AI is already a massive industry – valued at $136.55 billion (approx. €124.82 billion) as of 2022 – and it’s only going to get bigger as we come to grips with what AI can do. As a student, you stand on the cusp of the AI tidal wave and you have an opportunity to ride that wave into a decades-long career.

But you need a starting point for that career – a BSc computer science with artificial intelligence. The three courses discussed in this article are the best for budding AI masters.

Factors to Consider When Choosing a BSc Computer Science With AI Program

Before choosing your BSc, you need to know what to look for in a good course:

• Institution Accreditation – Whoever provides the course should offer solid accreditation so that you know you can trust the institution and that potential future employers actually respect the qualification you have on your VC.
• An AI-Focused Curriculum – Not all computer science bachelor’s degrees are the same. The one you choose needs to offer a specific focus on AI or machine learning so you can build the foundations for later specialization.
• Faculty Expertise – A course led by instructors who don’t know much about AI is like the blind leading the blind. Every mentor, instructor, and lecturer needs to have provable knowledge and industry experience.
• Job Opportunities – Every chance you have to “get your hands dirty” with AI is going to look great on your CV. Look for courses that create pathways into internships and job programs. Associations with organizations like IBM are a great place to start.
• Financial Aid – It isn’t cheap to study a BSc artificial intelligence and machine learning. Degrees cost thousands of Euros per year (the average in Europe is about €3,000, though prices can go higher) so the availability of financial aid is a huge help.

Top BSc Computer Science With AI Programs

Studying from the best is how you become a leader in the AI field. The combination of expert tuition and the name recognition that comes from having a degree from one of the following institutions stands you in good stead for success in the AI industry. Here are the top three organizations (with degrees available to overseas students) in the world.

Course 1 – BSc Artificial Intelligence – The University of Edinburgh

Named as one of the top 10 AI courses in the world by Forbes, The University of Edinburgh’s offering has everything you need from a great BSc computer science with artificial intelligence. It’s a four-year full-time course that focuses on the applications of AI in the modern world, with students developing the skills to build intelligent systems capable of making human-like decisions. The course is taught by the university’s School of Informatics, led by National Robotarium academic co-lead Professor Helen Hastie.

The course starts simple, with the first year dedicated to learning the language of computers before the second year introduces students to software development and data science concepts. By the third year, you’ll be digging deep into machine learning and robotics. That year also comes with opportunities to study abroad.

As for career prospects, The University of Edinburgh has a Careers Service department that can put you in line for internships at multi-national businesses. Add to that the university’s huge alumni network (essentially a huge group of professionals willing to help students with their careers) and this is a course that offers a great route into the industry.

Course 2 – Artificial Intelligence Program – Carnegie Mellon University

Ranked as the top university in the world for AI courses by Edurank, Carnegie Mellon University is a tough nut to crack if you want to study its world-renowned program. You’ll face a ton of competition, as evidenced by the university’s 17% acceptance rate, and the program is directed by Reid Simmons. For those who don’t recognize the name, he’s been a frontrunner in leveraging AI for NASA and was the creator of the “Robotceptionist.”

As for the course, it blends foundational mathematical, statistical, and computer science concepts with a wide variety of AI modules. It’s robotics-focused (that’s no surprise given the director), though you’ll also learn how AI applies on a perceptive level. The use of AI in speech processing, search engines, and even photography are just some examples of the concepts this course teaches.

Carnegie Mellon takes an interesting approach to internships, as it offers both career and academic internships. Career internships are what you’d expect – placements with major companies where you get to put your skills into practice. An academic internship is different because you’ll be based in the university and will work alongside its faculty on research projects.

Course 3 – BSc in Artificial Intelligence and Decision Making – Massachusetts Institute of Technology (MIT)

It should come as no surprise that MIT makes it onto the list given the school’s engineering and tech focus. Like Carnegie Mellon’s AI course, it’s tough to get into the MIT course (only a 7% acceptance rate) but simply having MIT on your CV makes you attractive to employers.

The course takes in multiple foundational topics, such as programming in Python and introductions to machine learning algorithms, before moving into a robotics focus in its application modules. But it’s the opportunities for research that make this one stand out. MIT has departments dedicated to the use of AI in society, healthcare, communications, and speech processing, making this course ideal for those who wish to pursue a specialization.

Networking opportunities abound, too. MIT’s AI faculty has 92 members, all with different types of expertise, who can guide you on your path and potentially introduce you to career opportunities. Combine that with the fact you’ll be working with some of the world’s best and brightest and you have a course that’s built for your success in the AI industry.

Emerging BSc Computer Science With AI programs

Given that AI is clearly going to be enormously important to developing industry in the coming years, it’s no surprise that many institutions are creating their own BSc computer science with artificial intelligence courses. In the UK alone, the likes of Queen’s University Belfast and Cardiff University are quickly catching up to The University of Edinburgh, especially in the robotics field.

In North America, the University of Toronto is making waves with a course that’s ranked the best in Canada and fifth in North America by EduRank. Interestingly, that course is a little easier to get into than many comparable North American courses, given its 43% acceptance rate.

Back in the UK, the University of Oxford is also doing well with AI, though its current courses tend to be shorter and specialized in areas like utilizing AI in business. We’re also seeing Asian universities make great progress with their courses, as both Tsinghua University and Nanyang Technological University are establishing themselves as leaders in the space.

Importance of Hands-On Experience and Internships

As important as foundational and theoretical knowledge is, it’s when you get hands-on that you start to understand how much of an impact AI will have on business and society at large. Good universities recognize this and offer hands-on experience (either via research or internship programs) that offer three core benefits:

• Gain Practical Skills – Becoming a walking encyclopedia for the theory of AI is great if you intend on becoming a teacher. But for everybody else, working with hands-on practical experiments and examples is required to develop the practical skills that employers seek.
• Networking – A strong faculty (ideally with industry as well as academic connections) will take you a long way in your BSc computer science with artificial intelligence. The more people you encounter, the more connections you build and the better your prospects are when you complete your course.
• Enhanced Job Prospects – Getting hands-on with real-world examples, and having evidence of that work, shows employers that you know how to use the knowledge you have knocking around your head. The more practical a course gets, the better it enhances your job prospects.

Scholarships and Financial Aid Opportunities

Due to BSc artificial intelligence and machine learning courses being so expensive (remember – an average of €3,000 per year), financial aid is going to be important for many students. In the UK, that aid often comes in the form of student loans, which you don’t have to start repaying until you hit a certain earnings threshold.

When we take things Europe-wide, more scholarship and financial aid programs become available. The Erasmus program offers funding for master’s students (assuming they meet the criteria) and there are several scholarship portals, such as EURAXESS and Scholarshipportal designed to help with financial aid.

If this is something you’re interested in, the following tips may help you obtain funding:

• Excel academically in pre-university studies to demonstrate your potential
• Speak to the finance teams at your university of choice to see what’s currently available
• Apply for as many scholarship and aid programs as you can to boost your chances of success

Try the Top BSc Artificial Intelligence and Machine Learning Programs

The three BSc computer science with artificial intelligence programs discussed in this article are among the best in the world for many reasons. They combine intelligence course focuses with faculty who not only know how to teach AI but have practical experience that helps you learn and can serve useful networking purposes.

