As computing technology evolved and the concept of linking multiple computers together into a “network” that could share data came into being, it was clear that a model was needed to define and enable those connections. Enter the OSI model in computer network idea.
This model allows various devices and software to “communicate” with one another by creating a set of universal rules and functions. Let’s dig into what the model entails.
History of the OSI Model
In the late 1970s, the continued development of computerized technology saw many companies start to introduce their own systems. These systems stood alone from others. For example, a computer at Retailer A has no way to communicate with a computer at Retailer B, with neither computer being able to communicate with the various vendors and other organizations within the retail supply chain.
Clearly, some way of connecting these standalone systems was needed, leading to researchers from France, the U.S., and the U.K. splitting into two groups – The International Organization for Standardization and the International Telegraph and Telephone Consultive Committee.
In 1983, these two groups merged their work to create “The Basic Reference Model for Open Systems Interconnection (OSI).” This model established industry standards for communication between networked devices, though the path to OSI’s implementation wasn’t as clear as it could have been. The 1980s and 1990s saw the introduction of another model – The TCP IP model – which competed against the OSI model for supremacy. TCP/IP gained so much traction that it became the cornerstone model for the then-budding internet, leading to the OSI model in computer network applications falling out of favor in many sectors. Despite this, the OSI model is still a valuable reference point for students who want to learn more about networking and still have some practical uses in industry.
The OSI Reference Model
The OSI model works by splitting the concept of computers communicating with one another into seven computer network layers (defined below), each offering standardized rules for its specific function. During the rise of the OSI model, these layers worked in concert, allowing systems to communicate as long as they followed the rules.
Though the OSI model in computer network applications has fallen out of favor on a practical level, it still offers several benefits:
- The OSI model is perfect for teaching network architecture because it defines how computers communicate.
- OSI is a layered model, with separation between each layer, so one layer doesn’t affect the operation of any other.
- The OSI model offers flexibility because of the distinctions it makes between layers, with users being able to replace protocols in any layer without worrying about how they’ll impact the other layers.
The 7 Layers of the OSI Model
The OSI reference model in computer network teaching is a lot like an onion. It has several layers, each standing alone but each needing to be peeled back to get a result. But where peeling back the layers of an onion gets you a tasty ingredient or treat, peeling them back in the OSI model delivers a better understanding of networking and the protocols that lie behind it.
Each of these seven layers serves a different function.
Layer 1: Physical Layer
Sitting at the lowest level of the OSI model, the physical layer is all about the hows and wherefores of transmitting electrical signals from one device to another. Think of it as the protocols needed for the pins, cables, voltages, and every other component of a physical device if said device wants to communicate with another that uses the OSI model.
Layer 2: Data Link Layer
With the physical layer in place, the challenge shifts to transmitting data between devices. The data layer defines how node-to-node transfer occurs, allowing for the packaging of data into “frames” and the correction of errors that may happen in the physical layer.
The data layer has two “sub-layers” of its own:
- MAC – Media Access Controls that offer multiplexing and flow control to govern a device’s transmissions over an OSI network.
- LLC – Logical Link Controls that offer error control over the physical media (i.e., the devices) used to transmit data across a connection.
Layer 3: Network Layer
The network layer is like an intermediary between devices, as it accepts “frames” from the data layer and sends them on their way to their intended destination. Think of this layer as the postal service of the OSI model in computer network applications.
Layer 4: Transport Layer
If the network layer is a delivery person, the transport layer is the van that the delivery person uses to carry their parcels (i.e., data packets) between addresses. This layer regulates the sequencing, sizing, and transferring of data between hosts and systems. TCP (Transmission Control Protocol) is a good example of a transport layer in practical applications.
Layer 5: Session Layer
When one device wants to communicate with another, it sets up a “session” in which the communication takes place, similar to how your boss may schedule a meeting with you when they want to talk. The session layer regulates how the connections between machines are set up and managed, in addition to providing authorization controls to ensure no unwanted devices can interrupt or “listen in” on the session.
