Artificial Intelligence - What Are Mobile Recommendation Assistants?

 

Mobile Recommendation Assistants, also known as Virtual Assistants, Intelligent Agents, or Virtual Personal Assistants, are a collection of software features that combine a conversational user interface with artificial intelligence to act on behalf of a user.

They may deliver what seems to a user as an agent when they work together.

In this sense, an agent differs from a tool in that it has the ability to act autonomously and make choices with some degree of autonomy.

Many qualities may be included into the design of mobile suggestion helpers to improve the user's impression of agency.

Using visual avatars to represent technology, incorporating features of personality such as humor or informal/colloquial language, giving a voice and a legitimate name, constructing a consistent method of behaving, and so on are examples of such tactics.

A human user can use a mobile recommendation assistant to help them with a wide range of tasks, such as opening software applications, answering questions, performing tasks (operating other software/hardware), or engaging in conversational commerce or entertainment (telling stories, telling jokes, playing games, etc.).

Apple's Siri, Baidu's Xiaodu, Amazon's Alexa, Microsoft's Cortana, Google's Google Assistant, and Xiaomi's Xiao AI are among the mobile voice assistants now in development, each designed for certain companies, use cases, and user experiences.

A range of user interface modali ties are used by mobile recommendation aides.

Some are completely text-based, and they are referred regarded as chatbots.

Business to consumer (B2C) communication is the most common use case for a chatbot, and notable applications include online retail communication, insurance, banking, transportation, and restaurants.

Chatbots are increasingly being employed in medical and psychological applications, such as assisting users with behavior modification.

Similar apps are becoming more popular in educational settings to help students with language learning, studying, and exam preparation.

Facebook Messenger is a prominent example of a chatbot on social media.

While not all mobile recommendation assistants need voice-enabled interaction as an input modality (some, such web site chatbots, may depend entirely on text input), many contemporary examples do.

A Mobile Recommendation Assistant uses a number similar predecessor technologies, including a voice-enabled user interface.

Early voice-enabled user interfaces were made feasible by a command syntax that was hand-coded as a collection of rules or heuristics in advance.

These rule-based systems allowed users to operate devices without using their hands by delivering voice directions.

IBM produced the first voice recognition program, which was exhibited during the 1962 World's Fair in Seattle.

The IBM Shoebox has a limited vocabulary of sixteen words and nine numbers.

By the 1990s, IBM and Microsoft's personal computers and software had basic speech recognition; Apple's Siri, which debuted on the iPhone 4s in 2011, was the first mobile application of a mobile assistant.

These early voice recognition systems were disadvantaged in comparison to conversational mobile agents in terms of user experience since they required a user to learn and adhere to a preset command language.

The consequence of rule-based voice interaction might seem mechanical when it comes to contributing to real humanlike conversation with computers, which is a feature of current mobile recommendation assistants.

Instead, natural language processing (NLP) uses machine learning and statistical inference to learn rules from enormous amounts of linguistic data (corpora).

Decision trees and statistical modeling are used in natural language processing machine learning to understand requests made in a variety of ways that are typical of how people regularly communicate with one another.

Advanced agents may have the capacity to infer a user's purpose in light of explicit preferences expressed via settings or other inputs, such as calendar entries.

Google's Voice Assistant uses a mix of probabilistic reasoning and natural language processing to construct a natural-sounding dialogue, which includes conversational components such as paralanguage ("uh", "uh-huh", "ummm").

To convey knowledge and attention, modern digital assistants use multimodal communication.

Paralanguage refers to communication components that don't have semantic content but are nonetheless important for conveying meaning in context.

These may be used to show purpose, collaboration in a dialogue, or emotion.

The aspects of paralanguage utilized in Google's voice assistant employing Duplex technology are termed vocal segre gates or speech disfluencies; they are intended to not only make the assistant appear more human, but also to help the dialogue "flow" by filling gaps or making the listener feel heard.

Another key aspect of engagement is kinesics, which makes an assistant feel more like an engaged conversation partner.

Kinesics is the use of gestures, movements, facial expressions, and emotion to aid in the flow of communication.

The car firm NIO's virtual robot helper, Nome, is one recent example of the application of face expression.

Nome is a digital voice assistant that sits above the central dashboard of NIO's ES8 in a spherical shell with an LCD screen.

It can swivel its "head" automatically to attend to various speakers and display emotions using facial expressions.

Another example is Dr. Cynthia Breazeal's commercial Jibo home robot from MIT, which uses anthropomorphism using paralinguistic approaches.