The latter will prove increasingly important as the AI industry grows and becomes more competitive. But as with any form of education, your own needs are paramount. Choose the best course for your needs (whether it’s one from this list or an online BSc) and focus your efforts on becoming the best you can be.

It’s hard to find a person who uses the internet but doesn’t enjoy at least one cloud computing service. “Cloud computing” sounds complex, but it’s actually all around you. The term encompasses every tool, app, and service that’s delivered via the internet.

The two popular examples are Dropbox and Google Drive. These cloud-based storage spaces allow you to keep your files at arm’s reach and access them in a few clicks. Zoom is also a cloud-based service – it makes communication a breeze.

Cloud computing can be classified into four types: public, private, hybrid, and community. These four types belong to one of the three cloud computing service models: infrastructure as a service, platform as a service, or software as a service.

It’s time to don a detective cap and explore the mystery hidden behind cloud computing.

Cloud Computing Deployment Models

• Public cloud
• Private cloud
• Hybrid cloud
• Community cloud

Public Cloud

The “public” in public cloud means anyone who wants to use that service can get it. Public clouds are easy to access and usually have a “general” purpose many can benefit from.

It’s important to mention that with public clouds, the infrastructure is owned by the service provider, not by consumers. This means you can’t “purchase” a public cloud service forever.

Advantages of Public Cloud

• Cost-effectiveness – Some public clouds are free. Those that aren’t free typically have a reasonable fee.
• Scalability – Public clouds are accommodating to changing demands. Depending on the cloud’s nature, you can easily add or remove users, upgrade plans, or manipulate storage space.
• Flexibility – Public clouds are suitable for many things, from storing a few files temporarily to backing up an entire company’s records.

Disadvantages of Public Cloud

• Security concerns – Since anyone can access public clouds, you can’t be sure your data is 100% safe.
• Limited customization – While public clouds offer many options, they don’t really allow you to tailor the environment to match your preferences. They’re made to suit broad masses, not particular individuals.

Examples of Public Cloud Providers

• Amazon Web Services (AWS)
• Microsoft Azure
• Google Cloud Platform

Private Cloud

If you’re looking for the complete opposite of public clouds, you’ve found it. Private clouds aren’t designed to fit general criteria. Instead, they’re made to please a single user. Some of the perks private clouds offer are exclusive access, exceptional security, and unmatched customization.

A private cloud is like a single-tenant building. The tenant owns the building and has complete control to do whatever they want. They can tear down walls, drill holes to hang pictures, paint the rooms, install tiles, and get new furniture. When needs change, the tenant can redecorate, no questions asked.

Advantages of Private Cloud

• Enhanced security – The company’s IT department oversees private clouds. They’re usually protected by powerful firewalls and protocols that minimize the risk of information breaches.
• Greater control and customization – Since private clouds are one-on-one environments, you can match them to your needs.
• Improved performance – Private clouds can have functions that suit your organization to the letter, resulting in high performance.

Disadvantages of Private Cloud

• Higher costs – The exclusive access and customization come at a cost (literally).
• Limited scalability – You can scale private clouds, but only up to a certain point.

Examples of Private Cloud Providers

• VMware
• IBM Cloud
• Dell EMC

Hybrid Cloud

Public and private clouds have a few important drawbacks that may be deal-breakers for some people. You may want to use public clouds but aren’t ready to compromise on security. On the other hand, you may want the perks that come with private clouds but aren’t happy with limited scalability.

That’s when hybrid clouds come into play because they let you get the best of both worlds. They’re the perfect mix of public and private clouds and offer their best features. You can get the affordability of public clouds and the security of private clouds.

Advantages of Hybrid Cloud

• Flexibility and scalability – Hybrid clouds are personalized environments, meaning you can adjust them to meet your specific needs. If your needs change, hybrid clouds can keep up.
• Security and compliance – You don’t have to worry about data breaches or intruders with hybrid clouds. They use state-of-the-art measures to guarantee safety, privacy, and security.
• Cost optimization – Hybrid clouds are much more affordable than private ones. You’ll need to pay extra only if you want special features.

Disadvantages of Hybrid Cloud

• Complexity in management – Since they combine public and private clouds, hybrid clouds are complex systems that aren’t really easy to manage.
• Potential security risks – Hybrid clouds aren’t as secure as private clouds.

Examples of Hybrid Cloud Providers

• Microsoft Azure Stack
• AWS Outputs
• Google Anthos

Community Cloud

Community clouds are shared by more than one organization. The organizations themselves manage them or a third party. In terms of security, community clouds fall somewhere between private and public clouds. The same goes for their price.

Advantages of Community Cloud

• Shared resources and costs – A community cloud is like a common virtual space for several organizations. By sharing the space, the organizations also share costs and resources.
• Enhanced security and compliance – Community clouds are more secure than public clouds.
• Collaboration opportunities – Cloud sharing often encourages organizations to collaborate on different projects.

Disadvantages of Community Cloud

• Limited scalability – Community clouds are scalable, but only to a certain point.
• Dependency on other organizations – As much as sharing a cloud with another organization(s) sounds exciting (and cost-effective), it means you’ll depend on them.

Examples of Community Cloud Providers

• Salesforce Community Cloud
• Rackspace
• IBM Cloud for Government

Cloud Computing Service Models

There are three types of cloud computing service models:

• Infrastructure as a Service (IaaS)
• Platform as a Service (PaaS)
• Software as a Service (SaaS)

IaaS

IaaS is a type of pay-as-you-go, third-party service. In this case, the provider gives you an opportunity to enjoy infrastructure services for your networking equipment, databases, devices, etc. You can get services like virtualization and storage and build a strong IT platform with exceptional security.

IaaS models give you the flexibility to create an environment that suits your organization. Plus, they allow remote access and cost-effectiveness.

What about their drawbacks? The biggest issue could be security, especially in multi-tenant ecosystems. You can mitigate security risks by opting for a reputable provider like AWS or Microsoft (Azure).

PaaS

Here, the provider doesn’t deliver the entire infrastructure to a user. Instead, it hosts software and hardware on its own infrastructure, delivering only the “finished product.” The user enjoys this through a platform, which can exist in the form of a solution stack, integrated solution, or an internet-dependent service.

Programmers and developers are among the biggest fans of PaaS. This service model enables them to work on apps and programs without dealing with maintaining complex infrastructures. An important advantage of PaaS is accessibility – users can enjoy it through their web browser.

As far as disadvantages go, the lack of customizability may be a big one. Since you don’t have control over the infrastructure, you can’t really make adjustments to suit your needs. Another potential drawback is that PaaS depends on the provider, so if they’re experiencing problems, you could too.

Some examples of PaaS are Heroku and AWS Elastic Beanstalk.

SaaS

Last but not least is SaaS. Thanks to this computing service model, users can access different software apps using the internet. SaaS is the holy grail for small businesses that don’t have the budget, bandwidth, workforce, or will to install and maintain software. Instead, they leave this work to the providers and enjoy only the “fun” parts.

