"As for why I tell a lot of stories, there's a joke about that. There was once a man who had a computer, and he asked it, 'Do you compute that you will ever be able to think like a human being?' And after assorted grindings and beepings, a slip of paper came out of the computer that said, 'That reminds me of a story . . . "
– from ANGELS FEAR: TOWARDS AN EPISTEMOLOGY OF THE SACRED. Gregory Bateson & Mary Catherine Bateson. (Part III 'Metalogue').
This endowment will empower the renowned academic institution with industry specific knowledge and resources to help create solutions to accelerate the growth and adoption of Data Science and AI globally, the company said in a statement. Through this endowment, Mindtree will help accelerate the development of technology innovation in fields like AI, data analytics, and machine learning, it added. "AI and Data Science are key priorities for our clients as these technologies offer immense potential to create new business opportunities. IIT Madras is one of the global leaders in this field and the collaboration between Mindtree and IIT Madras will help accelerate innovation and push the boundaries of knowledge," the company's CEO and Managing Director Rostow Ravanan said. Mindtree will further extend the partnership with IIT Madras to include research projects focusing on related topics such as personalisation, conversational interfaces and natural language generation, the statement said.
We present a data resource which can be useful for research purposes on language grounding tasks in the context of geographical referring expression generation. The resource is composed of two data sets that encompass 25 different geographical descriptors and a set of associated graphical representations, drawn as polygons on a map by two groups of human subjects: teenage students and expert meteorologists.
One of my biggest complaints about terminology in the industry is the claim that data from conversations is "unstructured data". After all, how do people communicate, either in voice or in a written language, if there was no structure that aids meaning? Syntax is the structure of language, and it clearly aids in defining semantics, or the meaning of the communications. To understand how computers are rapidly improving, it's important to look at how natural language is different from what computers have historically processed. From flat file sequential data storage models to relational databases (RDBMS), there is a decade's long history of rigidly structured data.
Natural language processing is a type of artificial intelligence in which computational and mathematical methods are used to analyze the human language. NPL's goal for end users is to facilitate interactions with computers using conversational language. Subtopics in this genre include natural language understanding, which is about understanding the inputs created by humans, and natural language generation, which focuses on generating natural language narratives. The most popular approaches to NLP use machine learning, said Adrian Bowles, vice president of Research and lead analyst at Aragon Research. "At the most advanced levels in the research labs today, we see applications or systems like Google Duplex, which can act as an agent to perform tasks like scheduling haircuts over the phone by engaging with humans, or IBM's Debater, which can detect patterns of logical arguments in free form text and construct a coherent and novel narrative position statement."
A New Zealand artificial intelligence company has become a world leader, and is about to list on the NZX. Arria NLG works in natural language generation and has been identified as one of two companies with artificial intelligence products ready for commercialisation. NLG chief executive Sharon Daniels told Mike Hosking the technology will increase the productivity of businesses. "Instead of having a fear around the technology taking over, it's mainly there to increase people's capabilities."
The Artificial Intelligence (AI) revolution is here – implementing an AI solution is no longer a nice-to-have for a business, it's a need-to-have. According to our Outlook on A.I. in the Enterprise Report, AI adoption grew over 60% from 2016 to 2017. With almost ⅔ of enterprises utilizing some form of artificial intelligence-- be it natural language processing (NLP), machine learning, image recognition, natural language generation (NLG), or some other machine-enabled capability meant to mimic human abilities-- it's clear that businesses understand the potential that AI can deliver. But do they understand the value? In fact, only 31% of respondents in our Outlook on AI survey cited that they are either not tracking ROI or not seeing returns on their AI investment.
Digital assistants, chatbots and other conversational interfaces have become the most widely-adopted technologies in the recent days. Their ability to carry human-like conversations in a seamless manner could be attributed to their tremendous popularity, which is in turn driven by two underlying technologies -- Natural Language Processing (NLP) and Natural Language Generation (NLG). These two branches of machine learning are enabling the conversion of human language to computer commands and vice versa. As these technologies are enabling humans to have a conversation with machines in an effective manner and augmenting human intelligence, we bring to you an article that discusses the differences between NLP and NLG, their working and some common use cases. The most popular definition of NLP describes it as a process which turns text into structured data when the computer reads the language.
Intelligent Process Automation (IPA) is a set of emerging Digital Technologies that combine process redesign with Robotic Process Automation (RPA) and Machine Learning (ML). IPA can be viewed as a suite of Business Process Improvements and Digital Transformation tools that assists the employee IPA essentially augments RPA technology with additional emerging Digital Transformation technologies. In its full extent, IPA is comprised of 5 core technologies: 1. Robotic Process Automation (RPA) 2. Smart Workflow 3. Machine Learning (ML) & Advanced Analytics 4. Natural Language Generation (NLG) 5. Cognitive Agents The disruptive power of IPA is that is supplements traditional rule-based automation with decision-making capabilities driven by emerging Deep Learning and Cognitive Technologies. The benefits of IPA are numerous and significant in impact. They center around enhanced productivity and efficiency, reduction in operational risks, and improved Customer Experiences.
Natural Language Generation (NLG) is the lesser known artificial intelligence (AI) technology that translates raw data into understandable text or spoken word. The Google Assistant is an example of NLG in action; for example, how it answers questions about yesterday's winning soccer team or summarizes the text in an email. There is also a lot of research being conducted on NLG to generate news articles, especially in the financial services space. To date, however, the technology has been most successful with structured data, e.g. From that type of data, NLG can generate a very specific template which says company X released its earnings and surpassed analysts' predictions by X%.
Natural Language Generation (NLG) is the lesser known artificial intelligence (AI) technology that translates raw data into understandable text or spoken word. How is NLG technology most commonly used? The Google Assistant is an example of NLG in action; for example, how it answers questions about yesterday's winning soccer team or summarizes the text in an email. There is also a lot of research being conducted on NLG to generate news articles, especially in the financial services space. To date, however, the technology has been most successful with structured data, e.g.