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#selfdrivingcars_2022-04-20_05-36-02.xlsx

#artificialintelligence

The graph represents a network of 1,840 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 20 April 2022 at 12:47 UTC. The requested start date was Wednesday, 20 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 25-day, 6-hour, 8-minute period from Friday, 25 March 2022 at 15:16 UTC to Tuesday, 19 April 2022 at 21:24 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#selfdrivingcars_2022-04-06_05-36-02.xlsx

#artificialintelligence

The graph represents a network of 1,781 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 06 April 2022 at 12:46 UTC. The requested start date was Wednesday, 06 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 24-day, 4-hour, 46-minute period from Saturday, 12 March 2022 at 16:24 UTC to Tuesday, 05 April 2022 at 21:11 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


#selfdrivingcars_2022-03-30_05-36-01.xlsx

#artificialintelligence

The graph represents a network of 1,744 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 30 March 2022 at 12:48 UTC. The requested start date was Wednesday, 30 March 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 21-day, 23-hour, 8-minute period from Monday, 07 March 2022 at 22:37 UTC to Tuesday, 29 March 2022 at 21:46 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


Why The Tesla Robot Won't Work

#artificialintelligence

"Tesla is arguably the world's biggest robotics company because our cars are like semi-sentient robots on wheels" -Elon Musk Last year during Tesla's open AI day, Elon Musk announced something new Tesla was working on called the Tesla robot. A lot of people I've spoken to have no idea what the Tesla robot is, what it is for, how it will work, and why it's not feasible. All they know is it's another cool piece of tech created by Tesla. In this article, I will be talking about what the Tesla robot is, what it will do and how it will work. I know pretty much anyone who is on the internet knows about Tesla, but for the sake of completion let's still go over what Tesla is.


3 trends shaping robotics demand in 2022

#artificialintelligence

With demand for robots growing as companies in multiple sectors look for new ways to enhance their productivity and competitiveness post-pandemic, ABB has compiled a set of growth predictions, looking at key trends driving demand for robots in the coming year. "The pandemic accelerated far-reaching global mega trends – from labor shortages and supply chain uncertainty, to the individualized consumer and growing pressure to operate sustainably and resiliently – leading new businesses to look to robotic automation," said Marc Segura, ABB's newly appointed robotics division President. "As technology opens new opportunities for meeting customer demands, new trends will continue to emerge that will further drive demand in areas where robots have traditionally not been used." Based on customer conversations, market research and a global survey of 250 companies across multiple industries, ABB has identified three key trends that will shape the demand for robots in 2022. With many countries restricting and phasing out the production of combustion engine vehicles over the next decade, the race towards electric cars has accelerated.


Internet of Production

Communications of the ACM

Making a high-quality gear cannot be learned simply from an Internet search. You may find guidelines, papers, rules, lectures, and videos. However, applying this general knowledge to a specific production process and dealing with uncertainties and disruptions requires special know-how, most of which resides in people's heads and networks and is acquired to a large extent through "learning by doing." Over 10 years ago, the vision of Industry 4.05 was announced at the Hannover Fair 2011 as part of the German/European High-Tech Strategy and adopted internationally by the Japanese Industrial Value Chain Initiative, the Advanced Manufacturing Initiative in the U.S., the Chinese Made in China 2025 strategy, the South Korean Manufacturing 3.0, and the U.K.'s High-Value Manufacturing Catapult research center. This "fourth industrial revolution" follows the earlier stages of mechanization (steam engine), mass production (assembly lines), and IT-based electronic automation.


Future Technology Trends: 10 Trends Mapping the Global Future

#artificialintelligence

Technological discoveries are the spermatozoa of social change, says C.L.R James. That means future technology trends will change with respect to dynamic social trends. And these social trends changes with respect to changing needs and demands. There are several reasons behind rapidly changing tech trends such as coping with scarcity of resources, improving life standards, increasing overall efficiency and cutting extra costs and much more. These days, both small scale and big companies are inventing innovative tech things that seem to be magical stuff for many people.


#selfdrivingcars_2022-02-02_05-36-01.xlsx

#artificialintelligence

The graph represents a network of 1,623 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 02 February 2022 at 13:49 UTC. The requested start date was Wednesday, 02 February 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 17-day, 22-hour, 51-minute period from Friday, 14 January 2022 at 22:39 UTC to Tuesday, 01 February 2022 at 21:31 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


The Morning After: Latest iOS beta supports FaceID with a mask

Engadget

Using your face to unlock your phone is great in normal times, but less than ideal when you're masked up and avoiding germs. Apple already has a workaround in place if you own a new enough Apple Watch, and now it's working on a fix for the rest of us. The most recent iOS developer beta enables users to open their device with just the geography of their eyes. The feature, which is currently being tested, will work with glasses users, although if you're wearing sunglasses, you might have to take them off first. At the same time, Apple has also reportedly been looking into enabling iPhones to work as standalone payment terminals. That way, it would be easier to settle bills between friends and, more importantly, enable small businesses to accept payments.


A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

arXiv.org Artificial Intelligence

Prescribing optimal operation based on the condition of the system and, thereby, potentially prolonging the remaining useful lifetime has a large potential for actively managing the availability, maintenance and costs of complex systems. Reinforcement learning (RL) algorithms are particularly suitable for this type of problems given their learning capabilities. A special case of a prescriptive operation is the power allocation task, which can be considered as a sequential allocation problem, where the action space is bounded by a simplex constraint. A general continuous action-space solution of such sequential allocation problems has still remained an open research question for RL algorithms. In continuous action-space, the standard Gaussian policy applied in reinforcement learning does not support simplex constraints, while the Gaussian-softmax policy introduces a bias during training. In this work, we propose the Dirichlet policy for continuous allocation tasks and analyze the bias and variance of its policy gradients. We demonstrate that the Dirichlet policy is bias-free and provides significantly faster convergence, better performance and better hyperparameters robustness over the Gaussian-softmax policy. Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems. The experimental results show the potential to prescribe optimal operation, improve the efficiency and sustainability of multi-power source systems.