Autonomous Vehicles

Data-driven experiences are rich, immersive and immediate. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data -- quickly. So they can't sustain latency as data travels to and from the cloud. That to-and-fro takes too long.

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.

China is deploying robots and drones to remotely disinfect hospitals, deliver food and enforce quarantine restrictions as part of the effort to fight coronavirus. Chinese state media has reported that drones and robots are being used by the government to cut the risk of person-to-person transmission of the disease. There are 780 million people that are on some form of residential lockdown in China. Wuhan, the city where the viral outbreak began, has been sealed off from the outside world for weeks. The global death toll from coronavirus topped 2,100 people this week, with over 74,000 infected.

While machine learning sounds impressive, the phrase has actually been around for a long time. In fact, the term "machine learning" came about in the 1950s, when IBM employee Arthur Samuel developed a program that could play checkers. The program was amazing for the time, especially considering the limitations of computer processing. It worked like this: the computer determined a score based on where the checker pieces were on the board. A better score meant it was more likely to win.

"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.

AI has grown to become quite the avid topic, especially with the way technology companies are using this intelligence in a variety of applications. But, prior to popular belief, AI has been around quite a while now, dating back to the 1980's. Do you remember the KITT car from the popular David Hasselhoff starrer – Knight Rider? The in-built AI in the car was able to have multi-lingual conversations with the driver and access every single nook and crevice of the vehicle. It could drive itself and take charge in tricky situations.