"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').
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 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.
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 (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.
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.
Natural Language Generation is a very important area to be explored in our time. It forms the basis of how a bot would communicate with -- not like how literates write books but like how we talk. In this Kernel, I'd like to show you a very simple but powerful Python module that does a similar exercise in (literally) a couple of lines of code. The Py module we use here is markovify. Markovify is a simple, extensible Markov chain generator.
Natural language generation (NLG) is an area of artificial intelligence (AI) concerned with enabling software to produce content or speech similar to that produced by humans. Depending on the particular technology, NLG software can access a knowledge base to create content, for example, or transform data and statistics into more user-friendly content. Explore 5 ways to see through AI washing, when to use AI vs. BI, deep learning vs. machine learning, and much more! You forgot to provide an Email Address. This email address doesn't appear to be valid.
Generative Adversarial Networks (GANs) have been promising in the field of image generation, however, they have been hard to train for language generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them which causes high levels of instability in training GANs. Consequently, past work has resorted to pre-training with maximum-likelihood or training GANs without pre-training with a WGAN objective with a gradient penalty. In this study, we present a comparison of those approaches. Furthermore, we present the results of some experiments that indicate better training and convergence of Wasserstein GANs (WGANs) when a weaker regularization term is enforcing the Lipschitz constraint.
In 2016 an artificial intelligence bot, "Hoffbot," used neural networks to write all of David Hasselhoff's lines for a bizarre short film called Sunspring. Just three months ago Botnik Studios used a predictive algorithm to create a four-page script performed by Zach Braff of Scrubs. By 2019, most leading AI providers will offer tools and libraries for building AI-powered natural-language generation, image manipulation, and other generative use cases. Artificial Intelligence exists as an aid to creativity across every discipline. This year more solutions will come to market--in all verticals--that use leading-edge AI approaches known as generative adversarial networks (GANs) to algorithmically create digital and analog objects of all sorts with astonishing accuracy.