Like a magician setting up a trick, Anuja Sonalker starts by making it clear that there is no hidden driver in her car's front or back seat. Next, she presses the phone camera up against the side window and waves it around until I reassure her that I'm satisfied. Sonalker then turns and strides away from the idling vehicle until she is maybe 10 or 15 feet away. Next, she holds up a smartphone displaying the STEER Tech app and taps it a couple of times. In the background, the car springs to life.
When most people think of machine learning in relation to themselves, something like the auto-correct peppered throughout their texts might come to mind. But these technologies are integrated into so many industries that touch us daily. In my previous article linked below, I talk about the broad strokes of machine learning by looking into the technologies of self driving cars, healthcare, and briefly touched on the YouTube algorithm. In this article, I'll be diving farther into that last concept by approaching three different violations of terms and services on a social media platform and the role that machine learning has in mitigating any hardships caused by these violations. To fully understand the decision making behavior, we must go over the basics of these algorithms.
With the recent tweet on Neuralink, Elon Musk again hit the headlines this week where he stated that the company is going to update on the progress of this mysterious company in the coming month. In fact, the last major update came from this brain-machine interface company last year around the same time, where he spoke about the technology "threads," surpassing the traditional ones, which can be implanted in human brains to solve some of the brain disorders people are facing. He ultimately revealed his interest "to achieve a symbiosis with AI" by merging technology with human brains and not taking over. This raises a serious question -- can Elon Musk build cyborgs in the near future? SpaceX and Tesla CEO, Elon Musk has been known for bringing extraordinary ideas to life like electric cars, sending rockets to Mars, and creating solar cities, to name a few.
Almost most of the major automakers are developing autonomous cars of some kind. Some, like Tesla's Autopilot and Google's Waymo, already are in use, though they're maybe not fully autonomous yet. Tesla and Waymo, like so many other automakers in the autonomous car race, remain ironing out the kinks. In the meantime, one of the biggest debates surrounding driverless cars is how they'll impact the insurance industry. If human error causes virtually all car accidents, then in theory, self-driving cars would be the solution.
Digital generated image of data. Lemonade is one of this year's hottest IPOs and a key reason for this is the company's heavy investments in AI (Artificial Intelligence). The company has used this technology to develop bots to handle the purchase of policies and the managing of claims. Then how does a company like this create AI models? Well, as should be no surprise, it is complex and susceptible to failure.
Its effects are already being felt in financial services, with the advent of robo-advisers in wealth management, online-only banks and peer-to-peer funding. This technological disruption is also blurring lines across sectors and industries, a development that is especially relevant to financial services. The use of common technologies and platforms is bringing global industries closer together and changing the competitive landscape.The new players include fintech and insurtech companies as well as disruptors in other industries. In Asia, for example, Tesla has partnered with established insurers to offer a vehicle package featuring customised motor insurance that accounts for its vehicles' autopilot safety features as well as maintenance costs. As it moves toward fully autonomous vehicles, Tesla is in a unique position to compete with property and casualty insurers in cases where traditional insurers are not willing to lower the risk premium1.
The potentially enormous safety benefits of self-driving vehicles have long been considered to be among the technology's biggest assets. Numerous research projects have found human error is a contributing factor in between 85% and 95% of current road collisions. The conventional thinking has been that if you remove human error through the use of fully autonomous technology, then the collision rate would fall by a similar amount. This has been a strong selling point for self-driving vehicles to a public which, so far, seems unwilling to trust the technology. For example, research conducted last year on behalf of the Institution of Mechanical Engineers found 60% of people said they would always prefer to drive themselves rather than use a self-driving vehicle, while two-thirds of people are uncomfortable with the idea of travelling in a driverless car.
Existing approaches to artificial intelligence for self-driving cars don't account for the fact that people might try to use the autonomous vehicles to do something bad, researchers report. For example, let's say that there is an autonomous vehicle with no passengers and it's about to crash into a car containing five people. It can avoid the collision by swerving out of the road, but it would then hit a pedestrian. Most discussions of ethics in this scenario focus on whether the autonomous vehicle's AI should be selfish (protecting the vehicle and its cargo) or utilitarian (choosing the action that harms the fewest people). But that either/or approach to ethics can raise problems of its own.
The context: One of the best unsolved defects of deep knowing is its vulnerability to so-called adversarial attacks. When included to the input of an AI system, these perturbations, apparently random or undetected to the human eye, can make things go totally awry. Stickers tactically put on a stop indication, for instance, can deceive a self-driving automobile into seeing a speed limitation indication for 45 miles per hour, while sticker labels on a roadway can puzzle a Tesla into drifting into the incorrect lane. Safety important: Most adversarial research study concentrates on image acknowledgment systems, however deep-learning-based image restoration systems are susceptible too. This is especially uncomfortable in healthcare, where the latter are typically utilized to rebuild medical images like CT or MRI scans from x-ray information.
Elon Musk delivers a remote speech at the World Artificial Intelligence Conference on Thursday. Tesla CEO Elon Musk delivered a brief speech via remote video to the attendants of this year's World Artificial Intelligence Conference (WAIC) in Shanghai, China on Thursday, kicking off the country's most important tech conference. Musk answered questions about Tesla's latest development in artificial intelligence, a central piece of technology behind the company's "Autopilot" semi-autonomous driving system. China is Tesla's biggest market outside the United States. Since introducing the first version of Autopilot in 2014, Tesla has upgraded the system multiple times.