Chatbot Name The Best Chatbot Name Ideas, Instantly!

Your chatbot should match up with your brand values, tone and style, helping customers deepen their connection with your business. It’s worth involving your marketing team or anyone responsible for branding from day one of the naming process. Creative chatbot names are effective for businesses looking to differentiate themselves from the crowd. These are perfect for the technology, eCommerce, entertainment, lifestyle, and hospitality industries. To make things easier, we’ve collected 365+ unique chatbot names for different categories and industries. Also, read some of the most useful tips on how to pick a name that best fits your unique business needs.

A good chatbot name will tell your website visitors that it’s there to help, but also give them an insight into your services. Different bot names represent different characteristics, so make sure your chatbot represents your brand. The customer service automation needs to match your brand image. If your company focuses on, for example, baby products, then you’ll need a cute name for it.

Why a versatile name will maximise your chatbot’s potential

You want the name to be easy to read and pronounce, so make sure you ask others to spell it or say it out loud to check they don’t struggle. Chatbots are quickly being displaced by more advanced AI assistants like ours, and “bot” can have negative connotations with spammers and trolls across all digital channels. Get your free guide on eight ways to transform your support strategy with messaging–from WhatsApp to live chat and everything in between. ManyChat offers templates that make creating your bot quick and easy.

Imagine your website visitors land on your website and find a customer service bot to ask their questions about your products or services. This is the reason online business owners prefer chatbots with artificial intelligence technology and creative bot names. You can foun additiona information about ai customer service and artificial intelligence and NLP. Gabi Buchner, user assistance development architect in the software industry and conversation designer for chatbots recommends looking through the dictionary for your chatbot name ideas. You could also look through industry publications to find what words might lend themselves to chatbot names. You could talk over favorite myths, movies, music, or historical characters. Don’t limit yourself to human names but come up with options in several different categories, from functional names—like Quizbot—to whimsical names.

Read moreFind out how to name and customize your Tidio chat widget to get a great overall user experience. Let’s have a look at the list of bot names you can use for inspiration. From there, you can create a shortlist based on the words that resonate best with you and follow the naming guidelines above. The first that come to mind for me are Alexa, Google, Nike, Apple – each unique in their own way (hence, easy to remember) , less than six characters and easy to spell. Consider avoiding long names as much as possible, as this will only lead your customers forgetting your name and feeling frustrated. Now that we’ve explored chatbot nomenclature a bit let’s move on to a fun exercise.

Cute names for chatbots

One of the effective ways is to give your chatbot an interesting name. This article looks into some interesting chatbot name ideas and how they are beneficial for your online business. With these swift steps, you can have a shortlist of potential chatbot names, maximizing productivity while maintaining creativity.

• While creating a unique and captivating chatbot name is essential, treading the fine line to avoid excessively complex or unusual names is equally significant.
• Depending on your brand voice, it also sets a tone that might vary between friendly, formal, or humorous.
• AI and machine learning technologies will help your bot sound like a human agent and eliminate repetitive and mechanical responses.
• The purpose for your bot will help make it much easier to determine what name you’ll give it, but it’s just the first step in our five-step process.

If you’re struggling to find the right bot name (just like we do every single time!), don’t worry. Do you remember the struggle of finding the right name or designing the logo for your business? It’s about to happen again, but this time, you can use what your company already has to help you out. Also, remember that your chatbot is an extension of your company, so make sure its name fits in well. Read moreCheck out this case study on how virtual customer service decreased cart abandonment by 25% for some inspiration.

Names matter, and that’s why it can be challenging to pick the right name—especially because your AI chatbot may be the first “person” that your customers talk to. You have the perfect chatbot name, but do you have the right ecommerce chatbot solution? The best ecommerce chatbots reduce support costs, resolve complaints and offer 24/7 support to your customers.

Customers who are unaware might attribute the chatbot’s inability to resolve complex issues to a human operator’s failure. This can result in consumer frustration and a higher churn rate. The ProProfs Live Chat Editorial Team is a diverse group of professionals passionate about customer support and engagement. We update you on the latest trends, dive into technical topics, and offer insights to elevate your business. For example, Function of Beauty named their bot Clover with an open and kind-hearted personality. You can see the personality drop down in the “bonus” section below.

