AI and machine learning are like an unstoppable tidal wave in today’s world. We’ve already seen the crest of that wave appear over the horizon with increased automation in businesses and the emergence of apps like ChatGPT. But in the coming years, the wave will engulf the world, making AI big business.


That’s supported by statistics from Statista, too, with reports that the AI market that was worth $200 billion (approx. €185 billion) in 2022 will be worth a staggering $2 trillion (approx. €1.85 trillion) in 2030. The point is that massive growth is coming in AI, and the right Master’s in AI is the key for you to be a part of that growth rather than getting stuck in an industry that gets consumed by it.


Top European Programs for Masters in AI and ML


In choosing the MSc artificial intelligence programs that appear on this list, we looked at factors ranging from the quality (and variety) of course content to who provides the degree. The three courses highlighted here are among Europe’s best to offer to European and overseas students.


Master in Artificial Intelligence (Universita di Bologna)


Though it’s held in Italy, this Master’s program is delivered in English as part of Universita di Bologna’s computer science program. It’s an on-campus course, meaning you’ll have to move to Bologna to attend.


The course provides a solid grounding in the foundations of AI over two years. You’ll get to grips with topics like machine learning and natural language processing, in addition to touching on the ethical and social issues that the rise of AI brings to the table.


The course is welcoming to international students, as it currently has a 77% ratio of international students who don’t come from Bologna. To apply, you must complete an application on the Studenti Online program, along with a mandatory form. Failure to follow this procedure leads to your application being discarded. Applicants don’t necessarily need to hold a Bachelor’s degree, though they must demonstrate a transcript of record that shows they have earned at least 150 ECTS or CFU credits in majors like computer science, mathematics, statistics, and physics.


The course page boasts that 90.5% of its 2021 graduates were happy with their degrees. It’s natural to assume most of these graduates leveraged their Master’s in artificial intelligence to move into careers in the field.


Master in Applied Data Science & AI (OPIT)


If you want to master artificial intelligence with a sprinkling of applying that mastery to the data science industry, OPIT’s course is right for you. It’s an 18-month course (though a 12-month fast-track version is available) that is fully online and delivers 90 ECTS credits. The first term covers the foundational aspects of AI, including subjects like machine learning and data science. But the second term stands out as it moves study from the theoretical to the practical by challenging you to solve real-world problems with your knowledge.


As an online program, it’s available to anybody anywhere, with entry requirements also being flexible. You’ll need a BSc degree, even one from a non-technical field, and should demonstrate English proficiency up to the B2 level with appropriate certification. Don’t worry if you don’t have an English language certification because OPIT offers its own that you can take before registering for the course.


Career-wise, the course is a good option because it occupies an interesting middle-ground between theory and practicality. The second term, in particular, equips you with skills that you can apply directly in fields as varied as IT business analysis, business intelligence, and data science.



MSc in Advanced Computer Science (University of Oxford)


Though it’s not marketed directly as a Master’s in machine learning and artificial intelligence, the University of Oxford’s program gives you excellent qualifications in both. It’s also delivered by an institution that EduRank names as the best for AI in the UK, and sixth-best in the world. The course examines advanced machine learning and computer security techniques, focusing on computational models and the algorithms behind them.


It’s a full-time program demanding 35 hours of weekly study, 15 of which you’ll spend on campus with the other 20 dedicated to self-study. It’s also a tough nut to crack for applicants, as the University of Oxford has a low 18% acceptance rate. You’ll need a first-class undergraduate degree with honors (or an equivalent) in mathematics or computer science to stand a chance of getting into one of the UK’s most prestigious universities.


Those tough entry requirements pay off later on, though, as the words “University of Oxford” on a CV immediately make employers stand up and pay attention. The wide-ranging approach of the program also means you’re not focusing solely on AI, opening up career opportunities in other fields related to math and statistical analysis.


