AI investment has become a must in the business world, and companies from all over the globe are embracing this trend. Nearly 90% of organizations plan to put more money into AI by 2025.

One of the main areas of investment is deep learning. The World Economic Forum approves of this initiative, as the cutting-edge technology can boost productivity, optimize cybersecurity, and enhance decision-making.

Knowing that deep learning is making waves is great, but it doesn’t mean much if you don’t understand the basics. Read on for deep learning applications and the most common examples.

Artificial Neural Networks

Once you scratch the surface of deep learning, you’ll see that it’s underpinned by artificial neural networks. That’s why many people refer to deep learning as deep neural networking and deep neural learning.

There are different types of artificial neural networks.

Perceptron

Perceptrons are the most basic form of neural networks. These artificial neurons were originally used for calculating business intelligence or input data capabilities. Nowadays, it’s a linear algorithm that supervises the learning of binary classifiers.

Convolutional Neural Networks

Convolutional neural network machine learning is another common type of deep learning network. It combines input data with learned features before allowing this architecture to analyze images or other 2D data.

The most significant benefit of convolutional neural networks is that they automate feature extraction. As a result, you don’t have to recognize features on your own when classifying pictures or other visuals – the networks extract them directly from the source.

Recurrent Neural Networks

Recurrent neural networks use time series or sequential information. You can find them in many areas, such as natural language processing, image captioning, and language translation. Google Translate, Siri, and many other applications have adopted this technology.

Generative Adversarial Networks

Generative adversarial networks are architecture with two sub-types. The generator model produces new examples, whereas the discriminated model determines if the examples generated are real or fake.

These networks work like so-called game theory scenarios, where generator networks come face-to-face with their adversaries. They generate examples directly, while the adversary (discriminator) tries to tell the difference between these examples and those obtained from training information.

Deep Learning Applications

Deep learning helps take a multitude of technologies to a whole new level.

Computer Vision

The feature that allows computers to obtain useful data from videos and pictures is known as computer vision. An already sophisticated process, deep learning can enhance the technology further.

For instance, you can utilize deep learning to enable machines to understand visuals like humans. They can be trained to automatically filter adult content to make it child-friendly. Likewise, deep learning can enable computers to recognize critical image information, such as logos and food brands.

Natural Language Processing

Artificial intelligence deep learning algorithms spearhead the development and optimization of natural language processing. They automate various processes and platforms, including virtual agents, the analysis of business documents, key phrase indexing, and article summarization.

Speech Recognition

Human speech differs greatly in language, accent, tone, and other key characteristics. This doesn’t stop deep learning from polishing speech recognition software. For instance, Siri is a deep learning-based virtual assistant that can automatically make and recognize calls. Other deep learning programs can transcribe meeting recordings and translate movies to reach wider audiences.

Robotics

Robots are invented to simplify certain tasks (i.e., reduce human input). Deep learning models are perfect for this purpose, as they help manufacturers build advanced robots that replicate human activity. These machines receive timely updates to plan their movements and overcome any obstacles on their way. That’s why they’re common in warehouses, healthcare centers, and manufacturing facilities.

Some of the most famous deep learning-enabled robots are those produced by Boston Dynamics. For example, their robot Atlas is highly agile due to its deep learning architecture. It can move seamlessly and perform dynamic interactions that are common in people.

Autonomous Driving

Self-driving cars are all the rage these days. The autonomous driving industry is expected to generate over $300 billion in revenue by 2035, and most of the credits will go to deep learning.

The producers of these vehicles use deep learning to train cars to respond to real-life traffic scenarios and improve safety. They incorporate different technologies that allow cars to calculate the distance to the nearest objects and navigate crowded streets. The vehicles come with ultra-sensitive cameras and sensors, all of which are powered by deep learning.

Passengers aren’t the only group who will benefit from deep learning-supported self-driving cars. The technology is expected to revolutionize emergency and food delivery services as well.

Deep Learning Algorithms

Numerous deep learning algorithms power the above technologies. Here are the four most common examples.

Backpropagation

Backpropagation is commonly used in neural network training. It starts from so-called “forward propagation,” analyzing its error rate. It feeds the error backward through various network layers, allowing you to optimize the weights (parameters that transform input data within hidden layers).

Stochastic Gradient Descent

The primary purpose of the stochastic gradient descent algorithm is to locate the parameters that allow other machine learning algorithms to operate at their peak efficiency. It’s generally combined with other algorithms, such as backpropagation, to enhance neural network training.

Reinforcement Learning

The reinforcement learning algorithm is trained to resolve multi-layer problems. It experiments with different solutions until it finds the right one. This method draws its decisions from real-life situations.