Layer 6: Presentation Layer
Presentation matters when sending data from one system to another. The presentation layer “pretties up” data by formatting and translating it into a syntax that the recipient’s application accepts. Encryption and decryption is a perfect example, as a data packet can be encrypted to be unreadable to anybody who intercepts it, only to be decrypted via the presentation layer so the intended recipient can see what the data packet contains.
Layer 7: Application Layer
The application layer is a front end through which the end user can interact with everything that’s going on behind the scenes in the network. It’s usually a piece of software that puts a user-friendly face on a network. For instance, the Google Chrome web browser is an application layer for the entire network of connections that make up the internet.
Interactions Between OSI Layers
Though each of the OSI layers in computer networks is independent (lending to the flexibility mentioned earlier), they must also interact with one another to make the network functional.
We see this most obviously in the data encapsulation and de-encapsulation that occurs in the model. Encapsulation is the process of adding information to a data packet as it travels, with de-encapsulation being the method used to remove that data added data so the end user can read what was originally sent. The previously mentioned encryption and decryption of data is a good example.
That process of encapsulation and de-encapsulation defines how the OSI model works. Each layer adds its own little “flavor” to the transmitted data packet, with each subsequent layer either adding something new or de-encapsulating something previously added so it can read the data. Each of these additions and subtractions is governed by the protocols set within each layer. A perfect network can only exist if these protocols properly govern data transmission, allowing for communication between each layer.
Real-World Applications of the OSI Model
There’s a reason why the OSI model in computer network study is often called a “reference” model – though important, it was quickly replaced with other models. As a result, you’ll rarely see the OSI model used as a way to connect devices, with TCP/IP being far more popular. Still, there are several practical applications for the OSI model.
Network Troubleshooting and Diagnostics
Given that some modern computer networks are unfathomably complex, picking out a single error that messes up the whole communication process can feel like navigating a minefield. Every wrong step causes something else to blow up, leading to more problems than you solve. The OSI model’s layered approach offers a way to break down the different aspects of a network to make it easier to identify problems.
Network Design and Implementation
Though the OSI model has few practical purposes, as a theoretical model it’s often seen as the basis for all networking concepts that came after. That makes it an ideal teaching tool for showcasing how networks are designed and implemented. Some even refer to the model when creating networks using other models, with the layered approach helping understand complex networks.
Enhancing Network Security
The concept of encapsulation and de-encapsulation comes to the fore again here (remember – encryption), as this concept shows us that it’s dangerous to allow a data packet to move through a network with no interactions. The OSI model shows how altering that packet as it goes on its journey makes it easier to protect data from unwanted eyes.
Limitations and Criticisms of the OSI Model
Despite its many uses as a teaching tool, the OSI model in computer network has limitations that are the reasons why it sees few practical applications:
- Complexity – As valuable as the layered approach may be to teaching networks, it’s often too complex to execute in practice.
- Overlap – The very flexibility that makes OSI great for people who want more control over their networks can come back to bite the model. The failure to implement proper controls and protocols can lead to overlap, as can the layered approach itself. Each of the computer network layers needs the others to work.
- The Existence of Alternatives – The OSI model walked so other models could run, establishing many fundamental networking concepts that other models executed better in practical terms. Again, the massive network known as the internet is a great example, as it uses the TCP/IP model to reduce complexity and more effectively transmit data.
Use the OSI Reference Model in Computer Network Applications
Though it has little practical application in today’s world, the OSI model in computer network terms is a theoretical model that played a crucial role in establishing many of the “rules” of networking still used today. Its importance is still recognized by the fact that many computing courses use the OSI model to teach the fundamentals of networks.
Think of learning about the OSI model as being similar to laying the foundations for a house. You’ll get to grips with the basic concepts of how networks work, allowing you to build up your knowledge by incorporating both current networking technology and future advancements to become a networking specialist.