The biggest advantage of SaaS is that it allows easy access to apps from anywhere. You’ll have no trouble using SaaS as long as you have internet. Plus, it saves a lot of money and time.

Nothing’s perfect, and SaaS is no exception. If you want to use SaaS without interruptions, you need to have a stable internet connection. Plus, with SaaS, you don’t have as much control over the software’s performance and security. Therefore, you need to decide on your priorities. SaaS may not be the best option if you want a highly-customizable environment with exceptional security.

The most popular examples of SaaS are Dropbox, Google Apps, and Salesforce.

Sit on the Right Cloud

Are high security and appealing customization features your priority? Or are you on the hunt for a cost-effective solution? Your answers can indicate which cloud deployment model you should choose.

It’s important to understand that models are not divided into “good” and “bad.” Each has unique characteristics that can be beneficial and detrimental at the same time. If you don’t know how to employ a particular model, you won’t be able to reap its benefits.

Gone are the days when you had to store boxes of documents in your office. Salvation came in the form of cloud computing in the 2000s. Since then, it’s made a world of difference for businesses across all industries, increasing productivity, organization, and decluttering the workspace. More importantly, it allows businesses to reduce various expenses by 30%-50%.

Cloud computing has countless benefits, but that doesn’t mean the technology is flawless. On the contrary, you should be aware of several disadvantages of cloud computing that can cause many problems with your implementation. Weighing up the pros and cons is essential – and we’ll do precisely that in this article.

Read on for the advantages and disadvantages of cloud computing.

Advantages of Cloud Computing

The cloud computing market is worth more than $540 billion. The main reason being that over 90% of all companies use some form of this technology. Here’s why they rely on cloud-based platforms.

Cost Efficiency

One of the greatest benefits of cloud computing is that it’s cost-efficient and allows you to reduce business expenses on three fronts.

Reduced Hardware and Software Expenses

You don’t need physical hardware to store your documents if you have a cloud computing platform. Likewise, the technology eliminates the need to run multiple software platforms because you can keep all your files in one place.

Lower Energy Consumption

In-house storage solutions can be convenient, but they consume a lot of electricity. Conversely, cloud computing systems help companies increase energy efficiency by over 90%.

Minimal Maintenance Costs

Maintaining such platforms is straightforward and affordable as cloud computing doesn’t involve heavy-duty software and hardware.

Scalability and Flexibility

Another reason cloud computing is popular is its scalability and flexibility. Here’s what underpins these advantages of cloud computing.

Easy Resource Allocation and Management

You don’t need to allocate your storage resources to numerous solutions if you have a unified cloud computing system. Managing your storage requirements becomes much easier with all your money going into one channel.

Pay-As-You-Go Pricing Model

Cloud-based platforms are available on a pay-as-you-go model. This reduces the risk of overpaying for your service because you’re only charged for the amount of data used.

Rapid Deployment of Applications and Services

Deploying cloud computing applications and services is simple. There’s no need for intense employee training, which further reduces your costs.

Accessibility and Mobility

Cloud computing is a highly accessible and mobile technology that can elevate your efficiency in a number of ways.

Access to Data and Applications From Anywhere

All it takes to access a cloud-based platform is a stable internet connection. As a result, you can retrieve key files virtually anywhere.

Improved Collaboration and Productivity

The ability to access data and applications from anywhere boosts collaboration and productivity. Your team gets a unified platform where they can share data with others much faster.

Support for Remote Work and Distributed Teams

Setting up a remote workspace is seamless with a cloud-computing solution. Employees no longer have to come to the office to perform repetitive tasks since they can do them from their computers.

Enhanced Security

If you want to address the most common security concerns in your organization, cloud computing is an excellent option.

Centralized Data Storage and Protection

By storing your information in a centralized location, you decrease the risk of data theft. In essence, you funnel all your resources into one platform rather than spread them out across multiple channels.

Regular Security Updates and Patches

Cloud computing providers offer regular updates to protect your information. Systems with the latest security patches are less prone to cyber attacks.

Advanced Encryption and Authentication Methods

You can also benefit from cloud computing tools due to their next-level encryption and authentication solutions. Most platforms feature AES 256-bit encryption, which is the most advanced and practically impregnable method. Furthermore, two-factor authentication lowers the chances of unauthorized access.

Disaster Recovery and Business Continuity

Business continuity and disaster recovery are two of the most pressing business challenges. Cloud computing solutions can help address these problems.

Automated Data Backup and Recovery

Many cloud storage systems are designed to automatically backup and recover your data. Hence, you don’t need to worry about losing your information in the event of a power outage.

Reduced Downtime and Data Loss

Since cloud computing helps prevent data loss, this technology also saves you less downtime. You don’t have to retrieve information manually because the platform does the work for you.

Simplified Disaster Recovery Planning

Although cloud computing tools are reliable, they’re not immune to failure caused by power loss, natural disasters, and other factors. Fortunately, these platforms have robust disaster recovery plans to get your system up and running in no time.

Disadvantages of Cloud Computing

Since the technology is so effective, you might be asking yourself: “Are there any disadvantages of cloud computing?” There are, and you need to understand these downsides to determine the best way to implement the technology. Here are the main drawbacks of cloud computing.

Data Privacy and Security Concerns

Like any other online technology, cloud computing can put users at risk of data privacy and security concerns.

Potential for Data Breaches and Unauthorized Access

While cloud apps have exceptional security practices, cyber criminals can bypass them with state-of-the-art technology and innovative hacking methods. Consequently, they may gain access to your information and steal your credentials.

Compliance With Data Protection Regulations

Your cloud computing tool may comply with many data protection regulations, but this doesn’t mean your information is 100% secure. Some standards only require apps to use robust password practices and fail to consider other attack methods, such as phishing.

Trusting Third-Party Providers With Sensitive Information

Online services require you to share your information to enable all features. Cloud computing is no different in this respect. You need to provide a third-party vendor with your data, which can be risky.

Limited Control and Customization

Cloud computing is a flexible and scalable technology. At the same time, it limits your control and customization options, which is why you might not be 100% happy with your platform.

Dependence on Cloud Service Providers

You decide what files you wish to share with your cloud-based solution. However, that’s pretty much it when it comes to the control you have over the platform. You depend on the vendor for every other aspect, including updates and patches.

Restrictions on Software and Hardware Customization

There aren’t many options to choose from when selecting a cloud storage plan. The price of your plan mostly depends on how much data you wish to share. Other than that, you get little-to-no hardware and software customization features.

Potential for Vendor Lock-In

Once you create an account with one cloud computing provider, you might not be happy with their services. As a result, you want to switch to a different platform. Many people think this is a simple transition, but that’s not always the case. Even though you can cancel your plan, migrating your data from one tool to the next can be difficult.

Network Dependency and Connectivity Issues

You might be relieved once you set up an account on a cloud-based platform: “I no longer need to clutter my office with masses of documents because I can now use an internet tool.” That said, using an online app also means you depend on network quality.

Reliance on Stable Internet Connection

A stable internet connection is essential for cloud computing. Internet problems can reduce or prevent you from accessing your files altogether.

