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The future of voice AI in healthcare


Famous social media influencer Gary Vayernurchuk says, "The future belongs to Voice." Look at all AI (artificial intelligence) driven assistants around us, from Alexa to Google Assistant, there is inherent convenience in just saying it out loud and having voice based conversations with your'virtual' assistant rather than writing commands or selecting a drop down menu. We all know that healthcare needs to be digitised in order to reach the next level of patient care. Technologies like AI and Blockchain need to be integrated into existing healthcare systems in order to make them more efficient. But these technologies can only work if all our processes are digitised first.

Podcast: How AI is giving a woman back her voice

MIT Technology Review

Voice technology is one of the biggest trends in the healthcare space. We look at how it might help care providers and patients, from a woman who is losing her speech, to documenting healthcare records for doctors. But how do you teach AI to learn to communicate more like a human, and will it lead to more efficient machines? This episode was reported and produced by Anthony Green with help from Jennifer Strong and Emma Cillekens. It was edited by Michael Reilly. Our mix engineer is Garret Lang and our theme music is by Jacob Gorski. Jennifer: Healthcare looks a little different than it did not so long ago…when your doctor likely wrote down details about your condition on a piece of paper...

Role of AI For Disabled Person


The term AI can be expanded as "Artificial Intelligence". Using AI, we can also process human intelligence and can be programmed to capture human actions. That's the reason this is also known as the artificial human brain. This helps in our daily life to manage new and simpler daily activities. We can easily automate tasks that require human intelligence using Ai such as text recognition, speech and voice recognition, the ability to make a decision, and other activities that can be done only with human intelligence.

AI for Trading


Stocks are shares or ownership certificates of a company, by buying stocks of a company a person becomes a shareholder of the company and gets profits if the stocks price increases and suffers loss if the stock price declines. The stock market is a very risky place as it is very hard to predict the trend or future price of any particular stock. Most people follow basic trends to invest in the stock market without sufficient data and a result they suffer loss.Those that understand AI technology and know how to handle its dangers will have opportunity throughout the early stages of its adoption. One disadvantage of AI-based trading systems is that they can provide models that are poorer than chance. Because methods based on chart patterns and indicators draw their rewards from a distribution with zero mean before transaction charges, traditional technical analysis is an unsuccessful technique of trading.

Role of AI for disabled persons


Artificial Intelligence has enabled us to perform our daily jobs in new and more efficient ways. AI can assist people with disabilities by significantly improving their ability to get around and participate in daily activities by automating the process that would typically need human intellect, such as speech recognition and several other functions. Artificial intelligence (AI) has the potential to change the lives of individuals with disabilities by facilitating the development of interactive technologies that promote accessibility and flexibility. For persons with impairments, AI-assisted voice-assist devices like Google Home, Alexa have opened new possibilities of accessibility. Because Artificial Intelligence is so important in communication and engagement, it makes it much easier for persons with disabilities to access information simply by speaking to their devices.

Oracle Announces New AI Services for OCI


Oracle has announced availability of Oracle Cloud Infrastructure (OCI) AI services, a collection of services that make it easier for developers to apply AI services to their applications without requiring data science expertise. The new OCI AI services give developers the choice of leveraging out-of-the-box models that have been pretrained on business-oriented data or custom training the services based on their organization's own data. The six new services help developers with a range of complex tasks from language to computer vision, and time-series forecasts. Companies today need AI to accelerate innovation, assess business conditions, and deliver new customer experiences. However, they frequently run into implementation issues ranging from a scarcity of data science expertise, difficulties in training models on relevant business data to getting their platform to work in a live environment or breaking down data silos.

How AI and NLP are helping healthcare call centers to be more efficient


Thirteen percent of calls in the healthcare industry are disconnected before the caller is routed to an agent, and 67% of callers hang up the phone because they are frustrated at not being able to speak to a representative, according to a 2019 survey finding from 8x8, a unified communications vendor. In 2021, call center frustration persists for most healthcare customers. "The most common issues in healthcare call centers revolve around inefficient and expensive operations," said Joe Hagan, chief product officer at LumenVox, a speech recognition vendor. "As a result of the rapid shift to remote work in early 2020, it became clear that more often than not, contact centers have disparate systems and incompatible software making it difficult to meet the increased call volumes and demands on live agents." Being in the midst of the COVID-19 pandemic hasn't helped, either.

5 Natural Language Processing Trends to Look Forward in 2022


Natural Language Processing is a way for computer systems like robots to learn and mimic the human language. It helps intelligent systems decode the meaning of human sentences and master communication. NLP tools are important for businesses that work with huge amounts of unstructured data, be it emails, social media conversations, survey responses, and data in other forms. Companies can analyze data to find what is trending amidst the pools of data and use those insights to automate tasks and make business decisions. Popular applications of NLP technology are in sentiment analysis, where machines learn to understand common human feelings like sarcasm to detect fake news online, text classification that brings order to unstructured data by making sense out of it, in chatbots and virtual assistants to make them smarter and obey commands better, and improves auto-correct and speech recognition software.

New Smithsonian exhibit features first 'genderless voice assistant'

FOX News

January Littlejohn's lawyer explained how the school treated her like she was a danger to her child on'Fox News Primetime.' A new exhibit at the Smithsonian Institution features an interactive display that incorporates the first "genderless voice assistant." The voice assistant, known as "Q," is located at the FUTURES exhibit and the Smithsonian's website describes it as a voice that "was synthesized by combining recordings of people who identify variously as male, female, transgender, or nonbinary." "By mixing multiple voices together, Q's makers have created a voice'for a future where we are no longer defined by gender, but rather by how we define ourselves,'" the website says. The genderless voice system was developed over the last few years by a Danish company called Virtue Nordic and it describes itself as "like Siri or Alexa but without the gender."

Why SoundHound is valued at 5 Shazams – TechCrunch


Voice AI company SoundHound is set to go public on the Nasdaq via a SPAC transaction at a nearly $2.1 billion valuation in early 2022, blank check company Archimedes and its target announced. The last time you heard SoundHound's name might have been several years ago when it was considered a lesser-known Shazam competitor. Now it's worth 5.25x what Apple paid for the leading music recognition service – some $400 million, in a transaction that closed in the fall of 2018. That was five years after TechCrunch reported on SoundHound's "struggles to exit Shazam's shadow," despite boasting more than 175 million users. So what happened that would make SoundHound now significantly more valuable than its British counterpart?