Cognitive automation is an extension of existing robotic process automation (RPA) technology. Machine learning enables bots to remember the best ways of completing tasks, while technology like optical character recognition increases the data formats with which bots can interact. Cognitive automation adds a layer of AI to RPA software to enhance the ability of RPA bots to complete tasks that require more knowledge and reasoning. These tasks can range from answering complex customer queries to extracting pertinent information from document scans. Some examples of mature cognitive automation use cases include intelligent document processing and intelligent virtual agents. In contrast, Modi sees intelligent automation as the automation of more rote tasks and processes by combining RPA and AI.

I was trying my hand on Optical Character Recognition on newspaper images when I realised that most documents have sections and text is not necessarily across the entire horizontal space of the page. Even though Tesseract was able to recognise the text it was jumbled up. To fix this the model should be able to identify sections on the document and draw a bounding box around it an perform OCR. It was this moment when applying Yolo Object detection on such images came into mind. YOLOv3 is extremely fast and accurate.

Artificial Intelligence (AI) -- and its attendant term, 'Machine Learning' (ML) -- is described as the capability of a computer system to perform tasks that normally require human intelligence, such as visual perception, speech recognition and decision-making. Almost all AI/ML examples in commercial as well as military use today rely on data stores that drive deep learning and natural language processing.[1] The defining feature of an AI/ML system is its ability to learn and solve problems. There has been a gradual change in our understanding of what exactly constitutes AI. While advancements in computer hardware and more efficient software have led to the development of AI systems, hitherto computer-resource-intensive tasks, such as optical character recognition (OCR) are now considered a routine technology and, hence, no longer included in any contemporary discussion of AI/ML.

In a world where new technology emerges at exponential rates, and our daily lives are increasingly mediated by speakers and sound waves, text to speech technology is the latest force evolving the way we communicate. Text to speech technology refers to a field of computer science that enables the conversion of language text into audible speech. Also known as voice computing, text to speech (TTS) often involves building a database of recorded human speech to train a computer to produce sound waves that resemble the natural sound of a human speaking. This process is called speech synthesis. The technology is trailblazing and major breakthroughs in the field occur regularly.

Abstract: Advanced text-to-speech (TTS) models such as FastSpeech can synthesize speech significantly faster than previous autoregressive models with comparable quality. The training of FastSpeech model relies on an autoregressive teacher model for duration prediction (to provide more information as input) and knowledge distillation (to simplify the data distribution in output), which can ease the one-to-many mapping problem (i.e., multiple speech variations correspond to the same text) in TTS. However, FastSpeech has several disadvantages: 1) the teacher-student distillation pipeline is complicated, 2) the duration extracted from the teacher model is not accurate enough, and the target mel-spectrograms distilled from teacher model suffer from information loss due to data simplification, both of which limit the voice quality. In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs during training and use predicted values during inference. We further design FastSpeech 2s, which is the first attempt to directly generate speech waveform from text in parallel, enjoying the benefit of full end-to-end training and even faster inference than FastSpeech.

Developers have taught artificial intelligence how to play an arcade pinball machine, which learned so quickly it could beat human players within four days. Speaking at Microsoft's developer conference, Build, which is being held virtually this week, Jack Skinner described how he and a team of developers in Sydney used artificial intelligence to control an actual pinball machine. The team took a regular arcade machine and adapted it, using a Windows computer to control the AI software, and a Raspberry Pi to control the flipper mechanism within the pinball machine. Two webcams were mounted on the pinball machine - one pointed at the scoreboard and one pointed down at the table - so that the AI could "see" the table like a human player would. Optical character recognition (OCR) software allowed the computer to read the current score from the pinball machine's electronic display.

The magnitude of the Covid-19 pandemic will largely depend on how quickly safe and effective vaccines and treatments can be developed and tested. Many assume a widely available vaccine is years away, if ever. Others believe that a 12- to 18-month development cycle is a given. Our best bet to reduce even that record-breaking timeline is by using artificial intelligence. The problem is twofold: discovering the right set of molecules among billions of possibilities, and then waiting for clinical trials. These processes ordinarily take several years, but AI holds the key to radically shortening both.

Tracking users' activities on the World Wide Web (WWW) allows researchers to analyze each user's internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as researchers may access to their participant's behaviors while browsing the web. Web search behavior has been a subject of interest because of its real-world applications in marketing, digital advertisement, and identifying potential threats online. In this paper, we present an image-processing based method to extract domains which are visited by a participant over multiple browsers during a lab session. This method could provide another way to collect users' activities during an online session given that the session recorder collected the data.

Optical Character Recognition and extraction is a key tool in the automatic evaluation of documents in a financial context. However, the image data provided to automated systems can have unreliable quality, and can be inherently low-resolution or downsampled and compressed by a transmitting program. In this paper, we illustrate the efficacy of a Gaussian Process upsampling model for the purposes of improving OCR and extraction through upsampling low resolution documents.

Welcome to the Newspaper Navigator dataset! This dataset consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project. The dataset also includes text corresponding to the visual content, identified by extracting the Optical Character Recognition, or OCR, within each predicted bounding box. For example, if the visual content recognition model predicted a bounding box around a headline, the corresponding textual content provides a machine-readable version of the headline; likewise, for a photograph, illustration, or map, this textual representation often contains the title and caption.