Look through the types of names in this article and pick the right one for your business. Every company is different and has a different target audience, so make sure your bot matches your brand and what you stand for. This might have been the case because it was just silly, or because it matched with the brand so cleverly that the name became humorous. Some of the use cases of the latter are cat chatbots such as Pawer or MewBot. It only takes about 7 seconds for your customers to make their first impression of your brand.

A well-chosen name encourages more customer interaction and creates positive associations. The name should match your brand’s values, tone, and style to deepen the connection with your brand. If you give your chatbot a human name, it’s important for the bot to introduce itself as an AI chatbot in a live chat, through whichever chatbot or messaging platform you’re using.

Our AI powered chatbot name generator will create unique chatbot business names – you just have to choose the one you like. However, there are some drawbacks to using a neutral name for chatbots. These names sometimes make it more difficult to engage with users on a personal level. They might not be able to foster engaging conversations like a gendered name. Today’s customers want to feel special and connected to your brand. A catchy chatbot name is a great way to grab their attention and make them curious.

Tips To Consider When Naming Your Chatbot Software:

We would love to have you onboard to have a first-hand experience of Kommunicate. You can signup here and start delighting your customers right away. Remember, emotions are a key aspect to consider when naming a chatbot. And this is why it is important to clearly define the functionalities of your bot. While a chatbot is, in simple words, a sophisticated computer program, naming it serves a very important purpose. In fact, chatbots are one of the fastest growing brand communications channels.

Siri, for example, means something anatomical and personal in the language of the country of Georgia. Wherever you hope to do business, it’s important to understand what your chatbot’s name means in that language. Doing research helps, as does including a diverse panel of people in the naming process, with different worldviews and backgrounds. Another important factor to consider is the function or purpose of your chat widget. Is it designed to provide customer support, answer frequently asked questions, or offer personalized recommendations? Tailoring the name to reflect the chatbot’s specific role can help users understand its capabilities and set appropriate expectations.

On the other hand, when building a chatbot for a beauty platform such as Sephora, your target customers are those who relate to fashion, makeup, beauty, etc. Here, it makes sense to think of a name that closely resembles such aspects. Now that you have a chatbot for customer assistance on your website, you must Chat PG note that they still cannot replace human agents. Online shoppers will not feel like they are talking to a robot and getting a mechanical response when their chatbot is humanized. However, you may not know the best way to humanize your chatbot and make your website visitors feel like talking to a human.

The mood you set for a chatbot should complement your brand and broadcast the vision of how the pain point should be solved. That is how people fall in love with brands – when they feel they found exactly what they were looking for. With Starter Story, you can see exactly how online businesses get to millions in revenue. I’m Pat Walls and I created Starter Story – a website dedicated to helping people start businesses.

A few online shoppers will want to talk with a chatbot that has a human persona. If you feel confused about choosing a human or robotic name for a chatbot, you should first determine the chatbot’s objectives. If your chatbot is going to act like a store representative in the online store, then choosing a human name is the best idea. Your online shoppers will converse with chatbots like talking with a sales rep and receive an immediate solution to their problems.

Most likely, the first one since a name instantly humanizes the interaction and brings a sense of comfort. The second option doesn’t promote a natural conversation, and you might be less comfortable talking to a nameless robot to solve your problems. It’s crucial to be transparent with your visitors and let them know upfront that they are interacting with a chatbot, not a live chat operator. A catchy or relevant name, on the other hand, will make your visitors feel more comfortable when approaching the chatbot.

If a customer knows they’re dealing with a bot, they may still be polite to it, even chatty. But don’t let them feel hoodwinked or that sense of cognitive dissonance that comes from thinking they’re talking to a person and realizing they’ve been deceived. The ProProfs Live Chat Editorial Team is a passionate group of customer service experts dedicated to empowering your live chat experiences with top-notch content. We stay ahead of the curve on trends, tackle technical hurdles, and provide practical tips to boost your business. With our commitment to quality and integrity, you can be confident you’re getting the most reliable resources to enhance your customer support initiatives. Detailed customer personas that reflect the unique characteristics of your target audience help create highly effective chatbot names.