Motion graphics or lighting animations are used to communicate states of communication such as listening, thinking, speaking, or waiting in less anthropomorphic uses of kinesics, such as the graphical user interface elements on Apple's Siri or illumination arrays on Amazon Alexa's physical interface Echo or in Xiami's Xiao AI.

The rising intelligence and anthropomorphism (or, in some circumstances, zoomorphism or mechanomorphism) that comes with it might pose some ethical issues about user experience.

The need for more anthropomorphic systems derives from the positive user experience of humanlike agentic systems whose communicative behaviors are more closely aligned with familiar interactions like conversation, which are made feasible by natural language and paralinguistics.

Natural conversation systems have the fundamental advantage of not requiring the user to learn new syntax or semantics in order to successfully convey orders and wants.

These more humanistic human machine interfaces may employ a user's familiar mental model of communication, which they gained through interacting with other people.

Transparency and security become difficulties when a user's judgments about a machine's behavior are influenced by human-to-human communication as machine systems become closer to human-to-human contact.

The establishment of comfort and rapport may obscure the differences between virtual assistant cognition and assumed motivation.

Many systems may be outfitted with motion sensors, proximity sensors, cameras, tiny phones, and other devices that resemble, replicate, or even surpass human capabilities in terms of cognition (the assistant's intellect and perceptive capacity).

While these can be used to facilitate some humanlike interaction by improving perception of the environment, they can also be used to record, document, analyze, and share information that is opaque to a user when their mental model and the machine's interface do not communicate the machine's operation at a functional level.

After a user interaction, a digital assistant visual avatar may shut his eyes or vanish, but there is no need to associate such behavior with the microphone's and camera's capabilities to continue recording.

As digital assistants become more incorporated into human users' daily lives, data privacy issues are becoming more prominent.

Transparency becomes a significant problem to solve when specifications, manufacturer data collecting aims, and machine actions are potentially mismatched with user expectations.

Finally, when it comes to data storage, personal information, and sharing methods, security becomes a concern, as hacking, disinformation, and other types of abuse threaten to undermine faith in technology systems and organizations.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Chatbots and Loebner Prize; Mobile Recommendation Assistants; Natural Language Processing and Speech Understanding.

References & Further Reading:

Lee, Gary G., Hong Kook Kim, Minwoo Jeong, and Ji-Hwan Kim, eds. 2015. Natural Language Dialog Systems and Intelligent Assistants. Berlin: Springer.

Leviathan, Yaniv, and Yossi Matias. 2018. “Google Duplex: An AI System for Accomplishing Real-world Tasks Over the Phone.” Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html.

Viken, Alexander. 2009. “The History of Personal Digital Assistants, 1980–2000.” Agile Mobility, April 10, 2009.

Waddell, Kaveh. 2016. “The Privacy Problem with Digital Assistants.” The Atlantic, May 24, 2016. https://www.theatlantic.com/technology/archive/2016/05/the-privacy-problem-with-digital-assistants/483950/.

Artificial Intelligence - What Is The Loebner Prize For Chatbots? Who Was Lili Cheng?

A chatbot is a computer software that communicates with people using artificial intelligence. Text or voice input may be used in the talks.

In certain circumstances, chatbots are also intended to take automatic activities in response to human input, such as running an application or sending an email.

Most chatbots try to mimic human conversational behavior, however no chatbot has succeeded in doing so flawlessly to far.

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Chatbots may assist with a number of requirements in a variety of circumstances.

The capacity to save time and money for people by employing a computer program to gather or disseminate information rather than needing a person to execute these duties is perhaps the most evident.

For example, a corporation may develop a customer service chatbot that replies to client inquiries with information that the chatbot believes to be relevant based on user queries using artificial intelligence.

The chatbot removes the requirement for a human operator to conduct this sort of customer service in this fashion.

Chatbots may also be useful in other situations since they give a more convenient means of interacting with a computer or software application.

A digital assistant chatbot, such as Apple's Siri or Google Assistant, for example, enables people to utilize voice input to get information (such as the address of a requested place) or conduct activities (such as sending a text message) on smartphones.

In cases when alternative input methods are cumbersome or unavailable, the ability to communicate with phones by speech, rather than needing to type information on the devices' displays, is helpful.

Consistency is a third benefit of chatbots.

Because most chatbots react to inquiries using preprogrammed algorithms and data sets, they will often respond with the same replies to the same questions.