Performance Issues Due to Network Latency

If your cloud network has high latency, sharing files can be challenging. In turn, latency reduces productivity and collaboration.

Vulnerability to Distributed Denial-of-Service (DDoS) Attacks

Cloud platforms are susceptible to so-called DDoS attacks. A cyber criminal can target your tool and keep you from accessing the service.

Downtime and Service Reliability

Not every cloud computing system performs the same in terms of reducing downtime and maximizing reliability.

Risk of Outages and Service Disruptions

While cloud-based solutions have exceptional recovery plans and backup methods, you’ll still face some downtime in case of outages. Even the shortest service disruption can cause major issues when working on certain projects.

Shared Resources and Potential for Performance Degradation

Cloud systems are convenient because they allow you to store your data in one place. Nonetheless, one of the key disadvantages of cloud computing is managing those shared resources. Accessing information can become difficult if you don’t stay on top of it.

Likewise, performance can drop at any point of your plan. App incompatibility and other issues can compromise data architecture and further compromise management.

Dependence on Provider’s Service Level Agreements (SLAs)

You’ll probably need to enter into an SLA when partnering with a cloud computing provider. These contracts can be rigid, meaning they may fail to recognize and adapt to evolving business needs.

Make an Informed Decision

Cloud computing has tremendous benefits, like improved data storage, collaboration, and cost reduction. The main drawbacks include hardware and software restrictions, connectivity issues, and potential downtime.

Therefore, you should understand the advantages and disadvantages of cloud computing before implementing a platform. Also, consider your business needs when partnering with a cloud provider to help prevent compatibility issues.

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.

Computer architecture forms the backbone of computer science. So, it comes as no surprise it’s one of the most researched fields of computing.

But what is computer architecture, and why does it matter?

Basically, computer architecture dictates every aspect of a computer’s functioning, from how it stores data to what it displays on the interface. Not to mention how the hardware and software components connect and interact.

With this in mind, it isn’t difficult to realize the importance of this structure. In fact, computer scientists did this even before they knew what to call it. The first documented computer architecture can be traced back to 1936, 23 years before the term “architecture” was first used when describing a computer. Lyle R. Johnson, an IBM senior staff member, had this honor, realizing that the word organization just doesn’t cut it.

Now that you know why you should care about it, let’s define computer architecture in more detail and outline everything you need to know about it.

Basic Components of Computer Architecture

Computer architecture is an elaborate system where each component has its place and function. You’re probably familiar with some of the basic computer architecture components, such as the CPU and memory. But do you know how those components work together? If not, we’ve got you covered.

Central Processing Unit (CPU)

The central processing unit (CPU) is at the core of any computer architecture. This hardware component only needs instructions written as binary bits to control all its surrounding components.

Think of the CPU as the conductor in an orchestra. Without the conductor, the choir is still there, but they’re waiting for instructions.

Without a functioning CPU, the other components are still there, but there’s no computing.

That’s why the CPU’s components are so important.

Arithmetic Logic Unit (ALU)

Since the binary bits used as instructions by the CPU are numbers, the unit needs an arithmetic component to manipulate them.

That’s where the arithmetic logic unit, or ALU, comes into play.

The ALU is the one that receives the binary bits. Then, it performs an operation on one or more of them. The most common operations include addition, subtraction, AND, OR, and NOT.

Control Unit (CU)

As the name suggests, the control unit (CU) controls all the components of basic computer architecture. It transfers data to and from the ALU, thus dictating how each component behaves.

Registers

Registers are the storage units used by the CPU to hold the current data the ALU is manipulating. Each CPU has a limited number of these registers. For this reason, they can only store a limited amount of data temporarily.

Memory

Storing data is the main purpose of the memory of a computer system. The data in question can be instructions issued by the CPU or larger amounts of permanent data. Either way, a computer’s memory is never empty.

Traditionally, this component can be broken into primary and secondary storage.

Primary Memory

Primary memory occupies a central position in a computer system. It’s the only memory unit that can communicate with the CPU directly. It stores only programs and data currently in use.

There are two types of primary memory:

• RAM (Random Access Memory). In computer architecture, this is equivalent to short-term memory. RAM helps start the computer and only stores data as long as the machine is on and data is being used.
• ROM (Read Only Memory). ROM stores the data used to operate the system. Due to the importance of this data, the ROM stores information even when you turn off the computer.

Secondary Memory

With secondary memory, or auxiliary memory, there’s room for larger amounts of data (which is also permanent). However, this also means that this memory is significantly slower than its primary counterpart.

When it comes to secondary memory, there’s no shortage of choices. There are magnetic discs (hard disk drives (HDDs) and solid-state drives (SSDs)) that provide fast access to stored data. And let’s not forget about optical discs (CD-ROMs and DVDs) that offer portable data storage.

Input/Output (I/O) Devices

The input/output devices allow humans to communicate with a computer. They do so by delivering or receiving data as necessary.

You’re more than likely familiar with the most widely used input devices – the keyboard and the mouse. When it comes to output devices, it’s pretty much the same. The monitor and printer are at the forefront.

Buses

When the CPU wants to communicate with other internal components, it relies on buses.

Data buses are physical signal lines that carry data. Most computer systems use three of these lines:

• Data bus – Transmitting data from the CPU to memory and I/O devices and vice versa
• Address bus – Carrying the address that points to the location the CPU wants to access
• Control bus – Transferring control from one component to the other

Types of Computer Architecture

There’s more than one type of computer architecture. These types mostly share the same base components. However, the setup of these components is what makes them differ.

Von Neumann Architecture

The Von Neumann architecture was proposed by one of the originators of computer architecture as a concept, John Von Neumann. Most modern computers follow this computer architecture.

The Von Neumann architecture has several distinguishing characteristics:

• All instructions are carried out sequentially.
• It doesn’t differentiate between data and instruction. They’re stored in the same memory unit.
• The CPU performs one operation at a time.

Since data and instructions are located in the same place, fetching them is simple and efficient. These two adjectives can describe working with the Von Neumann architecture in general, making it such a popular choice.

Still, there are some disadvantages to keep in mind. For starters, the CPU is often idle since it can only access one bus at a time. If an error causes a mix-up between data and instructions, you can lose important data. Also, defective programs sometimes fail to release memory, causing your computer to crash.

Harvard Architecture

Harvard architecture was named after the famed university. Or, to be more precise, after an IBM computer called “Harvard Mark I” located at the university.

The main difference between this computer architecture and the Von Neumann model is that the Harvard architecture separates the data from the instructions. Accordingly, it allocates separate data, addresses, and control buses for the separate memories.

The biggest advantage of this setup is that the buses can fetch data concurrently, minimizing idle time. The separate buses also reduce the chance of data corruption.

However, this setup also requires a more complex architecture that can be challenging to develop and implement.

Modified Harvard Architecture

Today, only specialty computers use the pure form of Harvard architecture. As for other machines, a modified Harvard architecture does the trick. These modifications aim to soften the rigid separation between data and instructions.

RISC and CISC Architectures

When it comes to processor architecture, there are two primary approaches.