For example, if your chat widget is primarily focused on customer support, you might want to choose a name that conveys reliability and helpfulness. This chatbot is on various social media channels such as WhatsApp and Instagram. CovidAsha helps people who want to reach out for medical emergencies. In the same way, choosing a creative chatbot name can either relate to their role or serve to add humor to your visitors when they read it. Nobody knows your customers better than your support teams, so why not bring them into the process and dedicate some time to brainstorming.

Unique Chatbot Names & Top 5 Tips to Create Your Own in 2024

But choosing the right name can be challenging, considering the vast number of options available. Name your chatbot as an actual assistant to make visitors feel as if they entered the shop. Consider simple names and build a personality around them that will match your brand. You most likely built your customer persona in the earlier stages of your business. If not, it’s time to do so and keep in close by when you’re naming your chatbot. And to represent your brand and make people remember it, you need a catchy bot name.

Keep in mind that about 72% of brand names are made-up, so get creative and don’t worry if your chatbot name doesn’t exist yet. It’s less confusing for the website visitor to know from the start that they are chatting to a bot and not a representative. This will show transparency of your company, and you will ensure that you’re not accidentally deceiving your customers. Additionally, we provide you with a free business name generator with an instant domain availability check to help you find a custom name for your chatbot software. So you know why your chatbot needs a fresh and compelling name. Though there are hundreds of free chatbot name idea generators available, coming up with an original name can help you stand out and convey your brand persona better.

To truly understand your audience, it’s important to go beyond superficial demographic information. You must delve deeper into cultural backgrounds, languages, preferences, and interests. Once the primary function is decided, you can choose a bot name that aligns with it.

Choosing a name not overtly tied to customer service means the chatbot can adapt and support different departments and tasks. Names designed to be memorable and relatable encourage more customers to interact with your chatbot, and your teams to create positive associations. Here are a few examples of chatbot names from companies to inspire you while creating your own. Similarly, naming your company’s chatbot is as important as naming your company, children, or even your dog.

Usually, a chatbot is the first thing your customers interact with on your website. So, cold or generic names like “Customer Service Bot” or “Product Help Bot” might dilute their experience. This demonstrates the widespread popularity of chatbots as an effective means of customer engagement.

Another method of choosing a chatbot name is finding a relation between the name of your chatbot and business objectives. While creating a unique and captivating chatbot name is essential, treading the fine line to avoid excessively complex or unusual names is equally significant. Innovation can be the key to standing out in the crowded world of chatbots. Start with a simple Google search to see if any other chatbots exist with the same name. This could be the perfect way to show off your chatbot’s capabilities, manage user expectations, and ensure they know they are interacting with AI.

Siri is a chatbot with AI technology that will efficiently answer customer questions. Artificial intelligence-powered chatbots use NLP to mimic humans. Online business owners use AI chatbots to reduce support ticket costs exponentially. Choosing a chatbot name is one of the effective ways to personalize it on websites.

Try to play around with your company name when deciding on your chatbot name. For example, if your company is called Arkalia, you can name your bot Arkalious. You can also brainstorm ideas with your friends, family members, and colleagues. This way, you’ll have a much longer list of ideas than if it was just you.

Apart from providing a human name to your chatbot, you can also choose a catchy bot name that will captivate your target audience to start a conversation. Online business owners usually choose catchy bot names that relate to business to intrigue their customers. Naming your chatbot, especially with a catchy, descriptive name, lends a personality to your chatbot, making it more approachable and personal for your customers. It creates a one-to-one connection between your customer and the chatbot.

Your rule-based bot is not just the only place where you would use decision trees. Psychology plays a significant role in how we perceive names and form associations. Certain sounds, syllables, and word structures can evoke specific emotions or impressions.

A well-named chatbot is not just an AI, and it’s a virtual entity with a promising identity that can provide value to users while representing your brand aptly. With an understanding of the importance of chatbot nomenclature and practical steps to name your bot, we’ve paved the groundwork for your chatbot naming process. Running a competition for customers is another fail-proof way of getting them engaged ― who knows what they’ll come up with. At the same time, you get real insight into how they experience your brand or how they feel about it, so it’s a win-win situation.

You can launch a chatbot in 10 minutes using only your website URL. Now you know how to name it too, you can transform your chatbot namen customer experience in no time at all. Internally, the AI chatbot helped Stena Line teams with cost-analysis systems.

