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Artificial intelligence (AI) is a modern-day monolith that is likely going to be as important to the world as the introduction of the internet. We already see it creeping into every aspect of industry, from the basic chatbots you find on many websites to the self-driving cars under production at companies like Tesla.
As an industry, AI looks set to zoom past its current global valuation of $100 billion, becoming worth a staggering $2 trillion by 2030. To ensure you enjoy a prosperous career in an increasingly computer-powered world, you need to learn about AI. That’s where each artificial intelligence tutorial in this list can help you.
Top AI Tutorials for Beginners
If you know nothing about AI beyond the name, these are the three tutorials to get you started with the subject.
Tutorial 1 – Artificial Intelligence Tutorial for Beginners: Learn the Basics of AI (Guru99)
You need to get to the grips with AI theory before you can start with more practical work. Guru99’s tutorial helps you there, with a set of 11 lessons that take you from the most basic of concepts (what is AI?) to digging into the various types of machine learning. It’s like a crib notes version of an AI book, as it takes you on a speedy flight through AI fundamentals before capping its offer with a look at some practical applications.
Key Topics
- The basic theory of AI and machine learning
- Different types of machine learning algorithms
- An introduction to neural networking
Why Take This Artificial Intelligence Tutorial?
The tutorial is completely free, with every lesson being accessible via the Guru99 website with the click of a mouse. It’s also a great choice for complete AI newbies. You’ll cover the basics first, getting a grounding in AI in the process, before moving on to more complicated aspects of machine learning.
Tutorial 2 – Artificial Intelligence Tutorial for Beginners (Simplilearn)
This 14-lesson tutorial may seem intimidating at first. However, those 14 lessons only take an hour to complete, and the course has no prerequisites. This combination of brevity and a lack of tutorial requirements make it ideal for beginners who want to get to grips with the theory of AI. It’ll also help you develop some programming skills useful in more advanced courses.
Key Topics
- Basic programming skills you can use to develop AI models
- An introduction to Big Data and Spark
- Basic AI concepts, including machine learning, linear algebra, and algorithms
Why Take This Artificial Intelligence Tutorial?
Many of the tutorials you come across online will ask you to have a basic understanding of probability theory and linear algebra. This course equips you with those skills, in addition to giving you a solid grounding in many of the AI concepts (and machine learning models) you’ll encounter when you reach the intermediate level. Think of it as a crash course in the basics of AI.
Top AI Tutorials for Intermediate Learners
If you have a grasp of the basics, meaning you can separate your supervised learning algorithms from your unsupervised ones, you’re ready for these intermediate-level tutorials.
Tutorial 1 – Intro to Artificial Intelligence (Udacity)
Don’t let the use of the word “intro” in this tutorial’s name fool you because this is more than a mere explanation of AI concepts. As a four-month course, it requires you to have a good understanding of concepts like linear algebra and probability theory. Assuming you have that understanding, you’ll embark on a four-month self-paced learning journey (that’s completely free) that delves deep into the applications of AI.
Key Topics
- The theoretical and practical applications of natural language processing
- How AI has uses in every aspect of modern life, from advanced research to gaming
- The fundamentals of AI that underpin the practical applications you learn about
Why Take This Artificial Intelligence Tutorial?
The price tag is right, as this is one of the few Udacity courses you can take without spending any money. It’s also created by two of the best minds in AI – Peter Norvig and Sebastian Thrun – who deliver a nice mix of content, including instructor-led videos, quizzes, and experiential learning. Granted, there’s a large time commitment. But that commitment pays off as the course delivers a solid understanding of AI’s fundamentals and practical applications.
Tutorial 2 – Natural Language Processing Specialization (Coursera)
Anybody who’s used ChatGPT or “spoken” to a chatbot knows that a lot of companies are interested in what AI can do to deliver written content. That’s where Natural Language Processing (NLP) comes in, and this course is ideal for understanding the techniques that allow you to build chatbots and similar technologies.
Key Topics
- How to use logistic regression (and other techniques) to conduct sentiment analysis
- Build autocomplete and autocorrect models
- Discover how to develop AI algorithms that both detect and use human language
Why Take This Artificial Intelligence Tutorial?
