I strongly believe the knowledge of AI tools is becoming mandatory for survival in any field, and not even only engineering or knowledge-based fields, soon.
What are the fundamental tools of AI? It's all about signal processing on graphs. In fact, graphs are already ubiquitous in software engineering. It's more a question of designing "tailor-made" graphs in conjunction with ad-hoc information processing in order to solve one purposed industrial application. AI can be considered as an applicative field of signal processing theory in the broadest sense...
Refer to:
- Leus et al., "Graph Signal Processing - History, development, Impact and Outlook", 2023 - https://read.nxtbook.com/ieee/signal_processing/signal_processing_jun_2023_v2/graph_signal_processing.html
Having knowledge of AI tools is not necessarily "mandatory" for survival in the software industry, but it is increasingly beneficial and important. This is due to the increasing adoption of AI and machine learning technologies across various sectors and functions. Here are a few reasons why knowledge of AI tools can be helpful:
However, it's also important to note that the software industry is very broad and diverse. There are many areas of software development where AI might not be as directly relevant or applicable, such as front-end development, user experience design, or certain kinds of system-level programming. In these areas, other skills might be more critical for success.
Remember that the best way to stay relevant in the software industry is to be a continuous learner. Stay curious, keep learning, and try to keep up-to-date with the latest trends and technologies. AI is one of those trends, but it's not the only one.
Graphs (at least in the form of lists, arrays and trees) are an integral part of any self-respecting software package. It's 'just' that the current trend (well-posed, it goes without saying) favours the blossoming and the diversification of extended polyadic graphs more than ever, compared with well-known classical dyadic graphs. These "new" types of graphs make it possible to fully integrate 'topology' in a structural way in addition to the conventional 'geometry' (alternatively ordered set).
In simpler and more pictorial terms, we are facing with the extension of wire graphs (edge networks) to both surface graphs and higher-order volume graphs.
As Subharun Pal wrote above, continuous learning is an integral part of the software industry...
For more information about AI tools:
- Graph Neural Networks — Introduction for Beginners - https://ai.plainenglish.io/graph-neural-networks-introduction-for-beginners-aa3791f196c3
- M. Bronstein, "A new computational fabric for Graph Neural Networks", 2022 - https://towardsdatascience.com/a-new-computational-fabric-for-graph-neural-networks-280ea7e3ed1a
Excerpt from the 2nd reference : "In the deep learning era, persistent homology has had a “second life” after it was shown that one can back-propagate through it, allowing thus to integrate the already established TDA [Topological Data Analysis] apparatus in deep learning frameworks [...]"
While AI knowledge isn't "mandatory" for survival in the software industry, it's becoming as essential as coffee on Monday mornings! Embracing AI tools can make your life smoother than a well-oiled robot, boosting your efficiency and problem-solving skills. Don't fret if you're not an AI expert yet; the software industry is a diverse ecosystem. However, keeping up with AI trends is like adding extra toppings to your career pizza—it never hurts! So, dip your toes into AI's wondrous pool; it's not just about survival; it's about thriving like a coding wizard with a sprinkle of futuristic charm!
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