If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Imagine that you are working on a project, with a team of 10 people. All members of this team, have to work from home now, because of the ongoing pandemic, so all of them have different laptops, different system specifications, different operating systems, etc. Now one fine day, a team member pushes a new change to GitHub, that adds some new functionality to your project. Unfortunately, these new changes do not work for some people, maybe because of different versions of the software installed on the different computers. So you have a very common problem, that many teams often face. "It works for him, but not for me" Docker was made specifically to solve this problem.
Meet the Toadi: an autonomous lawn mower robot that cuts your grass, so you don't have to. It comes with a 4K camera and AI for autonomous navigation. It cuts your grass but avoids objects and animals. The Toadi can maintain up to 1.2 acres of landscape. It has 4 easily replaceable titanium coated blades. The mowing height can be adjusted from 1.29″ to 3.54″.
There is no doubt that on the whole, the economic impacts from the lockdown and pandemic will be devastating. But while most leisure activities were throttled by the lockdown, others thrived -- just ask any of your friends that did Yoga With Adrienne (probably the same mates that brew their own kombucha). Tinder and Bumble usage alone spiked by over 20%, with Tinder registering 3 billion swipes on 28 March alone. However, the pandemic only accelerated a trend that was already in full force: finding love via apps. "Met online" is now the most common way that people report finding their significant other, streets ahead of boring old classics like "met in church" or "met in the neighbourhood". While there are a range of massively popular dating apps, including Bumble and Grindr, Tinder continues to be the most popular platform by a significant margin.
Whether you turn to news outlets, tech magazines, or academic sources for insight, you're likely to hear that the COVID-19 pandemic is going to drive massive growth in automation, especially via robots.1 The arguments in favor of this view seem reasonable: Main Street might look dead, but companies that provide shippable goods have been facing double, triple, or even 10 times their previous demand. Robots, the thinking goes, should be able to reliably do that repetitive physical work when many workers aren't safely able or willing to set foot in the building. What's more, access to the technology is getting less expensive, with "robots as a service" models allowing companies to pay per touch rather than dipping into precious capital reserves. And robots are becoming more capable. In just the past few years, for example, we've seen a small number of companies building and selling AI-enabled robots to pick things out of bins, handle parts, tend machines, and test the latest electronics.
The E.U. supports the Iranian nuclear deal as the Trump administration announces new sanctions. Iran's Revolutionary Guard on Saturday threatened to avenge the killing of its top general, saying it would go after everyone responsible for the January U.S. drone strike in Iraq. The guard's website quoted Gen. Hossein Salami as saying, "Mr. Our revenge for martyrdom of our great general is obvious, serious and real." FILE: Chief of Iran's Revolutionary Guard Gen. Hossein Salami speaks at a pro-government rally, in Tehran, Iran.
The data set would be astronomy sub-images that are either bad (edge of chip artifacts, bright star saturation and spikes, internal reflections, chip flaws) or good (populated with fuzzy-dot stars and galaxies and asteroids and stuff). Let's say the typical image is 512x512 but it varies a lot. Because the bad features tend to be big, I'd probably like to bin the images down to say 64x64 for compactness and speed. It has to run fast on tens of thousands of images. I'm sort of tempted by the solution of adopting PlaidML as my back end (if I understand what its role is), because it can compile the problem for many architectures, like CUDA, CPU-only, OpenCL.
There are various algorithms for object detection tasks and these algorithms have evolved in the last decade. To improve the performance further, and capture objects of different shapes and sizes, the algorithms predict multiple bounding boxes, of different sizes and aspect ratios. But of all the bounding boxes, how is the most appropriate and accurate bounding box selected? This is where NMS comes into the picture. The objects in the image can be of different sizes and shapes, and to capture each of these perfectly, the object detection algorithms create multiple bounding boxes.
Since the 1950s, science fiction has been telling the world we will soon be living with robots. While robots have emerged, they have been mostly kept to heavy industry, where machines can perform dangerous, hot and unpleasant repetitive tasks to a high standard. But China is pioneering the move to mainstream robots in more public spheres. And the country is promising big changes in the coming decade. Robots, strange as it may seem, can play a key role in development and fighting poverty.