Specialization is the key as you get deeper into the AI field. With this course, you focus your learning on language models and NLP, allowing you to dig deeper into an in-demand field that offers plenty of career opportunities. It’s somewhat intensive, requiring four months of study at about 10 hours per week to complete. But you get a shareable certificate at the end and develop a foundation in NLP that can apply in many business areas.
Top AI Tutorials for Advanced Learners
By the time you reach the advanced stage, you’re ready for your AI tutorials to teach you how to build and operate your own AI.
Tutorial 1 – Artificial Intelligence A-Z 2023: Build an AI With ChatGPT4 (Udemy)
With backing from a successful Kickstarter campaign, the Artificial Intelligence A-Z tutorial covers some of the fundamentals but focuses mostly on practical applications. You’ll create several types of AI, including a snazzy virtual self-driving car and an AI designed to beat simple games, helping you get to grips with how to put the theory you’ve learned into practice. The tutorial comes with 17 videos, a trio of downloadable resources, and 20 articles. All of which you can access whenever you need them.
Key Topics
- How to build practical AIs that actually do things
- The fundamentals of complex topics, such as Q-Learning
- How Asynchronous Advantage Actor Critic (AC3) applies to modern AI
Why Take This Artificial Intelligence Tutorial?
The two main reasons to take this tutorial are that it gives you hands-on experience with some exciting AI concepts, and you get a certificate you can put on your CV when you’ve finished. It’s well-structured and popular, with almost 204,000 students having already taken it from all over the world. And at just £59.99 (approx. €69), you get a lot of bang for your buck with videos, articles, and downloadable resources.
Tutorial 2 – A* Pathfinding Tutorial – Unity (YouTube)
Many prospective game developers will get their start with Unity, which is a free development tool that you can use to create surprisingly complex games. This YouTube tutorial series includes 10 videos, which walk you through how to use the A* algorithm to program AIs to determine the paths characters follow in a video game. It requires some programming knowledge, specifically C#, but it’s ideal for those who want to use their AI skills to transition into the world of gaming.
Key Topics
- Using the A* algorithm to create paths for AI-driven characters in video games
- Movement smoothing and terrain-related penalties
- Using multi-threading to improve pathfinding performance
Why Take This Artificial Intelligence Tutorial?
The price is certainly right for this tutorial, as the course creator (Sebastian Lague) makes all of his videos free to view on YouTube. But the biggest benefit of this tutorial is that it introduces complicated concepts that game developers use to determine character movement. If you’re interested in what makes video game characters “work” in terms of their actions in a game, this tutorial shows you the algorithm that underpins it all.
Additional AI Resources
The six tutorials in this list run the gamut from introducing you to the basics of AI to demonstrating specialized applications of the technology. Building on that knowledge requires you to go further, with the following books, podcasts, and websites all being great resources.
Great AI-Related Books
- Artificial Intelligence: A Modern Approach (Peter Norvig and Stuart Russell)
- Python: Advanced Guide to Artificial Intelligence (Giuseppe Bonaccorso)
- Neural Networks and Deep Learning (Charu C Aggarwal)
Great AI-Related Podcasts
- The AI Podcast (Noah Kravitz)
- Artificial Intelligence: AI Podcast (Lex Fridman)
- Eye on AI (Craig Smith)
Great AI-Related Websites and Blogs
- MIT News
- Analytics Vidhya
- KDnuggets
Understand Complex Concepts With an Artificial Intelligence Tutorial
AI is one of the world’s fastest-growing industries, with the previously-mentioned $2 trillion 2030 valuation representing a 20-fold growth from today. The point? Getting in close to the ground floor now by developing your understanding of AI concepts will set you up for a future in which many of the best jobs are in the AI field.
Each artificial intelligence tutorial in this list offers something different to students, from beginners who want to get to grips with AI to those who have a decent understanding and are ready to specialize. Regardless of the course you choose, the most important thing is that you keep learning. AI won’t stay static. It’s like a runaway locomotive that’s going to keep plowing forward, with nothing to stop it, to its next evolution. Use these tutorials to learn both basic and advanced concepts, then build on that learning with continued education.
-
From the same author
Read the articleSource:
- Agenda Digitale, published on May 16th, 2025
By Riccardo Ocleppo, Founder and Director of OPIT – Open Institute of Technology
AI ethics requires ongoing commitment. Organizations must integrate guidelines and a corporate culture geared towards responsibility and inclusiveness, preventing negative consequences for individuals and society.