The reason it’s called reinforcement learning is that it operates on a reward/penalty basis. It aims to maximize rewards to reinforce further training.

Transfer Learning

Transfer learning boils down to recycling pre-configured models to solve new issues. The algorithm uses previously obtained knowledge to make generalizations when facing another problem.

For instance, many deep learning experts use transfer learning to train the system to recognize images. A classifier can use this algorithm to identify pictures of trucks if it’s already analyzed car photos.

Deep Learning Tools

Deep learning tools are platforms that enable you to develop software that lets machines mimic human activity by processing information carefully before making a decision. You can choose from a wide range of such tools.

TensorFlow

Developed in CUDA and C++, TensorFlow is a highly advanced deep learning tool. Google launched this open-source solution to facilitate various deep learning platforms.

Despite being advanced, it can also be used by beginners due to its relatively straightforward interface. It’s perfect for creating cloud, desktop, and mobile machine learning models.

Keras

The Keras API is a Python-based tool with several features for solving machine learning issues. It works with TensorFlow, Thenao, and other tools to optimize your deep learning environment and create robust models.

In most cases, prototyping with Keras is fast and scalable. The API is compatible with convolutional and recurrent networks.

PyTorch

PyTorch is another Python-based tool. It’s also a machine learning library and scripting language that allows you to create neural networks through sophisticated algorithms. You can use the tool on virtually any cloud software, and it delivers distributed training to speed up peer-to-peer updates.

Caffe

Caffe’s framework was launched by Berkeley as an open-source platform. It features an expressive design, which is perfect for propagating cutting-edge applications. Startups, academic institutions, and industries are just some environments where this tool is common.

Theano

Python makes yet another appearance in deep learning tools. Here, it powers Theano, enabling the tool to assess complex mathematical tasks. The software can solve issues that require tremendous computing power and vast quantities of information.

Deep Learning Examples

Deep learning is the go-to solution for creating and maintaining the following technologies.

Image Recognition

Image recognition programs are systems that can recognize specific items, people, or activities in digital photos. Deep learning is the method that enables this functionality. The most well-known example of the use of deep learning for image recognition is in healthcare settings. Radiologists and other professionals can rely on it to analyze and evaluate large numbers of images faster.

Text Generation

There are several subtypes of natural language processing, including text generation. Underpinned by deep learning, it leverages AI to produce different text forms. Examples include machine translations and automatic summarizations.

Self-Driving Cars

As previously mentioned, deep learning is largely responsible for the development of self-driving cars. AutoX might be the most renowned manufacturer of these vehicles.

The Future Lies in Deep Learning

Many up-and-coming technologies will be based on deep learning AI. It’s no surprise, therefore, that nearly 50% of enterprises already use deep learning as the driving force of their products and services. If you want to expand your knowledge about this topic, consider taking a deep learning course. You’ll improve your employment opportunities and further demystify the concept.

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Agenda Digitale: The Five Pillars of the Cloud According to NIST – A Compass for Businesses and Public Administrations
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 26, 2025 7 min read

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By Lokesh Vij, Professor of Cloud Computing Infrastructure, Cloud Development, Cloud Computing Automation and Ops and Cloud Data Stacks at OPIT – Open Institute of Technology

NIST identifies five key characteristics of cloud computing: on-demand self-service, network access, resource pooling, elasticity, and metered service. These pillars explain the success of the global cloud market of 912 billion in 2025

In less than twenty years, the cloud has gone from a curiosity to an indispensable infrastructure. According to Precedence Research, the global market will reach 912 billion dollars in 2025 and will exceed 5.1 trillion in 2034. In Europe, the expected spending for 2025 will be almost 202 billion dollars. At the base of this success are five characteristics, identified by the NIST (National Institute of Standards and Technology): on-demand self-service, network access, shared resource pool, elasticity and measured service.

Understanding them means understanding why the cloud is the engine of digital transformation.

On-demand self-service: instant provisioning

The journey through the five pillars starts with the ability to put IT in the hands of users.

Without instant provisioning, the other benefits of the cloud remain potential. Users can turn resources on and off with a click or via API, without tickets or waiting. Provisioning a VM, database, or Kubernetes cluster takes seconds, not weeks, reducing time to market and encouraging continuous experimentation. A DevOps team that releases microservices multiple times a day or a fintech that tests dozens of credit-scoring models in parallel benefit from this immediacy. In OPIT labs, students create complete Kubernetes environments in two minutes, run load tests, and tear them down as soon as they’re done, paying only for the actual minutes.

