The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Boston Dynamics is known for the flashy videos of its robots doing impressive feats. Among Boston Dynamics' creations is Atlas, a humanoid robot that has become popular for showing unrivaled ability in jumping over obstacles, doing backflips, and dancing. The videos of Boston Dynamics robots usually go viral, accumulating millions of views on YouTube and generating discussions on social media. And the robotics company's latest video, which shows Atlas successfully running a parkour track, is no exception.

Boston Dynamics is known for the flashy videos of its robots doing impressive feats. Among Boston Dynamics' creations is Atlas, a humanoid robot that has become popular for showing unrivaled ability in jumping over obstacles, doing backflips, and dancing. The videos of Boston Dynamics robots usually go viral, accumulating millions of views on YouTube and generating discussions on social media. And the robotics company's latest video, which shows Atlas successfully running a parkour track, is no exception. Within hours of its release, it received hundreds of thousands of views and became one of the top-ten trends of U.S. Twitter.

Engineering Application of Data Science can be defined as using Artificial Intelligence and Machine Learning to model physical phenomena purely based on facts (field measurements, data). The main objective of this technology is the complete avoidance of assumptions, simplifications, preconceived notions, and biases. One of the major characteristics of Engineering Application of Data Science is its incorporation of Explainable Artificial Intelligence (XAI). While using actual field measurements as the main building blocks of modeling physical phenomena, Engineering Application of Data Science incorporates several types of Machine Learning Algorithms including artificial neural networks, fuzzy set theory, and evolutionary computing. Predictive models of Engineering Application of Data Science (data-driven predictive models) are not represented through unexplainable "Black Box". Predictive models of Engineering Application of Data Science are reasonably explainable.

MathWorks has introduced the new Release 2020a with expanded AI capabilities for deep learning. The release introduces an enhanced Deep Learning Toolbox that helps users manage multiple deep learning experiments, keep track of training parameters, analyse and compare results and code with the new Experiment Manager app. It can also interactively train a network for image classification, generate MATLAB code for training, and access pretrained models with Deep Network Designer app. Engineers can now train neural networks in the updated Deep Network Designer app, manage multiple deep learning experiments in a new Experiment Manager app, and choose from more network options to generate deep learning code. R2020a introduces new capabilities specifically for automotive and wireless engineers in addition to hundreds of new and updated features for all users of MATLAB and Simulink.

MathWorks today introduced Release 2020a with expanded AI capabilities for deep learning. Engineers can now train neural networks in the updated Deep Network Designer app, manage multiple deep learning experiments in a new Experiment Manager app, and choose from more network options to generate deep learning code. R2020a introduces new capabilities specifically for automotive and wireless engineers in addition to hundreds of new and updated features for all users of MATLAB and Simulink. More details are available in the Release 2020a video. "MathWorks provides a comprehensive platform for building AI-driven systems," said David Rich, MATLAB marketing director.

Many of the biggest tech companies like Google, Facebook and Amazon have realized the value of creating their own AI platforms for both internal and customer-facing services. Facebook's FBLearner Flow helps the social media site filter out offensive posts, while Uber's Michelangelo gives users time predictions for food deliveries. To keep up with the competition, eBay has unveiled its AI platform, Krylov, which has given the company a wide range of new capabilities from improved language translation services to searching with images. In a blog post, eBay's Sanjeev Katariya, vice president and chief architect of the eBay AI and platforms, and Ashok Ramani, director of product management, computer vision, natural and language processing, discussed the creation of Krylov and how it has changed things both inside eBay and for users of the site. "With computer vision powered by eBay's modern AI platform, the technology helps you find items based on the click of your camera or an image. Users can go onto the eBay app and take a photo of what they are looking for and within milliseconds, the platform surfaces items that match the image," Katariya and Ramani wrote in December.

BenevolentAI, founded in 2013, creates and applies AI technologies to transform the way medicines are discovered and developed. BenevolentAI seeks to improve patients' lives by applying technology designed to generate better data decision making and in doing so decrease drug discovery failure rates, lower drug development costs and increase the speed at which medicines are generated. The company has developed the Benevolent Platform - a discovery platform used by BenevolentAI scientists to find new ways to treat disease and personalise drugs for patients. BenevolentAI is HQ'd in London with a research facility in Cambridge (UK) and further offices in New York and Antwerp. BenevolentAI has active R&D drug programmes from discovery to Phase IIb in disease areas such as ALS, Atopic dermatitis, Ulcerative Colitis and Sarcopenia.

As AI becomes more prevalent in industry, more engineers and scientists – not just data scientists – will work on AI projects. They now have access to existing deep learning models and accessible research from the community, which allows a significant advantage than starting from scratch. While AI models were once majority image-based, most are also incorporating more sensor data, including time-series data, text and radar. Engineers and scientists will greatly influence the success of a project because of their inherent knowledge of the data, which is an advantage over data scientists not as familiar with the domain area. With tools such as automated labeling, they can use their domain knowledge to rapidly curate large, high-quality datasets.

Engineers and scientists who are modeling with machine learning often face challenges when working with data. Two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting. Classification models assign items to a discrete group or class based on a specific set of features.Determining the best classification model often presents difficulties given the uniqueness of each dataset and desired outcome. Overfitting occurs when the model is too closely aligned with limited training data that may contain noise or errors. An overfit model is not able to generalize well to data outside the training set, limiting its usefulness in a production system.

Understanding which AI technologies to use to advance a project can be challenging given the rapid growth and evolution of the science. This article outlines the differences between machine learning and deep learning, and how to determine when to apply each one. In both machine learning and deep learning, engineers use software tools, such as MATLAB, to enable computers to identify trends and characteristics in data by learning from an example data set. In the case of machine learning, training data is used to build a model that the computer can use to classify test data, and ultimately real-world data. Traditionally, an important step in this workflow is the development of features – additional metrics derived from the raw data – which help the model be more accurate.