Algorithms are the backbone behind technology that have helped establish some of the world’s most famous companies. Software giants like Google, beverage giants Coca Cola and many other organizations utilize proprietary algorithms to improve their services and enhance customer experience. Algorithms are an inseparable part of the technology behind organization as they help improve security, product or service recommendations, and increase sales.

Knowing the benefits of algorithms is useful, but you might also be interested to know what makes them so advantageous. As such, you’re probably asking: “What is an algorithm?” Here’s the most common algorithm definition: an algorithm is a set of procedures and rules a computer follows to solve a problem.

In addition to the meaning of the word “algorithm,” this article will also cover the key types and characteristics of algorithms, as well as their applications.

Types of Algorithms and Design Techniques

One of the main reasons people rely on algorithms is that they offer a principled and structured means to represent a problem on a computer.

Recursive Algorithms

Recursive algorithms are critical for solving many problems. The core idea behind recursive algorithms is to use functions that call themselves on smaller chunks of the problem.

Divide and Conquer Algorithms

Divide and conquer algorithms are similar to recursive algorithms. They divide a large problem into smaller units. Algorithms solve each smaller component before combining them to tackle the original, large problem.

Greedy Algorithms

A greedy algorithm looks for solutions based on benefits. More specifically, it resolves problems in sections by determining how many benefits it can extract by analyzing a certain section. The more benefits it has, the more likely it is to solve a problem, hence the term greedy.

Dynamic Programming Algorithms

Dynamic programming algorithms follow a similar approach to recursive and divide and conquer algorithms. First, they break down a complex problem into smaller pieces. Next, it solves each smaller piece once and saves the solution for later use instead of computing it.

Backtracking Algorithms

After dividing a problem, an algorithm may have trouble moving forward to find a solution. If that’s the case, a backtracking algorithm can return to parts of the problem it has already solved until it determines a way forward that can overcome the setback.

Brute Force Algorithms

Brute force algorithms try every possible solution until they determine the best one. Brute force algorithms are simpler, but the solution they find might not be as good or elegant as those found by the other types of algorithms.

Algorithm Analysis and Optimization

Digital transformation remains one of the biggest challenges for businesses in 2023. Algorithms can facilitate the transition through careful analysis and optimization.

Time Complexity

The time complexity of an algorithm refers to how long you need to execute a certain algorithm. A number of factors determine time complexity, but the algorithm’s input length is the most important consideration.

Space Complexity

Before you can run an algorithm, you need to make sure your device has enough memory. The amount of memory required for executing an algorithm is known as space complexity.

Trade-Offs

Solving a problem with an algorithm in C or any other programming language is about making compromises. In other words, the system often makes trade-offs between the time and space available.

For example, an algorithm can use less space, but this extends the time it takes to solve a problem. Alternatively, it can take up a lot of space to address an issue faster.

Optimization Techniques

Algorithms generally work great out of the box, but they sometimes fail to deliver the desired results. In these cases, you can implement a slew of optimization techniques to make them more effective.

Memorization

You generally use memorization if you wish to elevate the efficacy of a recursive algorithm. The technique rewrites algorithms and stores them in arrays. The main reason memorization is so powerful is that it eliminates the need to calculate results multiple times.

Parallelization

As the name suggests, parallelization is the ability of algorithms to perform operations simultaneously. This accelerates task completion and is normally utilized when you have a lot of memory on your device.

Heuristics

Heuristic algorithms (a.k.a. heuristics) are algorithms used to speed up problem-solving. They generally target non-deterministic polynomial-time (NP) problems.

Approximation Algorithms

Another way to solve a problem if you’re short on time is to incorporate an approximation algorithm. Rather than provide a 100% optimal solution and risk taking longer, you use this algorithm to get approximate solutions. From there, you can calculate how far away they are from the optimal solution.

Pruning

Algorithms sometimes analyze unnecessary data, slowing down your task completion. A great way to expedite the process is to utilize pruning. This compression method removes unwanted information by shrinking algorithm decision trees.

Algorithm Applications and Challenges

Thanks to this introduction to algorithm, you’ll no longer wonder: “What is an algorithm, and what are the different types?” Now it’s time to go through the most significant applications and challenges of algorithms.

Sorting Algorithms

Sorting algorithms arrange elements in a series to help solve complex issues faster. There are different types of sorting, including linear, insertion, and bubble sorting. They’re generally used for exploring databases and virtual search spaces.