Similarly, a biomedical research group can temporarily allocate hundreds of GPUs to train a deep-learning model and release them immediately afterwards, without tying up capital in hardware that will age rapidly. This flexibility allows the user to adapt resources to their needs in real time. There are no hard and fast constraints: you can activate a single machine and deactivate it when it is no longer needed, or start dozens of extra instances for a limited time and then release them. You only pay for what you actually use, without waste.

Wide network access: applications that follow the user everywhere

Once access to resources is made instantaneous, it is necessary to ensure that these resources are accessible from any location and device, maintaining a uniform user experience. The cloud lives on the network and guarantees ubiquity and independence from the device.

A web app based on HTTP/S can be used from a laptop, tablet or smartphone, without the user knowing where the containers are running. Geographic transparency allows for multi-channel strategies: you start a purchase on your phone and complete it on your desktop without interruptions. For the PA, this means providing digital identities everywhere, for the private sector, offering 24/7 customer service.

Broad access moves security from the physical perimeter to the digital identity and introduces zero-trust architecture, where every request is authenticated and authorized regardless of the user’s location.

All you need is a network connection to use the resources: from the office, from home or on the move, from computers and mobile devices. Access is independent of the platform used and occurs via standard web protocols and interfaces, ensuring interoperability.

Shared Resource Pools: The Economy of Scale of Multi-Tenancy

Ubiquitous access would be prohibitive without a sustainable economic model. This is where infrastructure sharing comes in.

The cloud provider’s infrastructure aggregates and shares computational resources among multiple users according to a multi-tenant model. The economies of scale of hyperscale data centers reduce costs and emissions, putting cutting-edge technologies within the reach of startups and SMBs.

Pooling centralizes patching, security, and capacity planning, freeing IT teams from repetitive tasks and reducing the company’s carbon footprint. Providers reinvest energy savings in next-generation hardware and immersion cooling research programs, amplifying the collective benefit.

Rapid Elasticity: Scaling at the Speed ​​of Business

Sharing resources is only effective if their allocation follows business demand in real time. With elasticity, the infrastructure expands or reduces resources in minutes following the load. The system behaves like a rubber band: if more power or more instances are needed to deal with a traffic spike, it automatically scales in real time; when demand drops, the additional resources are deactivated just as quickly.

This flexibility seems to offer unlimited resources. In practice, a company no longer has to buy excess servers to cover peaks in demand (which would remain unused during periods of low activity), but can obtain additional capacity from the cloud only when needed. The economic advantage is considerable: large initial investments are avoided and only the capacity actually used during peak periods is paid for.

In the OPIT cloud automation lab, students simulate a streaming platform that creates new Kubernetes pods as viewers increase and deletes them when the audience drops: a concrete example of balancing user experience and cost control. The effect is twofold: the user does not suffer slowdowns and the company avoids tying up capital in underutilized servers.

Metered Service: Transparency and Cost Governance

The dynamic scale generated by elasticity requires precise visibility into consumption and expenses : without measurement there is no governance. Metering makes every second of CPU, every gigabyte and every API call visible. Every consumption parameter is tracked and made available in transparent reports.

This data enables pay-per-use pricing , i.e. charges proportional to actual usage. For the customer, this translates into variable costs: you only pay for the resources actually consumed. Transparency helps you plan your budget: thanks to real-time data, it is easier to optimize expenses, for example by turning off unused resources. This eliminates unnecessary fixed costs, encouraging efficient use of resources.

The systemic value of the five pillars

When the five pillars work together, the effect is multiplier . Self-service and elasticity enable rapid response to workload changes, increasing or decreasing resources in real time, and fuel continuous experimentation; ubiquitous access and pooling provide global scalability; measurement ensures economic and environmental sustainability.

It is no surprise that the Italian market will grow from $12.4 billion in 2025 to $31.7 billion in 2030 with a CAGR of 20.6%. Manufacturers and retailers are migrating mission-critical loads to cloud-native platforms , gaining real-time data insights and reducing time to value .

From the laboratory to the business strategy

From theory to practice: the NIST pillars become a compass for the digital transformation of companies and Public Administration. In the classroom, we start with concrete exercises – such as the stress test of a video platform – to demonstrate the real impact of the five pillars on performance, costs and environmental KPIs.

The same approach can guide CIOs and innovators: if processes, governance and culture embody self-service, ubiquity, pooling, elasticity and measurement, the organization is ready to capture the full value of the cloud. Otherwise, it is necessary to recalibrate the strategy by investing in training, pilot projects and partnerships with providers. The NIST pillars thus confirm themselves not only as a classification model, but as the toolbox with which to build data-driven and sustainable enterprises.