-
From the same author
Read the articleAs computing technology evolved and the concept of linking multiple computers together into a “network” that could share data came into being, it was clear that a model was needed to define and enable those connections. Enter the OSI model in computer network idea.
This model allows various devices and software to “communicate” with one another by creating a set of universal rules and functions. Let’s dig into what the model entails.
History of the OSI Model
In the late 1970s, the continued development of computerized technology saw many companies start to introduce their own systems. These systems stood alone from others. For example, a computer at Retailer A has no way to communicate with a computer at Retailer B, with neither computer being able to communicate with the various vendors and other organizations within the retail supply chain.
Clearly, some way of connecting these standalone systems was needed, leading to researchers from France, the U.S., and the U.K. splitting into two groups – The International Organization for Standardization and the International Telegraph and Telephone Consultive Committee.
In 1983, these two groups merged their work to create “The Basic Reference Model for Open Systems Interconnection (OSI).” This model established industry standards for communication between networked devices, though the path to OSI’s implementation wasn’t as clear as it could have been. The 1980s and 1990s saw the introduction of another model – The TCP IP model – which competed against the OSI model for supremacy. TCP/IP gained so much traction that it became the cornerstone model for the then-budding internet, leading to the OSI model in computer network applications falling out of favor in many sectors. Despite this, the OSI model is still a valuable reference point for students who want to learn more about networking and still have some practical uses in industry.
The OSI Reference Model
The OSI model works by splitting the concept of computers communicating with one another into seven computer network layers (defined below), each offering standardized rules for its specific function. During the rise of the OSI model, these layers worked in concert, allowing systems to communicate as long as they followed the rules.
Though the OSI model in computer network applications has fallen out of favor on a practical level, it still offers several benefits:
- The OSI model is perfect for teaching network architecture because it defines how computers communicate.
- OSI is a layered model, with separation between each layer, so one layer doesn’t affect the operation of any other.
- The OSI model offers flexibility because of the distinctions it makes between layers, with users being able to replace protocols in any layer without worrying about how they’ll impact the other layers.
The 7 Layers of the OSI Model
The OSI reference model in computer network teaching is a lot like an onion. It has several layers, each standing alone but each needing to be peeled back to get a result. But where peeling back the layers of an onion gets you a tasty ingredient or treat, peeling them back in the OSI model delivers a better understanding of networking and the protocols that lie behind it.
Each of these seven layers serves a different function.
Layer 1: Physical Layer
Sitting at the lowest level of the OSI model, the physical layer is all about the hows and wherefores of transmitting electrical signals from one device to another. Think of it as the protocols needed for the pins, cables, voltages, and every other component of a physical device if said device wants to communicate with another that uses the OSI model.
Layer 2: Data Link Layer
With the physical layer in place, the challenge shifts to transmitting data between devices. The data layer defines how node-to-node transfer occurs, allowing for the packaging of data into “frames” and the correction of errors that may happen in the physical layer.
The data layer has two “sub-layers” of its own:
- MAC – Media Access Controls that offer multiplexing and flow control to govern a device’s transmissions over an OSI network.
- LLC – Logical Link Controls that offer error control over the physical media (i.e., the devices) used to transmit data across a connection.
Layer 3: Network Layer
The network layer is like an intermediary between devices, as it accepts “frames” from the data layer and sends them on their way to their intended destination. Think of this layer as the postal service of the OSI model in computer network applications.
Layer 4: Transport Layer
If the network layer is a delivery person, the transport layer is the van that the delivery person uses to carry their parcels (i.e., data packets) between addresses. This layer regulates the sequencing, sizing, and transferring of data between hosts and systems. TCP (Transmission Control Protocol) is a good example of a transport layer in practical applications.
Layer 5: Session Layer
When one device wants to communicate with another, it sets up a “session” in which the communication takes place, similar to how your boss may schedule a meeting with you when they want to talk. The session layer regulates how the connections between machines are set up and managed, in addition to providing authorization controls to ensure no unwanted devices can interrupt or “listen in” on the session.
