Artificial Intelligence
Artificial Intelligence definition and AI meaning
In general,
AI – Artificial Intelligence can be defined as a branch of Computer Science focused on creating intelligent machines that think and react like humans.But to define Artificial Intelligence, which is a very wide concept, many formulations in the literature try to explain what is AI technology.
Stuart Russel and Peter Norvig have systematized the many descriptions that explain artificial intelligence, identifying and arranging them into four categories:
– systems that think like humans;
– systems that think rationally– systems that act like human beings;
– systems that act rationally.According to the authors, these categories coincide with different phases of the historical evolution of A.I., starting from the 1950s up to today.
Jerry Kaplan, in his book “Artificial Intelligence.
A Guide to the Near Future,” agrees with the multiplicity of definitions that have revolved around AI since its inception but notes one element that unites them all: “creating computer programs or machines capable of behaviours that we would consider intelligent if enacted by human beings.”
Types of artificial intelligence
According to the European classification, there are two types of artificial intelligence: software and embedded intelligence.
By software we mean:
– virtual assistants: these are software that, by interpreting natural language, can converse with humans. The purposes can be multiple, from providing information to performing certain functions;
– image analysis software: mainly used in the security, medical, biomedical and aerospace sectors;
– Search engines: programs that can be accessed from appropriate sites to locate information in which the user may be interested;
– voice and facial recognition systems: software that uses biometric data for recognition.
Embedded intelligence, on the other hand, includes:
– robots: programmable mechanical and electronic devices that can be used to replace humans in performing repetitive or dangerous tasks:
– autonomous vehicles: capable of automatically matching the main transportation capabilities of a traditional car;– drones: remotely controlled aircraft capable of detecting information;
– Internet of Things (IoT): a network of objects capable of communicating and equipped with identification technologies.
In general,
AI – Artificial Intelligence can be defined as a branch of Computer Science focused on creating intelligent machines that think and react like humans.But to define Artificial Intelligence, which is a very wide concept, many formulations in the literature try to explain what is AI technology.
Stuart Russel and Peter Norvig have systematized the many descriptions that explain artificial intelligence, identifying and arranging them into four categories:
– systems that think like humans;
– systems that think rationally– systems that act like human beings;
– systems that act rationally.According to the authors, these categories coincide with different phases of the historical evolution of A.I., starting from the 1950s up to today.
Jerry Kaplan, in his book “Artificial Intelligence.
A Guide to the Near Future,” agrees with the multiplicity of definitions that have revolved around AI since its inception but notes one element that unites them all: “creating computer programs or machines capable of behaviours that we would consider intelligent if enacted by human beings.”
Types of artificial intelligence
According to the European classification, there are two types of artificial intelligence: software and embedded intelligence.
By software we mean:
– virtual assistants: these are software that, by interpreting natural language, can converse with humans. The purposes can be multiple, from providing information to performing certain functions;
– image analysis software: mainly used in the security, medical, biomedical and aerospace sectors;
– Search engines: programs that can be accessed from appropriate sites to locate information in which the user may be interested;
– voice and facial recognition systems: software that uses biometric data for recognition.
Embedded intelligence, on the other hand, includes:
– robots: programmable mechanical and electronic devices that can be used to replace humans in performing repetitive or dangerous tasks:
– autonomous vehicles: capable of automatically matching the main transportation capabilities of a traditional car;– drones: remotely controlled aircraft capable of detecting information;
– Internet of Things (IoT): a network of objects capable of communicating and equipped with identification technologies.
History
The 1950s
Turing TestThe history of artificial intelligence does not begin with the invention of the term, but a decade earlier, thanks to the experiments of mathematician Alan Turing.In 1950, Turing wrote an article entitled “Computing machinery and intelligence” to address the issue about AI, which at that time was little known to the point of not even having a name. The term “Artificial Intelligence” will be born, in fact, only six years later.He creates the “Turing Test” or “Imitation game”, to analyse artificial intelligence and human intelligence: the test consists of three participants of which one, at some point, is replaced by a machine, without the knowledge of the other two. The goal is to see if the “human” participants can realize that they are dealing with a machine.John McCarthy and Artificial IntelligenceAlthough the foundations of Artificial Intelligence technology had already been laid by Alan Turing, it is only with John McCarthy that this field of research finally has a name: “artificial intelligence”.He uses it for the first time during a conference on the subject held at Dartmouth in 1956 in which emerges the need for a name that differentiates AI from the already known cybernetics.A paper entitled “Dartmouth proposal” is produced in which, for the first time, the term “artificial intelligence” is used.Post-DartmouthThe Dartmouth conference sparks interest and enthusiasm for this new area of research and many people invest in the field and study the subject.Among these is Arthur Samuel, an American computer scientist, who in 1959 created the “checkers game“, a program created to self-learn to the point of surpassing the abilities of humans.Analyzing the possible moves in every moment of the game, computer intelligence can base its decisions on a large number of variables and information, which make it better than other players.But this is not the only contribution that Arthur Samuel has made to Artificial Intelligence: to give a name to his inventions, he also invented the term “machine learning.
