boundaries of what is possible and extract the full potential of robots. About us: Energy Robotics provides an end-to-end, robot-agnostic software solution for autonomous inspections in capital-intensive industries such as oil & gas, chemical and energy.


The Dominance of AI Chatbots Over Rule-Based Chatbots

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How is AI taking over rule-based chatbots? Is AI the future of chatbots? We will answer these questions in this article. The chatbot industry is growing really fast year by year as many companies try to use chatbots to reduce customer service costs. With rapid advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), more and more chatbots are made and released each day, and they serve a different purpose.


Post Graduate Program on Applied Data Science with Deep Learning and Specialisation(TEKS-RISE) - Teksands

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If it is your goal is to become a Data Scientist, you have to first understand what it takes to become one, the skills and competencies that you should learn. Data Science is an amazingly interesting field, full of interesting concepts and power to create magic from Data. Comprehensive knowledge on Deep Learning, ML-Ops and AI/ML Product Development are critical knowledge areas for any Data Scientist/Data Engineer/Machine-Learning Professional. This course places a lot of focus into these areas so that there is no learning gap when you start on a Data Science/Machine Learning role. The curriculum prepares you to be a leader in this field through mastery of core data science concepts like Statistical Analysis of Data, Exploratory Data Analysis Techniques using Python, powerful Visualizations, Machine Learning, Deep Learning and Model Deployment in Production.


How do Neural Networks really work? - Analytics Vidhya

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Neural networks form the core of deep learning, a subset of machine learning that I introduced in my previous article. People exposed to artificial intelligence generally have a good high-level idea of how a neural network works -- data is passed from one layer of the neural network to the next, and this data is propagated from the topmost layer to the bottom layer until, somehow, the algorithm outputs the prediction on whether an image is that of a chihuahua or a muffin. Seems like magic, isn't it? Surprisingly, neural networks for a computer vision model can be understood using high school math. It just requires the correct explanation in the simplest manner for everyone to understand how neural networks work under the hood.


Top AI-Powered Design Tools in 2022

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There are approximately 20 million more enjoyable things to do than manually removing the background. Avocode allows you to share design files, make changes that are automatically updated, and generate code styles for your design projects. This is yet another design to code converter that enables you to create web, iOS, and Android apps exactly as you want them, without leaving out any minor details. It also allows you to generate production-ready code in a variety of languages, including CSS, SCSS, CSS in JS, Android, and React Native. In this article, we discussed AI design tools that will assist you in creating some truly amazing designs. The best part about all of these tools is that they are all free to use, so you can start creating some awesome designs right away.


4 Reasons Your Deal Forecasts Probably Aren't Accurate

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Sales and deal forecasting are vital parts of any business's planning, but it is also hard to argue that there are major issues with how we prepare for the future. Based on a number of sources, the level of inaccuracy with current tools is astounding. A study found that only 28.1% of sales teams were within a 5% deviation of their forecast, and 47% of 90-day predictions were off by a margin of more than half -- and sales reps overestimated by an average $91,000 and underestimated by only $47,000. CSO Insights cites that 60% of forecast deals do not close, and even organizations that formally track and review their processes still lose 40% of predicted closures. A SiriusDecisions' analysis pegged that "79 percent of sales organizations miss their sales forecast by more than 10 percent", and in another analysis, an asset manager says he just cuts 20% off the top of a forecast since he doesn't think they're reliable.


Fundamentals in Neural Networks

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Beginner level audience that intends to obtain in-depth overview of Artificial Intelligence, Deep Learning, and three major types neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks, and (3) Recurrent Neural Networks.


Data Science using R Programming

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As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data. R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling. Data Science includes various fields such as mathematics, business insight, tools, processes and machine learning techniques.


Why Artificial Intelligence is Critical for Innovation and Growth

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AI is making its presence felt everywhere in the connected world whether via data-driven deep learning technologies, smart robots, or autonomous vehicles. Industries ranging from manufacturing, retail, to healthcare and aerospace have all witnessed some remarkable examples of how AI technology is changing the way they do business in recent times. The impact of AI on an organization's ability to harness data and unlock new opportunities is huge. The transformative powers of AI-enabled solutions in enhancing the capabilities of business analytics and business intelligence have helped them achieve a prominent place in the Gartner Hype Cycle for Emerging Technologies. The sudden surge in the volume and complexity of data is driving the commercial adoption of AI across many industries.


Working from home increases your risk of making mistakes, scientists say

Daily Mail - Science & tech

Working from home increases your risk of making mistakes, a study examining the quality of chess play has found. The standard was significantly worse when players competed online instead of face to face, researchers discovered, suggesting that not being in the office is harmful to productivity. They monitored nearly 215,000 chess moves made by players during in-person and digital tournaments, checking them against what was the best play by using artificial intelligence. Such was the impact on performance when playing remotely, it would have taken Norwegian grandmaster Magnus Carlsen, the world's top-rated player, to the same rating as the current 20th-best player, according to Dainis Zegners from Rotterdam School of Management, one of the study's co-authors. He said the research showed that remote working could hinder people's ability to carry out mentally-intense tasks while alone.