A.I.
AI poised to usher in new level of concierge services to the public
Researchers explore how intelligent systems can upgrade hospitality sector
OHIO STATE UNIVERSITY
COLUMBUS, Ohio – Concierge services built on artificial intelligence have the potential to improve how hotels and other service businesses interact with customers, a new paper suggests.
In the first work to introduce the concept, researchers have outlined the role an AI concierge, a technologically advanced assistant, may play in various areas of the service sector as well as the different forms such a helper might embody.
Their paper envisions a virtual caretaker that, by combining natural language processing, behavioral data and predictive analytics, would anticipate a customer’s needs, suggest certain actions, and automate routine tasks without having to be explicitly commanded to do so.
Though such a skilled assistant is still years away, Stephanie Liu, lead author of the paper and an associate professor of hospitality management at The Ohio State University, and her colleagues drew insight from several contemporary fields, including service management, psychology, human-computer interaction and ethics research, to detail what opportunities and challenges might arise from having an AI concierge manage human encounters.
“The traditional service industry uses concierges for high-end clients, meaning that only a few people have access to them,” Liu said. “Now with the assistance of AI technology, everybody can have access to a concierge providing superior experiences.”
On that premise, the benefits of incorporating AI into customer service are twofold: It would allow companies to offer around-the-clock availability and consistency in their operations as well as improve how individuals engage with professional service organizations, she said.
Moreover, as the younger workforce gravitates to more tech-oriented jobs and global travel becomes more common, generative AI could be an apt solution to deal with the escalating demands of evolving hospitality trends, said Liu.
“The development of AI technology for hotels, restaurants, health care, retail and tourism has a lot of potential,” she said.
The paper was published recently in the Journal of Service Management.
Despite the social and economic benefits associated with implementing such machines, how effective AI concierges may be at completing a task is dependent on both the specific situation and the type of interface consumers use, said Liu.
There are four primary forms a smart aide might take, each with distinctive attributes that would provide consumers with different levels of convenience, according to Liu.
The first type is a dialogue interface that uses only text or speech to communicate, such as ChatGPT, a conversational agent often used to make inquiries and garner real-time assistance. Many of these interactive devices are already used in hotels and medical buildings for contactless booking or to connect consumers with other services and resources.
The second is a virtual avatar that employs a vivid digital appearance and a fully formed persona to foster a deeper emotional connection with the consumer. This method is often utilized for telehealth consultations and online learning programs.
The third iteration is a holographic projection wherein a simulated 3D image is brought into the physical world. According to the paper, this is ideally suited for scenarios where the visual impact is desired, but physical assistance itself is not necessary.
The paper rounds out the list by suggesting an AI concierge that would present as a tangible, or touchable robot. This form would offer the most human-like sensory experiences and would likely be able to execute multiple physical tasks, like transporting heavy luggage.
Some international companies have already developed these cutting-edge tools for use in a limited capacity. One robotic concierge, known as Sam, was designed to aid those in senior living communities by helping them check in, make fall risk assessments and support staff with non-medical tasks. Another deployed at South Korea’s Incheon International Airport helped consumers navigate paths to their destination and offered premier shopping and dining recommendations.
Yet as advanced computing algorithms become more intertwined in our daily lives, industry experts will likely have to consider consumer privacy concerns when deciding when and where to implement these AI systems. One way to deal with these issues would be to create the AI concierge with limited memory or other safewalls to protect stored personal data, such as identity and financial information, said Liu.
“Different companies are at different stages with this technology,” said Liu. “Some have robots that can detect customers’ emotions or take biometric inputs and others have really basic ones. It opens up a totally different level of service that we have to think critically about.”
What’s more, the paper notes that having a diversity of concierge options available for consumers to choose from is also advantageous from a mental health standpoint.
Because AI is viewed as having less agency than their human counterparts, it might help mitigate psychologically uncomfortable service situations that could arise because of how consumers feel they might be perceived by a human concierge. This reduced apprehension regarding the opinion of a machine may encourage heightened comfort levels and result in more favorable responses about the success of the AI concierge, said Liu.
Ultimately, there’s still much multidisciplinary testing to be done to ensure these technologies can be applied in a widespread and equitable manner. Liu adds that future research should seek to determine how certain design elements, such as the perceived gender, ethnicity or voice of these robotic assistants, would impact overall consumer satisfaction.
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Contact: Stephanie Liu, Liu.6225@osu.edu
Written by: Tatyana Woodall, Woodall.52@osu.edu
JOURNAL
Journal of Service Management
ARTICLE TITLE
AI concierge in the customer journey: what is it and how can it add value to the customer?
Artificial intelligence resolves conflicts impeding animal behavior research
Algorithm automates research and reconciles differing results that often arise between various studies.
UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE/UW MEDICINE
IMAGE:
NEUROBIOLOGY RESEARCHERS SAM GOLDEN AND NASTACIA GOODWIN REVIEW LIGHT SHEET FLUORESCENT MICROSCOPY BRAIN IMAGES REVEALING THE ACTIVITY OF INDIVIDUAL NEURONS DURING DIFFERENT BEHAVIORS. THEY ARE IN A RESEARCH LABORATORY IN THE DEPARTMENT OF BIOLOGICAL STRUCTURE AT THE UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE IN SEATTLE.
view moreCREDIT: MICHAEL MCCARTHY/UW MEDICINE
Artificial intelligence software has been developed to rapidly analyze animal behavior so that behaviors can be more precisely linked to the activity of individual brain circuits and neurons, researchers in Seattle report.
The program promises not only to speed research into the neurobiology of behavior, but also to enable comparison and reconcile results that disagree due to differences in how individual laboratories observe, analyze and classify behaviors, said Sam Golden, assistant professor of biological structure at the University of Washington School of Medicine.
“The approach allows labs to develop behavioral procedures however they want and makes it possible to draw general comparisons between the results of studies that use different behavioral approaches,” he said.
A paper describing the program appears in the journal Nature Neuroscience. Golden and Simon Nilsson, a postdoctoral fellow in the Golden lab, are the paper’s senior authors. The first author is Nastacia Goodwin, a graduate student in the lab.
The study of the neural activity behind animal behavior has led to major advances in the understanding and treatment of such human disorders as addiction, anxiety and depression.
Much of this work is based on observations painstakingly recorded by individual researchers who watch animals in the lab and note their physical responses to different situations, then correlate that behavior with changes in brain activity.
For example, to study the neurobiology of aggression, researchers might place two mice in an enclosed space and record signs of aggression. These would typically include observations of the animals’ physical proximity to one another, their posture, and physical displays such as rapid twitching, or rattling, of the tail.
Annotating and classifying such behaviors is an exacting, protracted task. It can be difficult to accurately recognize and chronicle important details, Golden said. “Social behavior is very complicated, happens very fast and often is nuanced, so a lot of its components can be lost when an individual is observing it.”
To automate this process, researchers have developed AI-based systems to track components of an animal’s behavior and automatically classify the behavior, for example, as aggressive or submissive.
Because these programs can also record details more rapidly than a human, it is much more likely that an action can be closely correlated with neural activity, which typically occurs in milliseconds.
One such program, developed by Nilsson and Goodwin, is called SimBA, for Simple Behavioral Analysis. The open-source program features an easy-to-use graphical interface and requires no special computer skills to use. It has been widely adopted by behavioral scientists.
“Although we built SimBA for a rodent lab, we immediately started getting emails from all kinds of labs: wasp labs, moth labs, zebrafish labs,” Goodwin said.
But as more labs used these programs, the researchers found that similar experiments were yielding vastly different results.
“It became apparent that how any one lab or any one person defines behavior is pretty subjective, even when attempting to replicate well-known procedures,” Golden said.
Moreover, accounting for these differences was difficult because it is often unclear how AI systems arrive at their results, their calculations occurring in what is often characterized as “a black box.”
Hoping to explain these differences, Goodwin and Nilsson incorporated into SimBA a machine-learning explainability approach that produces what is called the Shapely Additive exPlanations (SHAP) score.
Essentially what this explainability approach does is determine how removing one feature used to classify a behavior, say tail rattling, changes the probability of an accurate prediction by the computer.
By removing different features from thousands of different combinations, SHAP can determine how much predictive strength is provided by any individual feature used in the algorithm that is classifying the behavior. The combination of these SHAP values then quantitatively defines the behavior, removing the subjectivity in behavioral descriptions.
“Now we can compare (different labs’) respective behavioral protocols using SimBA and see whether we’re looking, objectively, at the same or different behavior,” Golden said.
“This approach allows labs to design experiments however they like, but because you can now directly compare behavioral results from labs that are using different behavioral definitions, you can draw clearer conclusions between their results. Previously, inconsistent neural data could have been attributed to many confounds, and now we can cleanly rule out behavioral differences as we strive for cross-lab reproducibility and interpretability” Golden said.
This research was supported by grants from the National Institutes of Health (K08MH123791), the National Institute on Drug Abuse (R00DA045662, R01DA059374, P30DA048736), National Institute of Mental Health (1F31MH125587, F31AA025827, F32MH125634), National Institute of General Medicine Sciences (R35GM146751), Brain & Behavior Research Foundation, Burroughs Wellcome Fund, Simons Foundation, and Washington Research Foundation.
A video frame of two mice whose behavior is being analyzed by SimBA. The dots represent the body parts being tracked by the program.
CREDT
Nastacia Goodwi
JOURNAL
Nature Neuroscience
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Animals
ARTICLE TITLE
Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience
AI headphones let wearer listen to a single person in a crowd, by looking at them just once
IMAGE:
A UNIVERSITY OF WASHINGTON TEAM HAS DEVELOPED AN ARTIFICIAL INTELLIGENCE SYSTEM THAT LETS A USER WEARING HEADPHONES LOOK AT A PERSON SPEAKING FOR THREE TO FIVE SECONDS AND THEN HEAR JUST THE ENROLLED SPEAKER’S VOICE IN REAL TIME EVEN AS THE LISTENER MOVES AROUND IN NOISY PLACES AND NO LONGER FACES THE SPEAKER. PICTURED IS A PROTOTYPE OF THE HEADPHONE SYSTEM: BINAURAL MICROPHONES ATTACHED TO OFF-THE-SHELF NOISE CANCELING HEADPHONES.
view moreCREDIT: KIYOMI TAGUCHI/UNIVERSITY OF WASHINGTON
Noise-canceling headphones have gotten very good at creating an auditory blank slate. But allowing certain sounds from a wearer’s environment through the erasure still challenges researchers. The latest edition of Apple’s AirPods Pro, for instance, automatically adjusts sound levels for wearers — sensing when they’re in conversation, for instance — but the user has little control over whom to listen to or when this happens.