The 1960s
Machine learning
Machine learning was historically born in 1943, by Warren McCulloch and Walter Pitts, who noticed how the brain was sending digital and, precisely, binary signals (Kaplan, 2017).Frank Rosenblatt, a psychologist, took up the findings of the two scholars by implementing them and creating Perceptron, an electronic device capable of showing learning capabilities.The first wave of enthusiasm, however, is followed by one of stalemate, in which research on AI is halted and investment plummets. For the research field to become interesting again, it is necessary to wait until the ’80s and non-linear neural networks.
The 1970s
Expert systems
In the ‘70s expert systems arrived, intending to replace “artificially” a person expert in a particular field. Artificial Intelligence can detect specific solutions to a problem, without having to consult a person expert in the field.But how do expert systems work? They are composed of three sections:knowledge base: a knowledge base in which there is all the information the system needs to solve a problem;inference engine: more specific information related to the operation of the knowledge base;3. user interface: it is thanks to it that the interaction between the program and humans can take place.
The 1980s
Second-generation expert systems
In the 1970s the production of minicomputers increases and many companies began to produce them.This period saw the second-generation expert systems emerging, which differed from programming systems because “The common programming approach required that the programmer himself be an expert in the program’s area of expertise and that he always be readily available to make changes […]. In contrast, the concept behind expert systems was to explicitly represent scope knowledge, making it available for analysis and modification.”The Winters of AIIn 1984 we have the birth of a new term: “AI winter”. As we can guess from the name this is a period of cooling, in which there is a decline in investment and research in the field.Some examples are:– the mid-1960s: investment in AI is halted by the United States following a loss of confidence in the research field;– 1987: the U.S. Department of Defense government agency (DARPA), freezes investment by ousting AI from areas recognized as promising.However, like every season, winters end and, in the ’90s, innovations and new investments arrive, laying the foundations for the future of Artificial Intelligence.
The 1990s
Deep BlueIt is 1996 and a chess game is being held in Philadelphia. One of the two players is world champion Garri Kimovič Kasparov, known for being the youngest to have won the title, at 22 years and 210 days.Up to here, nothing special, except that the other player “Deep Blue” is a computer, designed by IBM to play chess.The challenge is won by Kasparov but the revenge does not delay to arrive: the following year Deep Blue, after an update, is able to overcome the world champion, winning the victory.The original project dates to the previous decade, in 1985, when the student Feng-Hsiung Hsu designs a machine to play chess, called ChipTest.In 1989, this project was joined by Murray Campbell, his classmate, and other computer scientists, including Joe Hoane, Jerry Brody, and CJ Tan.The chess player opens the way to a wide range of possible fields of use: the research has allowed developers to understand how to design a computer to solve complex problems using in-depth knowledge aimed at analyzing an increasing number of possible solutions.Such a revolutionary victory inevitably also generates a lot of criticism about what human supremacy over machines means and what it entails.There is also an attempt to downplay the event, focusing primarily on “the role of the supercomputer designed for the task, rather than the sophisticated techniques used by the team of programmers” (Kaplan, 2017).Weak AI and Strong AIAlready known to scholars, the debate between weak AI and strong AI further ignites in the 1990s. The human mind begins to be seen as something programmable and therefore replaceable by a machine.Let’s see together the characteristics of weak and strong AI and the main differences.Weak AIWeak A.I. – Artificial Intelligence simulates the functioning of some human cognitive functions and is related to the fulfilment of a specific task (Russel and Norvig, 2003);However, the goal is not to equal and exceed human intelligence, but rather to act as an intelligent subject, without having any importance if it really is.The machine in fact is not able to think independently, remaining bound to the presence of man.Strong AIAccording to John Searle, philosopher of language and mind, “the computer would not be only, in the study of mind, a tool; rather, a properly programmed computer is really a mind”.The A.I. – Strong Artificial Intelligence emulates the functioning of the human mind more completely, resulting autonomous and able to act as a human being (Russel and Norvig, 2003).The technology used is that of the expert systems we discussed in the chapter on the 1980s.
