Computer Science Online Course: Top Free Certified Options

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Jul 2, 2023 • 9 min read

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If we think of “computer science” as an umbrella term for so many aspects of computing today, its importance is immediately apparent. Artificial intelligence (and the programming that lies behind it) falls into the computer science category. The same goes for machine learning, data science, networking, cybersecurity, and so many other elements of what make modern computing technology tick.

You need a solid grounding in computer science – both general concepts and theory – to move into one of these areas of specialization. And if you need to get that grounding on a budget, these free computer science courses teach you what you need to know and come with a handy certification.

Top Free Certified Computer Science Online Courses

As surprising as it may seem, you don’t have to pay money to get an education in computer science that employers actually care about. Free courses exist. And many of these free online computer science courses deliver a certification that proves your knowledge and comes from an institution that employers respect.

Course 1 – CS50: Introduction to Computer Science (Harvard University)

We’re stretching the definition of “free and certified” with the first course on the list. Though it’s free to take (and you get an audit of your performance without paying a penny), the verified certification for Harvard’s CS50 course costs $189 (approx. €175).

Assuming you’re willing to part with the cash, this course gives you a certificate from one of the United States’ most respected institutions, in addition to a crash course in computer science fundamentals. Over 11 weeks of self-paced learning (you’ll need to commit at least 10 hours per week to the course) you’ll develop a fundamental understanding of computer science and the programming that underpins it.

Concepts covered include data structures, abstraction, web development, and algorithms, creating a course that melds the math of modern computing with the theoretical concepts you’ll apply in the real world. Prospective programmers enjoy some diversity, too, as the course teaches the basics of several languages. Python, C, JavaScript, and HTML are all covered, though not in enough detail for you to achieve mastery in any of them. Still, as online certified courses for computer science go, CS50 delivers a prestigious certificate and exposes you to ambitious peers who may offer networking potential beyond the course content.

Course 2 – CS50’s Computer Science for Business Professionals (Harvard University)

It’s hard to look beyond Harvard when it comes to free computer science courses because you’re getting education and certification from a top university. With CS50 Computer Science for Business Professionals, Harvard moves beyond the tech-centric approach of its usual CS50 course to demonstrate how computer science principles apply in a real-world setting.

It’s a short course, clocking in at six weeks of study and only requiring two-to-six hours of work per week. That makes it perfect for professionals who want to boost their knowledge without a full-time commitment. You’ll tackle more high-level concepts in computer science, including the fundamentals of cloud computing and how to build technology stacks. All of which makes this like a speed run through of what you need to know about computing on a business level.

That’s not to say you won’t learn any technical theory. Several programming languages are covered (albeit in short-form style), as are the basics of computational thinking. But like CS50 above, certification comes at a cost, even if the course itself is free. Paying for an optional upgrade with EDX (through which the course is offered) is the only way to nab your certificate, if you do get a free course audit to demonstrate completion regardless.

Course 3 – Introduction to Computer Science and Programming Using Python (Massachusetts Institute of Technology)

Offered in conjunction with the EDX platform, this computer science online course takes a Python-focused approach to its teaching. Unlike CS50, which covers a wide range of topics in brief, MIT’s course focuses on how computer science is like a tool that you can use to create software and algorithms. Python 3.5 is the technology behind that tool and you’ll learn how to use it by examining and analyzing real-world problems.

The nine-week course starts by demonstrating the basics of Python (some self-learning and expansion of these concepts may be required) before moving into algorithms. Once you’ve gotten to grips with basic algorithm creation, you’ll learn how to test what you create and how those algorithms become the building blocks of complex data structures.

You have to make a substantial time commitment with this course, with MIT requiring you to spend at least 14 hours per week on your studies if you wish to stick to the nine-week schedule. And though effective in teaching you the basics of Python, the course is really a primer for a second MIT course – Introduction to Computational Thinking and Data Science – that requires payment. But it’s a useful course as a standalone product, but you’ll have to pay a fee to EDX if you want a course-centric certificate.

Factors to Consider When Choosing a Free Certified Computer Science Online Course

The trio of free online computer science courses discussed above each offer something different. Depending on your choice, you’ll get a bottom-up crash course in the theory, a practical understanding of how computer science works in a business context, or an in-depth guide to using Python. But when choosing between the three courses above (or any other courses you find) you must consider the following factors.

The Course Content and Its Relevance to Your Goals

The big question here is – what do you want to achieve with the course?

