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    BCA vs. BSc Computer Science Key Differences: Which Is Better?

    OPIT - Open Institute of Technology
    OPIT - Open Institute of Technology
    Jun 22, 2023 • 9 min read
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    Do you tend to get all technical about how computers work? Or, do you prefer to put your thinking cap on and dig deep into theory and research?

    

    These questions matter because they can help you choose between BCA and BSc Computer Science. One focuses on practical knowledge, while the other explores the nitty-gritty behind technical concepts.
    

    

    In this BCA vs. BSc computer science: which is better guide, we’ll provide detailed information about the two courses and help point you in the right direction.
    

    

    BCA: Bachelor of Computer Applications
    

    

    To resolve the BSc computer science vs. BCA confusion, we need to discuss both in detail. Let’s start with BCA: Bachelor of Computer Applications.
    

    

    Overview of the BCA Program
    

    

    Duration
    

    

    BCA is typically a three-year professional undergraduate course focused on learning computer languages and applications. Since the focus is on applications, the BCA program is a software-oriented course (which is great for those who don’t enjoy learning too much theory).
    

    

    Course Structure
    

    

    The course structure depends on your chosen university. In most cases, you’ll have five core subjects per semester. Additionally, you’ll choose electives to learn more about specific computer-related topics.
    

    

    Eligibility Criteria
    

    

    Every university is free to set its own criteria for enrolling in a BCA program. Still, there are some tendencies you should know about. Students who studied arts, commerce, or science are most welcome to apply. Some universities may also have specific entrance exams that test subject-related knowledge.
    

    

    Key Subjects Covered in BCA
    

    

    As mentioned, the course structure in BCA programs varies (depending on the university). Regardless, every student needs to cover core subjects that will equip them to conquer the industry.
    

    

    Programming Languages
    

    

    Programming languages are like human languages. But rather than allowing communication among people, these languages let us “talk to” computers. This subject covers the basics of Java, HTML, C, C++, and others.
    

    

    Database Management
    

    

    Think of database management systems as computerized data-keeping solutions. Learning how to work with these systems is essential to ensure proper information storage and retrieval, and this is exactly what students learn on this course.
    

    

    Web Development
    

    

    Want to know how to create and maintain websites? This subject offers insight into behind-the-scenes work that goes into developing online stores, social networks, blogs, business websites, and others.
    

    

    Networking
    

    

    This subject explores the secret language in which computers, systems, and devices communicate with one another. All of which sheds light on how to connect them to share data.
    

    

    Career Prospects After BCA
    

    

    A degree in computer application opens doors to various career paths. Here are the job positions you can apply for after completing your studies:
    

      

    • Game Designer
      • 

    • System Specialist
      • 

    • Technical Support
      • 

    • Web Designer
      • 

    • Network Administrator
      • 

    • System Manager
      • 

    • Software Tester
      • 

    • Programmer
      • 

    

    

    In terms of continuing your education, these options are available:
    

      

    • Master in Computer Application
      • 

    • Master of Science in Information Technology
      • 

    • Master in Business Administration
      • 

    

    

    BSc Computer Science: Bachelor of Science in Computer Science
    

    

    If you want to know which is better: BCA or BSc Computer Science, you need to learn more about them. Since we’ve covered the BCA program, it’s time to discuss BSc Computer Science.
    

    

    Overview of the BSc Computer Science Program
    

    

    Duration
    

    

    Students can choose between two options: regular and fast-track. The former lasts three years, while the latter lasts two.
    

    

    Course Structure
    

    

    One of the first things students are interested in is the course structure. In most cases, you’ll have six terms – five terms of courses and one for the final project (dissertation).
    

    

    That being said, remember that every university sets its own course structure.
    

    

    Eligibility Criteria
    

    

    Universities set their eligibility criteria. Therefore, each institution has unique standards students have to meet to enroll. Some universities have strict requirements, the most common being a background in physics, chemistry, or mathematics.
    

    

    If you don’t meet these criteria, no worries. Online degree programs like the Open Institute of Technology (Opit) have easy-to-meet entry requirements, such as English proficiency (at least a B2 level) and high school education.
    

    

    Key Subjects Covered in BSc Computer Science
    

    

    The BSc Computer Science program features several core subjects.
    

    

    Programming Languages
    

    

    During this course, students learn how to “speak” programming languages. They’re introduced to fundamental concepts and common logical and/or syntactical problems they need to resolve.
    

    

    Data Structures and Algorithms
    

    

    If you want to learn how to organize data or solve a particular problem, you’ll find the answers to these questions and more in this course.
    

    

    Operating Systems
    

    

    Every OS is an entity of its own with unique anatomies, functions, and layers.
    

    

    Computer Networks
    

    

    A computer network is a cluster of interconnected dots that communicate with each other and transfer data. During this course, you’ll learn how this communication works.
    