In the world of artificial intelligence, concerns about algorithmic bias are coming to the forefront, calling for a collective effort to promote ethical practices in the development and use of AI.
This implies the need to understand the multiple causes and potential consequences of the biases themselves, identify concrete solutions and recognize the key role of academic institutions in this process.
Bias in AI is a form of injustice, often systemic, that can be embedded in algorithms. Its origins are many, but the main culprit is almost always the data set used to train the models. If this data reflects inequalities or prejudices present in society, the risk is that AI will absorb and reproduce them, consolidating these distortions.
But bias can also manifest itself in the opposite direction. This is what happened some time ago with Google Gemini. The generative AI system developed by Google, in an attempt to ensure greater inclusivity, ended up generating content and images completely disconnected from the reality it was supposed to represent.
Further complicating the picture is the very nature of AI models, which are often characterized by complex algorithms and opaque decision-making processes. This complexity makes it difficult to identify, and therefore correct, biases inherent in the systems.
Ethical Data Management to Reduce Bias in AI
Adopting good data management practices is essential to address these issues. The first step is to ensure that the datasets used for training are diverse and representative. This means actively seeking data that includes a wide variety of demographic, cultural, and social contexts, so as to avoid AI exclusively reproducing existing and potentially biased models.
Alongside data diversification, it is equally important to test models on different demographic groups. Only in this way can latent biases that would otherwise remain invisible be highlighted. Furthermore, promoting transparency in algorithms and decision-making processes is crucial. Transparency allows for critical control and makes all actors involved in the design and use of AI accountable.
Strategies for ethical and responsible artificial intelligence
Building ethical AI is not an isolated action, but an ongoing journey that requires constant attention and updating. This commitment is divided into several fundamental steps. First, ethical guidelines must be defined. Organizations must clearly establish the ethical standards to follow in the development and use of AI, inspired by fundamental values such as fairness, responsibility and transparency. These principles serve as a compass to guide all projects.
It is also essential to include a plurality of perspectives in the development of AI. Multidisciplinary teams, composed of technologists, ethicists, sociologists and representatives of the potentially involved communities, can help prevent and correct biases thanks to the variety of approaches. Last but not least, promote an ethical culture : in addition to establishing rules and composing diverse teams, it is essential to cultivate a corporate culture that places ethics at the center of every project. Only by integrating these values in the DNA of the organization can we ensure that ethics is a founding element of the development of AI.
The consequences of biased artificial intelligence
Ignoring the problem of bias can have serious and unpredictable consequences, with profound impacts on different areas of our lives. From the reinforcement of social inequalities to the loss of trust in AI-based systems, the risk is to fuel skepticism and resistance towards technological innovation. AI, if distorted, can negatively influence crucial decisions in sectors such as healthcare, employment and justice. Think, for example, of loan selection algorithms that unfairly penalize certain categories, or facial recognition software that incorrectly identifies people, with possible legal consequences. These are just some of the situations in which an unethical use of AI can worsen existing inequalities.
University training and research to counter bias in AI
Universities and higher education institutions have a crucial responsibility to address bias and promote ethical practices in AI development. Ethics must certainly be integrated into educational curricula. By including ethics modules in AI and computer science courses, universities can provide new generations of developers with the tools to recognize and address bias, contributing to more equitable and inclusive design. Universities can also be protagonists through research.
Academic institutions, with their autonomy and expertise, can explore the complexities of bias in depth, developing innovative solutions for detecting and mitigating bias. Since the topic of bias is multidimensional in nature, a collaborative approach is needed, thus fostering interdisciplinary collaboration. Universities can create spaces where computer scientists, ethicists, lawyers, and social scientists work together, offering more comprehensive and innovative solutions.
But that’s not all. As places of critical thinking and debate, universities can foster dialogue between developers, policy makers, and citizens through events, workshops, and conferences. This engagement is essential to raise awareness and promote responsible use of AI.
In this direction, several universities have already activated degree courses in artificial intelligence that combine advanced technical skills (in areas such as machine learning, computer vision and natural language processing) with training that is attentive to ethical and human implications.
Academic Opportunities for an Equitable AI Future
More and more universities around the world – including Yale and Oxford – are also creating research departments dedicated to AI and ethics.
The path to ethical AI is complex, but it also represents an opportunity to build a future where technology truly serves the common good.