Artificial intelligence today - An ethical issueIt has always been a matter of public debate to define what boundary Artificial Intelligence must respect.The fear that it will replace man, that technology will rebel, and other apocalyptic scenarios are the plot of many films on the subject that lead Artificial Intelligence to be seen as something to be feared.To dictate margins to the ethical dimension of AI, the European Union has stepped in, issuing the Code of Ethics in 2019, containing guidelines on the use and development of Artificial Intelligence systems.The document places humans at the centre and defines the purpose of AI use as increasing well-being and ensuring freedom.The main points of the Code are:– human control and oversight: Artificial Intelligence must be used to benefit human life. Therefore, only systems that protect fundamental rights and allow total human management and oversight can be developed;– Security: security must never be endangered, at any stage of the system life cycle;– Privacy: in case of the use of personal data, the involved subjects must be informed, in the maximum respect of the EU law on privacy;– Traceability: All data used must be tracked and documented;– Non-discrimination: AI systems must ensure accessibility to all and respect for diversity;– Environmental change: AI must support positive climate change;– Accountability: accountability mechanisms related to the algorithms used must be adopted in the use of data, to minimise any negative impacts.
Turing TestThe history of artificial intelligence does not begin with the invention of the term, but a decade earlier, thanks to the experiments of mathematician Alan Turing.In 1950, Turing wrote an article entitled “Computing machinery and intelligence” to address the issue about AI, which at that time was little known to the point of not even having a name. The term “Artificial Intelligence” will be born, in fact, only six years later.He creates the “Turing Test” or “Imitation game”, to analyse artificial intelligence and human intelligence: the test consists of three participants of which one, at some point, is replaced by a machine, without the knowledge of the other two. The goal is to see if the “human” participants can realize that they are dealing with a machine.John McCarthy and Artificial IntelligenceAlthough the foundations of Artificial Intelligence technology had already been laid by Alan Turing, it is only with John McCarthy that this field of research finally has a name: “artificial intelligence”.He uses it for the first time during a conference on the subject held at Dartmouth in 1956 in which emerges the need for a name that differentiates AI from the already known cybernetics.A paper entitled “Dartmouth proposal” is produced in which, for the first time, the term “artificial intelligence” is used.Post-DartmouthThe Dartmouth conference sparks interest and enthusiasm for this new area of research and many people invest in the field and study the subject.Among these is Arthur Samuel, an American computer scientist, who in 1959 created the “checkers game“, a program created to self-learn to the point of surpassing the abilities of humans.Analyzing the possible moves in every moment of the game, computer intelligence can base its decisions on a large number of variables and information, which make it better than other players.But this is not the only contribution that Arthur Samuel has made to Artificial Intelligence: to give a name to his inventions, he also invented the term “machine learning.
The 1960s
Machine learning
Machine learning was historically born in 1943, by Warren McCulloch and Walter Pitts, who noticed how the brain was sending digital and, precisely, binary signals (Kaplan, 2017).Frank Rosenblatt, a psychologist, took up the findings of the two scholars by implementing them and creating Perceptron, an electronic device capable of showing learning capabilities.The first wave of enthusiasm, however, is followed by one of stalemate, in which research on AI is halted and investment plummets. For the research field to become interesting again, it is necessary to wait until the ’80s and non-linear neural networks.
The 1970s
Expert systems
In the ‘70s expert systems arrived, intending to replace “artificially” a person expert in a particular field. Artificial Intelligence can detect specific solutions to a problem, without having to consult a person expert in the field.But how do expert systems work? They are composed of three sections:knowledge base: a knowledge base in which there is all the information the system needs to solve a problem;inference engine: more specific information related to the operation of the knowledge base;3. user interface: it is thanks to it that the interaction between the program and humans can take place.
The 1980s
Second-generation expert systems
In the 1970s the production of minicomputers increases and many companies began to produce them.This period saw the second-generation expert systems emerging, which differed from programming systems because “The common programming approach required that the programmer himself be an expert in the program’s area of expertise and that he always be readily available to make changes […]. In contrast, the concept behind expert systems was to explicitly represent scope knowledge, making it available for analysis and modification.”The Winters of AIIn 1984 we have the birth of a new term: “AI winter”. As we can guess from the name this is a period of cooling, in which there is a decline in investment and research in the field.Some examples are:– the mid-1960s: investment in AI is halted by the United States following a loss of confidence in the research field;– 1987: the U.S. Department of Defense government agency (DARPA), freezes investment by ousting AI from areas recognized as promising.However, like every season, winters end and, in the ’90s, innovations and new investments arrive, laying the foundations for the future of Artificial Intelligence.
