How can you prepare for the future of computer science?

I think the future of computers is the quantum computers. Therefore one should initially understand the quantum reality and uncertainty of the universe. Modern Physics = Computers

Computer Science isn’t just about computers, but computational thinking and problem-solving in everyday life. Start with the concept of understanding how all systems, including our body’s nervous system, follow the Input, Process, Output, and Storage model. Then use decomposition, abstraction, pattern recognition, and algorithmic thinking to understand, analyze, design, create, and reflect on the solutions. That way, learners can get a better understanding of why seemingly complex systems are, in fact, just a combination of multiple layers of smaller and simpler systems working together synchronously to provide the solutions that meet the needs of their users.

A complete rethinking of the competencies for software developers is required. The level of generalization of the tasks to be solved increases significantly. Critical thinking and the breadth of basic (background) education come to the fore. Digital skills are becoming mandatory for everyone and include synergy with artificial intelligence.

There are two aspects to consider. If we want to see ourself in future in an IT industry or in core computer science research. Fundamentals for someone who wants to see themselves an a Computer Engineer, there are bundles of opportunities and each opportunity has its own fundamentals that align with the fundamental of computer science. If the goal is being a computer scientist, than the fundamental of core computer subjects along with mathematics is a must.

This is a very important step, not only for working in the computer science field, but it is essential for adapting to new trends and changes. It's always about the fundamentals. By learning the fundamentals, not only can you adapt to the new trends more quickly and efficiently, but you can contribute to the process.

Living in the era of AI, one of the key ingredients in Computer Science will be the daily interaction with AI-powered algorithms, such as GitHub Co-pilot, which will do the coding heavy lifting. This new way of working, will increase productivity, albeit transforming many CS roles. To adapt to this new paradigm, the focus must shift towards higher and more abstract concepts. The future Computer Scientist will be an amalgam of high-level planner (something machines are as of yet incapable of), a creative (as it is now but not bogged down by the technical aspect of it) and an ethicist to keep machines in check with human values. This will enable new breakthroughs in many CS fields such as program synthesis and computer-automated design.

Computer science delves into scientific, technical, and even philosophical realms. Studying philosophy helps computer scientists understand the ethical implications of AI and other creations. Moreover, it hones their critical thinking skills, preventing them from succumbing to daily fads. It also provides clarity to the often-ambiguous and subjective concepts they encounter on a regular basis. Computer scientists without philosophical knowledge may find it challenging to answer questions such as: What is an algorithm? What is a program? What distinguishes specification from implementation? Incorporating philosophy into computer science is of utmost importance.

You definitely can't ignore the intersections of computer science any more. Each field has something useful to offer and it's in your best interest for you to learn everything you can about all the different fields. There's a lot of links that you might discover, and companies are valuing domain knowledge paired with tech skills, instead of just being a coder or a software engineer.

Existing educational institutions that train specialists in computer science should be radically changed to meet the requirements of digital online pedagogy, and practicing specialists should be retrained for new, more productive IT technologies.

Buscar estudar e explorar novos domínios de tecnologia, conhecer novas tecnologias, ser autodidata na resolu??o de novos problemas e oportunidades que esses domínios oferecem aos profissionais de TI, criar e testar antes de colocar em prática para rodar.

Preparing for the future of computer science means staying updated with the latest trends: 1. Continuous Learning: Embrace a lifelong learning mindset. Attend workshops, webinars, and online courses. 2. Tech News: Regularly read tech news and publications to understand emerging technologies. 3. Networking: Connect with peers and experts in the field through conferences and forums. 4. Experiment: Don't be afraid to experiment with new tools and languages. 5. Problem-Solving: Focus on honing your problem-solving skills, a timeless asset in computer science. By staying informed and adaptable, you'll be well-prepared for the ever-evolving world of computer science.

Staying up to date with the trends doesn't have to be hard. It can be a matter of subscribing to the TLDR daily newsletter for tech updates as well as checking Fireship for regular code reports. It's good to stay on top of it so that you aren't left behind and you're able to apply your foundational knowledge to new contexts.

Staying abreast of computer science trends involves the following considerations: 1) Responsibility: In the era of AI, introducing the human factor into the equation becomes imperative, as certain tools can significantly impact human life. 2) Enhance Critical Thinking: As an increasing array of tools emerges, many are saturated with marketing and false promises. Exercise caution not to be swayed by trends. 3) Opportunities to Expand Your Network: Never before have we had the chance to connect with individuals worldwide; seize this opportunity. In contrast to other fields of knowledge, computer science actively shapes our world by providing support to a multitude of disciplines, spanning from the sciences to the humanities.

