When visibility is low at night and you turn on your smartphone camera, the video preview is full of darkness, and the visibility is even lower than what you can see. With the surging of real-time video apps, we have seen various video enhancement technologies (such as beautification or AR stickers) that make a video look better than it is. You may wonder if there is a technology that can make video look clearer than it is in low light conditions. The answer is surely a YES. And there are a few more scenarios where there are strong demands for low-light image enhancement technology as follows.

This article is part of our Summer reads series. Visit our collection to discover "The Economist reads" guides, guest essays and more seasonal distractions. IN RECENT years artificial intelligence (AI) has undergone a revolution. After decades of modest progress that never quite lived up to its promise, a different approach--relying on big data and stats, not clever algorithms--made huge strides in solving real-world problems like voice- and image-recognition and self-driving cars. Also in the past ten years, a lot of books have been published that aim to explain what AI is, where it's going and why it matters.

Hi, I am navabhaarathi asokan. I will be publishing content about artificial intelligence from scratch. I will start with basic mathematics required for machine learning, followed by python programming, and then we will learn about machine learning from basic to advanced concepts.

Artificial intelligence is advancing at an incredible pace. If you haven't been following their progress, you'll be surprised to learn about AI's widespread adoption in every facet of life. A massive AI revolution is happening right now in the business world. According to the World Economic Forum, 85 million jobs will disappear, and 97 million new ones will emerge thanks to AI by 2025. We've entered an era where machines are no longer mere tools in the hands of humans but companions that work side by side and provide enormous value to any industry.

Free Coupon Discount - Ensemble Machine Learning in Python: Random Forest, AdaBoost, Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python Created by Lazy Programmer Inc. Students also bought Unsupervised Deep Learning in Python Machine Learning and AI: Support Vector Machines in Python Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Deep Learning Prerequisites: Linear Regression in Python Preview this Udemy Course GET COUPON CODE Description In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Baidu has obtained permits to run a fully driverless robotaxi service in China. It says it's the first company in the country to obtain such permissions. Back in April, Baidu received approval to run an autonomous taxi service in Beijing, as long as there was a human operator in the driver or front passenger seat. Now, it will be able to offer a service where the car's only occupants are passengers. There are some limits to the permits.

Data science is a branch of information technology that deals with the analysis and processing of large volumes of data, which may be structured or unstructured or a mix of both, in order to find unseen patterns and derive meaningful information. Data science is useful to identify market opportunities, for process optimisation and cost reduction, and to identify abnormal financial transactions, among others. A typical project involves components that require expertise from several of these areas in combinations of varying proportions from one project, to another. As technology finds its way into all our daily activities, so do the digital data trails we leave behind, be it at retail outlets, banks and many other places. Many organisations have realised they are sitting on a veritable gold mine of information that they can capitalise on and put to good use.

AI Ethics and the question of whether to ask AI if it is sentient. If I ask you whether you are sentient, you will undoubtedly assert that you are. Allow me to double-check that assumption. Perhaps the question itself seems a bit silly. The chances are that in our daily lives, we would certainly expect fellow human beings to acknowledge that they are sentient. This could be a humorous-inducing query that is supposed to imply that the other person is maybe not paying attention or has fallen off the sentience wagon and gone mentally out to lunch momentarily, as it were. Imagine that you walk up to a rock that is quietly and unobtrusively sitting on a pile of rocks and upon getting close enough to ask, you go ahead and inquire as to whether the rock is sentient. Assuming that the rock is merely a rock, we abundantly anticipate that the erstwhile but seemingly oddish question will be answered with rather stony silence (pun!).

We know when our pooch is giving us those puppy dog eyes – but having spent thousands of years with our canine friends, it turns out we really can read their moods. A study found slight changes, from wide eyes to a lolling tongue, can help to detect six different emotions in dogs. When 105 people were shown pictures of three different breeds, they correctly identified feelings of happiness, sadness, curiosity, fear, disgust and anger in the animals. Professor Harris Friedman, from the University of Florida and Harvard University, said: 'Our findings suggest people can naturally understand their dogs' emotions from their facial expressions.' Researchers recruited a dobermann, a Belgian shepherd called a malinois, and a floppy-eared Rhodesian ridgeback for their study.

Many predict a great future for machine learning and artificial intelligence. However, the best developments in this direction belong either to the academic community or too big business, mainly in the field of advertising. Therefore, there are not many working projects that would allow cryptocurrency traders to use artificial intelligence in their service. Let's figure out how the principle of machine learning works in cryptocurrency trading, and also consider one of the options for automatic trading. And in the next article, we will create and train our own bot, which in theory is able to show a positive result, however, its use is highly discouraged.