The 1990s
Deep BlueIt is 1996 and a chess game is being held in Philadelphia. One of the two players is world champion Garri Kimovič Kasparov, known for being the youngest to have won the title, at 22 years and 210 days.Up to here, nothing special, except that the other player “Deep Blue” is a computer, designed by IBM to play chess.The challenge is won by Kasparov but the revenge does not delay to arrive: the following year Deep Blue, after an update, is able to overcome the world champion, winning the victory.The original project dates to the previous decade, in 1985, when the student Feng-Hsiung Hsu designs a machine to play chess, called ChipTest.In 1989, this project was joined by Murray Campbell, his classmate, and other computer scientists, including Joe Hoane, Jerry Brody, and CJ Tan.The chess player opens the way to a wide range of possible fields of use: the research has allowed developers to understand how to design a computer to solve complex problems using in-depth knowledge aimed at analyzing an increasing number of possible solutions.Such a revolutionary victory inevitably also generates a lot of criticism about what human supremacy over machines means and what it entails.There is also an attempt to downplay the event, focusing primarily on “the role of the supercomputer designed for the task, rather than the sophisticated techniques used by the team of programmers” (Kaplan, 2017).Weak AI and Strong AIAlready known to scholars, the debate between weak AI and strong AI further ignites in the 1990s. The human mind begins to be seen as something programmable and therefore replaceable by a machine.Let’s see together the characteristics of weak and strong AI and the main differences.Weak AIWeak A.I. – Artificial Intelligence simulates the functioning of some human cognitive functions and is related to the fulfilment of a specific task (Russel and Norvig, 2003);However, the goal is not to equal and exceed human intelligence, but rather to act as an intelligent subject, without having any importance if it really is.The machine in fact is not able to think independently, remaining bound to the presence of man.Strong AIAccording to John Searle, philosopher of language and mind, “the computer would not be only, in the study of mind, a tool; rather, a properly programmed computer is really a mind”.The A.I. – Strong Artificial Intelligence emulates the functioning of the human mind more completely, resulting autonomous and able to act as a human being (Russel and Norvig, 2003).The technology used is that of the expert systems we discussed in the chapter on the 1980s.
Artificial intelligence today - An ethical issueIt has always been a matter of public debate to define what boundary Artificial Intelligence must respect.The fear that it will replace man, that technology will rebel, and other apocalyptic scenarios are the plot of many films on the subject that lead Artificial Intelligence to be seen as something to be feared.To dictate margins to the ethical dimension of AI, the European Union has stepped in, issuing the Code of Ethics in 2019, containing guidelines on the use and development of Artificial Intelligence systems.The document places humans at the centre and defines the purpose of AI use as increasing well-being and ensuring freedom.The main points of the Code are:– human control and oversight: Artificial Intelligence must be used to benefit human life. Therefore, only systems that protect fundamental rights and allow total human management and oversight can be developed;– Security: security must never be endangered, at any stage of the system life cycle;– Privacy: in case of the use of personal data, the involved subjects must be informed, in the maximum respect of the EU law on privacy;– Traceability: All data used must be tracked and documented;– Non-discrimination: AI systems must ensure accessibility to all and respect for diversity;– Environmental change: AI must support positive climate change;– Accountability: accountability mechanisms related to the algorithms used must be adopted in the use of data, to minimise any negative impacts.
AI and statistical approach
Rule-based
This type of interface is usually also called “flow chatbot” since it operates according to predefined rules. For their development are, in fact, built predefined dialogue paths, which can guide the user to perform certain actions during the interaction, which typically occurs with buttons and keywords, rather than freely typing a command.
Based on Artificial Intelligence
Text-based interfaces
So-called chatbots, or text-based web or mobile systems, are interfaces that rely on an impulse sent by the user in the form of written text to provide a response, which can be written or spoken.
Given the large availability of data in written form (which they draw on), these types of interfaces are faster to implement. Depending on the type of technology used, chatbots can learn information categorized by keywords, tags or specific terms, as is the case with NLU
– Natural language understanding. According to this approach, the information used to build the chatbot’s knowledge base must also be analyzed and “understood” from a semantic point of view. This results in a longer time for the implementation of such systems.For interfaces that are based on a statistical AI approach, the implementation time will be faster since it is based on the correlations present in the text (e.g.: question and answer pairs that the chatbot extracts from the documentation base).
Therefore, this type of interface does not require the user to use specific terms to perform the search. For further information: “Artificial intelligence with a statistical approach“.The information retrieved can be of a generic nature, as in the case of the Google Assistant, which opens a dialogue box for searching the web, or more specific, such as a portion of text, or a specific service.Similar to voice interfaces, they clearly differ in the type of visual front-end that the user uses to receive information.This type of natural language user interface is useful in cases where the company needs to convey more complex information to the user, as they can have the aid of text, links, and graphics.