    

    Career Prospects After BSc Computer Science
    

    

    What can you do after BSc Computer Science? The program allows you to explore a wide array of job positions:
    

      

    • Software Developer/Engineer
      • 

    • Web Developer
      • 

    • Data Scientist
      • 

    • Cyber Security Analyst
      • 

    • Database Architect
      • 

    • IT Business Analyst
      • 

    • App/Game Developer
      • 

    • Database Architect
      • 

    

    

    BSc Computer Science offers an excellent theoretical foundation. It’s no surprise, therefore, that many students decide to pursue higher education. Here are some of the available options:
    

      

    • Master of Science in Computer Science
      • 

    • Master in Computer Management
      • 

    • Bachelor of Technology in Computer Science
      • 

    

    


    Key Differences Between BCA and BCs Computer Science
    

    Want to know which is better: BSc Computer Science or BCA? To get the answer, you need to learn about the differences between the two degrees.
    

    

    Course Focus
    

    

    You wouldn’t be wrong if you said the two programs are similar. But there’s a significant difference between BCA and BSc Computer Science: course focus. BCA is all about application it centers on current technology, computing, and programming trends. The program is ideal for students who are more interested in practical knowledge.
    

    

    On the other hand, BSc Computer Science is perfect for those who like reading theory, doing research, and learning about different computer-related concepts.
    

    

    Curriculum
    

    

    The battle between theoretical and practical knowledge is (of course) reflected in the curriculum. The subjects BCA features develop practical, application-oriented skills, while BSc Computer Science prefers theory.
    

    

    Eligibility Criteria
    

    

    While trying not to sound like a broken record, let’s repeat it once again: it depends on the university. As a rule of thumb, BSc Computer Science has stricter criteria.
    

    

    Career Opportunities
    

    

    As far as career opportunities are concerned, both programs will set you up for success in the industry. Of course, each program opens doors to different fields. Students who complete the BCA program pursue jobs in IT or software development. Those who complete the BSc Computer Science program usually continue their education or work as researchers.
    

    

    Which Is Better: BCA or BSc Computer Science?
    

    

    Given that each program focuses on different aspects, it’s hard to say one stands out as “the best.” Every person is unique, and what suits you may not work for someone else. It all comes down to your future plans and ambitions. Going down the technical-heavy route is great for coding or anything else that calls for theoretical applications, but it won’t help much if you need experience in something practical, like game design. By the same token, you’ll need to draw from a well of technical knowledge when you’re working in data science or IT analysis. The choice comes down to balancing these three factors:
    

      

    • Personal interest and aptitude – Do you prefer theory or practice?
      • 

    • Career goals – What do you want to do after completing the program?
      • 

    • Future education plans – Do you want to continue your education after the program?
      • 

    

    

    Answer these questions to get a better idea of whether you should opt for BCA or BSc Computer Science. Remember, there’s no wrong answer. Picking a course isn’t like playing Russian roulette. It’s more like playing those little arcade machines that guarantee a prize with every turn – there’s no way to lose! Whatever you choose, you can build a fruitful career with computers. The path you decide to take solely depends on whether you prefer theory or practice.
    

    

    Pros and Cons of Each Course
    

    

    What are the strengths and weaknesses of each course? Find out here.
    

    

    BCA
    

    

    Pros:
    

      

    • Offers practical knowledge
      • 

    • Follow the latest industry trends
      • 

    • Less strict entry requirements
      • 

    • Job-oriented
      • 

    

    

    Cons:
    

      

    • Challenging course structure
      • 

    • Technology rapidly evolves, so you need to constantly update your skills to stay current
      • 

    

    

    BCs in Computer Science
    

    

    Pros:
    

      

    • Offers excellent theoretical knowledge
      • 

    • Great for those who want to continue their education
      • 

    • Ideal for researchers
      • 

    • Provides a strong foundation
      • 

    

    

    Cons:
    

      

    • Stricter eligibility criteria
      • 

    • Some consider it too theoretical
      • 

    

    

    Tips for Making the Right Choice
    

    

    These valuable tips can help you choose the right program:
    

      

    • Consider your expectations. Think about what you want to get from this course and where you want it to take you career-wise.
      • 

    • Study the curriculum. The curriculum is like a program’s CV. If you want to know whether it’s a good fit for you, you need to research it carefully.
      • 

    • Talk to someone who completed the program you’re interested in. A person who completed the course can offer valuable intel and help you determine whether you’re on the right track.
      • 

    • Consult an academic advisor. An advisor can analyze your preferences and put them into a concrete suggestion on which direction you should take.
      • 

    • Think about what you want to do next. Do you want to continue your education or find a job in the industry? The answer can help you make the right decision.
      • 

    

    


    Find Your Passion
    

    

    The biggest difference between BSc Computer Science and BCA lies in the curriculum. With the former, the subjects focus on obtaining theoretical knowledge to set the ground for further education or research. On the other hand, BCA focuses on practical skills and exploring current trends.
    

    

    Instead of wondering which is best: BCA or BSc Computer Science, think about your preferences and requirements. Explore your priorities, research both options, talk to professionals, and you’ll recognize the better fit.
    
                            
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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    	
        
        	
      
	
      
		
      
			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.
      

                                    
      
                                    
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							Francesco Profumo						
      
						
      
							
      
								Francesco Profumo							
      
							
      
															
      
						
      
					      
													
						
      
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								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).							
      
						
      
					      
													
						
      
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								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).							
      
						
      
					      
				
			
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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
                    
    
                                
    
                                    
    
                                        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.
    

                            
    
                            
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