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Personal Assistant Systems: AI-Alerts

Needy, overconfident voice assistants are wearing on their owners' last nerves

Washington Post - Technology News

Americans welcomed voice assistants into their homes on claims that Siri, Alexa and Google Assistant would be like quasi-human helpers, seamlessly managing our appointments, grocery lists and music libraries. From 2019 to 2021, the use of voice assistants among online adults in the United States rose to 30 percent from 21 percent, according to data from market research firm Forrester. Of the options, Siri is the most popular -- 34 percent of us have interacted with Apple's voice assistant in the last year. Amazon's Alexa is next with 32 percent; 25 percent have used Google Assistant; and Microsoft's Cortana and Samsung's Bixby trail behind with five percent each.

Why AI Isn't Providing Better Product Recommendations


If you're interested in obscure things, there are two reasons why your searches for items and products are likely to be less related to your interests than those of your'mainstream' peers; either you're a monetization'edge case' whose interests will only be catered to if you're also in the upper categories of economic purchasing power (for example, products and services related to'wealth management'); or the search algorithms that you're using are leveraging collaborative filtering (CF), which favors the interests of the majority. Since collaborative filtering is cheaper and more established than other potentially more capable algorithms and frameworks, it's possible that both these cases apply. CF-based search results will prioritize items that are perceived to be popular among'people like you', as best the host framework can understand what kind of a consumer you are. If you're wary of providing data profiling information to the host system – for instance, not inclined to press the'Like' buttons in Netflix and other video content services – you're likely to be classified quite generically in your earliest interactions with the system, and the recommendations you receive will reflect the most popular trends. On a streaming platform, that could mean being recommended whatever shows and movies are currently'hot', such as reality TV and forensic murder documentaries, irrespective of your interest in these.

Explainability in Music Recommender Systems


The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate.

Technical Perspective: Personalized Recommendation of PoIs to People with Autism

Communications of the ACM

Recommender systems are among the most pervasive machine learning applications on the Internet. Social media, audio and video streaming, news, and e-commerce are all heavily driven by the data-intensive personalization they enable, leveraging information drawn from the behavior of large user bases to offer a myriad of recommendation services. Point of Interest (PoI) recommendation is the task of recommending locations (business, cultural sites, natural areas) for a user to visit. This is a well-established sub-field within recommender systems, and as a domain of application, it provides a good introduction to the challenges of applying personalized recommendation in practical contexts. An effective PoI recommender must consider a user's interests and preferences, as in any personalized system, but also practical aspects of travel: weather, congestion, hours of operation, seasonality, to name a few.

The People in Intimate Relationships With AI Chatbots


Different from digital assistants, like Amazon's Alexa or Apple's Siri, artificially intelligent (AI) conversational chatbots learn by speaking with their user. Resembling animated sim-like avatars that blink and fidget as a real person would, users are invited to design their Replika's appearance when setting up the app – choosing its gender, hairstyle, ethnicity and eye colour. Later, you can use coins and gems to purchase add-ons like clothes, tattoos, facial hair, and interests (including anime, K-pop, gardening, and basketball). The more you chat, the more currency you receive – and the more intelligent your Replika becomes. Before you know it, they've developed an illusion of emotional awareness that's eerily similar to your conversations down the pub.

Amazon Research Introduces Deep Reinforcement Learning For NLU Ranking Tasks


In recent years, voice-based virtual assistants such as Google Assistant and Amazon Alexa have grown popular. This has presented both potential and challenges for natural language understanding (NLU) systems. These devices' production systems are often trained by supervised learning and rely significantly on annotated data. But, data annotation is costly and time-consuming. Furthermore, model updates using offline supervised learning can take long and miss trending requests.

How no-code, reusable AI will bridge the AI divide


In 1960, J.C.R. Licklider, an MIT professor and an early pioneer of artificial intelligence, already envisioned our future world in his seminal article, "Man-Computer Symbiosis": In the anticipated symbiotic partnership, men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinizable work that must be done to prepare the way for insights and decisions in technical and scientific thinking. In today's world, such "computing machines" are known as AI assistants. However, developing AI assistants is a complex, time-consuming process, requiring deep AI expertise and sophisticated programming skills, not to mention the efforts for collecting, cleaning, and annotating large amounts of data needed to train such AI assistants. It is thus highly desirable to reuse the whole or parts of an AI assistant across different applications and domains.

Google introduces Pathways, a new generation of AI


By enabling computers to perform each task intelligently, machine learning systems can carry out complex processes by learning from data. Recent years have seen exciting advances in machine learning, which have raised its capabilities across a suite of applications. For years, Google has been using machine learning for several tasks, including Autocorrecting misspelled words or showing useful results. Whats's more, they also created an individual's virtual assistant called Google Assistant. Just Say'OK Google,' and your assistant is ready to help you perform various tasks.

The Success of Conversational AI and the AI Evaluation Challenge it Reveals

Interactive AI Magazine

Research interest in Conversational AI has experienced a massive growth over the last few years and several recent advancements have enabled systems to produce rich and varied turns in conversations similar to humans. However, this apparent creativity is also creating a real challenge in the objective evaluation of such systems as authors are becoming reliant on crowd worker opinions as the primary measurement of success and, so far, few papers are reporting all that is necessary for others to compare against in their own crowd experiments. This challenge is not unique to ConvAI, but demonstrates as AI systems mature in more "human" tasks that involve creativity and variation, evaluation strategies need to mature with them. Conversational AI, or ConvAI as it has been abbreviated, is a sub-field of artificial intelligence (AI) where the goal is to build an autonomous agent that is capable of maintaining natural discourse with a human over some interface such as text or speech. The purpose may be to help humans perform tasks as a virtual/digital assistant, provide a natural language interface to another system as in information retrieval or navigation systems, or simply to converse like one would with an open domain chatbot.

Speech Study Using AI Technology to Spot ALS Biomarkers


A technology based on artificial intelligence is helping to spot biomarkers and document the progression of amyotrophic lateral sclerosis (ALS) in a large speech study being conducted by EverythingALS. The technology, developed by, is a web-based computer program that uses audio (speech) and video (facial) recordings to assess neurological states automatically through AI and machine learning algorithms. Its greatest advantage is that data can be collected remotely at home on any computer device with the help of a virtual assistant called "Tina." This is important for people with ALS, who often have limited mobility due to muscle weakness, which may affect their ability to participate in clinical studies. "Our mission is to discover and deploy initiatives that focus on new ways to diagnose and treat neurological disorders at the intersection of computing and brain science with a focus on ALS," Indu Navar, CEO and co-founder of EverythingALS, a U.S. nonprofit that is part of the Peter Cohen Foundation, said in a press release.