Generation


ROI Roadmap for Your AI Strategy

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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.


Understanding The Workings Of Natural Language Processing Vs Natural Language Generation

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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) (PowerPoint) - FlevyPro Document

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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.


What can NLG do for your business?

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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%.


Google talks NLG and what it can do for businesses

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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.


Visualizing Time Series Data of Stock Prices Byte Academy Top Coding School for Python, Blockchain & Fintech

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Tuesday, May 08, 2018 Top 10 Datasets for Deep Learning The strength and robustness of a machine learning algorithm often lies in the quality of the dataset used to train it. Therefore, it would suffice to say that to gain true mastery within these fields,... Friday, August 25, 2017 Data Scientist Interview: A Perspective On Artificial Intelligence (AI), Ethics & Healthcare Recently HealthTech Women, a national non-profit, sat down with our Data Science instructor, Lesley Cordero to get an inside look at Artificial Intelligence, or, "AI" and how it is impacting society, ... Wednesday, August 09, 2017 Overview of Natural Language Generation (NLG) NLG (Natural Language Generation), a subfield of Artificial Intelligence, is a hot topic in the technology news today. We hear a lot about AI that can soon replace writers and journalists beginning th... Tuesday, May 08, 2018 Top 10 Datasets for Deep Learning The strength and robustness of a machine learning algorithm often lies in the quality of the dataset used to train it. Therefore, it would suffice to say that to gain true mastery within these fields,... Friday, August 25, 2017 Data Scientist Interview: A Perspective On Artificial Intelligence (AI), Ethics & Healthcare Recently HealthTech Women, a national non-profit, sat down with our Data Science instructor, Lesley Cordero to get an inside look at Artificial Intelligence, or, "AI" and how it is impacting society, ... Wednesday, August 09, 2017 Overview of Natural Language Generation (NLG) NLG (Natural Language Generation), a subfield of Artificial Intelligence, is a hot topic in the technology news today. We hear a lot about AI that can soon replace writers and journalists beginning th...


Google I/O 2018 -- AI Highlights – Ahmet Kuzubaşlı – Medium

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Google unveils lots of big announcements at I/O on its 1st day and I am excited to summarize and discuss AI highlights on this story. For those following the Google's research blog (now it is Google AI), this creepy successful natural language generation examples have been a clear sign for such improvement in Google Assistant, showing that it could actually talk realistically to real people in automated voice calls. Google Assistant will be able to make appointments for people over the phone for things like reserving a table at a restaurant, or setting a time to get a haircut. It is creepy to hear the AI voice sounded just like a human, complete with put in words like "um" in the conversation. Google also introduced ML Kit today, a new software development kit (SDK) for app developers on iOS and Android that allows them to integrate into their apps a number of pre-built Google-provided machine learning models.


ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs

arXiv.org Machine Learning

Generative Adversarial Networks (GANs) have seen steep ascension to the peak of ML research zeitgeist in recent years. Mostly catalyzed by its success in the domain of image generation, the technique has seen wide range of adoption in a variety of other problem domains. Although GANs have had a lot of success in producing more realistic images than other approaches, they have only seen limited use for text sequences. Generation of longer sequences compounds this problem. Most recently, SeqGAN (Yu et al., 2017) has shown improvements in adversarial evaluation and results with human evaluation compared to a MLE based trained baseline. The main contributions of this paper are three-fold: 1. We show results for sequence generation using a GAN architecture with efficient policy gradient estimators, 2. We attain improved training stability, and 3. We perform a comparative study of recent unbiased low variance gradient estimation techniques such as REBAR (Tucker et al., 2017), RELAX (Grathwohl et al., 2018) and REINFORCE (Williams, 1992). Using a simple grammar on synthetic datasets with varying length, we indicate the quality of sequences generated by the model.


Artificial Intelligence–the Next Frontier In Content Marketing Marketo

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Artificial intelligence (AI) and machine learning are being used in all aspects of business and marketing. The technologies allow decision-makers to extract valuable insights from a large amount of data so businesses can stay on top of emerging trends. In this blog, I'll cover how you can leverage AI to help increase ROI and get better results. AI is an umbrella term to describe a suite of unique, but related, technologies that includes machine learning, deep learning, neural networks, natural language processing (NLP), and natural language generation (NLG). With the ability to process an enormous amount of unstructured data and decipher natural language, AI is used to extract insights and make recommendations based on previously established criteria.


Martech Archives - Marketing Technology

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If you're like the unbreakable Kimmy Schmidt and got stuck in a bomb shelter in 2017, it may be both a blessing and a curse that you missed the machine learning for marketing media frenzy. Machine learning showed up everywhere, rivaling electricity's systemic emergence a century ago, allegedly injecting sage-like wisdom into everything from sales forecasting tools to email subject lines generators. But buildup and hype aside, real progress was made in using machine learning for marketing purposes, infiltrating impactful areas as unprecedented investments poured in. More resources supporting great minds pushed forward innovation in areas like image recognition, voice technologies, and natural language generation (NLG). And savvy brands that mindfully wired these into marketing applications boosted performance, in some cases realizing 400 percent ROI.