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) …
In the past couple of years, large companies including Google, Facebook, Microsoft, and Amazon have been releasing libraries, frameworks, and services that enable other businesses to build machine learning (ML)models. What's great about these frameworks is that it's now cheaper and faster to run a machine learning experiment for your business. Building useful machine learning models often takes a lot of data -- thousands of examples -- as well as a lot of time to prep the data in a format that is appropriate for the system. The content needs to be carefully curated and high quality. This isn't always easy to come by.
Artificial Intelligence is on a rage! All of a sudden every one, whether understands or not, is talking about it. Understanding the latest advancements in artificial intelligence can seem overwhelming, but it really boils down to two very popular concepts Machine Learning and Deep Learning. But lately, Deep Learning is gaining much popularity due to it's supremacy in terms of accuracy when trained with huge amount of data. Machine Learning has become necessary in every sector as a way of making machines intelligent.
We need machine learning for tasks that are too complex for humans to code directly, i.e. tasks that are so complex that it is impractical, if not impossible, for us to work out all of the nuances and code for them explicitly. So instead, we provide a machine learning algorithm with a large amount of data and let it explore and search for a model that will work out what the programmers have set out to achieve. Let's look at these two examples: Then comes the Machine Learning Approach: instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical hand-written program -- it may contain millions of numbers. If we do it right, the program works for new cases, as well as the ones we trained it on. If the data changes, the program can change too by training from the new data.
Artificial intelligence (AI) promises to change nearly every enterprise workflow, but it isn't changing software and web development just yet. What other developers (and businesses for that matter) should take from this information is that it is OK if you haven't jumped on the AI hype train just yet. However, they should also take note that, despite low adoption rates, there is strong interest in the space, and it could be poised to grow rapidly. While many developers aren't using AI and machine learning tools, 81% said they are interested in learning more about them. Of those developers, 46% said they were specifically interested in automated machine learning, 22% in sentiment analysis and natural language processing, and 21% in hybrid and deep learning models.
Years ago, AI specialists worked at universities; now they are raffled in Silicon Valley. While large companies, such as Apple, Google or Facebook, are making significant investments in research and the acquisition of specialized startups in this field. On the other hand, words such as artificial intelligence, machine learning, deep learning or big data, appear more and more frequently in the media. Listed in this article are 5 large companies that already use artificial intelligence. Surely, the best known artificial intelligence product of Apple is Siri, its virtual personal assistant, included in the iPhone and the latest iPad.
According to Gartner, artificial intelligence will be the most disruptive class of technology over the next 10 years due to radical computational power, near-endless amounts of data, and unprecedented advances in deep learning. The rise of deep learning has been fueled by three recent trends: the explosion in the amount of training data; the use of accelerators such as graphics processing units (GPUs); and the advancement in training algorithms and neural network architectures. To realize the full potential of this rising trend, we want the technology to be easily accessible to the people it matters most to: data scientists and AI developers. Training deep neural networks, known as deep learning, is currently highly complex and computationally intensive. It requires a highly tuned system with the right combination of software, drivers, compute, memory, network, and storage resources.
It is not an easy task to get into Machine Learning and AI. Given the enormous amount of resources that are available today, many aspiring professionals and enthusiasts find it hard to establish a proper path into the field. The field is evolving at a constant pace and it is crucial that we keep up with this rapid development. In order to cope with the speed of evolution and innovation that is today so overwhelming, a good way to stay updated and knowledgeable on the advances that have taken place in ML is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals. Today, we discuss top 10 open-source projects on Python, Machine Learning and AI.
KITCHENER, Ontario--(BUSINESS WIRE)--Miovision, a global leader in smart city technology, today launched the next generation of traffic technology. Using a type of artificial intelligence (AI) called deep learning, Miovision SmartSense brings AI to the roadside to help cities sense and understand what's happening at the intersection in real time. SmartSense can detect the presence and movement of vehicles, pedestrians and cyclists and use this data to improve congestion and safety. The new Miovision SmartSense technology completes the company's TrafficLink solution, which also includes a 360-degree fisheye camera and an IoT connected hub that allows traffic professionals to remotely access the intersection. Together, these components make up a powerful AI toolkit that uncovers insights about the intersection.
Currently, the largest barrier to automating front-end development is computing power. However, we can use current deep learning algorithms, along with synthesized training data, to start exploring artificial front-end automation right now. In this post, we'll teach a neural network how to code a basic a HTML and CSS website based on a picture of a design mockup. We'll build the neural network in three iterations. First, we'll make a bare minimum version to get a hang of the moving parts. The second version, HTML, will focus on automating all the steps and explaining the neural network layers.