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--- career_directions_for_computer_science_students [2025.08.20 05:40] – Steve Isenberg
+++ career_directions_for_computer_science_students [2025.08.20 11:22] (current) – Steve Isenberg
@@ Line 4: / Line 4: @@
 {{counter|total| time| total times}} since 8/20/2025.</fs>//
-Much of this information, ironically, was supplied by ChatGPT with mods by steve.
+Much of this information, ironically, was supplied by ChatGPT with mods by steve. \\
+Updated 20aug2025.
+====Industry Advice====
+Advice from people currently in the industry, for your information only.
+  * The networking and system administration careers are fairly secure.
+  * Data analytics and cybersecurity are still growing fields. They will probably adapt and change with new AI tools but should still involve highly trained humans as well.
+  * There are likely additional areas for example SAP is growing market share like crazy versus Oracle.
 ====== Career Outlook for Graduate Students in Computer Science in the Age of AI ======
@@ Line 20: / Line 27: @@
   * *Machine Learning Engineer* – high demand, salaries ~$160k.
   * *AI Research Scientist* – fastest growth (~26% between 2023–2033).
-  * *Computer Vision & NLP Engineer* – widely used across industries.
+  * *Computer Vision & NLP((NLP: Natural Language Processing)) Engineer* – widely used across industries.
   * *Algorithm Engineer & Prompt Engineer* – vital for AI systems.
@@ Line 27: / Line 34: @@
   * *Quantum Computing* – algorithms, hardware/software integration.
   * *Bioinformatics / Computational Biology* – multidisciplinary growth.
-  * *Cybersecurity, Cloud/Edge Computing, IoT, XR, Blockchain* – expanding specializations.
+  * *Cybersecurity, Cloud/Edge Computing, IoT((IoT: Internet of Things)), XR((XR: Extended Reality; an umbrella term that covers VR (Virtual Reality): fully immersive digital environments; AR (Augmented Reality): overlaying digital content onto the real world; and MR (Mixed Reality): blending physical and digital worlds with interactive elements.)), Blockchain* – expanding specializations.
 === Leadership & Governance ===
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 ^ Specialization / Skill                       ^ Why It Helps                                   ^
-| ML / Deep Learning / MLOps                   | Automation-resilient, high demand, strong pay   |
+| ML((ML: Machine Learning)) / Deep Learning / MLOps                   | Automation-resilient, high demand, strong pay   |
 | NLP / Computer Vision / Algorithm Design     | Core AI skills needed across industries         |
 | Quantum / Bioinformatics / Cybersecurity     | Specialized, growing fields with research value |
@@ Line 71: / Line 78: @@
   * [[https://www.itpro.com/technology/artificial-intelligence/entry-level-jobs-ai-anthropic-dario-amodei|AI could wipe out half of entry-level jobs in 5 years (ITPro)]]
   * [[https://www.thetimes.co.uk/article/ai-adoption-could-add-billions-to-s-and-p-500-says-morgan-stanley-lmvhrvmvf|AI adoption could add billions to S&P 500 (The Times)]]
+——
+This is another take from Claude.ai 20aug2025
+======Career Guidance for CS Graduate Students in the AI Era======
+====Overview===
+The emergence of AI creates both challenges and tremendous opportunities for computer science graduates. Rather than viewing AI as a threat, students should see it as a field that's creating entirely new career paths while transforming existing ones.
+====High-Opportunity Focus Areas====
+===AI/ML Engineering and Research===
+The field needs people who can build, deploy, and improve AI systems. This includes roles in machine learning engineering, AI research, and developing specialized AI applications across industries.
+===AI Safety and Alignment===
+As AI systems become more powerful, there's growing demand for experts who can ensure these systems behave reliably and ethically. This is a rapidly expanding field with significant funding.
+===Human-AI Interaction==
+Designing interfaces and experiences that effectively combine human intelligence with AI capabilities. This spans UX design, prompt engineering, and building AI-augmented workflows.
+===AI Infrastructure and DevOps===
+The infrastructure needed to train, deploy, and scale AI systems requires specialized expertise in distributed computing, model optimization, and MLOps.
+===Domain-Specific AI Applications===
+Applying AI to specialized fields like healthcare, robotics, cybersecurity, or scientific research often requires deep technical knowledge beyond just AI fundamentals.
+===Core CS Fundamentals===
+Strong foundations in algorithms, systems design, databases, and software engineering become more valuable, not less, as they're needed to build robust AI-powered systems.
+===Job Outlook Perspective===
+Rather than AI eliminating programming jobs, it's creating new types of technical roles while making programmers more productive. Companies are hiring more developers, not fewer, as AI enables them to tackle previously impossible projects.
+===Key Takeaway===
+The key is embracing AI as a powerful tool while developing the deep technical skills needed to build the next generation of intelligent systems.