In this scenario, you can also name your chatbot in direct relation to your business. Online business owners also have the option of fixing a gender for the chatbot and choosing a bitmoji that will match the chatbots’ names. Apple named their iPhone bot Siri to make customers feel like talking to a human agent. In a business-to-business (B2B) website, most chatbots generate https://chat.openai.com/ leads by scheduling appointments and asking lead-qualifying questions to website visitors. This digital adventure unfurled the significance of choosing the perfect chatbot name and opened doors to boundless ideas, strategies, and steps to achieve the same. Real estate and education are two sectors where chatbots lend a hand in decisions that shape users’ lives.

Let’s consider an example where your company’s chatbots cater to Gen Z individuals. To establish a stronger connection with this audience, you might consider using names inspired by popular movies, songs, or comic books that resonate with them. For example, a legal firm Cartland Law created a chatbot Ailira (Artificially Intelligent Legal Information Research Assistant). It’s the a digital assistant designed to understand and process sophisticated technical legal questions without lawyers. When leveraging a chatbot for brand communications, it is important to remember that your chatbot name ideally should reflect your brand’s identity.

A Comprehensive Guide to Natural Language Generation by Sciforce Sciforce

The monolingual based approach is also far more scalable, as Facebook’s models are able to translate from Thai to Lao or Nepali to Assamese as easily as they would translate between those languages and English. As the number of supported languages increases, the number of language pairs would become unmanageable if each language pair had to be developed and maintained. Earlier iterations of machine translation models tended to underperform when not translating to or from English. I often work using an open source library such as Apache Tika, which is able to convert PDF documents into plain text, and then train natural language processing models on the plain text. However even after the PDF-to-text conversion, the text is often messy, with page numbers and headers mixed into the document, and formatting information lost. Natural language processing has been around for years but is often taken for granted.

You can learn more about noun phrase chunking in Chapter 7 of Natural Language Processing with Python—Analyzing Text with the Natural Language Toolkit. For this tutorial, you don’t need to know how regular expressions work, but they will definitely come in handy for you in the future if you want to process text. Chunking makes use of POS tags to group words and apply chunk tags to those groups. Chunks don’t overlap, so one instance of a word can be in only one chunk at a time.

What are the approaches to natural language processing?

If you’re not adopting NLP technology, you’re probably missing out on ways to automize or gain business insights. Natural Language Processing (NLP) is at work all around us, natural language example making our lives easier at every turn, yet we don’t often think about it. From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging.

And we’re finding that, a lot of the time, text produced by NLG can be flat-out wrong, which has a whole other set of implications. NLG derives from the natural language processing method called large language modeling, which is trained to predict words from the words that came before it. If a large language model is given a piece of text, it will generate an output of text that it thinks makes the most sense.

Explore NLP With Repustate

These services are connected to a comprehensive set of data sources. Sentiment analysis is an artificial intelligence-based approach to interpreting the emotion conveyed by textual data. NLP software analyzes the text for words or phrases that show dissatisfaction, happiness, doubt, regret, and other hidden emotions. Like most other artificial intelligence, NLG still requires quite a bit of human intervention. We’re continuing to figure out all the ways natural language generation can be misused or biased in some way.

Human language has several features like sarcasm, metaphors, variations in sentence structure, plus grammar and usage exceptions that take humans years to learn. Programmers use machine learning methods to teach NLP applications to recognize and accurately understand these features from the start. For SQL, we must assume that a database has been defined such that we can select columns from a table (called Customers) for rows where the Last_Name column (or relation) has ‘Smith’ for its value. For the Python expression we need to have an object with a defined member function that allows the keyword argument “last_name”. Until recently, creating procedural semantics had only limited appeal to developers because the difficulty of using natural language to express commands did not justify the costs.

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Second, it is useful to know what types of events or states are being mentioned and their semantic roles, which is determined by our understanding of verbs and their senses, including their required arguments and typical modifiers. For example, the sentence “The duck ate a bug.” describes an eating event that involved a duck as eater and a bug as the thing that was eaten. The most complete source of this information is the Unified Verb Index. This information is determined by the noun phrases, the verb phrases, the overall sentence, and the general context. The background for mapping these linguistic structures to what needs to be represented comes from linguistics and the philosophy of language.