Voice-based interfaces
Voice-based conversational interfaces are systems that allow the user to complete an action by uttering a command.Siri, launched in 2011 by Apple, was one of the first widely adopted voice assistants, initially available to all iPhone owners and later integrated into home devices as well. Numerous voice assistants, such as those belonging to Google and Amazon, were then developed to make users’ homes connected: thanks to the use of “smart” devices, they allow a whole series of actions to be carried out by pronouncing a simple voice command.Following this, great progress has been made, especially because this type of interface has been used extensively in the e-commerce sales sector, to ensure fast and effortless user interaction. A limitation is, however, represented by the lack of text and graphics: while for some simple actions, such as re-ordering an already known product, voice is sufficient, for others, such as examining a new article or choosing an item from a menu, this type of interface is less suitable.
Hybrid Interfaces
Hybrid interfaces are made up of mixed-type interactions with the user, who will have both the ability to type freely and interact with the chatbot, but in some cases can be guided in performing certain actions with selection buttons and keywords.
This type of interface is usually also called “flow chatbot” since it operates according to predefined rules. For their development are, in fact, built predefined dialogue paths, which can guide the user to perform certain actions during the interaction, which typically occurs with buttons and keywords, rather than freely typing a command.
Based on Artificial Intelligence
Text-based interfaces
So-called chatbots, or text-based web or mobile systems, are interfaces that rely on an impulse sent by the user in the form of written text to provide a response, which can be written or spoken.
Given the large availability of data in written form (which they draw on), these types of interfaces are faster to implement. Depending on the type of technology used, chatbots can learn information categorized by keywords, tags or specific terms, as is the case with NLU
– Natural language understanding. According to this approach, the information used to build the chatbot’s knowledge base must also be analyzed and “understood” from a semantic point of view. This results in a longer time for the implementation of such systems.For interfaces that are based on a statistical AI approach, the implementation time will be faster since it is based on the correlations present in the text (e.g.: question and answer pairs that the chatbot extracts from the documentation base).
Therefore, this type of interface does not require the user to use specific terms to perform the search. For further information: “Artificial intelligence with a statistical approach“.The information retrieved can be of a generic nature, as in the case of the Google Assistant, which opens a dialogue box for searching the web, or more specific, such as a portion of text, or a specific service.Similar to voice interfaces, they clearly differ in the type of visual front-end that the user uses to receive information.This type of natural language user interface is useful in cases where the company needs to convey more complex information to the user, as they can have the aid of text, links, and graphics.
Voice-based interfaces
Voice-based conversational interfaces are systems that allow the user to complete an action by uttering a command.Siri, launched in 2011 by Apple, was one of the first widely adopted voice assistants, initially available to all iPhone owners and later integrated into home devices as well. Numerous voice assistants, such as those belonging to Google and Amazon, were then developed to make users’ homes connected: thanks to the use of “smart” devices, they allow a whole series of actions to be carried out by pronouncing a simple voice command.Following this, great progress has been made, especially because this type of interface has been used extensively in the e-commerce sales sector, to ensure fast and effortless user interaction. A limitation is, however, represented by the lack of text and graphics: while for some simple actions, such as re-ordering an already known product, voice is sufficient, for others, such as examining a new article or choosing an item from a menu, this type of interface is less suitable.
Hybrid Interfaces
Hybrid interfaces are made up of mixed-type interactions with the user, who will have both the ability to type freely and interact with the chatbot, but in some cases can be guided in performing certain actions with selection buttons and keywords.
Features
Introduction:
Conversational interfaces play a crucial role in various sectors, enhancing both internal operations and customer interactions. The application areas of Conversational User Interface (CUI) technologies are diverse, offering benefits in different domains.
Sectors and Applications:Financial and Insurance Services:Chatbots assist in operations on websites, customer counters, and provide information on user profiles, transactions, and more.Customer assistance includes solving problems and suggesting personalized services.
E-commerce:Chatbots enhance the user experience by facilitating product searches, suggesting products, promotions, and personalized experiences.Post-purchase, chatbots assist with order tracking, invoice retrieval, and inquiries about return policies.
Human Resources:Chatbots manage HR tasks such as payroll issuance, leave counting, and assist in the recruitment process by screening resumes.
Public Administration:Conversational platforms support citizens by providing information and services on institutional websites, streamlining administrative tasks.