Layer 6: Presentation Layer
Presentation matters when sending data from one system to another. The presentation layer “pretties up” data by formatting and translating it into a syntax that the recipient’s application accepts. Encryption and decryption is a perfect example, as a data packet can be encrypted to be unreadable to anybody who intercepts it, only to be decrypted via the presentation layer so the intended recipient can see what the data packet contains.
Layer 7: Application Layer
The application layer is a front end through which the end user can interact with everything that’s going on behind the scenes in the network. It’s usually a piece of software that puts a user-friendly face on a network. For instance, the Google Chrome web browser is an application layer for the entire network of connections that make up the internet.
Interactions Between OSI Layers
Though each of the OSI layers in computer networks is independent (lending to the flexibility mentioned earlier), they must also interact with one another to make the network functional.
We see this most obviously in the data encapsulation and de-encapsulation that occurs in the model. Encapsulation is the process of adding information to a data packet as it travels, with de-encapsulation being the method used to remove that data added data so the end user can read what was originally sent. The previously mentioned encryption and decryption of data is a good example.
That process of encapsulation and de-encapsulation defines how the OSI model works. Each layer adds its own little “flavor” to the transmitted data packet, with each subsequent layer either adding something new or de-encapsulating something previously added so it can read the data. Each of these additions and subtractions is governed by the protocols set within each layer. A perfect network can only exist if these protocols properly govern data transmission, allowing for communication between each layer.
Real-World Applications of the OSI Model
There’s a reason why the OSI model in computer network study is often called a “reference” model – though important, it was quickly replaced with other models. As a result, you’ll rarely see the OSI model used as a way to connect devices, with TCP/IP being far more popular. Still, there are several practical applications for the OSI model.
Network Troubleshooting and Diagnostics
Given that some modern computer networks are unfathomably complex, picking out a single error that messes up the whole communication process can feel like navigating a minefield. Every wrong step causes something else to blow up, leading to more problems than you solve. The OSI model’s layered approach offers a way to break down the different aspects of a network to make it easier to identify problems.
Network Design and Implementation
Though the OSI model has few practical purposes, as a theoretical model it’s often seen as the basis for all networking concepts that came after. That makes it an ideal teaching tool for showcasing how networks are designed and implemented. Some even refer to the model when creating networks using other models, with the layered approach helping understand complex networks.
Enhancing Network Security
The concept of encapsulation and de-encapsulation comes to the fore again here (remember – encryption), as this concept shows us that it’s dangerous to allow a data packet to move through a network with no interactions. The OSI model shows how altering that packet as it goes on its journey makes it easier to protect data from unwanted eyes.
Limitations and Criticisms of the OSI Model
Despite its many uses as a teaching tool, the OSI model in computer network has limitations that are the reasons why it sees few practical applications:
- Complexity – As valuable as the layered approach may be to teaching networks, it’s often too complex to execute in practice.
- Overlap – The very flexibility that makes OSI great for people who want more control over their networks can come back to bite the model. The failure to implement proper controls and protocols can lead to overlap, as can the layered approach itself. Each of the computer network layers needs the others to work.
- The Existence of Alternatives – The OSI model walked so other models could run, establishing many fundamental networking concepts that other models executed better in practical terms. Again, the massive network known as the internet is a great example, as it uses the TCP/IP model to reduce complexity and more effectively transmit data.
Use the OSI Reference Model in Computer Network Applications
Though it has little practical application in today’s world, the OSI model in computer network terms is a theoretical model that played a crucial role in establishing many of the “rules” of networking still used today. Its importance is still recognized by the fact that many computing courses use the OSI model to teach the fundamentals of networks.
Think of learning about the OSI model as being similar to laying the foundations for a house. You’ll get to grips with the basic concepts of how networks work, allowing you to build up your knowledge by incorporating both current networking technology and future advancements to become a networking specialist.
