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) …
Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it's time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case. Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy.
Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.
Understanding text, images, and sounds is not a uniquely human prerogative anymore. Artificial intelligence is transforming virtually every business. AI's ability to derive data-driven insights is paving the road to better digital marketing. From the vast data analysis, marketers gain valuable consumer insights and change how they connect brands with their audiences. Why artificial intelligence cannot be separated from digital marketing anymore?
Artificial intelligence is about to change lead generation and conversion as you know it. In the process, it'll have a transformative impact on companies and careers. AI is a blanket term that covers several different technologies. You might have heard of some of them, like machine learning, computer vision, and natural language processing. Even if you don't know much about it, though, you probably use AI-powered technology dozens or hundreds of times per day.
With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they even be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.
For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions--about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.
Baidu (NASDAQ: BIDU) is making a big push into cutting-edge IT segments. The company announced Thursday that it will be allocating more capital to investments in developing corners of the market, particularly artificial intelligence (AI), cloud computing, and data centers. This project will unfold over the next 10 years, in an attempt by the China-based company to build out assets for future tech needs. This piggybacks on the Chinese government's ambition to develop what it calls "new infrastructure" throughout the country to dramatically modernize its economy. Baidu did not specify how much it would spend on its new infrastructure efforts.
When searching the keyword "machine learning" on Github, I found 246,632 machine learning repositories. Since these are top repositories in machine learning, I expect the owners and the contributors of these repositories to be experts or competent in machine learning. Thus, I decided to extract the profiles of these users to gain some interesting insights into their background as well as statistics. By removing duplicates as well as removing the profiles that are organizations like udacity, I obtain a list of 1208 users. After cleaning the data, it comes to the fun part: data visualization.
Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for Boltzmann machine learning to obtain a suitable probability distribution. The Boltzmann machine learning consists of calculating the gradient of the loss function given in terms of the thermal average, which is the most time consuming procedure. Here, we propose a method to implement the Boltzmann machine learning by using Noisy Intermediate-Scale Quantum (NISQ) devices. We prepare an initial pure state that contains all possible computational basis states with the same amplitude, and apply a variational imaginary time simulation. Readout of the state after the evolution in the computational basis approximates the probability distribution of the thermal equilibrium state that is used for the Boltzmann machine learning. We actually perform the numerical simulations of our scheme and confirm that the Boltzmann machine learning works well by our scheme.
One of the most important concepts in facial analysis using images, is to define our region of interest (ROI), we must define in our image a specific part where we will filter or perform some operation. For example, if we need to filter the license plate of a car, our ROI is only on the license plate. The street, the body of the car and anything else that is present in the image is just a supporting part in this operation. In our example, we will use the opencv library, which already has supported to partition our image and help us identify our ROI. In our project we will use the ready-made classifier known as: Haar cascade classifier. This specific classifier will always work with gray images.