Searching Algorithms

An algorithm in C or other programming languages can be used as a searching algorithm. They allow you to identify a small item in a large group of related elements.

Graph Algorithms

Graph algorithms are just as practical, if not more practical, than other types. Graphs consist of nodes and edges, where each edge connects two nodes.

There are numerous real-life applications of graph algorithms. For instance, you might have wondered how engineers solve problems regarding wireless networks or city traffic. The answer lies in using graph algorithms.

The same goes for social media sites, such as Facebook. Algorithms on such platforms contain nodes, which represent key information, like names and genders and edges that represent the relationships or dependencies between them.

Cryptography Algorithms

When creating an account on some websites, the platform can generate a random password for you. It’s usually stronger than custom-made codes, thanks to cryptography algorithms. They can scramble digital text and turn it into an unreadable string. Many organizations use this method to protect their data and prevent unauthorized access.

Machine Learning Algorithms

Over 70% of enterprises prioritize machine learning applications. To implement their ideas, they rely on machine learning algorithms. They’re particularly useful for financial institutions because they can predict future trends.

Famous Algorithm Challenges

Many organizations struggle to adopt algorithms, be it an algorithm in data structure or computer science. The reason being, algorithms present several challenges:

  • Opacity – You can’t take a closer look at the inside of an algorithm. Only the end result is visible, which is why it’s difficult to understand an algorithm.
  • Heterogeneity – Most algorithms are heterogeneous, behaving differently from one another. This makes them even more complex.
  • Dependency – Each algorithm comes with the abovementioned time and space restrictions.

Algorithm Ethics, Fairness, and Social Impact

When discussing critical characteristics of algorithms, it’s important to highlight the main concerns surrounding this technology.

Bias in Algorithms

Algorithms aren’t intrinsically biased unless the developer injects their personal biases into the design. If so, getting impartial results from an algorithm is highly unlikely.

Transparency and Explainability

Knowing only the consequences of algorithms prevents us from explaining them in detail. A transparent algorithm enables a user to view and understand its different operations. In contrast, explainability of an algorithm relates to its ability to provide reasons for the decisions it makes.

Privacy and Security

Some algorithms require end users to share private information. If cyber criminals hack the system, they can easily steal the data.

Algorithm Accessibility and Inclusivity

Limited explainability hinders access to algorithms. Likewise, it’s hard to include different viewpoints and characteristics in an algorithm, especially if it is biased.

Algorithm Trust and Confidence

No algorithm is omnipotent. Claiming otherwise makes it untrustworthy – the best way to prevent this is for the algorithm to state its limitations.

Algorithm Social Impact

Algorithms impact almost every area of life including politics, economic and healthcare decisions, marketing, transportation, social media and Internet, and society and culture in general.

Algorithm Sustainability and Environmental Impact

Contrary to popular belief, algorithms aren’t very sustainable. The extraction of materials to make computers that power algorithms is a major polluter.

Future of Algorithms

Algorithms are already advanced, but what does the future hold for this technology? Here are a few potential applications and types of future algorithms:

  • Quantum Algorithms – Quantum algorithms are expected to run on quantum computers to achieve unprecedented speeds and efficiency.
  • Artificial Intelligence and Machine Learning – AI and machine learning algorithms can help a computer develop human-like cognitive qualities via learning from its environment and experiences.
  • Algorithmic Fairness and Ethics – Considering the aforementioned challenges of algorithms, developers are expected to improve the technology. It may become more ethical with fewer privacy violations and accessibility issues.

Smart, Ethical Implementation Is the Difference-Maker

Understanding algorithms is crucial if you want to implement them correctly and ethically. They’re powerful, but can also have unpleasant consequences if you’re not careful during the development stage. Responsible use is paramount because it can improve many areas, including healthcare, economics, social media, and communication.

If you wish to learn more about algorithms, accredited courses might be your best option. AI and machine learning-based modules cover some of the most widely-used algorithms to help expand your knowledge about this topic.

