natural language


Generalized Language Models

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As a follow up of word embedding post, we will discuss the models on learning contextualized word vectors, as well as the new trend in large unsupervised pre-trained language models which have achieved amazing SOTA results on a variety of language tasks. I guess they are Elmo & Bert? (Image source: here) We have seen amazing progress in NLP in 2018. Large-scale pre-trained language modes like OpenAI GPT and BERT have achieved great performance on a variety of language tasks using generic model architectures. The idea is similar to how ImageNet classification pre-training helps many vision tasks (*). Even better than vision classification pre-training, this simple and powerful approach in NLP does not require labeled data for pre-training, allowing us to experiment with increased training scale, up to our very limit. In my previous NLP post on word embedding, the introduced embeddings are not context-specific -- they are learned based on word concurrency but not sequential context.


Setting Expectations Around AI in Healthcare

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There's a lot of hype and promises to artificial intelligence (AI) in healthcare. But what can be done to reap the full benefits of this technology? READ: What Is Being Done About Healthcare's Lack of Interoperability? Michael Doyle, CEO of COTA, told Healthcare Analytics News that interoperability is a factor in how AI works. AI needs data to learn.


Geo Super brings world's first WhatsApp ChatBot to its viewers

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In a first, famed Pakistani sports channel Geo Super has brought to its valuable viewers, the world's first ever Whatsapp-oriented interactive Chatbot. For increased viewer participation and satisfaction, the distinct Chatbot offers a wide range of features to its subscribers. In the wake of the on-going Pakistan Super League (PSL), the bot offers match summary alerts sent to all users every 20 minutes. The bot also allows for subscribers to cast their vote in various match polls where at the end results are shared. Using the bot, the subscribers also have a chance to get score updates anytime by typing'score' and sending it to the bot.


Amazon AI lead shares goals for company's healthcare services: Taha Kass-Hout, MD, former and first-ever CIO for the FDA and a senior leader of artificial intelligence at Amazon, detailed how the tech giant is using AI for its healthcare services in a recent interview with STAT.

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Taha Kass-Hout, MD, former and first-ever CIO for the FDA and a senior leader of artificial intelligence at Amazon, detailed how the tech giant is using AI for its healthcare services in a recent interview with STAT. A core component of Amazon's healthcare strategy is to support clinicians, according to Dr. Kass-Hout. "I hope we see that with AI we're finally getting to understand what patient has a disease, rather than what disease a patient has -- and truly start personalizing care to that level," Dr. Kass-Hout told STAT. "From a patient perspective and consumer perspective, AI is going to empower them, and for providers and healthcare systems, it's going to augment clinicians and bridge gaps." Dr. Kass-Hout also provided an update on how healthcare companies are using AWS' EHR-mining software Comprehend Medical, which launched in November 2018.


AI technique can identify alcohol misuse among trauma patients, study shows: Researchers from Maywood, Ill.-based Loyola Medicine and Loyola University Chicago used an artificial intelligence technique to identify alcohol misuse among trauma patients.

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Researchers from Maywood, Ill.-based Loyola Medicine and Loyola University Chicago used an artificial intelligence technique to identify alcohol misuse among trauma patients. The technique was able to differentiate between trauma patients who misused alcohol and those who did not in 78 percent of cases. Researchers published their findings in the Journal of the American Medical Informatics Association. One in three trauma patients misuse alcohol, and many trauma cases are alcohol-related, according to the study. These records included 91,405 EHR clinician notes.


