The danger of artificial intelligence is a common theme in science fiction because it allows authors and filmmakers to explore ethical and societal questions that arise when humans develop entities that can rival or surpass their own intelligence. There are various reasons why this keeps popping up. The biggest one is probably loss of control. Human beings fear the idea of losing control over the AI they create, which makes it a common theme of science fiction. This fear has often manifested in movies like "The Terminator," where an AI becomes so intelligent that it sees humanity as a threat to its survival and wages war against humans.

When (and if) if the Afeela electric car from Sony Honda Mobility goes on sale, it will use the Snapdragon Digital Chassis from Qualcomm -- an automotive technology platform that combines safety and connectivity systems, entertainment, customization, and upgradability all into one product. For automakers, finding the right technology partner means simplifying a vehicle's architecture and unlocking new revenue streams in the form of passenger entertainment and downloadable upgrades. The Qualcomm Snapdragon Digital Chassis is comprised of four automotive systems -- Snapdragon Auto Connectivity includes connected systems like 5G and vehicle-to-vehicle technologies, Snapdragon Cockpit Platform supplies the digital instrument clusters and infotainment controls, Snapdragon Car-to-Cloud makes over-the-air software updates possible, and the Snapdragon Ride Platform supplies driver assistance technologies and autonomous driving capability. "There is a tremendous amount of opportunity to reinvent the car," Nakul Duggal, Qualcomm's senior vice president and general manager for automotive, tells Wired. "And a tremendous amount of that reinvention is happening because the car is becoming a truly digital product."

We recently spilled a lot of virtual ink on Tesla AI Day 2022, but there are several other self-driving stories from the past few weeks that have been on the list waiting for their moment of glory. Let's catch up on some of these other pieces. On September 30, it was announced that Mobileye had filed for an IPO. In 2017, Intel bought Mobileye for a record $15.3 billion. Can Mobileye raise that much or more in its next IPO?

As the field of machine learning has grown, artificial neural networks have grown along with it.

Autonomous vehicles (AV) play an increasingly important role in food and parcel deliveries. In early December, Silicon Valley-based startup Nuro announced that it was launching the first commercial autonomous delivery in California. Partnering with 7-Eleven, the company provides the service for residents of Mountain View, where the business is located. According to a blog post from Nuro's co-founder Zhu Jiajun, customers can access the autonomous delivery through 7-Eleven's 7NOW delivery app. Nuro currently offers the service with its Prius vehicles in fully autonomous mode, expecting to replace them with its R2 autonomous cars later.

I wrote a version of this article below almost a year ago for another company in order to explain the EV market and its future potential. The electric vehicle market 10 years ago was basically nonexistent. Almost zero market analysts or investors were on the lookout for a promising electric vehicle startup. You couldn't find one person out of 100, probably not one out of 1,000, and maybe not even one out of 1 million, who expected an electric car to be the best selling automobile in some notable country and regional markets by 2020. Chances are good that you did not predict a Silicon Valley automaker would be outselling BMW, Mercedes, and Audi in the United States in the luxury car market.

Tesla is now worth more than half a trillion dollars on the stock market. Last year, Elon Musk predicted that self-driving Teslas would boost Tesla's market cap to $500 billion. Although Tesla's Full Self-Driving (FSD) suite is still in a very limited beta release, Elon's prediction has come true earlier than anticipated. In fact, with the rise in market cap, Elon Musk has moved past Bill Gates in the ranking of the richest people in the world, becoming second only to Jeff Bezos. In 2019, CNBC reported that Citigroup and Goldman Sachs, who just recently announced that Tesla being added into the S&P 500 could spark an $8 billion buying spree, were on a broad investor call with Elon Musk and Tesla CFO Zach Kirkhorn.

More than a dozen companies have long been approved to test out self-driving cars in California. Now, they can also charge passengers if they launch a robotaxi service. On Thursday, November 19, the California Public Utilities Commission (CPUC) approved both the ability to launch robotaxi services and charge for them after many months of these companies -- such as Cruise, Waymo, Aurora Innovation, Pony.ai, and Zoox -- lobbying for such policies. Of course, the companies still have to jump through various stacks of paperwork in order to be granted such approvals, but all in time. Waymo has been operating such a service in Arizona, Waymo One, for more than a year.

Tesla has released its quarterly "Tesla Vehicle Safety Report." One of the top reasons -- if not the #1 reason -- I bought a Tesla Model 3 last was because of its record-setting safety rating, so I'm always interested in seeing new stats on this topic. The second quarter of 2020 saw a slightly worse result for Tesla than the first quarter in terms of accidents per million miles driven with Tesla Autopilot engaged (see graph below), but keep in mind that the first quarter had a record result. Additionally, the difference was so small that it was probably not statistically significant. On the other hand, Tesla's Q2 figure was far better than the US average -- about 10 times better with Autopilot engaged.

Tesla's progress with artificial intelligence and neural nets has propelled its Autopilot and Full Self Driving solutions to the front of the pack. This is the result of the brilliant work of a large team of Autopilot directors and staff, including Tesla's Senior Director of AI, Andrej Karpathy. Karpathy presented Tesla's methods for training its AI at the Scaled ML Conference in February. Along the way, he shared specific insights into Tesla's methods for achieving the accuracy of traditional laser-based lidar with just a handful of cameras. The secret sauce in Tesla's ever-evolving solution is not the cameras themselves, but rather the advanced processing and neural nets they have built to make sense of the wide range and quality of inputs.