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Il Sole 24 Ore: Integrating Artificial Intelligence into the Enterprise – Challenges and Opportunities for CEOs and Management
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Expert Pierluigi Casale analyzes the adoption of AI by companies, the ethical and regulatory challenges and the differentiated approach between large companies and SMEs

By Gianni Rusconi

Easier said than done: to paraphrase the well-known proverb, and to place it in the increasingly large collection of critical issues and opportunities related to artificial intelligence, the task that CEOs and management have to adequately integrate this technology into the company is indeed difficult. Pierluigi Casale, professor at OPIT (Open Institute of Technology, an academic institution founded two years ago and specialized in the field of Computer Science) and technical consultant to the European Parliament for the implementation and regulation of AI, is among those who contributed to the definition of the AI ​​Act, providing advice on aspects of safety and civil liability. His task, in short, is to ensure that the adoption of artificial intelligence (primarily within the parliamentary committees operating in Brussels) is not only efficient, but also ethical and compliant with regulations. And, obviously, his is not an easy task.

The experience gained over the last 15 years in the field of machine learning and the role played in organizations such as Europol and in leading technology companies are the requirements that Casale brings to the table to balance the needs of EU bodies with the pressure exerted by American Big Tech and to preserve an independent approach to the regulation of artificial intelligence. A technology, it is worth remembering, that implies broad and diversified knowledge, ranging from the regulatory/application spectrum to geopolitical issues, from computational limitations (common to European companies and public institutions) to the challenges related to training large-format language models.

CEOs and AI

When we specifically asked how CEOs and C-suites are “digesting” AI in terms of ethics, safety and responsibility, Casale did not shy away, framing the topic based on his own professional career. “I have noticed two trends in particular: the first concerns companies that started using artificial intelligence before the AI ​​Act and that today have the need, as well as the obligation, to adapt to the new ethical framework to be compliant and avoid sanctions; the second concerns companies, like the Italian ones, that are only now approaching this topic, often in terms of experimental and incomplete projects (the expression used literally is “proof of concept”, ed.) and without these having produced value. In this case, the ethical and regulatory component is integrated into the adoption process.”

In general, according to Casale, there is still a lot to do even from a purely regulatory perspective, due to the fact that there is not a total coherence of vision among the different countries and there is not the same speed in implementing the indications. Spain, in this regard, is setting an example, having established (with a royal decree of 8 November 2023) a dedicated “sandbox”, i.e. a regulatory experimentation space for artificial intelligence through the creation of a controlled test environment in the development and pre-marketing phase of some artificial intelligence systems, in order to verify compliance with the requirements and obligations set out in the AI ​​Act and to guide companies towards a path of regulated adoption of the technology.

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The Lucky Future: How AI Aims to Change Everything
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Apr 10, 2025 7 min read

There is no question that the spread of artificial intelligence (AI) is having a profound impact on nearly every aspect of our lives.

But is an AI-powered future one to be feared, or does AI offer the promise of a “lucky future.”

That “lucky future” prediction comes from Zorina Alliata, principal AI Strategist at Amazon and AI faculty member at Georgetown University and the Open Institute of Technology (OPIT), in her recent webinar “The Lucky Future: How AI Aims to Change Everything” (February 18, 2025).

However, according to Alliata, such a future depends on how the technology develops and whether strategies can be implemented to mitigate the risks.

How AI Aims to Change Everything

For many people, AI is already changing the way they work. However, more broadly, AI has profoundly impacted how we consume information.

From the curation of a social media feed and the summary answer to a search query from Gemini at the top of your Google results page to the AI-powered chatbot that resolves your customer service issues, AI has quickly and quietly infiltrated nearly every aspect of our lives in the past few years.

While there have been significant concerns recently about the possibly negative impact of AI, Alliata’s “lucky future” prediction takes these fears into account. As she detailed in her webinar, a future with AI will have to take into consideration:

  • Where we are currently with AI and future trajectories
  • The impact AI is having on the job landscape
  • Sustainability concerns and ethical dilemmas
  • The fundamental risks associated with current AI technology

According to Alliata, by addressing these risks, we can craft a future in which AI helps individuals better align their needs with potential opportunities and limitations of the new technology.

Industry Applications of AI

While AI has been in development for decades, Alliata describes a period known as the “AI winter” during which educators like herself studied AI technology, but hadn’t arrived at a point of practical applications. Contributing to this period of uncertainty were concerns over how to make AI profitable as well.

That all changed about 10-15 years ago when machine learning (ML) improved significantly. This development led to a surge in the creation of business applications for AI. Beginning with automation and robotics for repetitive tasks, the technology progressed to data analysis – taking a deep dive into data and finding not only new information but new opportunities as well.