-
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Related posts
Source:
- Authority Magazine Medium, Published on September 15th, 2024.
Gaining hands-on experience through projects, internships, and collaborations is vital for understanding how to apply AI in various industries and domains. Use Kaggle or get a free cloud account and start experimenting. You will have projects to discuss at your next interviews.
By David Leichner, CMO at Cybellum
14 min read
Artificial Intelligence is now the leading edge of technology, driving unprecedented advancements across sectors. From healthcare to finance, education to environment, the AI industry is witnessing a skyrocketing demand for professionals. However, the path to creating a successful career in AI is multifaceted and constantly evolving. What does it take and what does one need in order to create a highly successful career in AI?
In this interview series, we are talking to successful AI professionals, AI founders, AI CEOs, educators in the field, AI researchers, HR managers in tech companies, and anyone who holds authority in the realm of Artificial Intelligence to inspire and guide those who are eager to embark on this exciting career path.
As part of this series, we had the pleasure of interviewing Zorina Alliata.
Zorina Alliata is an expert in AI, with over 20 years of experience in tech, and over 10 years in AI itself. As an educator, Zorina Alliata is passionate about learning, access to education and about creating the career you want. She implores us to learn more about ethics in AI, and not to fear AI, but to embrace it.
Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?
I was born in Romania, and grew up during communism, a very dark period in our history. I was a curious child and my parents, both teachers, encouraged me to learn new things all the time. Unfortunately, in communism, there was not a lot to do for a kid who wanted to learn: there was no TV, very few books and only ones that were approved by the state, and generally very few activities outside of school. Being an “intellectual” was a bad thing in the eyes of the government. They preferred people who did not read or think too much. I found great relief in writing, I have been writing stories and poetry since I was about ten years old. I was published with my first poem at 16 years old, in a national literature magazine.
Can you share with us the ‘backstory’ of how you decided to pursue a career path in AI?
I studied Computer Science at university. By then, communism had fallen and we actually had received brand new PCs at the university, and learned several programming languages. The last year, the fifth year of study, was equivalent with a Master’s degree, and was spent preparing your thesis. That’s when I learned about neural networks. We had a tiny, 5-node neural network and we spent the year trying to teach it to recognize the written letter “A”.
We had only a few computers in the lab running Windows NT, so really the technology was not there for such an ambitious project. We did not achieve a lot that year, but I was fascinated by the idea of a neural network learning by itself, without any programming. When I graduated, there were no jobs in AI at all, it was what we now call “the AI winter”. So I went and worked as a programmer, then moved into management and project management. You can imagine my happiness when, about ten years ago, AI came back to life in the form of Machine Learning (ML).
I immediately went and took every class possible to learn about it. I spent that Christmas holiday coding. The paradigm had changed from when I was in college, when we were trying to replicate the entire human brain. ML was focused on solving one specific problem, optimizing one specific output, and that’s where businesses everywhere saw a benefit. I then joined a Data Science team at GEICO, moved to Capital One as a Delivery lead for their Center for Machine Learning, and then went to Amazon in their AI/ML team.
Can you tell our readers about the most interesting projects you are working on now?
While I can’t discuss work projects due to confidentiality, there are some things I can mention! In the last five years, I worked with global companies to establish an AI strategy and to introduce AI and ML in their organizations. Some of my customers included large farming associations, who used ML to predict when to plant their crops for optimal results; water management companies who used ML for predictive maintenance to maintain their underground pipes; construction companies that used AI for visual inspections of their buildings, and to identify any possible defects and hospitals who used Digital Twins technology to improve patient outcomes and health. It is amazing to see how much AI and ML are already part of our everyday lives, and to recognize some of it in the mundane around us.
None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story about that?
When you are young, there are so many people who step up and help you along the way. I have had great luck with several professors who have encouraged me in school, and an uncle who worked in computers who would take me to his office and let me play around with his machines. I now try to give back and mentor several young people, especially women who are trying to get into the field. I volunteer with AnitaB and Zonta, as well as taking on mentees where I work.
