The space projects have been dominated by government bodies until we saw the ambitious companies such as SpaceX and Blue Origin diving into this diverse area. These two are the most prominent names in the private space community and are often put on a face-off due to the similarity of its founders in other areas as well. Owned by two of the most powerful businessmen of all time -- Elon Musk and Jeff Bezos, they have been on the competition radar for their interest in the area of autonomous vehicles. Similarly, in the space segment, while the two companies might look quite similar in its attempts to explore space, the ideology and the approach of these companies vary quite significantly. But one thing cannot be denied that they both are developing large, reusable vehicles capable of carrying people and satellites across space. While we have often heard about SpaceX's missions and launches over the past few years, Blue Origin does not come out to be so ambitious in gaining traction.
Before diving into Artificial Intelligence's future, Let's have a look at what is Artificial Intelligence. Artificial Intelligence is a machine Intelligence. In contrast with natural intelligence, machine intelligence is more accurate and efficient because it demonstrated by machines, not by humans or animals. Today, AI properly knowns as narrow AI (or weak AI), just because of designed it for narrow tasks. But, for a long-term goal, many researchers go for general AI (AGI or strong AI).
Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.
XAOS MOTORS, headquartered in KOREA, challenges the technological progress of autonomous driving. XAOS MOTORS, by launching XCAT LiDAR Sensor now, give OEMs to make fully self-driving cars earlier than the market expected. MEMS LiDAR Sensor XCAT was developed for self-driving cars. With the ability to scan over 300 meters, XCAT can safely cope with high-speed driving. XCAT is designed for mass production, and OEMs can adopt high-performance 3D LiDARs at a low cost.
DJI's new Mavic Air 2 folding-style drone is a huge improvement over the previous model--so much so that for most people, this is the perfect drone. The Mavic Air 2 is the middle child in DJI's consumer drone lineup, sitting between the smaller, lighter, but less capable Mavic Mini, and the more powerful, more capable, but also more expensive, Mavic 2. If you're just getting started with drones, the less expensive Mavic Mini (8/10 WIRED Recommends)--my previous top pick for most people--might be a better buy. That said, the Air 2 offers better collision avoidance systems, higher quality photos and video, and a wide assortment of automated flight features that newcomers and seasoned vets alike can appreciate. The Mavic Air 2 is slightly bigger than its predecessor, at least on paper. The folding design remains compact, and at 1.3 pounds, the drone is plenty portable.
For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions--about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.
The following was issued as a joint release from the MIT AgeLab and Toyota Collaborative Safety Research Center. How can we train self-driving vehicles to have a deeper awareness of the world around them? Can computers learn from past experiences to recognize future patterns that can help them safely navigate new and unpredictable situations? These are some of the questions researchers from the AgeLab at the MIT Center for Transportation and Logistics and the Toyota Collaborative Safety Research Center (CSRC) are trying to answer by sharing an innovative new open dataset called DriveSeg. Through the release of DriveSeg, MIT and Toyota are working to advance research in autonomous driving systems that, much like human perception, perceive the driving environment as a continuous flow of visual information. "In sharing this dataset, we hope to encourage researchers, the industry, and other innovators to develop new insight and direction into temporal AI modeling that enables the next generation of assisted driving and automotive safety technologies," says Bryan Reimer, principal researcher.
Few issues are as important to businesses today than sustainability. Because the modern consumer cares about the environment, companies need to meet higher expectations about eco-friendly practices. Supply chains, in particular, have a lot of room to improve. It's no secret that logistics chains aren't exactly eco-friendly. They account for more than 80% of carbon emissions globally. The modern business world can't exist without supply chains, but the natural world won't exist in the same way if they don't improve. The good news is there's an . . .
Silverstream Technologies, the leading air lubrication manufacturer for the shipping industry, in collaboration with the University of Southampton, has been awarded an Innovate UK Knowledge Transfer Partnership (KTP) grant to advance machine learning in the maritime sector, the organisations have announced today. The two-year partnership will see an Associate of the University of Southampton, secured under the programme, work with Silverstream's Technical Team with the goal to advance machine learning and artificial intelligence within the Silverstream System's control and automation module. The Silverstream System uses air lubrication to reduce frictional resistance between a vessel's hull and the water and delivers fuel savings of 5-10% depending on the vessel and its operating profile. The KTP will aim to increase this saving by analysing operational data taken from installed systems. This data, when combined with cutting edge machine learning techniques, will help to further increase Silverstream System performance during a voyage, with the goal of gaining the theoretical maximum savings associated with the technology every time it is operating.