The CISC (Complex Instruction Set Computer) processors have a single processing unit and are pretty straightforward. They tackle one task at a time. As a result, they use less memory. However, they also need more time to complete an instruction.

Over time, the speed of these processors became a problem. This led to a processor redesign, resulting in the RISC architecture.

The new and improved RISC (Reduced Instruction Set Computer) processors feature larger registers and keep frequently used variables within the processor. Thanks to these handy functionalities, they can operate much more quickly.

Instruction Set Architecture (ISA)

Instruction set architecture (ISA) defines the instructions that the processor can read and act upon. This means ISA decides which software can be installed on a particular processor and how efficiently it can perform tasks.

There are three types of instruction set architecture. These types differ based on the placement of instructions, and their names are pretty self-explanatory. For stack-based ISA, the instructions are placed in the stack, a memory unit within the address register. The same principle applies for accumulator-based ISA (a type of register in the CPU) and register-based ISA (multiple registers within the system).

The register-based ISA is most commonly used in modern machines. You’ve probably heard of some of the most popular examples. For CISC architecture, there are x86 and MC68000. As for RISC, SPARC, MIPS, and ARM stand out.

Pipelining and Parallelism in Computer Architecture

In computer architecture, pipelining and parallelism are methods used to speed up processing.

Pipelining refers to overlapping multiple instructions and processing them simultaneously. This couldn’t be possible without a pipeline-like structure. Imagine a factory assembly line, and you’ll understand how pipelining works instantly.

This method significantly increases the number of processed instructions and comes in two types:

• Instruction pipelines – Used for fixed-point multiplication, floating-point operations, and similar calculations
• Arithmetic pipelines – Used for reading consecutive instructions from memory

Parallelism entails using multiple processors or cores to process data simultaneously. Thanks to this collaborative approach, large amounts of data can be processed quickly.

Computer architecture employs two types of parallelism:

• Data parallelism – Executing the same task with multiple cores and different sets of data
• Task parallelism – Performing different tasks with multiple cores and the same or different data

Multicore processors are crucial for increasing the efficiency of parallelism as a method.

Memory Hierarchy and Cache

In computer system architecture, memory hierarchy is essential for minimizing the time it takes to access the memory units. It refers to separating memory units based on their response times.

The most common memory hierarchy goes as follows:

• Level 1: Processor registers
• Level 2: Cache memory
• Level 3: Primary memory
• Level 4: Secondary memory

The cache memory is a small and fast memory located close to a processor core. The CPU uses it to reduce the time and energy needed to access data from the primary memory.

Cache memory can be further broken into levels.

• L1 cache (the primary cache) – The fastest cache unit in the system
• L2 cache (the secondary cache) – The slower but more spacious option than Level 1
• L3 cache (a specialized cache) – The largest and the slowest cache in the system used to improve the performance of the first two levels

When it comes to determining where the data will be stored in the cache memory, three mapping techniques are employed:

• Direct mapping – Each memory block is mapped to one pre-determined cache location
• Associative mapping – Each memory block is mapped to a single location, but it can be any location
• Set associative mapping – Each memory block is mapped to a subset of locations

The performance of cache memory directly impacts the overall performance of a computing system. The following cache replacement policies are used to better process big data applications:

• FIFO (first in, first out) ­– The memory block first to enter the primary memory gets replaced first
• LRU (least recently used) – The least recently used page is the first to be discarded
• LFU (least frequently used) – The least frequently used element gets eliminated first

Input/Output (I/O) Systems

The input/output or I/O systems are designed to receive and send data to a computer. Without these processing systems, the computer wouldn’t be able to communicate with people and other systems and devices.

There are several types of I/O systems:

• Programmed I/O – The CPU directly issues a command to the I/O module and waits for it to be executed
• Interrupt-Driven I/O – The CPU moves on to other tasks after issuing a command to the I/O system
• Direct Memory Access (DMA) – The data is transferred between the memory and I/O devices without passing through the CPU

There are three standard I/O interfaces used for physically connecting hardware devices to a computer:

• Peripheral Component Interconnect (PCI)
• Small Computer System Interface (SATA)
• Universal Serial Bus (USB)

Power Consumption and Performance in Computer Architecture

Power consumption has become one of the most important considerations when designing modern computer architecture. Failing to consider this aspect leads to power dissipation. This, in turn, results in higher operating costs and a shorter lifespan for the machine.

For this reason, the following techniques for reducing power consumption are of utmost importance:

• Dynamic Voltage and Frequency Scaling (DVFS) – Scaling down the voltage based on the required performance
• Clock gating – Shutting off the clock signal when the circuit isn’t in use
• Power gating – Shutting off the power to circuit blocks when they’re not in use

Besides power consumption, performance is another crucial consideration in computer architecture. The performance is measured as follows:

• Instructions per second (IPS) – Measuring efficiency at any clock frequency
• Floating-point operations per second (FLOPS) – Measuring the numerical computing performance
• Benchmarks – Measuring how long the computer takes to complete a series of test programs

Emerging Trends in Computer Architecture

Computer architecture is continuously evolving to meet modern computing needs. Keep your eye out on these fascinating trends:

• Quantum computing (relying on the laws of quantum mechanics to tackle complex computing problems)
• Neuromorphic computing (modeling the computer architecture components on the human brain)
• Optical computing (using photons instead of electrons in digital computation for higher performance)
• 3D chip stacking (using 3D instead of 2D chips as they’re faster, take up less space, and require less power)

A One-Way Ticket to Computing Excellence

As you can tell, computer architecture directly affects your computer’s speed and performance. This launches it to the top of priorities when building this machine.

High-performance computers might’ve been nice-to-haves at some point. But in today’s digital age, they’ve undoubtedly become a need rather than a want.

In trying to keep up with this ever-changing landscape, computer architecture is continuously evolving. The end goal is to develop an ideal system in terms of speed, memory, and interconnection of components.

And judging by the current dominant trends in this field, that ideal system is right around the corner!

As artificial intelligence and machine learning are becoming present in almost every aspect of life, it’s essential to understand how they work and their common applications. Although machine learning has been around for a while, many still portray it as an enemy. Machine learning can be your friend, but only if you learn to “tame” it.

Regression stands out as one of the most popular machine-learning techniques. It serves as a bridge that connects the past to the present and future. It does so by picking up on different “events” from the past and breaking them apart to analyze them. Based on this analysis, regression can make conclusions about the future and help many plan the next move.

The weather forecast is a basic example. With the regression technique, it’s possible to travel back in time to view average temperatures, humidity, and other variables relevant to the results. Then, you “return” to present and tailor predictions about the weather in the future.

There are different types of regression, and each has unique applications, advantages, and drawbacks. This article will analyze these types.

Linear Regression

Linear regression in machine learning is one of the most common techniques. This simple algorithm got its name because of what it does. It digs deep into the relationship between independent and dependent variables. Based on the findings, linear regression makes predictions about the future.

There are two distinguishable types of linear regression:

• Simple linear regression – There’s only one input variable.
• Multiple linear regression – There are several input variables.