As with any career path, the AI industry comes with its own set of challenges. Could you elaborate on some of the significant challenges you faced in your AI career and how you managed to overcome them?
I think one major challenge in AI is the speed of change. I remember after spending my Christmas holiday learning and coding in R, when I joined the Data Science team at GEICO, I realized the world had moved on and everyone was now coding in Python. So, I had to learn Python very fast, in order to understand what was going on.
It’s the same with research — I try to work on one subject, and four new papers are published every week that move the goal posts. It is very challenging to keep up, but you just have to adapt to continuously learn and let go of what becomes obsolete.
Ok, let’s now move to the main part of our interview about AI. What are the 3 things that most excite you about the AI industry now? Why?
1. Creativity
Generative AI brought us the ability to create amazing images based on simple text descriptions. Entire videos are now possible, and soon, maybe entire movies. I have been working in AI for several years and I never thought creative jobs will be the first to be achieved by AI. I am amazed at the capacity of an algorithms to create images, and to observe the artificial creativity we now see for the first time.
2. Abstraction
I think with the success and immediate mainstream adoption of Generative AI, we saw the great appetite out there for automation and abstraction. No one wants to do boring work and summarizing documents; no one wants to read long websites, they just want the gist of it. If I drive a car, I don’t need to know how the engine works and every equation that the engineers used to build it — I just want my car to drive. The same level of abstraction is now expected in AI. There is a lot of opportunity here in creating these abstractions for the future.
3. Opportunity
I like that we are in the beginning of AI, so there is a lot of opportunity to jump in. Most people who are passionate about it can learn all about AI fully online, in places like Open Institute of Technology. Or they can get experience working on small projects, and then they can apply for jobs. It is great because it gives people access to good jobs and stability in the future.
What are the 3 things that concern you about the AI industry? Why? What should be done to address and alleviate those concerns?
1. Fairness
The large companies that build LLMs spend a lot of energy and money into making them fair. But it is not easy. Us, as humans, are often not fair ourselves. We even have problems agreeing what fairness even means. So, how can we teach the machines to be fair? I think the responsibility stays with us. We can’t simply say “AI did this bad thing.”
2. Regulation
There are some regulations popping up but most are not coordinated or discussed widely. There is controversy, such as regarding the new California bill SB1047, where scientists take different sides of the debate. We need to find better ways to regulate the use and creation of AI, working together as a society, not just in small groups of politicians.
3. Awareness
I wish everyone understood the basics of AI. There is denial, fear, hatred that is created by doomsday misinformation. I wish AI was taught from a young age, through appropriate means, so everyone gets the fundamental principles and understands how to use this great tool in their lives.
For a young person who would like to eventually make a career in AI, which skills and subjects do they need to learn?
I think maybe the right question is: what are you passionate about? Do that, and see how you can use AI to make your job better and more exciting! I think AI will work alongside people in most jobs, as it develops and matures.
But for those who are looking to work in AI, they can choose from a variety of roles as well. We have technical roles like data scientist or machine learning engineer, which require very specialized knowledge and degrees. They learn computing, software engineering, programming, data analysis, data engineering. There are also business roles, for people who understand the technology well but are not writing code. Instead, they define strategies, design solutions for companies, or write implementation plans for AI products and services. There is also a robust AI research domain, where lots of scientists are measuring and analyzing new technology developments.
With Generative AI, new roles appeared, such as Prompt Engineer. We can now talk with the machines in natural language, so speaking good English is all that’s required to find the right conversation.
With these many possible roles, I think if you work in AI, some basic subjects where you can start are:
- Analytics — understand data and how it is stored and governed, and how we get insights from it.
- Logic — understand both mathematical and philosophical logic.
- Fundamentals of AI — read about the history and philosophy of AI, models of thinking, and major developments.
As you know, there are not that many women in the AI industry. Can you advise what is needed to engage more women in the AI industry?
