The press release also states that the Dragon Drive AI enables drivers to access apps and services through voice commands, such as navigation, music, message dictation, calendar, weather, social media. One of the major considerations of this connected vehicle technology, Vlad says, is the interoperability between different AI systems.
According to Vlad, there are two hard problems to solve in this scenario: identifying the right software to delegate commands to and communicating to the system in the language it understands. Physician documentation is part of medical records that contain patient clinical status, such as improvements or declines in patient health.
CDI is the process of improving such healthcare records to ensure improved patient outcomes, data quality and accurate reimbursement. UHS wanted an advanced documentation capture tool to enable quick documentation of the patient story in real-time—one that could also be integrated with the electronic health record EHR. According to the case study, Dragon Medical One enables physicians to dictate progress notes, history of present illness, etc.
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Nuance CAPD reportedly offers physicians real-time intelligence by automatically prompting them with clarifying questions while they are documenting. However, to minimize obstruction during caregiving, Dragon Medical One asks clarifying questions in specific circumstances, such as possibilities of different diagnosis or a different piece of medical information that the physician should consider. Vlad has three important points for businesses to consider before integrating existing NLP technologies.
In order to advance existing NLP technologies, Vlad thinks that the businesses today could either:. Here are some of the future possibilities of NLP that Vlad discusses in our interview:. It is not uncommon for medical personnel to pore over various sources trying to find the best viable treatment methods for a complex medical condition, variations of certain diseases, complicated surgeries, and so on. Information discovery and retrieval: A plausible application of NLP technologies here could be real-time information discovery and retrieval.
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That is, the healthcare AI solution will be able to understand the medical terminology and retrieve relevant medical information from the most reputable sources in real time. Diagnostic assistance: Another near-term and practical NLP application in healthcare, according to Vlad, is diagnostic assistance.
For example, a radiologist looking at a report could take the help of AI to pull up diagnostic guidelines from the American College of Radiology database. The AI system will periodically ask the medical examiner clarifying questions to make appropriate, relevant diagnostic suggestions. Virtual healthcare assistant: These NLP capabilities could be extended further to create an intelligent healthcare AI assistant. This AI medical assistant will understand conversations using NLU models enabled with medical vocabulary.
Trained on medical terminology and data, it would be able to listen and interpret conversations between a doctor and a patient with consent so that it can transcribe, summarize the conversation as notes for future reference, and even create structured draft reports which could take hours to manually create. This would minimize the manual labor of healthcare personnel so they can invest their time in catering to patients. Image classification and report generation: Extending existing NLP technologies such as automated image captioning to healthcare AI systems would be extremely useful in report generation from images or X-rays.
The AI would be able to understand medical images and electronic health records. Vlad believes that tying up all the above potential NLP applications in healthcare would be difficult because the systems are heterogenous a wide variety of different software from different vendors in the medical field. That is, they execute one command at a time. However, to take on more complex tasks, they have to be able to converse, much like a human.
Hybrid Natural Language Generation in a Spoken Language Dialog System | SpringerLink
Real-time vocal communication is riddled with imperfections such as slang, abbreviations, fillers, mispronunciations, and so on, which can be understood by a human listener sharing the same language as the speaker. In the future, this NLP capability of understanding the imperfections of real-time vocal communication will be extended to the conversational AI solutions. For example, to plan a series of events, a user will be able to converse with the AI like he would with a human assistant.
Vlad gives a common example of a colloquial command:. Take care of it. The AI would be able to comprehend the command, divide the complex task into simpler subtasks and execute them. To achieve this, the virtual assistant would have to consult the calendars of both the user and the friend to determine a common time when they are both available, know the end time of the last meeting on the specified day or date, check the availability of restaurants, present the user with the list of nearby restaurants, etc.
The 4 Biggest Open Problems in NLP
Vlad is convinced that cars will be increasingly used as autonomous robots whose transportational capabilities could be augmented with other onboard computational capabilities and sensors. He states. The user should be able to do this while the music is playing or when the co-passengers are talking either to their virtual assistant or among themselves.
Therefore, it would identify and interact with the appropriate AI software that can open the garage door. In fact, Amazon recently announced that BMW will integrate Alexa into their vehicles in , which will give the drivers access to their Alexa from their cars through voice commands. In the customer service field, Vlad believes that advanced NLP technologies could be used to analyze voice calls and emails in terms of customer happiness quotient, prevalent problem topics, sentiment analysis, etc. For example, NLP could be used to extract insights from the tone and words of customers in textual messages and voice calls that can be used to analyze the frequency of the problem topic at hand and which features and services receive the most complaints, etc.
Vlad elaborates that using clustering in NLP for broad information search, businesses can coax out patterns in the problem topics, tracking the biggest concerns among customers, etc. Many such voice-controlled NLP systems have already made their way into the market, including apps that help control smart machines like washing machines, thermostats, ovens, pet monitoring systems, etc. Human-like virtual assistants: Virtual assistants will become better at understanding and responding to complex and long-form natural language requests, which use conversational language, in real time.
These assistants will be able to converse more like humans, take notes during dictation, analyze complex requests and execute tasks in a single context, suggest important improvements to business documents, and more. Information retrieval from unstructured data: NLP solutions will increasingly gather useful intelligence from unstructured data such as long-form texts, videos, audios, etc.
They will be able to analyze the tone, voice, choice of words, and sentiments of the data to gather analytics, such as gauging customer satisfaction or identifying problem areas. Digital Language Typology investigates methods for automatically discovering family relationships between languages from text and speech material, with little or no prior linguistic analysis or resources.
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It focuses on low-resourced Uralic languages. Immersive Automation concerns building tools for the newsroom of the future, in particular using Natural Language Generation to automatically generate news articles from data. Anna Kantosalo started working in the Discovery Group in She approaches the topic from a practical angle drawing methodology from her background in Interaction Design, as well as from a more theoretical angle, looking at abstract interaction in mathematical terms.
Simo's doctoral studies consider the intersection of computational creativity, autonomous agents and multi-agent systems.
From a single agent perspective he is interested in how autonomous and self-adaptive agents can exhibit creativity both in their outputs and in their internal processes. In multi-agent settings his main focus is on how a group of creative agents can work together in novel ways to accomplish tasks that are not easily fulfilled by any single agent alone.
Khalid's research is focused on Linguistic Creativity, e. His research explores how Natural Language Processing and Machine Learning can be utilised on corpora of text to interpret and generate figurative language automatically. Leo's current research interests lie in the fields of Natural Language Generation and Data Science, as well as their applications to different domains, especially automated journalism and automated report generation. He is working on the Digital Language Typology project concerned with the computational discovery of structural relationships between languages, in terms of various typological dimensions.
The project is focused on low-resourced languages, calling for language-independent methods applicable to unannotated data. NB: Applications for internships are only taken via the joint call of the department, usually in Jan-Feb. New PhD students are taken only in exceptional cases. If you are interested in joining the research group, please contact Prof. Hannu Toivonen. Regardless of the nature of the position you are looking for, please. Due to the large number of applications, we only reply to messages that follow the above instructions.
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Hannu Toivonen, Professor. Mark Granroth-Wilding, Postdoc. Lidia Pivovarova, Postdoc. Anna Kantosalo, Postdoc. Simo Linkola, PhD student. Khalid Alnajjar, PhD student. Eliel Soisalon-Soininen, PhD student. Elaine Zosa, PhD student.