Data Science Master – Europe’s Best Options


Data science is an industry that requires you to translate your understanding of algorithmic theory to transform complex data sets into actionable insights. It’s also an industry that’s making increasingly heavy use of AI tools, making a Master’s in data science a great companion (or alternative) to the best artificial intelligence Master in Europe. As you noticed above, OPIT’s MSc AI program includes elements of data science, though the two programs here (covered in brief) are excellent choices as standalone programs.


MSc Data Sciences and Business Analytics (Essec Business School)


This hybrid course lasts for either one or two years, depending on your background, and focuses on the application of data sciences in a business context. It’s also ranked as the fourth-best Master’s in business analytics in the world by QS World University Rankings.


That high ranking is backed up by the university’s own statistics, which state that over half of its students get jobs before they even complete the course. Essec has a 100% career success rate for graduates in less than six months from completion of the Master’s, making this a great choice for career-focused students. Google, Amazon, JP Morgan Chase, and PwC count as some of the top recruiters that keep their eye on graduates from this program.


Admission requires a degree in a related technical subject, such as engineering, science, or business, from a leading university. That degree also impacts the version of the program you take, as a three-year BSc means you take the two-year Master’s, while those who have a four-year BSc under their belts take the one-year version, assuming they meet other requirements.


Data Science, Technology, and Innovation (University of Edinburgh)


With over 13,000 international students, the University of Edinburgh welcomes overseas students who want to expand their knowledge. Its MSc data science program is no different, buoyed by the fact that it’s an online course that doesn’t require you to move to the less-than-sunny climate of Edinburgh.


It’s a part-time program that relies on self-study, though it provides you with plenty of interactive resources to help along the way. The program is something of an umbrella course as it focuses on equipping students with the knowledge they need to enter the data science field across industries as diverse as medicine, science, and even the arts.


You’ll need the equivalent of an Upper Second-Class Honors degree that has elements of programming before applying. Ideally, you’ll also have evidence of mathematical skill, either through taking math classes in your undergraduate studies or by demonstrating the equivalent of an English A-Level in math through other qualifications.

 

Factors to Consider When Choosing an Artificial Intelligence Master’s


The five programs highlighted here all help you master artificial intelligence, with many also providing a practical grounding that puts you in good stead for your future career. But if you want to do more research (and that’s always a good idea), the following factors should be on your mind when checking other programs:

  • Course Curriculum – The content of your course impacts what you can do once you have your MSc under your belt. Focus on programs that teach tangible skills applicable to the field you wish to enter.
  • Faculty – Always check the credentials of the program’s creators and administrators, particularly in terms of industry experience, to confirm they have the relevant tools.
  • Tuition and Financial Aid – Master’s programs aren’t cheap (you’ll pay several thousand euros for even an online course), so check you can budget accordingly for the program. Many universities offer financial aid options, from scholarships to student loans, that can help in this area.
  • Location – The location isn’t really an issue if you take an online course, but it impacts your decision if you decide to study on-campus. Remember that you’ll spend at least a year of your life on the course (often two years) so you need to gel well with the place in which you’ll live.
  • Networking and Industry – Does the course provider have connections to major industry players? Does it offer career advice, ideally via a specialized office or program? These are the types of questions to ask when assessing a university’s capacity for networking and career advancement.


Become a Master in Artificial Intelligence


A Master’s degree in artificial intelligence is your entry point into a growing industry that’s already on the verge of taking the world by storm. That is, assuming you choose the right program. The five highlighted here all land in the “right program” category by virtue of the tuition you receive, the reputation of the institution, and their accessibility to European and overseas students.


Research each program (and any others you consider) extensively before making a choice. Remember that it’s not always about the course or its reputation – it’s about how the course helps you achieve the specific learning goals you need to achieve to get ahead in your chosen career.

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Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management
OPIT - Open Institute of Technology
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Apr 14, 2025 6 min read

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Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

Read the full article below (in Italian):

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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