Artificial intelligence (AI) permeates every aspect of modern society, with that effect only becoming more pronounced as we move deeper into the 21st century. That’s a statement supported by the Brookings Institute, which asserts that whoever rules AI by 2030 (be it a country or corporation) will rule the global roost until at least 2100.

The point is that AI is already everywhere, even if in limited capacities, and you need to be ready for an AI-centric world to unfold ahead of you in the future. The right AI courses ensure you’re ready, so let’s look at four that you can complete today.

What Is Artificial Intelligence (AI)?

As humans, our brains give us the ability to learn and adapt to everything around us. For computers, AI achieves the same thing, equipping machines with the ability to take in datasets, learn from the data, and apply what it learns to real-world scenarios. There are many types of AI, with the following three being among the most prominent:

  • Narrow AI – An AI system that’s dedicated to performing a single task, like a chatbot that delivers stock responses based on user queries. Think of these AI as the “manual labor” machines that exist to do the same thing over and over again.
  • General AI – With general AI, we move closer to AI that has the same capacities to learn and apply that humans have. Multi-functional is the keyword here, as these AIs will be capable of completing multiple tasks at a human level.
  • Superintelligent AI – Though not in existence yet, superintelligent AI is the pinnacle of AI research, or the peak on the Mount Everest of AI. In addition to bringing the multi-functional talents that humans have to the table, these AI will have an unlimited capacity for learning.

We’re nowhere near the superintelligent AI level yet (some even say that this type of AI will be more of a threat than a help to humanity), but we can see AI in so many industries already. Self-driving cars, automated stock checkers, and even email spam filters are all examples of narrow AI in action, with each having specific functions. As the technology evolves, and it’s already doing so at a rapid pace, we’ll see more multi-function AI come to the fore.

Factors to Consider When Choosing an AI Course

When choosing a course, the key question is always what is artificial intelligence course criteria that actually matters? Here are five things to look for in an artificial intelligence course:

  • Quality course content – In this context, “quality” doesn’t solely mean “good” (though that’s a part of it). Your course also needs to deliver an educational experience that furthers whatever goals you’ve set for yourself in your career.
  • Course flexibility – Some people can commit themselves fully to an AI course. Others need to fit their learning around work, family, and other commitments. Figure out which category you slot into and search for courses that offer the flexibility (or lack thereof) that you need.
  • Instructor expertise – Good instructors bring a combination of theoretical mastery and industry experience to their courses. That’s why the best AI courses are usually created, and run, by people who currently work in the field.
  • Course reviews and ratings – Online reviews and ratings are the modern “word of mouth,” with global courses benefitting (or otherwise) from what their students have to say online. A few minutes of research can tell you if other students consider your chosen course to be a dud or an AI masterclass.
  • Pricing – As attractive as a full Master’s degree may be, the five-figure pricing may feel prohibitive. Other courses, such as a short-term artificial intelligence online course, may offer snippets of what you need to know at a much lower price. Balance your needs against your budget to make your choice.

Top AI Online Courses

There is no such thing as the “best” artificial intelligence course because every course offers something different that may or may not align with your needs. But these four run the gamut, from full-blown Master’s degrees (with accreditation) to crash courses designed to get you up to speed as fast as possible.

Course 1 – CS50’s Introduction to Artificial Intelligence With Python (Harvard)

There are few educational institutions as prestigious as Harvard University, and its CS50 course is perfect for those who already have a grasp of the Python programming language. Offered completely online, it’s a self-paced course that comes with a verified certificate (assuming you’re willing to pay an extra $199/€180).

Key Topics Covered

  • Reinforcement learning as it applies to machine learning
  • The core principles of artificial intelligence
  • Creating Python programs that use AI
  • An in-depth study into graph search algorithms

Course Duration and Pricing

Harvard advertises the course as a seven-week-long self-paced online program and recommends between 10 and 30 hours of study per week. How much time you actually spend on your studies depends on how quickly you pick up the concepts. It’s free to enroll (though a certificate costs money, as mentioned) and enrollment is open between May and December of each year.