Healthcare:Chatbots aid communication with patients, assisting in diagnostics, monitoring, and information retrieval, especially during health emergencies
.Entertainment, Tourism, and Information:Chatbots offer suggestions in media services, facilitate ticket purchases for events, and assist in the tourism sector by providing information and booking services.
Logistics Sector:Chatbots provide valuable information on operations, warehouse management, shipping, and order changes in the logistics sector.
Operational Sector:Voice assistants assist operators in construction sites and fields, providing information on machinery and recording operations through voice commands.
Operating Systems - Integrated Applications:Virtual assistants like Siri and Cortana are integrated into operating systems, allowing users to access applications and device features through voice commands.
Main advantages and problems solved with a Natural Language Interface
The use of conversational interfaces has become more and more frequent in the business world, also given the number of advantages that can derive from the use of these tools that have become, to all effects, allies in working activities.
Some of the main points in favour of AI-based CUIs can be summarized as follows:– as opposed to humans, they are available 24/7;
– they respond very quickly and simultaneously to a large number of users;
– they are able to mimic human behaviour (in fact they are artificial intelligence), improving the user experience;– can be integrated into any type of service and are multiplatform, to adapt to the needs of use;
– they can automate repetitive work: all those activities performed frequently can be performed by the chatbot, reducing the possibility of errors;
– they can use multiple communication channels, such as text, audio, and images;Consequently, many problems inherent to productivity and customer satisfaction can be solved, thanks to the introduction of conversational interfaces in the business environment:
– Enhance work and increase productivity: the use of interfaces such as chatbots can support employees in their search for information and decrease repetitive tasks, as in the case of Help Desk agents in technical support requests. If you have all the information you need to do your job quickly, you’ll be able to reduce the workload for all business units, which can then focus on higher value-added activities;
– Transforming the Customer Experience and Increasing Sales: especially in the B2C sector, the use of chatbots and conversational solutions helps to improve the user experience, to whom personalized suggestions are made that guide him in the purchase path. Moreover, even about assistance and Customer Care, a chatbot can be a quick and effective solution to answer simple customer requests, improving the customer’s perception of the company;
– Reduce costs: by automating low-level tasks and repetitive actions, such as administrative activities, updating data in CRM, generating documents and consulting them within the company’s knowledge base, conversational interfaces also save costs related to the use of personnel to perform all these activities;
– Facilitating access to corporate knowledge and Knowledge Sharing: conversational platforms based on artificial intelligence can also be used in the area of Knowledge Management, which enables the management and organization of all corporate intellectual capital. Conversational interfaces can be used as true search engines in documentation, providing a centralized access point to knowledge and helping to disseminate information among employees;
– Facilitate onboarding and circulate company culture: conversational solutions such as chatbots can also be used for outreach purposes, for example to answer new employees’ questions about particular company policies and procedures. This will make it easier for the HR department to onboard and train new employees, helping to circulate information and spread company culture values.
Challenges in designing conversational interfaces
Some of the key features that emerged from the opinions of CUI experts and leaders relate to both the design phase of interfaces and the focus on how the user will interact with them.
To talk about challenges, it is useful to introduce the concept of “pervasiveness” and “information architecture.”The latter indicates precisely the design of information systems, software, websites, intranets, online communities based on usability and availability, therefore as usable as possible for users.At this point, we can say that information architecture is pervasive when it becomes usable by the user on multiple channels and through different modalities.
The “multi-channel“, also mentioned by Henry Jenkins in his book “Convergence culture”, is the characteristic that concerns a service or a system, which can be used by the user through different channels (for example, website, application, telephone call).At this point, it is useful to introduce the fundamental difference with the concept of “cross-channel“, which instead concerns the use of multiple channels to complete a fruition experience.
The user will complete part of his journey on one channel, and part on another, since they offer different experiences and functions, but are complementary for the achievement of the user’s objective.Regarding, in particular, conversational interfaces, a striking example of these concepts is provided by Peter Morville, father of information architecture.
Referring to a well-known and commonly used voice assistant, he notes how the impossibility of using multiple channels (auditory and visual) represents an obstacle for the user, whose experience is relegated to the use of voice alone, for example when searching for information.Hence, one of the design challenges is to create interfaces that mix audiovisual input and output to enable better visualization and search, thus creating a cross-channel information architecture.
Conversational interfaces play a crucial role in various sectors, enhancing both internal operations and customer interactions. The application areas of Conversational User Interface (CUI) technologies are diverse, offering benefits in different domains.
Sectors and Applications:Financial and Insurance Services:Chatbots assist in operations on websites, customer counters, and provide information on user profiles, transactions, and more.Customer assistance includes solving problems and suggesting personalized services.
