One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Follow up on my previous post discussing the key technologies around the conversational AI solution, I will be dive into the typical challenges the AI Engineer team would encounter when building a virtual agent or a chatbot solution for your clients or customers. Let firstly define the scope and goal of the conversational application. The conversational agents can be categorized into two main streams. The typical agents for Open Domain Conversation are Siri, Google Assistant, BlenderBot from Facebook, Meena from Google. Users can start a conversation without a clear goal, and the topics are unrestricted.
The impact of the COVID-19 pandemic on education has been profound, with new ways of thinking about how best to teach students reverberating in institutions of higher learning, K-12 classrooms and in the business community. The role of AI is central to the discussion on every level. For the K-12 classroom, teachers are thinking about how to use AI as a teaching tool. For example, Deb Norton of the Oshkosh Area school district in Wisconsin, was asked several years ago by the International Society for Technology in Education to lead a course on the uses of AI in K-12 classrooms, according to a recent account in Education Week. The course includes sections on the definition of artificial intelligence, machine learning, voice recognition, chatbots and the role of data in AI systems.
Dave Ryan leads the Global Health & Life Sciences business unit at Intel that focuses on digital transformation from edge-to-cloud in order to make precision, value-based care a reality. His customers are the manufacturers who build life sciences instruments, medical equipment, clinical systems, compute appliances and devices used by research centers, hospitals, clinics, residential care settings and the home. Dave has served on the boards of Consumer Technology Association Health & Fitness Division, HIMSS' Personal Connected Health Alliance, the Global Coalition on Aging and the Alliance for Connected Care. What is Intel's Health & Life Sciences Business? Intel's Health & Life Sciences business helps customers create solutions in the areas of medical imaging, clinical systems, and lab and life sciences, enabling distributed, intelligent, and personalized care.
Humans interact with each other through several means (e.g., voice, gestures, written text, facial expressions, etc.) and a natural human-machine interaction system should preserve the same modality. However, traditional Natural Language Processing (NLP) focuses on analyzing textual input to solve language understanding and reasoning tasks, and other modalities are only partially targeted. This workshop aims to be a forum for both academia and industry researchers where new and unfinished research in the area of Multi/Cross-Modal NLP can be discussed. In particular, the focus of this workshop are (i) studying how to bridge the gap between NLP on spoken and written language and (ii) exploring how NLU models can be empowered by jointly analyzing multiple input sources, including language (spoken or written), vision (gestures and expressions) and acoustic (paralingustic) modalities. All deadlines must be considered at 11.59pm GMT-12 (anywhere on Earth).
This article builds upon my previous two articles where I share some tips on how to get started with data analysis in Python (or R) and explain some basic concepts on text analysis in Python. In this article, I want to go a step further and talk about how to get started with text classification with the help of machine learning. The motivation behind writing this article is the same as the previous ones: because there are enough people out there who are stuck with tools that are not optimal for the given task, e.g. using MS Excel for text analysis. I want to encourage people to use Python, not be scared of programming, and automate as much of their work as possible. Speaking of automation, in my last article I presented some methods on how to extract information out of textual data, using railroad incident reports as an example.
This multi-part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published) neural networks, computer vision, natural language processing, algorithms, and artificial general intelligence. Among the most common misconceptions surrounding machine learning technology is the idea that video games dating back to the 1970s and 1980s had built-in "artificial intelligence" capable of interacting with a human user. If you're curious but in a hurry, video game "AI," in the traditional sense, is not what people refer to in the modern era when they're talking about artificial intelligence. The "bots" in an online multiplayer game, the enemies in a first-person-shooter, and the CPU-controlled characters in old-school Nintendo games are not examples of artificial intelligence, they're just clever programming tricks.
Artificial intelligence (AI) and financial services have only formed a coherent whole for a handful of years. Yet, the role of machine learning and AI-based recommendation has become central to how the finance industry approaches revenue, sales, marketing, security and customer satisfaction. The main reason for this shift in perspective is the emergence of well-adapted tools that allow banks and other actors to harness the full potential of this technology. One such tool is Explainable AI, which bridges the gap between AI and financial services by providing a completely transparent and compliant solution to assist in decision-making processes. Machine learning and algorithm-based technologies are just as promising.
It's no secret that healthcare costs have risen faster than inflation for decades. Some experts estimate that healthcare will account for over 20% of the US GDP by 2025. Meanwhile, doctors are working harder than ever before to treat patients as the U.S. physician shortage continues to grow. Many medical professionals have their schedules packed so tightly that much of the human element which motivated their pursuit of medicine in the first place is reduced. In healthcare, artificial intelligence (AI) can seem intimidating.
Amazon Comprehend now supports Amazon Virtual Private Cloud (Amazon VPC) endpoints via AWS PrivateLink so you can securely initiate API calls to Amazon Comprehend from within your VPC and avoid using the public internet. Amazon Comprehend is a fully managed natural language processing (NLP) service that uses machine learning (ML) to find meaning and insights in text. You can use Amazon Comprehend to analyze text documents and identify insights such as sentiment, people, brands, places, and topics in text. Using AWS PrivateLink, you can access Amazon Comprehend easily and securely by keeping your network traffic within the AWS network, while significantly simplifying your internal network architecture. It enables you to privately access Amazon Comprehend APIs from your VPC in a scalable manner by using interface VPC endpoints.
Lexalytics, the leader in "words-first" machine learning and artificial intelligence, announced that Zignal Labs, creator of the Impact Intelligence platform for measuring the evolution of opinion in real time, has chosen Lexalytics to extend its natural language processing (NLP) and text analytics capabilities to help marketers, communicators and analysts gain a greater understanding of perceptions across traditional and social media. Zignal Labs has incorporated Lexalytics' on-premises Salience engine to analyze media in real time, across multiple industries, including financial services, technology, healthcare, consumer products, sports, entertainment and more. With Lexalytics, Zignal's customers can understand what people are saying about products, services or current events, categorize discussions into separate groupings and themes, and evaluate the sentiment of media coverage across multiple languages. "With more people working from home, and the increase in online discourse caused by the COVID-19 crisis and social justice movements, we've seen an explosion in the amount of content we're analyzing for our customers in all parts of the world and had a need to expand our NLP capabilities for international languages," said Jonathan Dodson, CTO of Zignal Labs. "We chose Lexalytics because out of all of the market leaders we evaluated, they have the best combination of accuracy and performance, breadth of foreign language capabilities, scale and price, as well as an on-premises solution, offering maximum tuning and features while keeping data processing costs to a minimum."