Linear regression has proven useful in various spheres. Its most popular applications are:

• Predicting salaries
• Analyzing trends
• Forecasting traffic ETAs
• Predicting real estate prices

Polynomial Regression

At its core, polynomial regression functions just like linear regression, with one crucial difference – the former works with non-linear datasets.

When there’s a non-linear relationship between variables, you can’t do much with linear regression. In such cases, you send polynomial regression to the rescue. You do this by adding polynomial features to linear regression. Then, you analyze these features using a linear model to get relevant results.

Here’s a real-life example in action. Polynomial regression can analyze the spread rate of infectious diseases, including COVID-19.

Ridge Regression

Ridge regression is a type of linear regression. What’s the difference between the two? You use ridge regression when there’s high colinearity between independent variables. In such cases, you have to add bias to ensure precise long-term results.

This type of regression is also called L2 regularization because it makes the model less complex. As such, ridge regression is suitable for solving problems with more parameters than samples. Due to its characteristics, this regression has an honorary spot in medicine. It’s used to analyze patients’ clinical measures and the presence of specific antigens. Based on the results, the regression establishes trends.

LASSO Regression

No, LASSO regression doesn’t have anything to do with cowboys and catching cattle (although that would be interesting). LASSO is actually an acronym for Least Absolute Shrinkage and Selection Operator.

Like ridge regression, this one also belongs to regularization techniques. What does it regulate? It reduces a model’s complexity by eliminating parameters that aren’t relevant, thus concentrating the selection and guaranteeing better results.

Many choose ridge regression when analyzing a model with numerous true coefficients. When there are only a few of them, use LASSO. Therefore, their applications are similar; the real difference lies in the number of available coefficients.

Elastic Net Regression

Ridge regression is good for analyzing problems involving more parameters than samples. However, it’s not perfect; this regression type doesn’t promise to eliminate irrelevant coefficients from the equation, thus affecting the results’ reliability.

On the other hand, LASSO regression eliminates irrelevant parameters, but it sometimes focuses on far too few samples for high-dimensional data.

As you can see, both regressions are flawed in a way. Elastic net regression is the combination of the best characteristics of these regression techniques. The first phase is finding ridge coefficients, while the second phase involves a LASSO-like shrinkage of these coefficients to get the best results.

Support Vector Regression

Support vector machine (SVM) belongs to supervised learning algorithms and has two important uses:

• Regression
• Classification problems

Let’s try to draw a mental picture of how SVM works. Suppose you have two classes of items (let’s call them red circles and green triangles). Red circles are on the left, while green triangles are on the right. You can separate these two classes by drawing a line between them.

Things get a bit more complicated if you have red circles in the middle and green triangles wrapped around them. In that case, you can’t draw a line to separate the classes. But you can add new dimensions to the mix and create a circle (rectangle, square, or a different shape encompassing just the red circles).

This is what SVM does. It creates a hyperplane and analyzes classes depending on where they belong.

There are a few parameters you need to understand to grasp the reach of SVM fully:

• Kernel – When you can’t find a hyperplane in a dimension, you move to a higher dimension, which is often challenging to navigate. A kernel is like a navigator that helps you find the hyperplane without plummeting computational costs.
• Hyperplane – This is what separates two classes in SVM.
• Decision boundary – Think of this as a line that helps you “decide” the placement of positive and negative examples.

Support vector regression takes a similar approach. It also creates a hyperplane to analyze classes but doesn’t classify them depending on where they belong. Instead, it tries to find a hyperplane that contains a maximum number of data points. At the same time, support vector regression tries to lower the risk of prediction errors.

SVM has various applications. It can be used in finance, bioinformatics, engineering, HR, healthcare, image processing, and other branches.

Decision Tree Regression

This type of supervised learning algorithm can solve both regression and classification issues and work with categorical and numerical datasets.

As its name indicates, decision tree regression deconstructs problems by creating a tree-like structure. In this tree, every node is a test for an attribute, every branch is the result of a test, and every leaf is the final result (decision).

The starting point of (the root) of every tree regression is the parent node. This node splits into two child nodes (data subsets), which are then further divided, thus becoming “parents” to their “children,” and so on.

You can compare a decision tree to a regular tree. If you take care of it and prune the unnecessary branches (those with irrelevant features), you’ll grow a healthy tree (a tree with concise and relevant results).

Due to its versatility and digestibility, decision tree regression can be used in various fields, from finance and healthcare to marketing and education. It offers a unique approach to decision-making by breaking down complex datasets into easy-to-grasp categories.

Random Forest Regression

Random forest regression is essentially decision tree regression but on a much bigger scale. In this case, you have multiple decision trees, each predicting a certain output. Random forest regression analyzes the outputs of every decision tree to come up with the final result.

Keep in mind that the decision trees used in random forest regression are completely independent; there’s no interaction between them until their outputs are analyzed.

Random forest regression is an ensemble learning technique, meaning it combines the results (predictions) of several machine learning algorithms to create one final prediction.

Like decision tree regression, this one can be used in numerous industries.

The Importance of Regression in Machine Learning Is Immeasurable

Regression in machine learning is like a high-tech detective. It travels back in time, identifies valuable clues, and analyzes them thoroughly. Then, it uses the results to predict outcomes with high accuracy and precision. As such, regression found its way to all niches.

You can use it in sales to analyze the customers’ behavior and anticipate their future interests. You can also apply it in finance, whether to discover trends in prices or analyze the stock market. Regression is also used in education, the tech industry, weather forecasting, and many other spheres.

Every regression technique can be valuable, but only if you know how to use it to your advantage. Think of your scenario (variables you want to analyze) and find the best actor (regression technique) who can breathe new life into it.

Thanks to many technological marvels of our era, we’ve moved from writing important documents using pen and paper to storing them digitally.

Database systems emerged as the amount and complexity of information we need to keep have increased significantly in the last decades. They represent virtual warehouses for storing documents. Database management systems (DBMS) and relational database management systems (RDBMS) were born out of a burning need to easily control, organize, and edit databases.

Both DBMS and RDBMS represent programs for managing databases. But besides the one letter in the acronym, the two terms differ in several important aspects.

Here, we’ll outline the difference between DBMS and RDBMS, help you learn the ins and outs of both, and choose the most appropriate one.

Definition of DBMS (Database Management Systems)

While working for General Electric during the 1960s, Charles W. Bachman recognized the importance of proper document management and found that the solutions available at the time weren’t good enough. He did his research and came up with a database management system, a program that made storing, editing, and retrieving files a breeze. Unknowingly, Bachman revolutionized the industry and offered the world a convenient database management solution with amazing properties.

Key Features

Over the years, DBMSs have become powerful beasts that allow you to enhance performance and efficiency, save time, and handle huge amounts of data with ease.

One of the key features of DBMSs is that they store information as files in one of two forms: hierarchical or navigational. When managing data, users can use one of several manipulation functions the systems offer:

• Inserting data
• Deleting data
• Updating data

DBMSs are simple structures ideal for smaller companies that don’t deal with huge amounts of data. Only a single user can handle information, which can be a deal-breaker for larger entities.

