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) …
It has been always difficult to consume TensorFlow or ONNX models without the help of tools like TensorFlow Serving or gRPC and all the fun that comes with protocol buffers. Hosting deep learning models to be consumed using REST was very hard although this is probably the most common approach application developers would start with. Microsoft has recently released Azure Machine Learning service which comes with heaps of features to facilitate development and deployment of machine learning models. One of those features is hosting ONNX models in docker containers to be consumed using REST. In this post, we go through an end to end workflow of hosting a sample ONNX model and consuming it from a .NET application.
A mid-sized company with about 5,000 employees gets approximately 1,000 to 2,000 security incidents per day. This equates to nearly 60,000 threat incidents per month and as many as 720,000 per year. That number has increased dramatically because of automated security attacks using bots. According to The Cybersecurity Intelligence Report from Oracle Dyn "over 50% of internet traffic is bots. With these huge numbers, there are just too many threat incidents for the typical security operations team to manage with any level of precision.
In spite of the fact that the idea of Artificial Intelligence has been around for a long time, it is just in the most recent years that it has gotten on the tech charts and is trending in each and every industry conceivable. Getting to be noticeably extraordinary compared to other cherished techs among the ingenious minds all over the world, Artificial Intelligence demands a mix of computer science, mathematics, cognitive psychology, and engineering. There is no doubt about that soon the demand for experts prepared in Artificial Intelligence would beat supply. In spite of the fact that there is some overlap of Artificial Intelligence with analytics, a capable Artificial Intelligence expert would have profound knowledge on spheres like computer vision, natural language processing, robotics automation, and machine learning. Artificial Intelligence education is still in its youthful days.
As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)--with over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You'll hear success stories about machine learning on AWS firsthand from customers and partners such as Sony, Moody's, NFL, Intuit, 21st Century Fox, Toyota, and more. This year's re:Invent also includes the AI Summit, where thought leaders in the academic community will share their perspectives on the future of AI. Here are a few highlights of this year's lineup from the re:Invent session catalog to help you plan your event agenda.
How the features and benefits of data virtualization can make working with data easier and more efficient. Data lakes have become the principal data management architecture for data science. A data lake's primary role is to store raw structured and unstructured data in one central location, making it easy for data scientists and other investigative and exploratory users to analyze data. The data lake can store vast amounts of data affordably. It can potentially store all data of interest to data scientists in a single physical repository, making discovery easier.
In last week's post, we discussed if machine learning was right for your business. As part of that effort, I recently went through the process of learning the ins-and-outs of machine learning and realized most information out there is technical and aimed at developers or data scientists. I thought an explanation from a non-technical person might be of interest. Machine learning is "[…] the branch of AI that explores ways to get computers to improve their performance based on experience". Let's break that down to set some foundations on which to build our machine learning knowledge.
Efforts to develop artificial intelligence (AI) are increasingly being framed as a global race, or even a new Great Game. In addition to the race between countries to build national competencies and establish a competitive advantage, firms are also in a contest to acquire AI talent, leverage data advantages, and offer unique services. In both cases, success will depend on whether AI solutions can be democratized and distributed across sectors. The global AI race is unlike any other global competition, because the extent to which innovation is being driven by the state, the corporate sector, or academia differs substantially from country to country. On average, though, the majority of innovations so far have emerged from academia, with governments contributing through procurement, rather than internal research and development.
Deep reinforcement learning(DRL) has been categorized many times as the future of artificial intelligence(AI). Some of the most important AI breakthroughs of the last few years such as DeepMind's AlphaGo or OpenAI's Dota Five have been based on DRL applications. Despite its importance, the implementation of DRL models remains an incredibly challenging exercise and, for the most part, we have very little ideas about the pieces that make an efficient DRL solution. Earlier this week, DeepMind open sourced TRFL(pronounced truffle, of course), a framework that compiles a series of useful building blocks of DRL models. Most of the current wave of DRL methods have had their origin in the academic environments and they haven't been tested in real world implementations.