Human operators cannot always be relied to act in the same manner; one person's response to a query may differ from another's, or the same person's replies may change from day to day.

Chatbots may aid with consistency in experience and information for the users with whom they communicate in this way.

However, chatbots that employ neural networks or other self-learning techniques to answer to inquiries may "evolve" over time, with the consequence that a query given to a chatbot one day may get a different response from a question posed the next day.

However, just a handful chatbots have been built to learn on their own thus far.

Some, such as Microsoft Tay, have proved to be ineffective.

Chatbots may be created using a number of ways and can be built in practically any programming language.

However, to fuel their conversational skills and automated decision-making, most chatbots depend on a basic set of traits.

Natural language processing, or the capacity to transform human words into data that software can use to make judgments, is one example.

Writing code that can process natural language is a difficult endeavor that involves knowledge of computer science, linguistics, and significant programming.

It requires the capacity to comprehend text or speech from individuals who use a variety of vocabulary, sentence structures, and accents, and who may talk sarcastically or deceptively at times.

Because programmers had to design natural language processing software from scratch before establishing a chatbot, the problem of creating good natural language processing engines made chatbots difficult and time-consuming to produce in the past.

Natural language processing programming frameworks and cloud-based services are now widely available, considerably lowering this barrier.

Modern programmers may either employ a cloud-based service like Amazon Comprehend or Azure Language Understanding to add the capability necessary to read human language, or they can simply import a natural language processing library into their apps.

Most chatbots also need a database of information to answer to queries.

They analyze their own data sets to choose which information to provide or which action to take in response to the inquiry after using natural language processing to comprehend the meaning of input.

Most chatbots do this by matching phrases in queries to predefined tags in their internal databases, which is a very simple process.

More advanced chatbots, on the other hand, may be programmed to continuously adjust or increase their internal databases by evaluating how users have reacted to previous behavior.

For example, a chatbot may ask a user whether the answer it provided in response to a specific query was helpful, and if the user replies no, the chatbot would adjust its internal data to avoid repeating the response the next time a user asks a similar question.

Although chatbots may be useful in a variety of settings, they are not without flaws and the potential for abuse.

One obvious flaw is that no chatbot has yet been proven to be capable of perfectly simulating human behavior, and chatbots can only perform tasks that they have been programmed to do.

They don't have the same aptitude as humans to "think outside the box" or solve issues imaginatively.

In many cases, people engaging with a chatbot may be looking for answers to queries that the chatbot was not designed to answer.

Chatbots raise certain ethical issues for similar reasons.

Chatbot critics have claimed that it is immoral for a computer program to replicate human behavior without revealing to individuals with whom it communicates that it is not a real person.

Some have also stated that chatbots may contribute to an epidemic of loneliness by replacing real human conversations with chatbot conversations that are less intellectually and socially gratifying for human users.

Chatbots, on the other hand, such as Replika, were designed with the express purpose of providing lonely people with an entity to communicate to when real people are unavailable.

Another issue with chatbots is that, like other software programs, they might be utilized in ways that their authors did not anticipate.

Misuse could occur as a result of software security flaws that allow malicious parties to gain control of a chatbot; for example, an attacker seeking to harm a company's reputation might try to compromise its customer-support chatbot in order to provide false or unhelpful support services.

In other circumstances, simple design flaws or oversights may result in chatbots acting unpredictably.

When Microsoft debuted the Tay chatbot in 2016, it learnt this lesson.

The Tay chatbot was meant to teach itself new replies based on past discussions.

When users engaged Tay in racist conversations, Tay began making public racist or inflammatory remarks of its own, prompting Microsoft to shut down the app.

The word "chatbot" was first used in the 1990s as an abbreviated version of chatterbot, a phrase invented in 1994 by computer scientist Michael Mauldin to describe a chatbot called Julia that he constructed in the early 1990s.

Chatbot-like computer programs, on the other hand, have been around for a long time.

The first was ELIZA, a computer program created by Joseph Weizenbaum at MIT's Artificial Intelligence Lab between 1964 and 1966.

Although the software was confined to just a few themes, ELIZA employed early natural language processing methods to participate in text-based discussions with human users.

Stanford psychiatrist Kenneth Colby produced a comparable chatbot software called PARRY in 1972.

It wasn't until the 1990s, when natural language processing techniques had advanced, that chatbot development gained traction and programmers got closer to their goal of building chatbots that could participate in discussion on any subject.

