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) …
What's dangerous is not to evolve. The darling of consumers and investors alike, Mr. Bezos sure seems to have the future figured out. Amazon today is the most AI driven customer experience platform in the world, besides just being an e-commerce marketplace. Retail has moved from the brick-and-mortar store front to beautiful and highly aesthetic web portals, accessible on any device you use, that seem, at times, shockingly intuitive. The secret ingredient here is AI.
There are two schools of thought on the impact of artificial intelligence (AI). The first is pessimistic: AI will turn into the all-powerful computer SkyNet that takes over Earth in the Terminator movies. The second is optimistic: AI will help humans become much more than they could be without it. In this school of thought, we live in a state of blissful "augmented humanity." The truth, of course, lies somewhere in the middle.
This course is designed to equip you with the theoretical and practical knowledge of Machine Learning as applied for geospatial analysis, namely Geographic Information Systems (GIS) and Remote Sensing. By the end of the course, you will feel confident and completely understand the Machine Learning applications in GIS technology and how to use Machine Learning algorithms for various geospatial tasks, such as land use and land cover mapping (classifications) and object-based image analysis (segmentation). This course will also prepare you for using GIS with open source and free software tools. In the course, you will be able to apply such Machine Learning algorithms as Random Forest, Support Vector Machines and Decision Trees (and others) for classification of satellite imagery. On top of that, you will practice GIS by completing an entire GIS project by exploring the power of Machine Learning, cloud computing and Big Data analysis using Google Erath Engine for any geographic area in the world.
In 2019, I was asked to write the Foreword for the book "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", by Mark Needham and Amy E. Hodler. I wrote an extensive piece on the power of graph databases, linked data, graph algorithms, and various significant graph analytics applications. In their wisdom, the editors of the book decided that I wrote "too much". So, they correctly shortened my contribution by about half in the final published version of my Foreword for the book. The book is awesome, an absolute must-have reference volume, and it is free (for now, downloadable from Neo4j).
The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days. But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas needs to be studied prior to that.
Every technology vendor, it seems, is taking advantage of the buzz around machine learning (ML), especially in cybersecurity, where vendors know that their clients need to keep pace with ever more intelligent threats. Including machine learning as a feature-benefit of a product has almost become "table stakes," meaning that companies that don't claim some element of machine learning may be at a huge disadvantage compared to competitors. As a result, marketing teams are stretching the limits of what "machine learning" means, and potential product users are left alone to separate fact from fiction. Machine learning is a large, active area of research that is developing quickly, which means that capabilities and outputs from one company vary significantly. If you're evaluating a product that promotes machine learning as a feature-benefit and want to ensure you'll get what you'll pay for, there are a few things you should know.
Socionext Inc. has developed a prototype chip that incorporates newly-developed quantized Deep Neural Network (DNN) technology, enabling highly-advanced AI processing for small and low-power edge computing devices. The prototype is a part of a research project on "Updatable and Low Power AI-Edge LSI Technology Development" commissioned by the New Energy and Industrial Technology Development Organization (NEDO) of Japan. The chip features a "quantized DNN engine" optimized for deep learning inference processing at high speeds with low power consumption. Today's edge computing devices are based on conventional, general-purpose GPUs. These processors are not generally capable of supporting the growing demand for AI-based processing requirements, such as image recognition and analysis, which need larger devices at higher cost due to increases in power consumption and heat generation.
On March 16, 2020, the White House Office of Science and Technology Policy (OSTP) announced the availability of an open research dataset on COVID-19, as well as stated a call to action for the nation's artificial intelligence (AI) researchers to help scientists fight the disease. Established in 1976, the OSTP provides scientific and technological advice to the President and the Executive Office, among other duties. The CORD-19 (COVID-19 Open Research Dataset) is the result of a collaboration between the Allen Institute for Artificial Intelligence (AI2), Microsoft, the National Library of Medicine at the National Institutes of Health (NIH), Georgetown University's Center for Security and Emerging Technology (CSET), and the Chan Zuckerberg Initiative in response to a request by the White House Office of Science and Technology Policy. Together these institutions issued a joint call to action to the world's AI researchers to create text and data mining tools to help accelerate COVID-19 research. The Allen Institute's Semantic Scholar has an adaptive feed on COVID-19 research.
Solving complex, temporally-extended tasks is a long-standing problem in reinforcement learning (RL). We hypothesize that one critical element of solving such problems is the notion of compositionality. With the ability to learn sub-skills that can be composed to solve longer tasks, i.e. hierarchical RL, we can acquire temporally-extended behaviors. However, acquiring effective yet general abstractions for hierarchical RL is remarkably challenging. In this paper, we propose to use language as the abstraction, as it provides unique compositional structure, enabling fast learning and combinatorial generalization, while retaining tremendous flexibility, making it suitable for a variety of problems.
In today's cluttered marketing world, there are many buzzwords that are commonly used by the industry professionals. To ease up the process, we'll first start with the basics before diving into the context of behavioral segmentation marketing. Market segmentation refers to the process of dividing a market of potential customers into groups, or segments, based on different characteristics. Simply put, any market segmentation based on customer behavior or customer buying behavior is behavioral segmentation. With the use of behavioral segmentation marketing, an e-business owner can quickly categorize his customers on the basis of number of times they have visited their online store, what products they have bought, what categories they prefer, if they are registered members and target them accordingly through several marketing channels.