Sure, having a certificate, especially one with a major university’s name on it, is nice. But if that certificate demonstrates that you’ve learned skills that you don’t need for your intended career path then it’s not worth the paper it’s printed on.

Think of choosing a course like making an investment on which you expect a return. Outline your goals – both learning-centric and career-based – for taking the course. Then, find a course that helps you to reach those goals through laser-focused learning on topics you’ll use in the future.

Course Duration and Flexibility

For a young learner without full-time work or family commitments, taking on a computer science online course that requires months of study may not be a big deal. But that’s not the case for everybody. If you have limited hours available during the week, you need a course that you can fit into those hours rather than one that forces you to fit your life around the course.

Thankfully, most free online computer science courses make allowances for schedule flexibility by taking a self-paced learning approach. You’ll get access to all of the course resources upfront, allowing you to choose when you study. You may be able to get ahead during one week in preparation for a week where you know you can’t commit as much time, giving you the flexibility you need to fit the course into your schedule.

The Instructors and Their Expertise

Would you want to learn the theory of how to pilot a plane from somebody who’s never been up in the air? Of course you wouldn’t, and you must adopt the same attitude when choosing a computer science course.

Check the faculty list associated with the course (most reputable courses tell you who created them) and dig into their individual credentials. What have they done in the computer science industry? Where did they learn what they know? The answers to these questions tell you if your instructors and, by extension, your course are credible.

The Value of the Certification

When it comes to certification, look beyond the website that offers the course and instead focus on the institution that created it. For example, CS50’s Computer Science for Business Professionals is offered via the EDX platform, which doesn’t mean much to potential employers. But that certificate comes with a stamp of approval from Harvard University, which is a school that’s going to immediately raise eyebrows if it’s on your CV.

The point is that reputation matters, though it’s the reputation of the course creator that matters above that of the course platform. The more prestigious the name on the piece of paper, the more valuable the certificate is in the eyes of employers.

Tips for Successfully Completing a Free Certified Computer Science Online Course

With the tips for sifting through the sands of free computer science courses established, let’s round things off with some quick tips that’ll help you succeed in your studies:

Set clear goals for your education from the outset, with those goals aligning with your current experience level and desired outcomes.
Create a study schedule that fits around your commitments and stick to it as closely as you can.
Don’t skip assignments or practical sessions because everything included in the course is there to teach you something valuable.
Engage with the course community both to get advice from your peers and to potentially create networking opportunities.
Dedicate time to revision and research when preparing for exams or practical assessments to ensure you fully understand the course content.

Get Certified for Free and Improve Your Job Prospects

Given the importance of computer science to modern business – even the simplest of companies use software and have networks – it’s reasonable to want to build your knowledge of the subject. Free online computer science courses allow you to do that in exchange for a time commitment, with many allowing you to inject some flexibility into your study schedule.

Explore the three courses highlighted here, and look beyond them to more specialized courses once you’re confident in the foundational knowledge you’ve built. And remember – even a certificate from a free course has value in the job market if that course was created by a recognized institution.

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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

April 14, 2025
Source:
- Il Sole 24 Ore, published on April 14th, 2025
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.

“CEOs who already work with AI – explains the OPIT expert – see the investment in compliance as necessary, the latter as an opportunity: and the latter, on paper, have the advantage of being able to define a strategy also in terms of ethics and civil responsibility”

SMEs in greater difficulty

There is a further theme that accompanies the diffusion of AI in the European corporate fabric and that is the difference in “approach” between large companies and SMEs. The top executives of enterprise-class organizations, as Casale confirms, have budgets and resources to adequately address and manage the challenge of compliance. Small and medium-sized businesses, on the other hand, are in a waiting position to understand how to carry forward the development process and the risk that many may slow down in the innovation path, and that a conservative attitude towards the use of artificial intelligence prevails, is clear.

Lastly, no less important is the issue of skills, which is very much felt in both the technical and legal fields. “The skills and requirements of AI Engineers and Data Scientists, with all their variations on the theme, are now defined and it is clear – Casale assures – that there is a strong increase in demand. However, I am of the opinion that there cannot be training that is only engineering-based or focused solely on regulatory aspects: instead, we need paths that combine the two dimensions and that lead to preparing the new Chief AI Officers, that is, high-level responsibility figures called to govern all activities related to the use of artificial intelligence”. Nonetheless, according to the expert, we need a layer of specialists who study the impacts and implications of technology on society, anticipating possible risks.