Course 2 – Expand Your Knowledge of Artificial Intelligence (Udacity)

Marketed as a “nanodegree” program, which basically means it packs a lot of information into a short timeframe. Expand Your Knowledge gives you access to a digital classroom. It comes with some prerequisites, such as an understanding of Python and statistics, but it’s a course designed for those taking their first steps into applied AI.

Key Topics Covered

  • Foundational AI algorithms that power things like NASA’s Mars Rover
  • An introduction to AI concepts using Python as your base programming language
  • Classical graph search algorithms
  • Project reviews and feedback from over 1,400 people in the AI field

Course Duration and Pricing

This is a three-month course, with estimated study hours of between 12 and 15 per week, making it ideal for part-time learners who want to grasp the fundamentals of AI. Pricing is flexible, too. You can subscribe to the monthly version of the course via Udacity at a cost of £329 (approx. €377) per month or buy the whole thing upfront for £837 (approx. €959).

Course 3 – Master in Applied Data Science & AI (OPIT)

Those who’ve already completed a Bachelor’s degree in a computing or statistical subject may want to continue their full-time studies. OPIT’s Master’s program offers that opportunity, with its 100% online course being supported by experienced tutors who are available literally whenever you need them. The course contains both live and prerecorded content and the degree you receive carries European Qualification Framework accreditation.

Key Topics Covered

  • Real-life business problems (and solutions) that use both AI and data science
  • Python programming in the context of AI and data science
  • Business-related topics, such as the ethics surrounding AI usage and project management
  • Applied machine learning and artificial intelligence techniques

Course Duration and Pricing

OPIT’s Master’s program is a full-time postgraduate course. The regular version takes 18 months of self-timed study to complete. A fast-track version is available, lasting for 12 months, for those who want a more intensive educational experience. The cost varies depending on when you enroll. Intakes occur in October of each year, with early birds paying a discounted price of €4,950, to save almost €1,500 on the usual €6,500 price.

Course 4 – AI Engineering Professional Certificate (IBM via Coursera)

For those looking for direct tutelage from professionals who already work in the AI field, IBM’s offering is one of the best AI courses online. It’s also ideal for beginners, with no experience in computing needed and a flexible schedule allows you to learn as and how you want. Those studying for formal degrees aren’t left out. The certificate you earn through this course counts toward your degree credit.

Key Topics Covered

  • The foundations of machine learning and neural networks
  • Machine learning algorithm deployment
  • Neural network development using PyTorch, Keras, and TensorFlow
  • Implementation of both supervised and unsupervised machine learning models

Course Duration and Pricing

Flexibility is the name of the game with this course. It lasts for eight months, with three hours of learning per week, though fast and full-time learners may be able to complete it much quicker. Enrollment begins in May of each year, and the first seven days of the course act as a free trial so you can get a taste of what it has to offer. It’s also fairly cheap, with the course costing around €125 if you go for the full eight-month option.

Benefits of Taking AI Courses

There’s no use looking for the best artificial intelligence course if you don’t understand how that course will help you in the future. These are four benefits of studying AI:

  • Develop a skillset that will not only be important as we move toward an AI-driven future, but will serve as a foundation for the skills you’ll need to develop as AI evolves.
  • Combine theoretical and practical knowledge of AI to make your CV sparkle when it’s in front of employers.
  • Create the problem-solving skills that are essential in the tech industry, with those skills often being transferable to other sectors.
  • Follow whatever path you want in the constantly branching AI field.

Take Your Next Career Step With an Artificial Intelligence Online Course

Each of the four courses highlighted here offers something different. Some are short-term introductory courses while others allow full-time students to continue in-depth formal education. Whichever you choose serves as an investment into your future. AI is already causing ripples in the industrial ocean, and those ripples will grow into a tidal wave of opportunity for those who are prepared for the explosive growth of the industry. By investing in yourself today, through education and career foresight, you set yourself up for an amazing future tomorrow.

