People want increased regulation and more accountability in the field of artificial intelligence (AI), new research by Fountech.ai The AI firm commissioned an independent survey among 2,000 UK adults to uncover their attitudes towards the current state of AI development. It found that the majority (64%) want to see more regulation introduced so that the technology is safer to use and does not pose threats to society. Those aged over 55 appear more sceptical of AI, with almost three quarters (73%) keen to see additional guidelines introduced to improve safety standards. This is in comparison to just over half (53%) of those aged between 18 and 34 who held this view.
Brisbane-based drone company Emesent has launched what it has dubbed as the "first plug-and-play payload" that enables industrial drones to fly beyond communications range and into unmapped areas. Built on Emesent's Hovermap simultaneous localisation and mapping (SLAM) autonomous flight system, the autonomy level 2 (AL2) technology was designed to enable companies to map, navigate, and collect data in challenging environments, such as mines, civil construction works, telecommunications infrastructure, and areas hit by natural disasters. "With the intelligence to navigate environments without a prior map, customers can use the system to carry out complex missions, secure the safety of personnel, and drive greater efficiency in their operations," Emesent co-founder and CEO Stefan Hrabar said. Emesent added that using AL2 would mean the drone processes data on-board in real-time to stream a 3D map of the environment back to the operator's tablet. It also touted that the ability for a drone to fly beyond line of sight allows workers to avoid hazardous environments while also enhancing visibility.
Aicha Evans who is the CEO of the self-driving technology development company Zoox, talks about ... [ ] autonomous cars during a keynote session at the Amazon Re:MARS conference on robotics and artificial intelligence at the Aria Hotel in Las Vegas, Nevada on June 6, 2019. On June 26th, Amazon announced via their blog they are acquiring autonomous ride-hailing vehicle startup Zoox. Financial terms of the acquisition were disclosed. However, the Financial Times says Amazon paid $1.2B for Zoox. Launched in 2014, Zoox began with the vision of producing zero-emissions vehicles for autonomous ride-hailing services.
Robotic vehicles including unmanned ground, air, and sea vehicles, robotics and automation, intelligent control systems, intelligent manufacturing, intelligent transportation systems, weapon systems are some of the wonder deployments of robotics that have caught the attention of businesses. Robotic products under development include collaborating with intelligent systems to control complex systems of systems that serve as decision tools for human decision-makers, and autonomous intelligent robots and vehicles for military and civil applications. Robots are helpful to make repetitive activities much easier, for instance, assembly lines in a factory or collecting large amounts of mundane data, can be boring. Multiple pieces of research have connected mundane tasks associated with negative behaviours and lethargy, thus impacting production capabilities. This holds especially true for rule-based duties that require continuous attention, leaving the manual workforce tired and agitated.
In their book The End of Capitalism (As We Knew It), J.K. Gibson-Graham, a two-person writing team, examine a conundrum: after innumerable examinations of capitalism's inherent contradictions, and despite decades of projects devoted specifically to accelerating its demise, capitalism seems as vibrant as ever. Gibson-Graham ask, "In the face of these efforts, how has capitalism maintained such a strong grip on political economy?" The answer they offer is oblique but striking: perhaps it hasn't. More precisely, they suggest that the conventional wisdom that economic life is dominated by capitalist relations is not, in fact, true. They point to the wide range of forms of economic engagement that fall outside the limits of traditional political economy -- domestic activity, relations of care, mutual support, self-sustenance, and more -- to argue that capitalism is only one amongst a range of concurrent forms of economic life -- and perhaps not even the most common.
In recent times we've seen airports and railways stations trying to detect whether people are maintaining social distance, wearing masks using cameras. These are real-time videos captured by cameras where constant movement exists. We've also seen the research going towards developing self-driving cars where the car needs to detect an obstacle in its way and drive accordingly. How does all this happen? This is where Object Detection comes into the picture.
Different algorithms have been introduced to segment objects based on the criteria like uniform colors, regular shapes, and nearby shadows. Since these algorithms are whether hard-coded or learned from selected features, have some limitations in terms of generalization. Furthermore, these algorithms' accuracies are low, and results are dependent on parameter selection due to manual feature extraction and the lack of big datasets and capable computers. For these reasons, the field of automatic feature extraction has been extensively studied for a long time. In addition, by emerging the neural networks and computers, which can process a huge amount of data in per seconds, a new approach was presented which stacks each layer of neural networks on top of each other to extract features automatically.
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.