This article will help you understand the basic and advanced NLP concepts and show you how to implement using the most advanced and popular NLP libraries – spaCy, Gensim, Huggingface and NLTK. In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code. This content has been made available for informational purposes only.

Natural language processing with Python

The following is a list of some of the most commonly researched tasks in natural language processing. Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks. The proposed test includes a task that involves the automated interpretation and generation of natural language.

Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. As we explore in our open step on conversational interfaces, 1 in 5 homes across the UK contain a smart speaker, and interacting with these devices using our voices has become commonplace. Whether it’s through Siri, Alexa, Google Assistant or other similar technology, many of us use these NLP-powered devices. There are, of course, far more steps involved in each of these processes. A great deal of linguistic knowledge is required, as well as programming, algorithms, and statistics.

Natural Language Generation Use Cases

A direct word-for-word translation often doesn’t make sense, and many language translators must identify an input language as well as determine an output one. As we explored in our post on what different programming languages are used for, the languages of humans and computers are very different, and programming languages exist as intermediaries between the two. We examine the potential influence of machine learning and AI on the legal industry.

The potential of machine learning in services operations

It works the same way as humans learn using some labeled data points of the training set. It helps in optimizing the performance of models using experience and solving various complex computation problems. Coming as a solution to all this chaos is Machine Learning proposing smart alternatives to analyzing vast volumes of data. It is a leap forward from computer science, statistics, and other emerging applications in the industry. Machine learning can produce accurate results and analysis by developing efficient and fast algorithms and data-driven models for real-time processing of this data. Among the association rule learning techniques discussed above, Apriori [8] is the most widely used algorithm for discovering association rules from a given dataset [133].

An unsupervised learning model’s goal is to identify meaningful

patterns among the data. In other words, the model has no hints on how to

categorize each piece of data, but instead it must infer its own rules. Performing machine learning can involve creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.[46] In other words, it is a process of reducing the dimension of the feature set, also called the « number of features ». Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction.

How Machine Learning Works?

The model is sometimes trained further using supervised or

reinforcement learning on specific data related to tasks the model might be

asked to perform, for example, summarize an article or edit a photo. However, there are many caveats to these beliefs functions when compared to Bayesian approaches in order to incorporate ignorance and Uncertainty quantification. Algorithms trained on data sets that exclude certain populations or contain errors can lead to inaccurate models of the world that, at best, fail and, at worst, are discriminatory. When an enterprise bases core business processes on biased models, it can suffer regulatory and reputational harm. Machine learning also performs manual tasks that are beyond our ability to execute at scale — for example, processing the huge quantities of data generated today by digital devices. Machine learning’s ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery.

• The advent of high-throughput sequencing has ushered in an era of unprecedented data generation, which has been utilized to probe the etiology of diverse human diseases.
• The challenge posed by the massive data volume is to identify meaningful insights, a task of utmost importance in the medical …
• An example comes from the field of financial modelling, with a manifesto elaborated in the aftermath of the 2008 financial crisis (Derman and Wilmott, 2009).
• To analyze the data and extract insights, there exist many machine learning algorithms, summarized in Sect.
• Machine learning relies on a large amount of data, which is fed into algorithms in order to produce a model off of which the system predicts its future decisions.
• Computation in general enhances several key areas of clinical research, and AI-based methods promise even more applications for researchers.

Reverse-engineering exercises have been run so as to understand what are the key drivers on the observed scores. Rudin (2019) found that the algorithm seemed to behave differently from the intentions of their creators (Northpointe, 2012) with a non-linear dependence on age and a weak correlation with one’s criminal history. These exercises (Rudin, 2019; Angelino et al., 2018) showed that it is possible to implement interpretable classification algorithms that lead to a similar accuracy as COMPAS. Dressel and Farid (2018) achieved this result by using a linear predictor-logistic regressor that made use of only two variables (age and total number of previous convictions of the subject). Raji et al. (2020) suggest that a process of algorithmic auditing within the software-development company could help in tackling some of the ethical issues raised.