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ChatGPT Action Figures & Responsible Artificial Intelligence
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Jun 23, 2025 6 min read

You’ve probably seen two of the most recent popular social media trends. The first is creating and posting your personalized action figure version of yourself, complete with personalized accessories, from a yoga mat to your favorite musical instrument. There is also the Studio Ghibli trend, which creates an image of you in the style of a character from one of the animation studio’s popular films.

Both of these are possible thanks to OpenAI’s GPT-4o-powered image generator. But what are you risking when you upload a picture to generate this kind of content? More than you might imagine, according to Tom Vazdar, chair of cybersecurity at the Open Institute of Technology (OPIT), in a recent interview with Wired. Let’s take a closer look at the risks and how this issue ties into the issue of responsible artificial intelligence.

Uploading Your Image

To get a personalized image of yourself back from ChatGPT, you need to upload an actual photo, or potentially multiple images, and tell ChatGPT what you want. But in addition to using your image to generate content for you, OpenAI could also be using your willingly submitted image to help train its AI model. Vazdar, who is also CEO and AI & Cybersecurity Strategist at Riskoria and a board member for the Croatian AI Association, says that this kind of content is “a gold mine for training generative models,” but you have limited power over how that image is integrated into their training strategy.

Plus, you are uploading much more than just an image of yourself. Vazdar reminds us that we are handing over “an entire bundle of metadata.” This includes the EXIF data attached to the image, such as exactly when and where the photo was taken. And your photo may have more content in it than you imagine, with the background – including people, landmarks, and objects – also able to be tied to that time and place.

In addition to this, OpenAI also collects data about the device that you are using to engage with the platform, and, according to Vazdar, “There’s also behavioral data, such as what you typed, what kind of image you asked for, how you interacted with the interface and the frequency of those actions.”

After all that, OpenAI knows a lot about you, and soon, so could their AI model, because it is studying you.

How OpenAI Uses Your Data

OpenAI claims that they did not orchestrate these social media trends simply to get training data for their AI, and that’s almost certainly true. But they also aren’t denying that access to that freely uploaded data is a bonus. As Vazdar points out, “This trend, whether by design or a convenient opportunity, is providing the company with massive volumes of fresh, high-quality facial data from diverse age groups, ethnicities, and geographies.”

OpenAI isn’t the only company using your data to train its AI. Meta recently updated its privacy policy to allow the company to use your personal information on Meta-related services, such as Facebook, Instagram, and WhatsApp, to train its AI. While it is possible to opt-out, Meta isn’t advertising that fact or making it easy, which means that most users are sharing their data by default.

You can also control what happens with your data when using ChatGPT. Again, while not well publicized, you can use ChatGPT’s self-service tools to access, export, and delete your personal information, and opt out of having your content used to improve OpenAI’s model. Nevertheless, even if you choose these options, it is still worth it to strip data like location and time from images before uploading them and to consider the privacy of any images, including people and objects in the background, before sharing.

Are Data Protection Laws Keeping Up?

OpenAI and Meta need to provide these kinds of opt-outs due to data protection laws, such as GDPR in the EU and the UK. GDPR gives you the right to access or delete your data, and the use of biometric data requires your explicit consent. However, your photo only becomes biometric data when it is processed using a specific technical measure that allows for the unique identification of an individual.

But just because ChatGPT is not using this technology, doesn’t mean that ChatGPT can’t learn a lot about you from your images.

AI and Ethics Concerns

But you might wonder, “Isn’t it a good thing that AI is being trained using a diverse range of photos?” After all, there have been widespread reports in the past of AI struggling to recognize black faces because they have been trained mostly on white faces. Similarly, there have been reports of bias within AI due to the information it receives. Doesn’t sharing from a wide range of users help combat that? Yes, but there is so much more that could be done with that data without your knowledge or consent.

One of the biggest risks is that the data can be manipulated for marketing purposes, not just to get you to buy products, but also potentially to manipulate behavior. Take, for instance, the Cambridge Analytica scandal, which saw AI used to manipulate voters and the proliferation of deepfakes sharing false news.

Vazdar believes that AI should be used to promote human freedom and autonomy, not threaten it. It should be something that benefits humanity in the broadest possible sense, and not just those with the power to develop and profit from AI.

Responsible Artificial Intelligence

OPIT’s Master’s in Responsible AI combines technical expertise with a focus on the ethical implications of AI, diving into questions such as this one. Focusing on real-world applications, the course considers sustainable AI, environmental impact, ethical considerations, and social responsibility.

Completed over three or four 13-week terms, it starts with a foundation in technical artificial intelligence and then moves on to advanced AI applications. Students finish with a Capstone project, which sees them apply what they have learned to real-world problems.

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