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CCN: Australia Tightens Crypto Oversight as Exchanges Expand, Testing Industry’s Appetite for Regulation
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 3 min read

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  • CCN, published on March 29th, 2025

By Kurt Robson

Over the past few months, Australia’s crypto industry has undergone a rapid transformation following the government’s proposal to establish a stricter set of digital asset regulations.

A series of recent enforcement measures and exchange launches highlight the growing maturation of Australia’s crypto landscape.

Experts remain divided on how the new rules will impact the country’s burgeoning digital asset industry.

New Crypto Regulation

On March 21, the Treasury Department said that crypto exchanges and custody services will now be classified under similar rules as other financial services in the country.

“Our legislative reforms will extend existing financial services laws to key digital asset platforms, but not to all of the digital asset ecosystem,” the Treasury said in a statement.

The rules impose similar regulations as other financial services in the country, such as obtaining a financial license, meeting minimum capital requirements, and safeguarding customer assets.

The proposal comes as Australian Prime Minister Anthony Albanese’s center-left Labor government prepares for a federal election on May 17.

Australia’s opposition party, led by Peter Dutton, has also vowed to make crypto regulation a top priority of the government’s agenda if it wins.

Australia’s Crypto Growth

Triple-A data shows that 9.6% of Australians already own digital assets, with some experts believing new rules will push further adoption.

Europe’s largest crypto exchange, WhiteBIT, announced it was entering the Australian market on Wednesday, March 26.

The company said that Australia was “an attractive landscape for crypto businesses” despite its complexity.

In March, Australia’s Swyftx announced it was acquiring New Zealand’s largest cryptocurrency exchange for an undisclosed sum.

According to the parties, the merger will create the second-largest platform in Australia by trading volume.

“Australia’s new regulatory framework is akin to rolling out the welcome mat for cryptocurrency exchanges,” Alexander Jader, professor of Digital Business at the Open Institute of Technology, told CCN.

“The clarity provided by these regulations is set to attract a wave of new entrants,” he added.

Jader said regulatory clarity was “the lifeblood of innovation.” He added that the new laws can expect an uptick “in both local and international exchanges looking to establish a foothold in the market.”

However, Zoe Wyatt, partner and head of Web3 and Disruptive Technology at Andersen LLP, believes that while the new rules will benefit more extensive exchanges looking for more precise guidelines, they will not “suddenly turn Australia into a global crypto hub.”

“The Web3 community is still largely looking to the U.S. in anticipation of a more crypto-friendly stance from the Trump administration,” Wyatt added.

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Agenda Digitale: Generative AI in the Enterprise – A Guide to Conscious and Strategic Use
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Mar 31, 2025 6 min read

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By Zorina Alliata, Professor of Responsible Artificial Intelligence e Digital Business & Innovation at OPIT – Open Institute of Technology

Integrating generative AI into your business means innovating, but also managing risks. Here’s how to choose the right approach to get value

The adoption of generative AI in the enterprise is growing rapidly, bringing innovation to decision-making, creativity and operations. However, to fully exploit its potential, it is essential to define clear objectives and adopt strategies that balance benefits and risks.

Over the course of my career, I have been fortunate to experience firsthand some major technological revolutions – from the internet boom to the “renaissance” of artificial intelligence a decade ago with machine learning.

However, I have never seen such a rapid rate of adoption as the one we are experiencing now, thanks to generative AI. Although this type of AI is not yet perfect and presents significant risks – such as so-called “hallucinations” or the possibility of generating toxic content – ​​it fills a real need, both for people and for companies, generating a concrete impact on communication, creativity and decision-making processes.

Defining the Goals of Generative AI in the Enterprise

When we talk about AI, we must first ask ourselves what problems we really want to solve. As a teacher and consultant, I have always supported the importance of starting from the specific context of a company and its concrete objectives, without inventing solutions that are as “smart” as they are useless.

AI is a formidable tool to support different processes: from decision-making to optimizing operations or developing more accurate predictive analyses. But to have a significant impact on the business, you need to choose carefully which task to entrust it with, making sure that the solution also respects the security and privacy needs of your customers .

