The field of Computer Science and Engineering has been pushing the envelope of almost all other disciplines, which use computing as a fundamental means of inquiry and tool for discovery. Computer science and engineering offers exciting, intellectually challenging, and rapidly growing career opportunities. It is the heart of where the action is: whether this means intelligent game playing, mobile applications, smart robots, cloud computing, data security, social networks, or bioinformatics.
Computer Science and Engineering (CSE) is an academic program at some universities that combines aspects of both computer science and computer engineering programs. Computer science programs typically centers primarily around theory and software, with only some hardware; upper division courses tend to allow a lot of freedom to specialize in software and theory related areas (e.g. algorithms, artificial intelligence, cryptography/security, graphics/visualization, numerical and symbolic computing, operating systems/distributed processing, software engineering).
Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering. When created, IBM stated that, “more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses”. In recent years, the Watson capabilities have been extended and the way in which Watson works has been changed to take advantage of new deployment models (Watson on IBM Cloud) and evolved machine learning capabilities and optimized hardware available to developers and researchers. It is no longer purely a question answering (QA) computing system designed from Q&A pairs but can now 'see', 'hear', 'read', 'talk', 'taste', 'understand', 'reason', 'interpret', 'learn' and 'recommend'. Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework. The system was written in various languages, including Java, C++, and Prolog, and runs on the SUSE Linux Enterprise Server 11 operating system using the Apache Hadoop framework to provide distributed computing.
Amazon Web Services (AWS) is a subsidiary of Amazon.com that provides on-demand cloud computing platforms to individuals, companies and governments, on a paid subscription basis with a free-tier option available for 12 months. The technology allows subscribers to have at their disposal a full-fledged virtual cluster of computers, available all the time, through the Internet. AWS's version of virtual computers have most of the attributes of a real computer including hardware (CPU(s) & GPU(s) for processing, local/RAM memory, hard-disk/SSD storage); a choice of operating systems; networking; and pre-loaded application software such as web servers, databases, CRM, etc. Each AWS system also virtualizes its console I/O (keyboard, display, and mouse), allowing AWS subscribers to connect to their AWS system using a modern browser. The browser acts as a window into the virtual computer, letting subscribers log-in, configure and use their virtual systems just as they would a real physical computer. They can choose to deploy their AWS systems to provide internet-based services for their own and their customers' benefit. The AWS technology is implemented at server farms throughout the world, and maintained by the Amazon subsidiary
Tensor Flow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and also used for machine learning applications such as neural networks. It is used for both research and production at Google often replacing its closed-source predecessor, DistBelief. Tensor Flow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source license on November 9, 2015. Tensor Flow is Google Brain's second generation system. Version 1.0.0 was released on February 11, 2017. While the reference implementation runs on single devices, Tensor Flow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units).Tensor Flow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. Tensor Flow computations are expressed as stateful dataflow graphs. The name Tensor Flow derives from the operations that such neural networks perform on multidimensional data arrays. These arrays are referred to as "tensors". In June 2016, Dean stated that 1,500 repositories on GitHub mentioned Tensor Flow, of which only 5 were from Google.