Although fairly simple, DBMSs bring a lot to the table. They allow you to access, edit, and share data in the blink of an eye. Moreover, DBMSs let you unify your team and have accurate and reliable information on the record, ensuring nobody is left out. They also help you stay compliant with different security and privacy regulations and lower the risk of violations. Finally, having an efficient database management system leads to wiser decision-making that can ultimately save you a lot of time and money.

Examples of Popular DBMS Software

When DBMSs were just becoming a thing, you had software like Clipper and FoxPro. Today, the most popular (and simplest) examples of DBMS software are XML, Windows Registry, and file systems.

Definition of RDBMS (Relational Database Management Systems)

Not long after DBMS came into being, people recognized the need to keep data in the form of tables. They figured storing info in rows (tuples) and columns (attributes) allows a clearer view and easier navigation and information retrieval. This idea led to the birth of relational database management systems (RDBMS) in the 1970s.

Key Features

As mentioned, the only way RDBMSs store information is in the form of tables. Many love this feature because it makes organizing and classifying data according to different criteria a piece of cake. Many companies that use RDBMSs utilize multiple tables to store their data, and sometimes, the information in them can overlap. Fortunately, RDBMSs allow relating data from various tables to one another (hence the name). Thanks to this, you’ll have no trouble adding the necessary info in the right tables and moving it around as necessary.

Since you can relate different pieces of information from your tables to each other, you can achieve normalization. However, normalization isn’t the process of making your table normal. It’s a way of organizing information to remove redundancy and enhance data integrity.

In this technological day and age, we see data growing exponentially. If you’re working with RDBMSs, there’s no need to be concerned. The systems can handle vast amounts of information and offer exceptional speed and total control. Best of all, multiple users can access RDBMSs at a time and enhance your team’s efficiency, productivity, and collaboration.

Simply put, an RDBMS is a more advanced, powerful, and versatile version of DBMS. It offers speed, plenty of convenient features, and ease of use.

Examples of Popular RDBMS Software

As more and more companies recognize the advantages of using RDBMS, the availability of software grows by the day. Those who have tried several options agree that Oracle and MySQL are among the best choices.

Key Differences Between DBMS and RDBMS

Now that you’ve learned more about DBMS and RDBMS, you probably have an idea of the most significant differences between them. Here, we’ll summarize the key DBMS vs. RDBMS differences.

Data Storage and Organization

The first DBMS and RDBMS difference we’ll analyze is the way in which the systems store and organize information. With DBMS, data is stored and organized as files. This system uses either a hierarchical or navigational form to arrange the information. With DBMS, you can access only one element at a time, which can lead to slower processing.

On the other hand, RDBMS uses tables to store and display information. The data featured in several tables can be related to each other for ease of use and better organization. If you want to access multiple elements at the same time, you can; there are no constraints regarding this, as opposed to DBMS.

Data Integrity and Consistency

When discussing data integrity and consistency, it’s necessary to explain the concept of constraints in DBMS and RDBMS. Constraints are sets of “criteria” applied to data and/or operations within a system. When constraints are in place, only specific types of information can be displayed, and only specific operations can be completed. Sounds restricting, doesn’t it? The entire idea behind constraints is to enhance the integrity, consistency, and correctness of data displayed within a database.

DBMS lacks constraints. Hence, there’s no guarantee the data within this system is consistent or correct. Since there are no constraints, the risk of errors is higher.

RDBMS have constraints, resulting in the reliability and integrity of the data. Plus, normalization (removing redundancies) is another option that contributes to data integrity in RDBMS. Unfortunately, normalization can’t be achieved in DBMS.

Query Language and Data Manipulation

DBMS uses multiple query languages to manipulate data. However, none of these languages offer the speed and convenience present in RDBMS.

RDBMS manipulates data with structured query language (SQL). This language lets you retrieve, create, insert, or drop data within your relational database without difficulty.

Scalability and Performance

If you have a small company and/or don’t need to deal with vast amounts of data, a DBMS can be the way to go. But keep in mind that a DBMS can only be accessed by one person at a time. Plus, there’s no option to access more than one element at once.

With RDBMSs, scalability and performance are moved to a new level. An RDBMS can handle large amounts of information in a jiff. It also supports multiple users and allows you to access several elements simultaneously, thus enhancing your efficiency. This makes RDBMSs excellent for larger companies that work with large quantities of data.

Security and Access Control

Last but not least, an important difference between DBMS and RDBMS lies in security and access control. DBMSs have basic security features. Therefore, there’s a higher chance of breaches and data theft.

RDBMSs have various security measures in place that keep your data safe at all times.

Choosing the Right Database Management System

The first criterion that will help you make the right call is your project’s size and complexity. Small projects with relatively simple data are ideal for DBMSs. But if you’re tackling a lot of complex data, RDBMSs are the logical option.

Next, consider your budget and resources. Since they’re simpler, DBMSs are more affordable, in both aspects. RDBMSs are more complex, so naturally, the price of software is higher.

Finally, the factor that affects what option is the best for you is the desired functionality. What do you want from the program? Is it robust features or a simple environment with a few basic options? Your answer will guide you in the right direction.

Pros and Cons of DBMS and RDBMS

DBMS

Pros:

• Doesn’t involve complex query processing
• Cost-effective solution
• Ideal for processing small data
• Easy data handling via basic SQL queries

Cons:

• Doesn’t allow accessing multiple elements at once
• No way to relate data
• Doesn’t inherently support normalization
• Higher risk of security breaches
• Single-user system

RDBMS

Pros:

• Advanced, robust, and well-organized
• Ideal for large quantities of information
• Data from multiple tables can be related
• Multi-user system
• Supports normalization

Cons:

• More expensive
• Complex for some people

Examples of Use Cases

DBMS

DBMS is used in many sectors where more basic storing and management of data is required, be it sales and marketing, education, banking, or online shopping. For instance, universities use DBMS to store student-related data, such as registration details, fees paid, attendance, exam results, etc. Libraries use it to manage the records of thousands of books.

RDBMS

RDBMS is used in many industries today, especially those continuously requiring processing and storing large volumes of data. For instance, Airline companies utilize RDBMS for passenger and flight-related information and schedules. Human Resource departments use RDBMS to store and manage information related to employees and their payroll statistics. Manufacturers around the globe use RDBMS for operational data, inventory management and supply chain information.

Choose the Best Solution

An RDBM is a more advanced and powerful younger sibling of a DBMS. While the former offers more features, convenience, and the freedom to manipulate data as you please, it isn’t always the right solution. When deciding which road to take, prioritize your needs.

Tens of thousands of businesses go under every year. There are various culprits, but one of the most common causes is the inability of companies to streamline their customer experience. Many technologies have emerged to save the day, one of which is natural language processing (NLP).

But what is natural language processing? In simple terms, it’s the capacity of computers and other machines to understand and synthesize human language.

It may already seem like it would be important in the business world and trust us – it is. Enterprises rely on this sophisticated technology to facilitate different language-related tasks. Plus, it enables machines to read and listen to language as well as interact with it in many other ways.

The applications of NLP are practically endless. It can translate and summarize texts, retrieve information in a heartbeat, and help set up virtual assistants, among other things.