Understanding Generative AI to Adopt It Effectively

A widespread risk, in fact, is that of being guided by enthusiasm and deploying sophisticated technology where it is not really needed. For example, designing a system of reviews and recommendations for films requires a certain level of attention and consumer protection, but it is very different from an X-ray reading service to diagnose the presence of a tumor. In the second case, there is a huge ethical and medical risk at stake: it is necessary to adapt the design, control measures and governance of the AI ​​to the sensitivity of the context in which it will be used.

The fact that generative AI is spreading so rapidly is a sign of its potential and, at the same time, a call for caution. This technology manages to amaze anyone who tries it: it drafts documents in a few seconds, summarizes or explains complex concepts, manages the processing of extremely complex data. It turns into a trusted assistant that, on the one hand, saves hours of work and, on the other, fosters creativity with unexpected suggestions or solutions.

Yet, it should not be forgotten that these systems can generate “hallucinated” content (i.e., completely incorrect), or show bias or linguistic toxicity where the starting data is not sufficient or adequately “clean”. Furthermore, working with AI models at scale is not at all trivial: many start-ups and entrepreneurs initially try a successful idea, but struggle to implement it on an infrastructure capable of supporting real workloads, with adequate governance measures and risk management strategies. It is crucial to adopt consolidated best practices, structure competent teams, define a solid operating model and a continuous maintenance plan for the system.

The Role of Generative AI in Supporting Business Decisions

One aspect that I find particularly interesting is the support that AI offers to business decisions. Algorithms can analyze a huge amount of data, simulating multiple scenarios and identifying patterns that are elusive to the human eye. This allows to mitigate biases and distortions – typical of exclusively human decision-making processes – and to predict risks and opportunities with greater objectivity.

At the same time, I believe that human intuition must remain key: data and numerical projections offer a starting point, but context, ethics and sensitivity towards collaborators and society remain elements of human relevance. The right balance between algorithmic analysis and strategic vision is the cornerstone of a responsible adoption of AI.

Industries Where Generative AI Is Transforming Business

As a professor of Responsible Artificial Intelligence and Digital Business & Innovation, I often see how some sectors are adopting AI extremely quickly. Many industries are already transforming rapidly. The financial sector, for example, has always been a pioneer in adopting new technologies: risk analysis, fraud prevention, algorithmic trading, and complex document management are areas where generative AI is proving to be very effective.

Healthcare and life sciences are taking advantage of AI advances in drug discovery, advanced diagnostics, and the analysis of large amounts of clinical data. Sectors such as retail, logistics, and education are also adopting AI to improve their processes and offer more personalized experiences. In light of this, I would say that no industry will be completely excluded from the changes: even “humanistic” professions, such as those related to medical care or psychological counseling, will be able to benefit from it as support, without AI completely replacing the relational and care component.

Integrating Generative AI into the Enterprise: Best Practices and Risk Management

A growing trend is the creation of specialized AI services AI-as-a-Service. These are based on large language models but are tailored to specific functionalities (writing, code checking, multimedia content production, research support, etc.). I personally use various AI-as-a-Service tools every day, deriving benefits from them for both teaching and research. I find this model particularly advantageous for small and medium-sized businesses, which can thus adopt AI solutions without having to invest heavily in infrastructure and specialized talent that are difficult to find.

Of course, adopting AI technologies requires companies to adopt a well-structured risk management strategy, covering key areas such as data protection, fairness and lack of bias in algorithms, transparency towards customers, protection of workers, definition of clear responsibilities regarding automated decisions and, last but not least, attention to environmental impact. Each AI model, especially if trained on huge amounts of data, can require significant energy consumption.

Furthermore, when we talk about generative AI and conversational models , we add concerns about possible inappropriate or harmful responses (so-called “hallucinations”), which must be managed by implementing filters, quality control and continuous monitoring processes. In other words, although AI can have disruptive and positive effects, the ultimate responsibility remains with humans and the companies that use it.

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