Over 72.5 million connected car units are estimated to be sold by 2023, enabling nearly 70% of all passenger vehicles to actively exchange data with external sources. The amount of data resulting from these smart vehicles will be overwhelming for traditional data processing solutions to gather and analyze, as well as the associated latency of processing this data-- leading to potential life-or-death scenarios, according to Ramya Ravichandar, from Foghorn. We speak with Ravichandar, about how connected car manufacturers are implementing edge AI solutions for real-time video recognition, multi-factor authentication, and other innovative capabilities to decrease network latency and optimize data gathering, analyzing and security. Digital Journal: What are the current trends with autonomous and connected cars? Ramya Ravichandar: Automotive companies are looking to improve real-time functionalities and accelerate autonomous operations of passenger vehicles.
In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.
Autonomous technology continues to make an impact on the supply chain. The autonomous supply chain, applies to moving goods without human intervention (to some degree at least) or aiding in achieving inventory accuracy. One of the more interesting examples is the Belgian brewery De Halve Maan, which in an effort to reduce congestion on the city streets, built a beer pipeline under the streets. The pipeline is capable of carrying 1,500 gallons of beer an hour at 12 mph to a bottling facility two miles away. Autonomous technology is seen in warehouses and stores, on highways and in mines, and in last mile deliveries.
The big rub on the first generation of graph databases was that although RDF triple stores were great at storing the simple sentence, they had a hard time with the adverbs, adjectives and clarifying phrases of your data story. If I wanted to store'John is a carpenter since 2001' or'John from Alberta Canada is a carpenter liked by 702 people', the syntax of old-school triple stores had a more tedious, but not impossible way of handling it. It involved creating extra nodes that were confusing to some and a process called reification. Until about a year ago, labeled property graphs (LPG) were better at color and detail than RDF, having a more intuitive syntax for clarifying adverbs, adjectives, and phrases. That was, of course, until recently.
"AI is a fundamental risk to the existence of human civilization in a way that car accidents, airplane crashes, faulty drugs or bad food were not -- they were harmful to a set of individuals within society, of course, but they were not harmful to society as a whole", said Musk at the National Governors Association. At the moment Elon Musk is working hard to combat coming to AI with his nonprofit Open AI and is planning to link the human mind to computers at his company Neuralink.
"The future is already being automated, and it's enabled by AI" Uber, whose AI is so central to its business model that employees "…don't even think about it anymore," is betting big on self-driving cars driving down costs. As their core driver of competitiveness, it stands to reason that if Artificial Intelligence is smart enough to drive a car it can surely help the shop owner who doubles as its sole mechanic. Our previous entry explored how AI will impact the manufacturing and distribution of auto parts, but what about the businesses that purchase and use them on a daily basis? For service centers doing everything they can to move jobs out of the bays and customers through their doors, activities that add value or increase average ticket prices can fall by the wayside. "Advances in computing power will give machines abilities once reserved for humans--the ability to understand and organize unstructured data such as photos and speech, to recognize patterns, and to learn from past experiences how to improve future performance."
You'd thinking flying in a plane would be more dangerous than driving a car. In reality it's much safer, partly because the aviation industry is heavily regulated. Airlines must stick to strict standards for safety, testing, training, policies and procedures, auditing and oversight. And when things do go wrong, we investigate and attempt to rectify the issue to improve safety in the future. Other industries where things can go very badly wrong, such as pharmaceuticals and medical devices, are also heavily regulated.
Gradient Dissent by Weights and Biases We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they're working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. Today our guest is Nicolas Koumchatzky.
New-age Digital Transformation journeys are hinged on AI capabilities. While most businesses automatically relate to AI for Artificial Intelligence, there are two more AI scenarios that you should be aware of. In this article, we will tell about these three A.I. scenarios that involve Cognitive Learning and Intelligent Automation -- While digital transformation by harnessing AI and Intelligent Automation seems to be the most obvious path to sustain businesses, focusing on just one AI scenario can expose companies to unforeseeable challenges in the future. That's why you should be able to define and distinguish between these three AI scenarios. I always refer to this Venn Diagram from Peter Sommer (2017) to distinguish between AI and ML.