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

To help you get a better idea of how these types differ from one another, here’s an overview of the four different types of machine learning primarily in use today. The all new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models. Although all of these methods have the same goal – to extract insights, patterns and relationships that can be used to make decisions – they have different approaches and machine learning importance abilities. Fueled by the massive amount of research by companies, universities and governments around the globe, machine learning is a rapidly moving target. Breakthroughs in AI and ML seem to happen daily, rendering accepted practices obsolete almost as soon as they’re accepted. One thing that can be said with certainty about the future of machine learning is that it will continue to play a central role in the 21st century, transforming how work gets done and the way we live.

This would prevent the algorithm-learning process from conflicting with the standards agreed. Making mandatory to deposit these algorithms in a database owned and operated by this entrusted super-partes body could ease the development of this overall process. Many algorithms have been proposed to reduce data dimensions in the machine learning and data science literature [41, 125]. In the following, we summarize the popular methods that are used widely in various application areas. Many clustering algorithms have been proposed with the ability to grouping data in machine learning and data science literature [41, 125]. Usually, the availability of data is considered as the key to construct a machine learning model or data-driven real-world systems [103, 105].

Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications

In a bank, for example, regulatory requirements mean that developers can’t “play around” in the development environment. At the same time, models won’t function properly if they’re trained on incorrect or artificial data. Even in industries subject to less stringent regulation, leaders have understandable concerns about letting an algorithm make decisions without human oversight. In the semi-supervised learning method, a machine is trained with labeled as well as unlabeled data.

It’s often used in gaming environments where an algorithm is provided with the rules and tasked with solving the challenge in the most efficient way possible. The model will start out randomly at first, but over time, through trial and error, it will learn where and when it needs to move in the game to maximise points. An example of a supervised learning model is the K-Nearest Neighbors (KNN) algorithm, which is a method of pattern recognition.

In the following section, we discuss several application areas based on machine learning algorithms. Deep learning is a specific application of the advanced functions provided by machine learning algorithms. « Deep » machine learning  models can use your labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require labeled data. Deep learning can ingest unstructured data in its raw form (such as text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets.

Restricted Boltzmann machines (RBM) [46] can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. A deep belief network (DBN) is typically composed of simple, unsupervised networks such as restricted Boltzmann machines (RBMs) or autoencoders, and a backpropagation neural network (BPNN) [123]. A generative adversarial network (GAN) [39] is a form of the network for deep learning that can generate data with characteristics close to the actual data input.

SAS analytics solutions transform data into intelligence, inspiring customers around the world to make bold new discoveries that drive progress. ArXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

As a human, and as a user of technology, you complete certain tasks that require you to make an important decision or classify something. For instance, when you read your inbox in the morning, you decide to mark that ‘Win a Free Cruise if You Click Here’ email as spam. Machine learning is comprised of algorithms that teach computers to perform tasks that human beings do naturally on a daily basis. Several different types of machine learning power the many different digital goods and services we use every day. While each of these different types attempts to accomplish similar goals – to create machines and applications that can act without human oversight – the precise methods they use differ somewhat.

Artificial intelligence (AI), particularly, machine learning (ML) have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [95]. “Industry 4.0” [114] is typically the ongoing automation of conventional manufacturing and industrial practices, including exploratory data processing, using new smart technologies such as machine learning automation. Thus, to intelligently analyze these data and to develop the corresponding real-world applications, machine learning algorithms is the key.

As with the use of machine language in clinical diagnosis, work in prognosis promises many improvements ahead. One should also not forget that these algorithms are learning by direct experience and they may still end up conflicting with the initial set of ethical rules around which they have been conceived. Learning may occur through algorithms interaction taking place at a higher hierarchical level than the one imagined in the first place (Smith, 2018). This aspect would represent a further open issue to be taken into account in their development (Markham et al., 2018). It also poses further tension between the accuracy a vehicle manufacturer seeks and the capability to keep up the agreed fairness standards upstream from the algorithm development process. Artificial intelligence (AI) is the branch of computer science that deals with the simulation of intelligent behaviour in computers as regards their capacity to mimic, and ideally improve, human behaviour.

• Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.[46] In other words, it is a process of reducing the dimension of the feature set, also called the « number of features ».
• Machine learning algorithms are trained to find relationships and patterns in data.
• Data can be of various forms, such as structured, semi-structured, or unstructured [41, 72].
• This can include statistical algorithms, machine learning, text analytics, time series analysis and other areas of analytics.