Tesla Inc.'s Elon Musk said the carmaker is on the verge of developing technology to render its vehicles fully capable of driving themselves, repeating a claim he's made for years but been unable to achieve. The chief executive officer has long offered exuberant takes on the capabilities of Tesla cars, even going so far as to start charging customers thousands of dollars for a "Full Self Driving" feature in 2016. Years later, Tesla still requires users of its Autopilot system to be fully attentive and ready to take over the task of driving at any time. Tesla's mixed messages have drawn controversy and regulatory scrutiny. In 2018, the company blamed a driver who died after crashing a Model X while using Autopilot for not paying attention to the road.
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.
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.
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.
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.