E-commerce:Chatbots enhance the user experience by facilitating product searches, suggesting products, promotions, and personalized experiences.Post-purchase, chatbots assist with order tracking, invoice retrieval, and inquiries about return policies.
Human Resources:Chatbots manage HR tasks such as payroll issuance, leave counting, and assist in the recruitment process by screening resumes.
Public Administration:Conversational platforms support citizens by providing information and services on institutional websites, streamlining administrative tasks.
Healthcare:Chatbots aid communication with patients, assisting in diagnostics, monitoring, and information retrieval, especially during health emergencies
.Entertainment, Tourism, and Information:Chatbots offer suggestions in media services, facilitate ticket purchases for events, and assist in the tourism sector by providing information and booking services.
Logistics Sector:Chatbots provide valuable information on operations, warehouse management, shipping, and order changes in the logistics sector.
Operational Sector:Voice assistants assist operators in construction sites and fields, providing information on machinery and recording operations through voice commands.
Operating Systems - Integrated Applications:Virtual assistants like Siri and Cortana are integrated into operating systems, allowing users to access applications and device features through voice commands.
Main advantages and problems solved with a Natural Language Interface
The use of conversational interfaces has become more and more frequent in the business world, also given the number of advantages that can derive from the use of these tools that have become, to all effects, allies in working activities.
Some of the main points in favour of AI-based CUIs can be summarized as follows:– as opposed to humans, they are available 24/7;
– they respond very quickly and simultaneously to a large number of users;
– they are able to mimic human behaviour (in fact they are artificial intelligence), improving the user experience;– can be integrated into any type of service and are multiplatform, to adapt to the needs of use;
– they can automate repetitive work: all those activities performed frequently can be performed by the chatbot, reducing the possibility of errors;
– they can use multiple communication channels, such as text, audio, and images;Consequently, many problems inherent to productivity and customer satisfaction can be solved, thanks to the introduction of conversational interfaces in the business environment:
– Enhance work and increase productivity: the use of interfaces such as chatbots can support employees in their search for information and decrease repetitive tasks, as in the case of Help Desk agents in technical support requests. If you have all the information you need to do your job quickly, you’ll be able to reduce the workload for all business units, which can then focus on higher value-added activities;
– Transforming the Customer Experience and Increasing Sales: especially in the B2C sector, the use of chatbots and conversational solutions helps to improve the user experience, to whom personalized suggestions are made that guide him in the purchase path. Moreover, even about assistance and Customer Care, a chatbot can be a quick and effective solution to answer simple customer requests, improving the customer’s perception of the company;
– Reduce costs: by automating low-level tasks and repetitive actions, such as administrative activities, updating data in CRM, generating documents and consulting them within the company’s knowledge base, conversational interfaces also save costs related to the use of personnel to perform all these activities;
– Facilitating access to corporate knowledge and Knowledge Sharing: conversational platforms based on artificial intelligence can also be used in the area of Knowledge Management, which enables the management and organization of all corporate intellectual capital. Conversational interfaces can be used as true search engines in documentation, providing a centralized access point to knowledge and helping to disseminate information among employees;
– Facilitate onboarding and circulate company culture: conversational solutions such as chatbots can also be used for outreach purposes, for example to answer new employees’ questions about particular company policies and procedures. This will make it easier for the HR department to onboard and train new employees, helping to circulate information and spread company culture values.
Challenges in designing conversational interfaces
Some of the key features that emerged from the opinions of CUI experts and leaders relate to both the design phase of interfaces and the focus on how the user will interact with them.
To talk about challenges, it is useful to introduce the concept of “pervasiveness” and “information architecture.”The latter indicates precisely the design of information systems, software, websites, intranets, online communities based on usability and availability, therefore as usable as possible for users.At this point, we can say that information architecture is pervasive when it becomes usable by the user on multiple channels and through different modalities.
The “multi-channel“, also mentioned by Henry Jenkins in his book “Convergence culture”, is the characteristic that concerns a service or a system, which can be used by the user through different channels (for example, website, application, telephone call).At this point, it is useful to introduce the fundamental difference with the concept of “cross-channel“, which instead concerns the use of multiple channels to complete a fruition experience.
The user will complete part of his journey on one channel, and part on another, since they offer different experiences and functions, but are complementary for the achievement of the user’s objective.Regarding, in particular, conversational interfaces, a striking example of these concepts is provided by Peter Morville, father of information architecture.
