Tag Archives: Natural Language Processing

How Chatbots Make Healthcare More Efficient

In the mid 1960s, Joseph Weizenbaum of the MIT Artificial Intelligence Laboratory created ELIZA, an early natural language processing computer program and the first chatbot therapist. While ELIZA did not change therapy forever, it was a major step forward and one of the first programs capable of taking the Turing Test. Researchers were surprised by the amount of people who attributed human-like feelings to the computer’s responses.

Fast-forward 50 years later, advancements in artificial intelligence and natural language processing enable chatbots to become useful in a number of scenarios. Interest in chatbots has increased by 500% in the past 10 years and the market size is expected reach $1.3 billion by 2025.

Chatbots are becoming commonplace in marketing, customer service, real estate, finance, and more. Healthcare is one of the top 5 industries where chatbots are expected to make an impact. This week, we explore why chatbots appeal to help healthcare providers run a more efficient operation.

SCALABILITY

Chatbots can interact with a large number of users instantly. Their scalability equips them to handle logistical problems with ease. For example, chatbots can make mundane tasks such as scheduling easier by asking basic questions to understand a user’s health issues, matching them with doctors based on available time slots, and integrating with both doctor and patient calendars to create an appointment.

At the onset of the pandemic, Intermountain Healthcare was receiving an overload of inquiries from people who were afraid they may have contracted Covid-19. In order to facilitate the inquiries, Intermountain added extra staff and a dedicated line to their call center, but it wasn’t enough. Ultimately, they turned to artificial intelligence in the form of Scout, a conversational chatbot made by Gyant, to facilitate a basic coronavirus screening which determined if patients were eligible to get tested at a time when the number of tests were limited.

Ultimately, Scout only had to ask very basic questions—but it handled the bevy of inquiries with ease. Chatbots have proved themselves to be particularly useful for understaffed healthcare providers. As they employ AI to learn from previous interactions, they will become more sophisticated which will enable them to take on more robust tasks.

ACCESS

Visiting a doctor can be challenging due to the considerable amount of time it takes to commute. Working people and those without access to reliable transport may prevent them from taking on the hassle of the trip. Chatbots and telehealth in general provide a straightforward solution to these issues, enabling patients to receive insight as to whether an in-person consultation will be necessary.

While chatbots cannot provide medical insight and prognoses, they are effective in collecting and encouraging an awareness of basic data, such as anxiety and weight changes. They can help effectively triage patients through preliminary stages using automated queries and store information which doctors can later reference with ease. Their ability to proliferate information and handle questions will only increase as natural language processing improves.

A PERSONALIZED APPROACH — TO AN EXTENT

Chatbot therapists have come a long way since ELIZA. Developments in NLP, machine learning, and more enable chatbots to deliver helpful, personalized responses to user messages. Chatbots like Woebot are trained to employ cognitive-behavioral therapy (CBT) to aid patients suffering from emotional distress by offering prompts and exercises for reflection. The anonymity of chatbots can help encourage patients to provide more candid answers unafraid of human judgment.

However, chatbots have yet to achieve one of the most important features a medical provider should have: empathy. Each individual is different, some may be scared away by formal talk and prefer casual conversation while for others, formality may be of the utmost importance. Given the delicacy of health matters, a lack of human sensitivity is a major flaw.

While chatbots can help manage a number of logistical tasks to make life easier for patients and providers, their application will be limited until they can gauge people’s tone and understand context. If recent advances in NLP and AI serve any indication, that time is soon to come.

How AI Revolutionizes Music Streaming

In 2020, worldwide music streaming revenue hit 11.4 billion dollars, a 2800% growth over the course of a decade. Three hundred forty-one million paid online streaming subscribers get their music from top services like Apple Music, Spotify, and Tidal. The competition for listeners is fierce. Each company looks to leverage every advantage they can in pursuit of higher market share.

Like all major tech conglomerates, music streaming services collect an exceptional amount of user data through their platforms and are creating elaborate AI algorithms designed to improve user experience on a number of levels. Spotify has emerged as the largest on-demand music service active today and bolstered its success through the innovative use of AI.

Here are the top ways in which AI has changed music streaming:

COLLABORATIVE FILTERING

AI has the ability to sift through a plenitude of implicit consumer data, including:

  • Song preferences
  • Keyword preferences
  • Playlist data
  • Geographic location of listeners
  • Most used devices

AI algorithms can analyze user trends and identify users with similar tastes. For example, if AI deduces that User 1 and User 2 have similar tastes, then it can infer that songs User 1 has liked will also be enjoyed by User 2. Spotify’s algorithms will leverage this information to provide recommendations for User 2 based on what User 1 likes, but User 2 has yet to hear.

via Mehmet Toprak (Medium)
via Mehmet Toprak (Medium)

The result is not only improved recommendations, but greater exposure for artists that otherwise may not have been organically found by User 2.

NATURAL LANGUAGE PROCESSING

Natural Language Processing is a burgeoning field in AI. Previously in our blog, we covered GPT-3, the latest Natural Language Processing (NLP) technology developed by OpenAI. Music streaming services are well-versed in the technology and leverage it in a variety of ways to enhance UI.

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Algorithms scan a track’s metadata, in addition to blog posts, discussions, and news articles about artists or songs on the internet to determine connections. When artists/songs are mentioned alongside artists/songs the user likes, algorithms make connections that fuel future recommendations.

GPT-3 is not perfect; its ability to track sentiments lacks nuance. As Sonos Radio general manager Ryan Taylor recently said to Fortune Magazine: “The truth is music is entirely subjective… There’s a reason why you listen to Anderson .Paak instead of a song that sounds exactly like Anderson .Paak.”

As NLP technology evolves and algorithms extend their grasp of the nuances of language, so will the recommendations provided to you by music streaming services.

AUDIO MODELS

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AI can study audio models to categorize songs exclusively based on their waveforms. This scientific, binary approach to analyzing creative work enables streaming services to categorize songs and create recommendations regardless of the amount of coverage a song or artist has received.

BLOCKCHAIN

Artist payment of royalties on streaming services poses its own challenges, problems, and short-comings. Royalties are deduced from trillions of data points. Luckily, blockchain is helping to facilitate a smoother artist’s payment process. Blockchain technology can not only make the process more transparent but also more efficient. Spotify recently acquired blockchain company Mediachain Labs, which will, many pundits are saying, change royalty payments in streaming forever.

MORE TO COME

While AI has vastly improved streaming ability to keep their subscribers compelled, a long road of evolution lies ahead before it can come to a deep understanding of what motivates our musical tastes and interests. Today’s NLP capabilities provided by GPT-3 will probably become fairly archaic within three years as the technology is pushed further. One thing is clear: as streaming companies amass decades’ worth of user data, they won’t hesitate to leverage it in their pursuit of market dominance.