A.L.I.C.E., a chat bot debuted in 1995, and Jabberwacky, a chatbot created in the early 1980s and made accessible to users on the web in 1997, both have this purpose in mind.

The second significant wave of chatbot invention occurred in the early 2010s, when increased smartphone usage fueled demand for digital assistant chatbots that could engage with people through voice interactions, beginning with Apple's Siri in 2011.

The Loebner Prize competition has served to measure the efficacy of chatbots in replicating human behavior throughout most of the history of chatbot development.

The Loebner Prize, which was established in 1990, is given to computer systems (including, but not limited to, chatbots) that judges believe demonstrate the most human-like behavior.

A.L.I.C.E, which won the award three times in the early 2000s, and Jabberwacky, which won twice in 2005 and 2006, are two notable chatbots that have been examined for the Loebner Prize.

Lili Cheng

Lili Cheng is the Microsoft AI and Research division's Corporate Vice President and Distinguished Engineer.

She is in charge of the company's artificial intelligence platform's developer tools and services, which include cognitive services, intelligent software assistants and chatbots, as well as data analytics and deep learning tools.

Cheng has emphasized that AI solutions must gain the confidence of a larger segment of the community and secure users' privacy.

Her group is focusing on artificial intelligence bots and software apps that have human-like dialogues and interactions, according to her.

The ubiquity of social software—technology that lets people connect more effectively with one another—and the interoperability of software assistants, or AIs that chat to one another or pass tasks to one another, are two further ambitions.

Real-time language translation is one example of such an application.

Cheng is also a proponent of technical education and training for individuals, especially women, in order to prepare them for future careers (Davis 2018).

Cheng emphasizes the need of humanizing AI.

Rather than adapting human interactions to computer interactions, technology must adapt to people's working cycles.

Language recognition and conversational AI, according to Cheng, are insufficient technical advancements.

Human emotional needs must be addressed by AI.

One goal of AI research, she says, is to understand "the rational and surprising ways individuals behave." Cheng graduated from Cornell University with a bachelor's degree in architecture."

She started her work as an architect/urban designer at Nihon Sekkei International in Tokyo.

She also worked in Los Angeles for the architectural firm Skidmore Owings & Merrill.

Cheng opted to pursue a profession in information technology while residing in California.

She thought of architectural design as a well-established industry with well-defined norms and needs.

Cheng returned to school and graduated from New York University with a master's degree in Interactive Telecommunications, Computer Programming, and Design.

Her first position in this field was at Apple Computer in Cupertino, California, where she worked as a user experience researcher and designer for QuickTime VR and QuickTime Conferencing in the Advanced Technology Group-Human Interface Group.

In 1995, she joined Microsoft's Virtual Worlds Group, where she worked on the Virtual Worlds Platform and Microsoft V-Chat.

Kodu Game Lab, an environment targeted at teaching youngsters programming, was one of Cheng's efforts.

In 2001, she founded the Social Computing group with the goal of developing social networking prototypes.

She then worked at Microsoft Research-FUSE Labs as the General Manager of Windows User Experience for Windows Vista, eventually ascending to the post of Distinguished Engineer and General Manager.

Cheng has spoken at Harvard and New York Universities and is considered one of the country's top female engineers 

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

Cheng, Lili; ELIZA; Natural Language Processing and Speech Understanding; PARRY; Turing Test.

Further Reading

Abu Shawar, Bayan, and Eric Atwell. 2007. “Chatbots: Are They Really Useful?” LDV Forum 22, no.1: 29–49.

Abu Shawar, Bayan, and Eric Atwell. 2015. “ALICE Chatbot: Trials and Outputs.” Computación y Sistemas 19, no. 4: 625–32.

Deshpande, Aditya, Alisha Shahane, Darshana Gadre, Mrunmayi Deshpande, and Prachi M. Joshi. 2017. “A Survey of Various Chatbot Implementation Techniques.” Inter￾national Journal of Computer Engineering and Applications 11 (May): 1–7.

Shah, Huma, and Kevin Warwick. 2009. “Emotion in the Turing Test: A Downward Trend for Machines in Recent Loebner Prizes.” In Handbook of Research on Synthetic Emotions and Sociable Robotics: New Applications in Affective Computing and Artificial Intelligence, 325–49. Hershey, PA: IGI Global.

Zemčík, Tomáš. 2019. “A Brief History of Chatbots.” In Transactions on Computer Science and Engineering, 14–18. Lancaster: DEStech.