Blogging Birds

Communications of the ACM

Blogging birds is a novel artificial intelligence program that generates creative texts to communicate telemetric data derived from satellite tags fitted to red kites -- a medium-size bird of prey -- as part of a species reintroduction program in the U.K. We address the challenge of communicating telemetric sensor data in real time by enriching it with meteorological and cartographic data, codifying ecological knowledge to allow creative interpretation of the behavior of individual birds in respect to such enriched data, and dynamically generating informative and engaging data-driven blogs aimed at the general public. Geospatial data is ubiquitous in today's world, with vast quantities of telemetric data collected by GPS receivers on, for example, smartphones and automotive black boxes. Adoption of telemetry has been particularly striking in the ecological realm, where the widespread use of satellite tags has greatly advanced our understanding of the natural world.14,23 Despite its increasing popularity, GPS telemetry involves the important shortcoming that both the handling and the interpretation of often large amounts of location data is time consuming and thus done mostly long after the data has been gathered.10,24 This hampers fruitful use of the data in nature conservation where immediate data analysis and interpretation are needed to take action or communicate to a wider audience.25,26 The widespread availability of GPS data, along with associated difficulties interpreting and communicating it in real time, mirrors the scenario seen with other forms of numeric or structured data. It should be noted that the use of computational methods for data analysis per se is hardly new; much of science depends on statistical analysis and associated visualization tools. However, it is generally understood that such tools are mediated by human operators who take responsibility for identifying patterns in data, as well as communicating them accurately.


Technical Perspective: Borrowing Big Code to Automate Programming Activities

Communications of the ACM

Big data combined with machine learning has revolutionized fields such as computer vision, robotics, and natural language processing. In these fields, automated techniques that detect and exploit complex patterns hidden within large datasets have repeatedly outperformed techniques based on human insight and intuition. But despite the availability of enormous amounts of code (big code) that could, in theory, be leveraged to deliver similar advances for software, programming has proved to be remarkably resistant to this kind of automation. Much programming today consists of developers deploying keyword searches against online information aggregators such as Stack Overflow to find, then manually adapt, code sequences that implement desired behaviors. The following paper presents new techniques for leveraging big code to automate two programming activities: selecting understandable names for JavaScript identifiers and generating type annotations for JavaScript variables.


Talking with machines with Dr. Layla El Asri - Microsoft Research

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Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you'd talk to another person. The hard part is getting your machine to understand and talk back to you like it's that other person. Today, Dr. El Asri talks about the particular challenges she and other scientists face in building sophisticated dialogue systems that lay the foundation for talking machines. She also explains how reinforcement learning, in the form of a text game generator called TextWorld, is helping us get there, and relates a fascinating story from more than fifty years ago that reveals some of the safeguards necessary to ensure that when we design machines specifically to pass the Turing test, we design them in an ethical and responsible way. Layla El Asri: In a video game, most of the time you only have a few actions that you can take. You just need to learn when you should go right, when you should go left, when you should go up, when you should go down. But when it comes to dialogue, you need to learn how to make a sentence that is grammatically correct, and then you need to learn how to make a sentence that makes sense in the global context of the dialogue, or a sentence that brings new information in the dialogue that is going to make the person you are talking to satisfied with the sentence. Your action space is just huge because it's not just up/down, right/left, it's all the sentences you could imagine! Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Humans are unique in their ability to learn from, understand the world through and communicate with language… Or are they? Perhaps not for long, if Dr. Layla El Asri, a Research Manager at Microsoft Research Montreal, has a say in it. She wants you to be able to talk to your machine just like you'd talk to another person.


How AI can benefit marketers in 2019

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Drill into your head that a customer has a lot of brands to pick from. He thinks from a heart that throbs for a particular one. That brand might have put its 110 per cent in getting connected with its customers emotionally. So, it's up to you to come with a blockbuster upselling and cross-selling idea. What I mean to say is that the knowledge economy is offbeat.


What is a Chatbot?

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If you've been keeping up with the news lately, reading blogs or the occasional legal industry related tweets on twitter, you've heard the deafening cries and dizzying excitement about artificial intelligence, machine learning and chatbots. You see, it all started with Alan Turing, the original computer scientist on which the movie The Imitation Game was based. Turing developed his test in 1950 for intelligence in a computer. The idea is if a human is unable to distinguish machine from another human being through engaging it in a dialog of questions and and replies, then the computer could be deemed to be "intelligent." Turing predicted by the year 2000, a computer would pass the test on five-minute keyboard conversations 30% of the time.