Engaging more women in the AI industry is absolutely crucial if you want to build any successful AI products. In my twenty years career, I have seen changes in the tech industry to address this gender discrepancy. For example, we do well in school with STEM programs and similar efforts that encourage girls to code. We also created mentorship organizations such as AnitaB.org who allow women to connect and collaborate. One place where I think we still lag behind is in the workplace. When I came to the US in my twenties, I was the only woman programmer in my team. Now, I see more women at work, but still not enough. We say we create inclusive work environments, but we still have a long way to go to encourage more women to stay in tech. Policies that support flexible hours and parental leave are necessary, and other adjustments that account for the different lives that women have compared to men. Bias training and challenging stereotypes are also necessary, and many times these are implemented shoddily in organizations.
Ethical AI development is a pressing concern in the industry. How do you approach the ethical implications of AI, and what steps do you believe individuals and organizations should take to ensure responsible and fair AI practices?
Machine Learning and AI learn from data. Unfortunately, lot of our historical data shows strong biases. For example, for a long time, it was perfectly legal to only offer mortgages to white people. The data shows that. If we use this data to train a new model to enhance the mortgage application process, then the model will learn that mortgages should only be offered to white men. That is a bias that we had in the past, but we do not want to learn and amplify in the future.
Generative AI has introduced a new set of fresh risks, the most famous being the “hallucinations.” Generative AI will create new content based on chunks of text it finds in its training data, without an understanding of what the content means. It could repeat something it learned from one Reddit user ten years ago, that could be factually incorrect. Is that piece of information unbiased and fair?
There are many ways we fight for fairness in AI. There are technical tools we can use to offer interpretability and explainability of the actual models used. There are business constraints we can create, such as guardrails or knowledge bases, where we can lead the AI towards ethical answers. We also advise anyone who build AI to use a diverse team of builders. If you look around the table and you see the same type of guys who went to the schools, you will get exactly one original idea from them. If you add different genders, different ages, different tenures, different backgrounds, then you will get ten innovative ideas for your product, and you will have addressed biases you’ve never even thought of.
Read the full article below:
Source:
- Il Sole 24 Ore, Published on July 29th, 2024 (original article in Italian).
By Filomena Greco
It is called OPIT and it was born from an idea by Riccardo Ocleppo, entrepreneur, director and founder of OPIT and second generation in the company; and Francesco Profumo, former president of Compagnia di Sanpaolo, former Minister of Education and Rector of the Polytechnic University of Turin. “We wanted to create an academic institution focused on Artificial Intelligence and the new formative paths linked to this new technological frontier”.
How did this initiative come about?
“The general idea was to propose to the market a new model of university education that was, on the one hand, very up-to-date on the topic of skills, curricula and professors, with six degree paths (two three-year Bachelor degrees and four Master degrees) in areas such as Computer Science, AI, Cybersecurity, Digital Business; on the other hand, a very practical approach linked to the needs of the industrial world. We want to bridge a gap between formal education, which is often too theoretical, and the world of work and entrepreneurship.”
What characterizes your didactic proposal?
“Ours is a proprietary teaching model, with 45 teachers recruited from all over the world who have a solid academic background but also experience in many companies. We want to offer a study path that has a strong business orientation, with the aim of immediately bringing added value to the companies. Our teaching is entirely in English, and this is a project created to be international, with the teachers coming from 20 different nationalities. Italian students last year were 35% but overall the reality is very varied.”
Can you tell us your numbers?
“We received tens of thousands of applications for the first year but we tried to be selective. We started the first two classes with a hundred students from 38 countries around the world, Italy, Europe, USA, Canada, Middle East and Africa. We aim to reach 300 students this year. We have accredited OPIT in Malta, which is the only European country other than Ireland to be native English speaking – for us, this is a very important trait. We want to offer high quality teaching but with affordable costs, around 4,500 euros per year, with completely online teaching.”
Read the full article below (in Italian):
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