Abstract: Properly handling del i cate produce with robotic manipulators is a major part of the future role of automation in agricultural harvesting and processing . Grasping with the correct amount of force is crucial in not only ensuring proper grip on the object, but also to avoid damaging or bruising the product . In this work, a flexible pressure sensor that is both low cost and easy to fabricate is integrated with robotic grippers for work ing with produce of varying shape s, sizes, and stiffness es . The sensor is successfully integrated with both a rigid robotic gripper, as well as a pneumatically actuated soft finger. Furthermore, an algorithm is proposed for acce lerated estimation of the steady - state value of the sensor output based on the transient response data, to enable real - time applications. The sensor is shown to be effective in incorporating feedback to correctly grasp objects of unknown sizes and stiffnesses . At the same time, the sensor provid es estimates for these values which can be utilized for identification of qualities such as ripeness levels and bruising . It is also shown to be able to provide force feedback for objects of variable stiffness es . Th is enables future use not only for produce identification, but also for tasks such as quality control and selective distribution based on ripeness levels . Keywords: Robotics, sensing, p roduce handling, grasping Highlights: Low - cost and easy - to - fabricate sensor for easy implementation with a variety of robotic grippers Fast estimation of settled resistance using exponential decay curve fit Measurements of grasping force and stiffness of a held object V arious produce handling features such as ripeness monitoring, bruising detection, and size estimation 1. Introduction: The use of robotic end - effectors for securely grasping objects is a pivotal component in manipulation tasks .

Amazon is the Standard Oil of the 21st century. Its business operations and global reach dwarf those of virtually every other company on the planet -- and exceed the GDP of more than a few countries -- illustrating the vital importance innovation has on the modern economy. In his latest book, The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society, author Azeem Azhar examines how the ever-increasing pace of technological progress is impacting, influencing -- and often rebuilding -- our social, political and economic mores from the ground up. Excerpted from The Exponential Age: How Accelerating Technology is Transforming Business, Politics and Society by Azeem Azhar. In 2020, Amazon turned twenty-six years old.

When it comes to leading in this moment, a lot of information is swirling around on the topic of how to work from home or how to lead through disruption -- like contingency planning. These are of course very helpful and important topics, but they miss the point and the soul of leadership right now. As a futurist, humanist, innovation and leadership expert, I've spent the last 20 years working with chief executives from many Fortune 500 organizations on how to develop exceptional leaders that can lead through and respond to the mega-shifts coming in the future. The parallels between what we need to do to navigate the Coronavirus pandemic and AI are endless. Here we are at a moment where the disruptions I have anticipated have been accelerated -- not by the exponential curve of technology, but by the exponential curve of a virus.

It's very abstract and it's something that deep neural networks can't do even in the simplest form. They can't recognize same… If you give them an example of something in which two things are the same versus two things are different in general, it can't do that task.

As every single network training begins, let's start with the data. Data might give some explanation as to why you're having problems while training. Your data might give insights into why your model isn't behaving as it is expected to. After training a classification model, there might be situations where the output of the model completely or mostly belongs to one class ie, the situation where the model is biased. This is primarily due to an imbalanced dataset.

Last year, I participated in a discussion of The Human Use of Human Beings, Norbert Weiner's groundbreaking book on cybernetics theory. Out of that grew what I now consider a manifesto against the growing singularity movement, which posits that artificial intelligence, or AI, will supersede and eventually displace us humans. The notion of singularity – which includes the idea that AI will supercede humans with its exponential growth, making everything we humans have done and will do insignificant – is a religion created mostly by people who have designed and successfully deployed computation to solve problems previously considered impossibly complex for machines. They have found a perfect partner in digital computation, a seemingly knowable, controllable, machine-based system of thinking and creating that is rapidly increasing in its ability to harness and process complexity and, in the process, bestowing wealth and power on those who have mastered it. In Silicon Valley, the combination of groupthink and the financial success of this cult of technology has created a feedback loop, lacking in self-regulation (although #techwontbuild, #metoo and #timesup are forcing some reflection).

Andrew Yan-Tak Ng, regarded as one of the world's foremost experts on Artificial Intelligence (AI), firmly believes that despite the widespread mistrust of AI, it is good for governments, companies and individuals. Currently co-chairman and co-founder of the online learning platform Coursera and an adjunct professor at Stanford University's computer science department, Ng served as chief scientist and vice-president at Chinese tech company Baidu and was founding lead of the Google Brain team. In a phone interview from the Coursera headquarters in Mountain View, California, Ng spoke about the need for the Indian government to invest in education. He also shared his perspective on the potential of AI and the fears surrounding it. We would like you to propose one big idea to mark India's Independence Day.

By now, most of us are familiar with Moore's Law, the famous maxim that the development of computing power follows an exponential curve, doubling in price-performance (that is, speed per unit cost) every 18 months or so. When it comes to applying Moore's Law to their own business strategies, however, even visionary thinkers frequently suffer from a giant "AI blind spot." I give a lot of talks to successful, strategically-minded business people who can see around corners in their own industries, yet they struggle to grasp what exponential improvement really means. And a lot is riding on this exponential curve, but one technology that is particularly benefiting from it is artificial intelligence. One reason people do not grasp how rapidly artificial intelligence is developing is so simple it's almost laughable: Exponential curves don't fare well when we humans try to capture them on paper. For very practical reasons, it's virtually impossible to fully depict the steep trajectory of an exponential curve in a small space such as a chart or a slide.

By now, most of us are familiar with Moore's Law, the famous maxim that the development of computing power follows an exponential curve, doubling in price-performance (that is, speed per unit cost) every 18 months or so. When it comes to applying Moore's Law to their own business strategies, however, even visionary thinkers frequently suffer from a giant "AI blind spot." I give a lot of talks to successful, strategically-minded business people who can see around corners in their own industries, yet they struggle to grasp what exponential improvement really means. And a lot is riding on this exponential curve, but one technology that is particularly benefiting from it is artificial intelligence. One reason people do not grasp how rapidly artificial intelligence is developing is so simple it's almost laughable: Exponential curves don't fare well when we humans try to capture them on paper. For very practical reasons, it's virtually impossible to fully depict the steep trajectory of an exponential curve in a small space such as a chart or a slide.

A few weeks ago, for the first time ever, a computer beat the world champion of Go, one of the most complex games known to man. This was another watershed moment in the progress of artificial intelligence. To give you an idea how complex Go is, there are 2.082 10 170 possible board configurations. That is 2 with 170 zeroes after it. Chances are your brain cannot even conceive of a number that large (but a computer can). Or to give you an idea of how big of a number that is, there are only 10 80 atoms in the universe -- that is, one followed by 80 zeroes. The reason this is such a big deal is that Go is so complicated that in order to beat a top human player, a machine would have to learn how to think creatively, improvising and adapting to the situation at hand without being able to calculate every possible outcome; i.e., there has to be some serious artificial intelligence going on -- like real, creative intelligence.