A University of Washington team has developed an artificial intelligence system that lets a user wearing headphones look at a person speaking for three to five seconds to “enroll” them. The system, called “Target Speech Hearing,” then cancels all other sounds in the environment and plays just the enrolled speaker’s voice in real time even as the listener moves around in noisy places and no longer faces the speaker.
The team presented its findings May 14 in Honolulu at the ACM CHI Conference on Human Factors in Computing Systems. The code for the proof-of-concept device is available for others to build on. The system is not commercially available.
“We tend to think of AI now as web-based chatbots that answer questions,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “But in this project, we develop AI to modify the auditory perception of anyone wearing headphones, given their preferences. With our devices you can now hear a single speaker clearly even if you are in a noisy environment with lots of other people talking.”
To use the system, a person wearing off-the-shelf headphones fitted with microphones taps a button while directing their head at someone talking. The sound waves from that speaker’s voice then should reach the microphones on both sides of the headset simultaneously; there’s a 16-degree margin of error. The headphones send that signal to an on-board embedded computer, where the team’s machine learning software learns the desired speaker’s vocal patterns. The system latches onto that speaker’s voice and continues to play it back to the listener, even as the pair moves around. The system’s ability to focus on the enrolled voice improves as the speaker keeps talking, giving the system more training data.
The team tested its system on 21 subjects, who rated the clarity of the enrolled speaker’s voice nearly twice as high as the unfiltered audio on average.
This work builds on the team’s previous “semantic hearing” research, which allowed users to select specific sound classes — such as birds or voices — that they wanted to hear and canceled other sounds in the environment.
Currently the TSH system can enroll only one speaker at a time, and it’s only able to enroll a speaker when there is not another loud voice coming from the same direction as the target speaker’s voice. If a user isn’t happy with the sound quality, they can run another enrollment on the speaker to improve the clarity.
The team is working to expand the system to earbuds and hearing aids in the future.
Additional co-authors on the paper were Bandhav Veluri, Malek Itani and Tuochao Chen, UW doctoral students in the Allen School, and Takuya Yoshioka, director of research at AssemblyAI. This research was funded by a Moore Inventor Fellow award, a Thomas J. Cabel Endowed Professorship and a UW CoMotion Innovation Gap Fund.
For more information, contact tsh@cs.washington.edu.
TSH_demonstration [VIDEO] |
A University of Washington team has developed an artificial intelligence system that lets a user wearing headphones look at a person speaking for three to five seconds and then hear just the enrolled speaker’s voice in real time even as the listener moves around in noisy places and no longer faces the speaker. In this video, co-lead authors Malek Itani, a UW doctoral student in the electrical and computer engineering department, and Bandhav Veluri, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering, demonstrate the system.
CREDIT
Kiyomi Taguchi/University of Washington
ARTICLE TITLE
Look Once to Hear: Target Speech Hearing with Noisy Examples
Artificial intelligence approaches demonstrate how plant science has evolved
Machine learning also reveals how model systems have changed and how countries differ in terms of research focus and impact
IMAGE:
RESEARCHERS HAVE USED ARTIFICIAL INTELLIGENCE TO REVEAL TRENDS IN PLANT RESEARCH TOPICS OVER THE PAST SEVERAL DECADES.
view moreCREDIT: CREATED WITH IMAGES BY JACKIE DILORENZO/UNSPLASH (PLANT PHOTO) AND ALEXANDRA KOCH/PIXABAY (ELECTRONIC BRAIN ILLUSTRATION) (CC-BY 4.0, HTTPS://CREATIVECOMMONS.ORG/LICENSES/BY/4.0/)
Artificial intelligence approaches demonstrate how plant science has evolved
Machine learning also reveals how model systems have changed and how countries differ in terms of research focus and impact
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In your coverage, please use this URL to provide access to the freely available paper in PLOS Biology: http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002612
Article Title: Assessing the evolution of research topics in a biological field using plant science as an example
Author Countries: United States
Funding: This work was supported by the National Science Foundation (IOS-2107215 and MCB-2210431 to MDL and SHS; DGE-1828149 and IOS-2218206 to SHS), Department of Energy grant Great Lakes Bioenergy Research Center (DE-SC0018409 to SHS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
JOURNAL
PLoS Biology
METHOD OF RESEARCH
Meta-analysis
SUBJECT OF RESEARCH
Not applicable
COI STATEMENT
Competing interests: The authors have declared that no competing interests exist.
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