Looking to learn more about these applications? You’ve come to the right place. Besides use cases, this introduction to natural language processing will cover the history, components, techniques, and challenges of NLP.

History of Natural Language Processing

Before getting to the nuts and bolts of NLP basics, this introduction to NLP will first examine how the technology has grown over the years.

Early Developments in NLP

Some people revolutionized our lives in many ways. For example, Alan Turing is credited with several groundbreaking advancements in mathematics. But did you also know he paved the way for modern computer science, and by extension, natural language processing?

In the 1950s, Turing wanted to learn if humans could talk to machines via teleprompter without noticing a major difference. If they could, he concluded the machine would be capable of thinking and speaking.

Turin’s proposal has since been used to gauge this ability of computers and is known as the Turing Test.

Evolution of NLP Techniques and Algorithms

Since Alan Turing set the stage for natural language processing, many masterminds and organizations have built upon his research:

• 1958 – John McCarthy launched his Locator/Identifier Separation Protocol.
• 1964 – Joseph Wizenbaum came up with a natural language processing model called ELIZA.
• 1980s – IBM developed an array of NLP-based statistical solutions.
• 1990s – Recurrent neural networks took center stage.

The Role of Artificial Intelligence and Machine Learning in NLP

Discussing NLP without mentioning artificial intelligence and machine learning is like leaving a glass half empty. So, what’s the role of these technologies in NLP? It’s pivotal, to say the least.

AI and machine learning are the cornerstone of most NLP applications. They’re the engine of the NLP features that produce text, allowing NLP apps to turn raw data into usable information.

Key Components of Natural Language Processing

The phrase building blocks get thrown around a lot in the computer science realm. It’s key to understanding different parts of this sphere, including natural language processing. So, without further ado, let’s rifle through the building blocks of NLP.

Syntax Analysis

An NLP tool without syntax analysis would be lost in translation. It’s a paramount stage since this is where the program extracts meaning from the provided information. In simple terms, the system learns what makes sense and what doesn’t. For instance, it rejects contradictory pieces of data close together, such as “cold Sun.”

Semantic Analysis

Understanding someone who jumbles up words is difficult or impossible altogether. NLP tools recognize this problem, which is why they undergo in-depth semantic analysis. The network hits the books, learning proper grammatical structures and word orders. It also determines how to connect individual words and phrases.

Pragmatic Analysis

A machine that relies only on syntax and semantic analysis would be too machine-like, which goes against Turing’s principles. Salvation comes in the form of pragmatic analysis. The NLP software uses knowledge outside the source (e.g., textbook or paper) to determine what the speaker actually wants to say.

Discourse Analysis

When talking to someone, there’s a point to your conversation. An NLP system is just like that, but it needs to go through extensive training to achieve the same level of discourse. That’s where discourse analysis comes in. It instructs the machine to use a coherent group of sentences that have a similar or the same theme.

Speech Recognition and Generation

Once all the above elements are perfected, it’s blast-off time. The NLP has everything it needs to recognize and generate speech. This is where the real magic happens – the system interacts with the user and starts using the same language. If each stage has been performed correctly, there should be no significant differences between real speech and NLP-based applications.

Natural Language Processing Techniques

Different analyses are common for most (if not all) NLP solutions. They all point in one direction, which is recognizing and generating speech. But just like Google Maps, the system can choose different routes. In this case, the routes are known as NLP techniques.

Rule-Based Approaches

Rule-based approaches might be the easiest NLP technique to understand. You feed your rules into the system, and the NLP tool synthesizes language based on them. If input data isn’t associated with any rule, it doesn’t recognize the information – simple as that.

Statistical Methods

If you go one level up on the complexity scale, you’ll see statistical NLP methods. They’re based on advanced calculations, which enable an NLP platform to predict data based on previous information.

Neural Networks and Deep Learning

You might be thinking: “Neural networks? That sounds like something out of a medical textbook.” Although that’s not quite correct, you’re on the right track. Neural networks are NLP techniques that feature interconnected nodes, imitating neural connections in your brain.

Deep learning is a sub-type of these networks. Basically, any neural network with at least three layers is considered a deep learning environment.

Transfer Learning and Pre-Trained Language Models

The internet is like a massive department store – you can find almost anything that comes to mind here. The list includes pre-trained language models. These models are trained on enormous quantities of data, eliminating the need for you to train them using your own information.

Transfer learning draws on this concept. By tweaking pre-trained models to accommodate a particular project, you perform a transfer learning maneuver.

Applications of Natural Language Processing

With so many cutting-edge processes underpinning NLP, it’s no surprise it has practically endless applications. Here are some of the most common natural language processing examples:

• Search engines and information retrieval – An NLP-based search engine understands your search intent to retrieve accurate information fast.
• Sentiment analysis and social media monitoring – NLP systems can even determine your emotional motivation and uncover the sentiment behind social media content.
• Machine translation and language understanding – NLP software is the go-to solution for fast translations and understanding complex languages to improve communication.
• Chatbots and virtual assistants – A state-of-the-art NLP environment is behind most chatbots and virtual assistants, which allows organizations to enhance customer support and other key segments.
• Text summarization and generation – A robust NLP infrastructure not only understands texts but also summarizes and generates texts of its own based on your input.

Challenges and Limitations of Natural Language Processing

Natural language processing in AI and machine learning is mighty but not almighty. There are setbacks to this technology, but given the speedy development of AI, they can be considered a mere speed bump for the time being:

• Ambiguity and complexity of human language – Human language keeps evolving, resulting in ambiguous structures NLP often struggles to grasp.
• Cultural and contextual nuances – With approximately 4,000 distinct cultures on the globe, it’s hard for an NLP system to understand the nuances of each.
• Data privacy and ethical concerns – As every NLP platform requires vast data, the methods for sourcing this data tend to trigger ethical concerns.
• Computational resources and computing power – The more polished an NLP tool becomes, the greater the computing power must be, which can be hard to achieve.

The Future of Natural Language Processing

The final part of our take on natural language processing in artificial intelligence asks a crucial question: What does the future hold for NLP?

• Advancements in artificial intelligence and machine learning – Will AI and machine learning advancements help NLP understand more complex and nuanced languages faster?
• Integration of NLP with other technologies – How well will NLP integrate with other technologies to facilitate personal and corporate use?
• Personalized and adaptive language models – Can you expect developers to come up with personalized and adaptive language models to accommodate those with speech disorders better?
• Ethical considerations and guidelines for NLP development – How will the spearheads of NLP development address ethical problems if the technology requires more and more data to execute?

The Potential of Natural Language Processing Is Unrivaled

It’s hard to find a technology that’s more important for today’s businesses and society as a whole than natural language processing. It streamlines communication, enabling people from all over the world to connect with each other.

The impact of NLP will amplify if the developers of this technology can address the above risks. By honing the software with other platforms while minimizing privacy issues, they can dispel any concerns associated with it.

If you want to learn more about NLP, don’t stop here. Use these natural language processing notes as a stepping stone for in-depth research. Also, consider an NLP course to gain a deep understanding of this topic.