By recognizing the root causes of bias , adopting responsible data practices, and engaging in ongoing and vigilant development, we can reduce the unintended effects of biased algorithms. In this process, academic institutions – thanks to their expertise and authority – are at the forefront, helping to shape a more equitable and inclusive digital age.
Read the articleSource:
- TechFinancials, published on May 16th, 2025
By Zorina Alliata
Artificial intelligence (AI) used to be the stuff of science fiction. Stories about rogue machines and robot uprisings were once a source of amusement, not anxiety. But over recent years, AI has quietly embedded itself in our daily lives.
From the algorithms behind social media feeds, to the voice assistants managing our calendars. This quiet takeover has become something far louder: fear.
Headlines around AI are often alarmist. Statements such as “AI will take your job”, “AI will end education”, or “AI is dangerous and unregulated” are thrown around regularly. These narratives feed on uncertainty and fuel distrust.
But it doesn’t have to be this way. The hyper-fixation on the never-ending negative aspects of AI is the wrong approach to take. What if AI isn’t the villain? What if, at this stage, it’s simply a child?
AI, in many ways, is still learning. It mimics human behaviour, absorbs language, and forms patterns based on what it sees. Its current capabilities, however powerful they may seem, are not equivalent to human intelligence. It has limitations. It makes mistakes. It can even be manipulated and misled. It reflects our world; flaws and all. In that sense, AI is less an omnipotent force and more in line with a toddler trying to find its way.
And, like any child, it needs guidance.
This is especially evident in education. The emergence of AI tools such as ChatGPT has caused a stir in higher education institutions and universities, sparking fears about plagiarism and the erosion of critical thinking. Some institutions have responded with strict bans, while others have embraced cautious integration. The panic is understandable, but is it misplaced?
Rather than jumping to conclusions, educators should consider shifting the conversation. AI can, in fact, become an ally in learning. Instead of assuming students will cheat, we can teach them to use AI responsibly. Most of us educators can already recognise the signs of AI-generated work: excessive use of numbered lists, repetitive language and poor comparison skills. So why not use this as a teaching opportunity?
Encouraging students to engage with AI critically, understanding what it’s good at, where it falls short, and how to improve its output, can strengthen their own analytical skills. It invites them to become more active participants in their learning, not passive consumers of machine generated answers. Teaching young people how to work with AI is arguably more important than shielding them from it.
Outside the classroom, AI’s impact on the workforce is another growing concern. Stories about AI replacing jobs often dominate the news cycle. But these conversations often ignore a key point: AI is not autonomous. AI needs human designers, engineers, analysts, and ethicists to guide it. For every job that AI may eliminate, others will emerge to support and direct it.
More importantly, there are many things AI simply cannot do. It doesn’t understand nuance, morality or emotion. It can’t make ethical decisions without human input. These aren’t minor gaps, they’re fundamental. That’s why we must stop imagining AI as an unstoppable force and start thinking about how to raise it responsibly.
When considering how to raise our AI child responsibly, we need to acknowledge the issue of the algorithm being biased. Critics often point out that AI reproduces prejudices and errors, and whilst this is true, the source of that bias is us. It is important to remember that AI learns from historical data created by us, much of which reflects deeply ingrained societal inequalities.
Take, for example, mortgage lending in the US, where decades of discriminatory practices have skewed the data. Unless we intervene, AI trained on this information will inevitably reflect those same biases.
That’s not a reason to reject AI. It’s a reason to be more involved in its development, like any good parent. The responsibility lies with us.
Parenting is not about control for control’s sake; it’s about nurturing growth while setting boundaries. AI, like a child, needs feedback, accountability, and care. It will grow, but how it grows is up to us.
It’s tempting to view technology as something that happens to us, rather than something we can shape. But AI doesn’t exist outside of society, it’s a product of our values, decisions, and input. If we treat it as a monster, it may become one. If we treat it as a mirror, it will reflect what we show it. And if we treat it as a child, we may be able to raise it into something better.
So instead of fearmongering, let’s ask ourselves a better question: Are we raising AI correctly?
- Zorina Alliata, Professor of Responsible AI at OPIT– Open Institute of Technology
Read the full article below:
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Source:
- Agenda Digitale, published on May 16th, 2025
By Riccardo Ocleppo, Founder and Director of OPIT – Open Institute of Technology
AI ethics requires ongoing commitment. Organizations must integrate guidelines and a corporate culture geared towards responsibility and inclusiveness, preventing negative consequences for individuals and society.
