We all know Elon Musk to be a very ambitious guy. I mean, seriously, the guy has a company which specializes in electric car manufacturing, you've heard of Tesla, right? He also runs an aerospace manufacturing and space transportation services company called SpaceX. I am sure you've heard about it in the news or somewhere else. SolarCity, a solar energy company, now owned by Tesla.
And yet, AI's current automated task-mastering was first posited by the French philosopher René Descartes almost 400 years ago. Descartes, who famously coined, "I think, therefore I am," pondered about the ability of machines to reason. While machines may be able to "do some things as well, or better, than humans, they would inevitably fail in others," whereas human reason can universally adapt to any task. Though Descartes' idea of machines differs from today's reality, some say he threw down the gauntlet for what we now refer to as general AI--or machines that can think like humans. Though Descartes' idea of machines differs from today's reality, some say he threw down the gauntlet for what we now refer to as general AI--or machines that can think like humans.
Machine Learning helps your company create entirely new products to increase revenue. An example is new mobility services powered by self-driving cars, also called Robo-Taxi. Without Machine Learning, this new product is hard to create. In this case, Machine Learning allows the company to develop an entirely new product to increase revenue. The holy grail of Artificial Intelligence ("AI")-powered products is a product that enters the Virtuous Circle of AI.
The self-driving freight truck startup TuSimple has been carrying mail across the state of Arizona for several weeks. UPS announced on Thursday that its venture capital arm has made a minority investment in TuSimple. The announcement also revealed that since May TuSimple autonomous trucks have been hauling UPS loads on a 115-mile route between Phoenix and Tucson. UPS confirmed to Gizmodo this is the first time UPS has announced it has been using TuSimple autonomous trucks to deliver packages in the state. Around the same time as the UPS and TuSimple program began, the United States Postal Service and TuSimple publicized a two-week pilot program to deliver mail between Phoenix and Dallas, a 1,000 mile trip.
"We demand rigidly defined areas of doubt and uncertainty!" Let's imagine for a second that we're building a computer vision model for a construction company, ABC Construction. The company is interested in automating its aerial site surveillance process, and would like our algorithm to run on their drones. We happily get to work, and deploy our algorithm onto their fleets of drones, and go home thinking that the project is a great success. A week later, we get a call from ABC Construction saying that the drones keep crashing into the white trucks that they have parked on all their sites.
In recent years, autonomous driving and so-called robotaxis have become one of the hottest topics in the automotive industry - and beyond! Recent autonomous vehicle forecasts call for sales of more than 30 million autonomous vehicles in 2040. Although the sharpest gains are expected to occur after 2030 compared to one million in 2025, commercial market introduction is announced by several OEMs for 2021. Traditional car manufacturers and established suppliers are not the only ones who are trying hard to find the sweet spots in this new emerging mobility value chain. Tech giants like Nvidia and Intel, leading software and internet players like Google (Waymo) and new mobility startups such as Aurora, Cruise and Uber are also on the verge of reaping the rewards of an entirely new future mobility era.
Utilizing ALCF supercomputing resources, Argonne researchers are developing the deep learning framework MaLTESE with autonomous -- or self-driving -- and cloud-connected vehicles in mind. This work could help meet demand to deliver better engine performance, fuel economy and reduced emissions. Researchers used nearly the full capacity of the ALCF's Theta system to simulate a typical 25-minute drive cycle of 250,000 vehicles. Researchers at Argonne are developing the deep learning framework MaLTESE (Machine Learning Tool for Engine Simulations and Experiments) to meet ever-increasing demands to deliver better engine performance, fuel economy and reduced emissions. Automotive manufacturers are facing an ever-increasing demand to deliver better engine performance, fuel economy and reduced emissions.
The first autonomous vehicles were built in the 1980s and since then, major companies and research organizations have started developing prototype autonomous vehicles: General Motors, Bosch, Nissan, Audi, Volvo, Oxford University, Google, and more. Recent car models are equipped with cruise control features that follow road markings and automatically adjust speed to maintain a proper distance between vehicles in the same lane. An autonomous vehicle must be competent in a wide range of machine learning processes before it can drive safely. Multi-step AI systems allow autonomous vehicles to process image and video data in real time and safely coordinate with other vehicles on the road. We as humans have no problem recognizing other vehicles, pedestrians, trees, road signs, and other objects when we're driving.
Yet, many still believe commons myths surrounding AI or are unsure of how AI within a DAM can actually benefit them. In our webinar "Work Smarter, Not Harder" with Forrester Senior Analyst, Nick Barber, we tackled the challenges that marketers and creatives face when it comes to managing thousands of creative assets and how AI can lessen administrative work while providing opportunities for more complex projects. Following the webinar, we asked Nick to address some of the questions we heard from the audience around common AI misconceptions and how more advanced AI within DAMs can offer brand-specific opportunities. Q: Do I really need AI and will it help me with my library of creative assets? Barber: Artificial intelligence offers a lot of promise for companies of all sizes because it can enrich a large library of content where metadata governance has been historically not very good.
Motorsport has long been at the bleeding edge of innovation and Brent Pittman, director of engineering, automotive and concept design at Autodesk suggests that remains the case. Motorsport is more than just blazing heat, screeching brakes, a roar of engines, and the test of a driver's skill and bravery. It is positioned as the pinnacle of technological innovation coming out of the automotive industry. But for a sport that uniquely has'Constructor Championships' to reward the work of the team behind the athlete, it is interesting that the value of new technologies hasn't been realised fully yet. Indeed, when it all boils down, an athlete may be the most talented individual, but it is technology that is the real driver behind the sport's success.