Computer programming is an exercise of the mind. As with the gym, consistency is key. Make sure you write at least one line of code every day. Just make it a ritual if you wish. What matters is that it will add up over time. One of the best ways is to make this practice enjoyable. Contribute to an open-source codebase of a library that you use, build a new project on a new technology you like, or dedicate time to learn online and then publish the code along with a curated README about what you have learned. Remember that it is the repetition that makes the coding craft (and the coders) achieve expertise, rather than talent.

Practice is so crucial. It's important that you try to do what you can to apply your skills, even if it's on a small scale. It doesn't need to be a big project, it can be a few small ones that showcase different sides of your abilities. It's important to also document this through a blog or a newsletter so that you have a record of what you've done.

Computer science is not solely about programming. Some practical and valuable skills include: 1) Learn to read scientific papers: Begin by reading the title, then the abstract, followed by the conclusions, and conclude with the introduction. If it piques your interest, delve into the other sections. 2) Do not neglect the theoretical aspect: Theory underpins practice, as understanding it provides context and significance to practical applications. 3) Writing: The most effective way to comprehend something is by teaching it, and one way to achieve this is through writing. Writing is a reflection of the clarity of your thoughts.

A general discussion on this topic is needed to involve the maximum number of stakeholders and reduce the transition period to new AI-oriented technologies.

Receiving feedback helps improving and sharpening skills and perspectives. But not everyone has the chance to have a mentor available. So, what could be a possible solution? You might consider to join at least one (though multiple would be even better) data science community. There are many LinkedIn Groups focused on Data Science, where you can post your projects, and ask for feedback. At the same time, you can have a look at the publications of fellow professionals and extend your knowledge of alternative methods for analyzing and visualizing data.

As a computer scientist, it's tempting to prioritize beloved tools over problem-solving. However, it's vital to resist this urge. Exercising skepticism is crucial when confronted with assertions that one tool is better than another. The importance of a neutral and objective evaluation of every tool cannot be overstated. It is important to make decisions based on rational thinking rather than emotions. Consistently asking questions is a great way to cultivate curiosity and promote daily learning. Keep exploring and creating. Never stop.

The key to staying ahead of the curve is to keep up to date. Curiosity drives us to step out of the comfort zone, going beyond the already consolidated, known, and familiar methods and tools, to experiment and explore new solutions. This demands extra time, beyond the daily workload, often taken from evenings and weekends. Moreover, it's not always plain sailing, the new solutions might not align or benefit. Without curiosity as the driving force, time might feel wasted. But it's never, ever time wasted. It's an investment, experience gained, and knowledge acquired. Attitude is the key to staying motivated. Curiosity enables consistency, while adaptability facilitates the integration of new solutions into our processes.

In today’s fast paced world where technology is evolving every second, one should be prepared to come out of comfort zones. One needs to be hands-on with new technologies. Expand your network to include people who are at forefront of emerging technologies. Don’t compromise on learning advancement in a domain unrelated to yours. After all, new discoveries can always be found at merging of inter-disciplinary domains.

While I agree with "Learn the fundamentals at first" statement However, I would press more to Choose a technology aligned with personal interest and delve deep. Mastery, not mere familiarity. The allure of multitasking with various technologies often leads to scattered knowledge. Depth trumps breadth. Becoming an expert in one technology holds more value than a superficial grasp of many. Commit to the chosen technology. In the rapidly evolving landscape, stay focused. Avoid the distraction of shiny new technologies until mastery is attained. The journey to technological mastery is deliberate, demanding patience, dedication, and consistent effort. This approach fosters a successful and fulfilling career in computer science

One more thing I want to add. While we prepare for the future of computer science we need to follow the global trends on where the world is advancing. Any areas that technology is being used and is being popular, computer science comes as a key resource. If we are advancing towards augmented reality and artifical intelligence, computer science should also think about the fundamentals on how we can blend our algorithms, techniques, practices and resources for the betterment of the same.

I might have not seen it (, and it might be here already), but at the end of the day, you are solving someone's problems with software. If computer science just stays in the realm of the theoretical, then I would argue it is just (intellectual) chit-chat. On the other hand, if you are solving (interesting) problems, then you also have to bear in mind that a software project is also a socio-technical system, and as such, the social variables you can think of, really have a role/weight in the success of our activity. See the work done so far by Evelyn van Kelle & Kenny Baas-Schwegler in this respect.

Synthesizing insights, both traditional and AI-guided: This amalgamation underscores the potential of AI-driven exploration in broadening horizons. By embracing similar AI-informed approaches, individuals can uncover multifaceted strategies to navigate the dynamic landscape of computer science. Exploration of these insights can further inspire proactive steps toward success in the future of computer science, similar to the additional insights below.