In the world of artificial intelligence, concerns about algorithmic bias are coming to the forefront, calling for a collective effort to promote ethical practices in the development and use of AI.
This implies the need to understand the multiple causes and potential consequences of the biases themselves, identify concrete solutions and recognize the key role of academic institutions in this process.
Bias in AI is a form of injustice, often systemic, that can be embedded in algorithms. Its origins are many, but the main culprit is almost always the data set used to train the models. If this data reflects inequalities or prejudices present in society, the risk is that AI will absorb and reproduce them, consolidating these distortions.
But bias can also manifest itself in the opposite direction. This is what happened some time ago with Google Gemini. The generative AI system developed by Google, in an attempt to ensure greater inclusivity, ended up generating content and images completely disconnected from the reality it was supposed to represent.
Further complicating the picture is the very nature of AI models, which are often characterized by complex algorithms and opaque decision-making processes. This complexity makes it difficult to identify, and therefore correct, biases inherent in the systems.
Ethical Data Management to Reduce Bias in AI
Adopting good data management practices is essential to address these issues. The first step is to ensure that the datasets used for training are diverse and representative. This means actively seeking data that includes a wide variety of demographic, cultural, and social contexts, so as to avoid AI exclusively reproducing existing and potentially biased models.
Alongside data diversification, it is equally important to test models on different demographic groups. Only in this way can latent biases that would otherwise remain invisible be highlighted. Furthermore, promoting transparency in algorithms and decision-making processes is crucial. Transparency allows for critical control and makes all actors involved in the design and use of AI accountable.
Strategies for ethical and responsible artificial intelligence
Building ethical AI is not an isolated action, but an ongoing journey that requires constant attention and updating. This commitment is divided into several fundamental steps. First, ethical guidelines must be defined. Organizations must clearly establish the ethical standards to follow in the development and use of AI, inspired by fundamental values such as fairness, responsibility and transparency. These principles serve as a compass to guide all projects.
It is also essential to include a plurality of perspectives in the development of AI. Multidisciplinary teams, composed of technologists, ethicists, sociologists and representatives of the potentially involved communities, can help prevent and correct biases thanks to the variety of approaches. Last but not least, promote an ethical culture : in addition to establishing rules and composing diverse teams, it is essential to cultivate a corporate culture that places ethics at the center of every project. Only by integrating these values in the DNA of the organization can we ensure that ethics is a founding element of the development of AI.
The consequences of biased artificial intelligence
Ignoring the problem of bias can have serious and unpredictable consequences, with profound impacts on different areas of our lives. From the reinforcement of social inequalities to the loss of trust in AI-based systems, the risk is to fuel skepticism and resistance towards technological innovation. AI, if distorted, can negatively influence crucial decisions in sectors such as healthcare, employment and justice. Think, for example, of loan selection algorithms that unfairly penalize certain categories, or facial recognition software that incorrectly identifies people, with possible legal consequences. These are just some of the situations in which an unethical use of AI can worsen existing inequalities.
University training and research to counter bias in AI
Universities and higher education institutions have a crucial responsibility to address bias and promote ethical practices in AI development. Ethics must certainly be integrated into educational curricula. By including ethics modules in AI and computer science courses, universities can provide new generations of developers with the tools to recognize and address bias, contributing to more equitable and inclusive design. Universities can also be protagonists through research.
Academic institutions, with their autonomy and expertise, can explore the complexities of bias in depth, developing innovative solutions for detecting and mitigating bias. Since the topic of bias is multidimensional in nature, a collaborative approach is needed, thus fostering interdisciplinary collaboration. Universities can create spaces where computer scientists, ethicists, lawyers, and social scientists work together, offering more comprehensive and innovative solutions.
But that’s not all. As places of critical thinking and debate, universities can foster dialogue between developers, policy makers, and citizens through events, workshops, and conferences. This engagement is essential to raise awareness and promote responsible use of AI.
In this direction, several universities have already activated degree courses in artificial intelligence that combine advanced technical skills (in areas such as machine learning, computer vision and natural language processing) with training that is attentive to ethical and human implications.
Academic Opportunities for an Equitable AI Future
More and more universities around the world – including Yale and Oxford – are also creating research departments dedicated to AI and ethics.