Referring to a well-known and commonly used voice assistant, he notes how the impossibility of using multiple channels (auditory and visual) represents an obstacle for the user, whose experience is relegated to the use of voice alone, for example when searching for information.Hence, one of the design challenges is to create interfaces that mix audiovisual input and output to enable better visualization and search, thus creating a cross-channel information architecture.
Conclusion: AI in everyday life
After outlining what artificial intelligence is, we have analyzed all the steps that, starting from the 1950s, have made AI great.This historical excursus has allowed us to understand the purpose of artificial intelligence development and how the advancement of technological progress has allowed AI to be more and more present in our everyday lives.The European Parliament has carried out an analysis of the main fields in which artificial intelligence and everyday life intersect, allowing technology to increasingly come alongside us:
– Web shopping and advertising: AI, as we have seen in the previous paragraphs, is also used to make future predictions, based on previously collected data. And this is also applied to product suggestions, made based on purchases, search intent, online behaviours, and more.
– Online searches: as with shopping, search engines use the data collected to “learn” what the user is interested in and propose results that are similar to it;
– Virtual assistants: to provide answers to users and customers, they answer questions in a personalized way;– Machine translation: AI software automatically generates translations of text, video and audio. The most common example is youtube’s auto-generated subtitles;
– Smart infrastructure: from tech tools inside smart homes that learn the behaviours of those living in the home, to using AI to improve the viability of cities;
– Cyber security: using artificial intelligence to recognize and block cyber threats, learning from previous attacks and how to recognize them;
– COVID-19 emergency: AI in the fight against the pandemic has been used in a variety of ways, from monitoring restricted entrances to temperature detection to more specific applications in the healthcare system, such as recognizing infections starting with CT scans of the lungs;
– Fighting misinformation: a valuable aid to recognising fake news, monitoring and analyzing social content, identifying suspicious or alarming expressions, aimed at recognizing authoritative sources.Artificial intelligence is not only present in our personal and work routines.
More and more companies are using AI to offer better services to customers and increase employee productivity.One example is knowledge management, or the management of corporate knowledge, which can be implemented with artificial intelligence systems with a statistical approach, to allow users to find the information they are looking for within the corporate database more quickly. One thing is now certain: AI is present in so many aspects of our daily lives, increasing our security and allowing us to have support in numerous activities.
We don’t know what the future holds for us, but what is certain is that Artificial Intelligence will not stop its expansion and, as we have seen in tracing the history of its evolution, this is faster than it seems.Sources:Jerry Kaplan, Artificial Intelligence. What Everyone Needs to Know, 2017
Sources:
https://www.imperva.com/learn/data-security/structured-and-unstructured-data/
https://en.wikipedia.org/wiki/Natural_language_processing
https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explainedhttps://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence
– Web shopping and advertising: AI, as we have seen in the previous paragraphs, is also used to make future predictions, based on previously collected data. And this is also applied to product suggestions, made based on purchases, search intent, online behaviours, and more.
– Online searches: as with shopping, search engines use the data collected to “learn” what the user is interested in and propose results that are similar to it;
– Virtual assistants: to provide answers to users and customers, they answer questions in a personalized way;– Machine translation: AI software automatically generates translations of text, video and audio. The most common example is youtube’s auto-generated subtitles;
– Smart infrastructure: from tech tools inside smart homes that learn the behaviours of those living in the home, to using AI to improve the viability of cities;
– Cyber security: using artificial intelligence to recognize and block cyber threats, learning from previous attacks and how to recognize them;
– COVID-19 emergency: AI in the fight against the pandemic has been used in a variety of ways, from monitoring restricted entrances to temperature detection to more specific applications in the healthcare system, such as recognizing infections starting with CT scans of the lungs;
– Fighting misinformation: a valuable aid to recognising fake news, monitoring and analyzing social content, identifying suspicious or alarming expressions, aimed at recognizing authoritative sources.Artificial intelligence is not only present in our personal and work routines.
More and more companies are using AI to offer better services to customers and increase employee productivity.One example is knowledge management, or the management of corporate knowledge, which can be implemented with artificial intelligence systems with a statistical approach, to allow users to find the information they are looking for within the corporate database more quickly. One thing is now certain: AI is present in so many aspects of our daily lives, increasing our security and allowing us to have support in numerous activities.
We don’t know what the future holds for us, but what is certain is that Artificial Intelligence will not stop its expansion and, as we have seen in tracing the history of its evolution, this is faster than it seems.Sources:Jerry Kaplan, Artificial Intelligence. What Everyone Needs to Know, 2017
Sources:
https://www.imperva.com/learn/data-security/structured-and-unstructured-data/
https://en.wikipedia.org/wiki/Natural_language_processing
https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explainedhttps://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence
