Me: Alexa please remind me my morning yoga sculpt class is at 5:30am. Alexa: I have added Tequila to your shopping list. We talk to our devices, and sometimes they recognize what we are saying correctly. We use free services to translate foreign language phrases encountered online into English, and sometimes they give us an accurate translation. Although natural language processing has been improving by leaps and bounds, it still has considerable room for improvement.
Healthcare analytics company Pulse8 is offering a tool to identify and code patient conditions by accessing content from their clinical data and converting it to XML schema for integration with a variety of systems. Called Popul8, the software leverages machine learning, natural language processing as well as optical character and pattern recognition technologies to create what the company described as a data-driven view of healthcare processes. "The goal is to reduce waste, eliminate unnecessary interventions, and improve patient and provider visibility by easily extracting clinical information from both structured and unstructured data," Pulse8 CEO John Criswell said. Popul8 parses and processes XML schema with a 2-stage coding engine. The first stage uses an ICD parser to discover conditions present in or implied by the chart and physician notes.
The term'Artificial Intelligence' was originally coined in the 1950s by the computer scientist John McCarthy. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Human-style intelligence, is the desire for people to create human-like consciousness in a machine, enabling it to apply common sense, work out varied problems and even have emotional intelligence, sometimes referred to as'general' or'strong' AI, and Task-orientated intelligence, is the ability to do a limited range of tasks very well, such as the ability to drive a car, answer questions or to make health diagnoses, referred to as'narrow' or'weak' AI. Today, the hype around artificial intelligence (AI) is ramping up, especially as big tech companies like Apple, Amazon, Google, Facebook, IBM and Microsoft attempt to commercialize its use. Digital Ad Agencies are also starting to figure out how they can leverage Artificial Intelligence techniques to make their clients' marketing and advertising efforts more effective. So far in 2016, Artificial Intelligence technology has grabbed headlines as the focus of Apple's first acquisition--in the form of Emotient Inc--and Facebook CEO Mark Zuckerberg has resolved to build an AI assistant to run his home and help him at work. Google has also gone down in the history books after its DeepMind team developed an AI program capable of defeating human world champions of complex Chinese board game Go. It's an achievement reminiscent of IBM's milestone moment when its cognitive system IBM's Watson thrashed human contestants in the U.S. game show Jeopardy in 2011.
My work aims to create a scaffold for deployable intelligent systems using crowdsourcing. Current approaches in artificial intelligence (AI) typically focus on solving a narrow subset of problems in a given space - for example: automatic speech recognition as part of a conversational assistant, machine vision as part of a question answering service for blind people, or planning as part of a home assistive robot. This approach is necessary to scope the solution, but often results in a large number of systems that are rarely deployed in real-world setting, but instead operate in toy domains, or in situations where other parts of the problem are assumed to be solved. The framework I have developed aims to use the crowd to help in two ways: (i) make it possible to use human intelligence to power parts of a system that automated approaches cannot or do not yet handle, and (ii) provide a means of enabling more effective deployable systems by people to provide reliable training data on-demand. This summary begins with a brief review of prior work, then outlines a number of different system that I have developed to demonstrate the capabilities of this framework, and concludes with future work to be completed as part of my thesis.