Old universities are not enough

In conclusion, it remains to be asked whether Italy is up to speed in meeting this need, and in this regard Casale’s answer is once again precise and transparent: “Old-style universities can no longer be functional for this purpose, because we need to understand how to teach a new mindset and not a technology as such, given the speed of its evolution. We are in the midst of a phase of great change and evolution towards dynamic study paths, and in other European countries this transformation has already begun”. In the meantime, artificial intelligence continues on its course and suggests that it will not be a passing fad. On the contrary. “AI – concludes the OPIT professor – exists and is here to remain a protagonist: to teach it and bring it to the ground in an adequate way, correct governance of the technology is necessary and the AI Act is an important first step in this direction. But there is still a lot of work to do”.

Read the full article below (in Italian):
- Il Sole 24 Ore
Check out OPIT degrees

BSc in Digital Business MSc in Digital Business and Innovation MSc in Responsible Artificial Intelligence MSc in Applied Data Science and AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI
- Career aligned
- Fully Online
- EU-accredited institution
Read the article
The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology

April 10, 2025
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.
Check out OPIT degrees

MSc in Digital Business and Innovation MSc in Responsible Artificial Intelligence MSc in Applied Data Science and AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI MSc in Data Science & AI
- Career aligned
- Fully Online
- EU-accredited institution
Read the article
Categories
Authors

Francesco Profumo

John Loewen

Professor @OPIT, PhD in Computer Science, Former Professor @Chulalongkorn University and @Vancouver Island University. Location: Italy Teaches Computer Architectures (BSc).

Karim Bouzoubaa

Professor @ OPIT, Professor of Data Science & AI @ Mohammed V University in Rabat, PhD @ Laval University. Location: Canada, Morocco. Teaches Programming courses (BSc).

Khaled Elbehiery

Professor @ OPIT, Senior Director & Network Engineer @ Charter Communications, Professor @ DeVry University and @ Park University. Location: USA. Teaches: Computer Networks (BSc).

Lokesh Vij

Professor @ OPIT, Software Engineer @ Symantec, Faculty @ Seneca College. Location: Canada. Teaches Cloud Computing courses (BSc), Big Data and Cloud Computing Infrastructure (MSc).

Go to all authors

Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management

OPIT - Open Institute of Technology

Apr 14, 2025 • 6 min read

Source:

Il Sole 24 Ore, published on April 14th, 2025

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.

“CEOs who already work with AI – explains the OPIT expert – see the investment in compliance as necessary, the latter as an opportunity: and the latter, on paper, have the advantage of being able to define a strategy also in terms of ethics and civil responsibility”

SMEs in greater difficulty

There is a further theme that accompanies the diffusion of AI in the European corporate fabric and that is the difference in “approach” between large companies and SMEs. The top executives of enterprise-class organizations, as Casale confirms, have budgets and resources to adequately address and manage the challenge of compliance. Small and medium-sized businesses, on the other hand, are in a waiting position to understand how to carry forward the development process and the risk that many may slow down in the innovation path, and that a conservative attitude towards the use of artificial intelligence prevails, is clear.

Lastly, no less important is the issue of skills, which is very much felt in both the technical and legal fields. “The skills and requirements of AI Engineers and Data Scientists, with all their variations on the theme, are now defined and it is clear – Casale assures – that there is a strong increase in demand. However, I am of the opinion that there cannot be training that is only engineering-based or focused solely on regulatory aspects: instead, we need paths that combine the two dimensions and that lead to preparing the new Chief AI Officers, that is, high-level responsibility figures called to govern all activities related to the use of artificial intelligence”. Nonetheless, according to the expert, we need a layer of specialists who study the impacts and implications of technology on society, anticipating possible risks.

Old universities are not enough

In conclusion, it remains to be asked whether Italy is up to speed in meeting this need, and in this regard Casale’s answer is once again precise and transparent: “Old-style universities can no longer be functional for this purpose, because we need to understand how to teach a new mindset and not a technology as such, given the speed of its evolution. We are in the midst of a phase of great change and evolution towards dynamic study paths, and in other European countries this transformation has already begun”. In the meantime, artificial intelligence continues on its course and suggests that it will not be a passing fad. On the contrary. “AI – concludes the OPIT professor – exists and is here to remain a protagonist: to teach it and bring it to the ground in an adequate way, correct governance of the technology is necessary and the AI Act is an important first step in this direction. But there is still a lot of work to do”.

Read the full article below (in Italian):

Il Sole 24 Ore

Read the article

The Lucky Future: How AI Aims to Change Everything

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.