This further developed into generative AI capable of completing creative tasks. Generative AI now produces around one billion words per day, compared to the one trillion produced by humans.

We are now at the stage where AI can complete complex tasks involving multiple steps. In her webinar, Alliata gave the example of a team creating storyboards and user pathways for a new app they wanted to develop. Using photos and rough images, they were able to use AI to generate the code for the app, saving hundreds of hours of manpower.

The next step in AI evolution is Artificial General Intelligence (AGI), an extremely autonomous level of AI that can replicate or in some cases exceed human intelligence. While the benefits of such technology may readily be obvious to some, the industry itself is divided as to not only whether this form of AI is close at hand or simply unachievable with current tools and technology, but also whether it should be developed at all.

This unpredictability, according to Alliata, represents both the excitement and the concerns about AI.

The AI Revolution and the Job Market

According to Alliata, the job market is the next area where the AI revolution can profoundly impact our lives.

To date, the AI revolution has not resulted in widespread layoffs as initially feared. Instead of making employees redundant, many jobs have evolved to allow them to work alongside AI. In fact, AI has also created new jobs such as AI prompt writer.

However, the prediction is that as AI becomes more sophisticated, it will need less human support, resulting in a greater job churn. Alliata shared statistics from various studies predicting as many as 27% of all jobs being at high risk of becoming redundant from AI and 40% of working hours being impacted by language learning models (LLMs) like Chat GPT.

Furthermore, AI may impact some roles and industries more than others. For example, one study suggests that in high-income countries, 8.5% of jobs held by women were likely to be impacted by potential automation, compared to just 3.9% of jobs held by men.

Is AI Sustainable?

While Alliata shared the many ways in which AI can potentially save businesses time and money, she also highlighted that it is an expensive technology in terms of sustainability.

Conducting AI training and processing puts a heavy strain on central processing units (CPUs), requiring a great deal of energy. According to estimates, Chat GPT 3 alone uses as much electricity per day as 121 U.S. households in an entire year. Gartner predicts that by 2030, AI could consume 3.5% of the world’s electricity.

To reduce the energy requirements, Alliata highlighted potential paths forward in terms of hardware optimization, such as more energy-efficient chips, greater use of renewable energy sources, and algorithm optimization. For example, models that can be applied to a variety of uses based on prompt engineering and parameter-efficient tuning are more energy-efficient than training models from scratch.

Risks of Using Generative AI

While Alliata is clearly an advocate for the benefits of AI, she also highlighted the risks associated with using generative AI, particularly LLMs.

  • Uncertainty – While we rely on AI for answers, we aren’t always sure that the answers provided are accurate.
  • Hallucinations – Technology designed to answer questions can make up facts when it does not know the answer.
  • Copyright – The training of LLMs often uses copyrighted data for training without permission from the creator.
  • Bias – Biased data often trains LLMs, and that bias becomes part of the LLM’s programming and production.
  • Vulnerability – Users can bypass the original functionality of an LLM and use it for a different purpose.
  • Ethical Risks – AI applications pose significant ethical risks, including the creation of deepfakes, the erosion of human creativity, and the aforementioned risks of unemployment.

Mitigating these risks relies on pillars of responsibility for using AI, including value alignment of the application, accountability, transparency, and explainability.

The last one, according to Alliata, is vital on a human level. Imagine you work for a bank using AI to assess loan applications. If a loan is denied, the explanation you give to the customer can’t simply be “Because the AI said so.” There needs to be firm and explainable data behind the reasoning.

OPIT’s Masters in Responsible Artificial Intelligence explores the risks and responsibilities inherent in AI, as well as others.

A Lucky Future

Despite the potential risks, Alliata concludes that AI presents even more opportunities and solutions in the future.

Information overload and decision fatigue are major challenges today. Imagine you want to buy a new car. You have a dozen features you desire, alongside hundreds of options, as well as thousands of websites containing the relevant information. AI can help you cut through the noise and narrow the information down to what you need based on your specific requirements.

Alliata also shared how AI is changing healthcare, allowing patients to understand their health data, make informed choices, and find healthcare professionals who meet their needs.

It is this functionality that can lead to the “lucky future.” Personalized guidance based on an analysis of vast amounts of data means that each person is more likely to make the right decision with the right information at the right time.

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