The path to ethical AI is complex, but it also represents an opportunity to build a future where technology truly serves the common good.
By recognizing the root causes of bias , adopting responsible data practices, and engaging in ongoing and vigilant development, we can reduce the unintended effects of biased algorithms. In this process, academic institutions – thanks to their expertise and authority – are at the forefront, helping to shape a more equitable and inclusive digital age.
Read the full article below:
Source:
- TechFinancials, published on May 16th, 2025
By Zorina Alliata
Artificial intelligence (AI) used to be the stuff of science fiction. Stories about rogue machines and robot uprisings were once a source of amusement, not anxiety. But over recent years, AI has quietly embedded itself in our daily lives.
From the algorithms behind social media feeds, to the voice assistants managing our calendars. This quiet takeover has become something far louder: fear.
Headlines around AI are often alarmist. Statements such as “AI will take your job”, “AI will end education”, or “AI is dangerous and unregulated” are thrown around regularly. These narratives feed on uncertainty and fuel distrust.
But it doesn’t have to be this way. The hyper-fixation on the never-ending negative aspects of AI is the wrong approach to take. What if AI isn’t the villain? What if, at this stage, it’s simply a child?
AI, in many ways, is still learning. It mimics human behaviour, absorbs language, and forms patterns based on what it sees. Its current capabilities, however powerful they may seem, are not equivalent to human intelligence. It has limitations. It makes mistakes. It can even be manipulated and misled. It reflects our world; flaws and all. In that sense, AI is less an omnipotent force and more in line with a toddler trying to find its way.
And, like any child, it needs guidance.
This is especially evident in education. The emergence of AI tools such as ChatGPT has caused a stir in higher education institutions and universities, sparking fears about plagiarism and the erosion of critical thinking. Some institutions have responded with strict bans, while others have embraced cautious integration. The panic is understandable, but is it misplaced?
Rather than jumping to conclusions, educators should consider shifting the conversation. AI can, in fact, become an ally in learning. Instead of assuming students will cheat, we can teach them to use AI responsibly. Most of us educators can already recognise the signs of AI-generated work: excessive use of numbered lists, repetitive language and poor comparison skills. So why not use this as a teaching opportunity?
Encouraging students to engage with AI critically, understanding what it’s good at, where it falls short, and how to improve its output, can strengthen their own analytical skills. It invites them to become more active participants in their learning, not passive consumers of machine generated answers. Teaching young people how to work with AI is arguably more important than shielding them from it.
Outside the classroom, AI’s impact on the workforce is another growing concern. Stories about AI replacing jobs often dominate the news cycle. But these conversations often ignore a key point: AI is not autonomous. AI needs human designers, engineers, analysts, and ethicists to guide it. For every job that AI may eliminate, others will emerge to support and direct it.
More importantly, there are many things AI simply cannot do. It doesn’t understand nuance, morality or emotion. It can’t make ethical decisions without human input. These aren’t minor gaps, they’re fundamental. That’s why we must stop imagining AI as an unstoppable force and start thinking about how to raise it responsibly.
When considering how to raise our AI child responsibly, we need to acknowledge the issue of the algorithm being biased. Critics often point out that AI reproduces prejudices and errors, and whilst this is true, the source of that bias is us. It is important to remember that AI learns from historical data created by us, much of which reflects deeply ingrained societal inequalities.
Take, for example, mortgage lending in the US, where decades of discriminatory practices have skewed the data. Unless we intervene, AI trained on this information will inevitably reflect those same biases.
That’s not a reason to reject AI. It’s a reason to be more involved in its development, like any good parent. The responsibility lies with us.
Parenting is not about control for control’s sake; it’s about nurturing growth while setting boundaries. AI, like a child, needs feedback, accountability, and care. It will grow, but how it grows is up to us.
It’s tempting to view technology as something that happens to us, rather than something we can shape. But AI doesn’t exist outside of society, it’s a product of our values, decisions, and input. If we treat it as a monster, it may become one. If we treat it as a mirror, it will reflect what we show it. And if we treat it as a child, we may be able to raise it into something better.
So instead of fearmongering, let’s ask ourselves a better question: Are we raising AI correctly?
- Zorina Alliata, Professor of Responsible AI at OPIT– Open Institute of Technology
Read the full article below:
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