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    Data Science and Machine Learning Course: The Top Picks for Success

    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Jul 2, 2023 • 9 min read
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    As the world becomes increasingly data-driven and computing power advances beyond all expectations, two intriguing fields are at the center of attention – data science and machine learning.

    These fields are often grouped together as they have numerous contact points. First and foremost, both areas are all about data. But data science primarily focuses on extracting valuable insights from data, while machine learning aims to use the data to make predictions and decisions without explicit programming.

    These revolutionary technologies have seeped into (and revolutionized) virtually every existing sector: healthcare, business, finance, retail, IT, and the list can go on and on. So, no wonder companies are constantly seeking highly skilled professionals in these fields.

    If you’d like to build a career in these highly lucrative fields, improving your skills and knowledge is an absolute must.

    Luckily, nowadays, you don’t have to leave your home to achieve this level of expertise. Just pick a data science and machine learning course from this list (or do all three!), and you’ll be well on your way toward a bright future in these burgeoning fields.

    Top Data Science and Machine Learning Courses

    Whether you’ve just started to dip your toes in these fields or want to take your skills to the next level, you’ll find the perfect data science and machine learning course on our list.

    Data Science: Machine Learning by Harvard University

    The first data science and machine learning course on the list is classified as an introductory course. In other words, it’s ideal for beginners.

    The course first tackles the basics of machine learning, gradually digging deeper into popular algorithms, principal component analysis, and building recommendation systems. You’ll finish this course with fundamental data science and machine learning skills.

    The class lasts eight weeks and is entirely self-paced. The recommended time commitment is two to four hours per week, but every learner can tailor it to their needs. Another great option is auditing this data science and machine learning course for free. But you’ll have to pay a fee for a verified certificate and unlimited access to the materials.

    The $109 (a little over €101) cost is a small price for the theoretical and hands-on knowledge you’ll gain after this course.

    Unfortunately, not everyone will be given a chance to gain this knowledge. Due to some licensing issues, this course isn’t available for learners in Iran, Cuba, and Ukraine (the Crimea region). Another potential downside is that the class is a section of a nine-part data science program. And most of those nine parts precede this course. Although not obligatory, the program creators recommend taking these courses in order, which can be too much time and financial commitment for some learners.

    Machine Learning, Data Science, and Deep Learning With Python by Udemy

    Do you feel like you need more hands-on experience in machine learning and data science? Have you had to pass on promising job applications because you don’t meet the listing requirements? If you’ve answered positively to both questions, here’s some good news. This data science and machine learning course was custom-made for you.

    And no, these aren’t empty promises à-la infomercials you see on TV. This course covers all the most common requirements big-tech companies seek in data scientist job listings. Implementing machine learning at a massive scale, making predictions, visualizing data, classifying images and data — you name it, this course will teach it.

    Naturally, this is the single most considerable advantage of this course. It will give you the necessary skills to successfully navigate the lucrative career paths of data science and machine learning. But this only goes if you already have some experience with coding and scripting. Unfortunately, this course isn’t beginner-friendly (in terms of Python, not data science), so not everyone can take it immediately.

    Those who do will enjoy over 100 on-demand video lectures, followed by several additional resources. For a $119.99 (approximately €112) fee, you’ll also receive a shareable certificate and full lifetime access to the course.

    Data Science and Machine Learning: Making Data-Driven Decisions by MIT

    The last item on our list is a big-league data science and machine learning course. The word “course” might even be an understatement, as it’s closer to an entire learning program encompassing a broad set of educational activities.

    For starters, the course involves a mentorship program with leading industry experts as guides. And this isn’t a one-and-done type of program either; you’ll have weekly online meetings in small groups. The course itself is taught by MIT faculty and industry experts with years of experience under their belts.

    In 12 weeks, you’ll significantly grow your data science and machine learning portfolio, examine numerous case studies, acquire valuable knowledge in applying multiple skills (clustering, regression, classification, etc.), and receive a professional certificate to prove it.

    The only notable downside of this extensive data science and machine learning course is its price. With a $2,300 (around €2,142) fee, this course is far from accessible for an average learner. However, those who can afford it should consider it a long-term investment, as this course can be a one-way ticket to a successful career in data science and machine learning.

