'Scout', a six-legged autonomous home-delivery robot by Amazon, started delivering packages in Snohomish County, Washington, for the first time in January 2019. Scout, like a good samaritan, used the sidewalks to travel. Upon reaching its destination, Scout would stop at the front door of its customer and open the lid so that the customer could collect their parcel. However, three years later, before Scout could fulfil its potential to be fully autonomous, Amazon just scrapped the whole project. Amazon spokesperson Alisa Carroll said there were aspects of the programme that weren't meeting customers' needs.

January 22, 2018 in Culver City: A Tesla Model S hit the back of a fire truck parked at an accident in Culver City around 8:30 am on Interstate 405 using the cars Autopilot system. The Tesla, which was going 65mph, suffered'significant damage' and the firetruck was taken out of service for body work. May 30, 2018 in Laguna Beach: Authorities said a Tesla sedan in Autopilot mode crashed into a parked police cruiser in Laguna Beach. Laguna Beach Police Sgt. Jim Cota says the officer was not in the cruiser during the crash. He said the Tesla driver suffered minor injuries.

A Tesla Model 3 car in'Full Self-Driving' mode has been captured colliding with a bike lane barrier post, in a potential setback for Elon Musk's firm. The footage was captured during a drive in downtown San Jose, California, by a YouTuber who goes by the name AI Addict, and provides the first recorded evidence that the feature has been directly responsible for an accident. It shows the latest version of Tesla's self-driving software, Full Self-Driving (FSD) Beta version 10.10, veering the Model 3 into the bollard separating a bike lane. Even though the driver is hitting the brakes and furiously spins the steering wheel away from the obstacle, the AI-powered FSD system hits the bollard with a big thud. Worryingly, at other points in the video the Model 3 appears to run a red light and attempts to go down a railroad track and later a tram lane.

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.

A Tesla Model 3 driving on'autopilot' smacked into a Florida Highway Patrol cruiser on Saturday morning, narrowly missing the driver of the cruiser who had stopped in order to help a disabled vehicle. The incident is the 12th such smash involving a Tesla on autopilot mode and an emergency vehicle. All the cars which have been struck had their lights flashing, or had deployed an emergency flare, illuminated warning sign or cones, raising questions about whether they may have confused the Tesla's sensors. Saturday's smash happened after when the 28-year-old trooper, who has not been named, stopped shortly after 5 am on August 28 on I-4 near downtown Orlando while responding to a broken down car. He put his emergency lights and was walking over to a disabled vehicle when the Tesla hit the cruiser's left side, according to a copy of the police report seen by DailyMail.com.

ARLINGTON, VIRGINIA - OCTOBER 31: A voter walks out of a polling station during early voting for the ... [ ] U.S. Presidential election on October 31, 2020 in Arlington, United States. With predictions of record turnout of 150 million people, representing 65% of eligible voters, we have to ask ourselves why we continue to rely on antiquated systems, paper ballots and inadequate machines to handle the most important day of our democracy. There is technology available today that can make every election day going forward safe, efficient, and most importantly, secure. If we look to AI and innovation, we can see the future of election day. No long lines, no waiting on ballots to be dumped and counted.

Amazon Scout, the e-commerce giant's fully-electric autonomous delivery robots, are heading south. Announced Tuesday in a blog post from Amazon Scout VP Sean Scott, Amazon says that it is set to start delivering to select customers in these markets as part of the company's continuing "field test" rollout. It has also been delivering packages in Snohomish County, Washington, and the Irvine-area of California. "We're thrilled to bring Amazon Scout to two new communities," Scott writes. "Adding Atlanta and Franklin to our existing operations gives Scout devices the opportunity to operate in varied neighborhoods with different climates than they operate in today. Amazon also has a significant presence in these areas through our corporate offices and logistics facilities. And, we know they are both great places to find world-class talent that can help us continue inventing for customers."

If you live in Atlanta, Georgia or Franklin, Tennessee, your next Amazon order might arrive in one of the company's Scout delivery robots. Amazon began testing its cooler-sized delivery bots in Snohomish County, Washington last year. They've been making deliveries in the Irvine area of California, and this week they popped up in Atlanta and Franklin. Only a handful of Amazon Scout devices will operate in each city. They'll be accompanied by a human, travel at walking speed and make deliveries Monday through Friday, during daylight hours.