    Factors to Consider When Choosing a Course

    Online learning platforms have democratized the world of learning. Now, you can learn whatever you want from wherever you are and at whatever pace works best for you.

    But keep in mind that this goes for instructors as well. Anyone can now teach anything. To avoid wasting your time and money on a subpar course, consider these factors when choosing the perfect data science and machine learning course.

    Course Content and Curriculum

    First things first: check what the course is about. The course’s description will usually contain a “Curriculum” section where you can clearly see whether it delves into topics that interest you. If you have experience in the field, you’ll immediately know if the course spends too much time on skills you’ve already mastered.

    Course Duration and Flexibility

    Most online courses are self-paced. Sure, this kind of flexibility is mostly a good thing. But if you lack discipline, it can also be detrimental. So, before starting the course, check its duration and make sure you can fully commit to it from beginning to end.

    Instructor Quality and Expertise

    A data science and machine learning course will undoubtedly contain portions some learners might perceive as challenging or tedious. If there’s one thing that can help them breeze through these parts, it’s an engaging and personable instructor.

    So, before committing to a course, research the instructor(s) a little bit. Check their bios and play a video to ensure their teaching style works for you.

    Cost and Return on Investment

    A data science and machine learning course can cost upwards of thousands of dollars. To ensure you’ll get your money’s worth, check how well it will prepare you for finding a job in the field.

    Does it come with a highly requested certification? Does it cover the skills your future employers seek? These are just some of the questions you should consider before investing in a data science and machine learning course.

    Hands-On Experience and Real-World Projects

    This is another factor that can make investing in a data science and machine learning course well worth it. As valuable as theory is, hands-on experience is king in these fields. Working on real-world projects and building a rock-solid portfolio opens up new doors for you, even before leaving the course.

    Networking Opportunities and Job Placement Assistance

    A strong support system and direct contact with instructors and mentors should be a course must-have for anyone interested in a data science and machine learning career. Meet notable figures in the industry and stand out among the course goers, and incredible job opportunities should follow suit.

    Tips for Success in Data Science and Machine Learning Courses

    You can get straight to learning after selecting the perfect data science and machine learning course. Sure, closely following the curriculum will help you gain the necessary knowledge and skills in these fields. But following these tips while studying will do wonders for your future career prospects:

    • Develop a strong foundation in mathematics and programming: This will allow you to take more advanced courses and breeze through the rest.
    • Stay up-to-date with industry trends and advancements: Despite being updated frequently, the courses can barely keep up with the innovations in the field.
    • Engage in online forums and communities for support and networking: Sharing ideas and receiving feedback can help you overcome learning challenges.
    • Practice your skills through personal projects and competitions: Challenge yourself to go beyond the scope of the course.
    • Seek internships and job opportunities to gain real-world experience: Besides looking great on your resume, these will help you get the hang out of things much quicker.

    Learn, Practice, Excel

    A carefully selected data science and machine learning course is an excellent opportunity to enter these booming fields with a bang. Developing data science and machine learning skills further will help you stay there and enjoy a successful and rewarding career for years to come.

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    • From the same author

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

      Source:

      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):

      Read the article
      The Lucky Future: How AI Aims to Change Everything
      OPIT - Open Institute of Technology
      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.

      Read the article
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    • Authors
      Francesco Profumo
      Francesco Profumo
      Avatar
      John Loewen
      Professor @OPIT, PhD in Computer Science, Former Professor @Chulalongkorn University and @Vancouver Island University. Location: Italy Teaches Computer Architectures (BSc).
      Karim Bouzoubaa
      Karim Bouzoubaa
      Professor @ OPIT, Professor of Data Science & AI @ Mohammed V University in Rabat, PhD @ Laval University. Location: Canada, Morocco. Teaches Programming courses (BSc).
      Avatar
      Khaled Elbehiery
      Professor @ OPIT, Senior Director & Network Engineer @ Charter Communications, Professor @ DeVry University and @ Park University. Location: USA. Teaches: Computer Networks (BSc).
      Lokesh Vij
      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

    Related posts

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

    Source:

    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):

    Read the article
    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.

    Read the article

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