Dear Shah Jahan ,

Generative AI is at a crossroads. It's now more than two years since ChatGPT's launch, and the initial optimism about AI's potential is decidedly tempered by an awareness of its limitations and costs.

The 2025 AI landscape reflects that complexity. While excitement still abounds -- particularly for emerging areas, like agentic AI and multimodal models -- it's also poised to be a year of growing pains.

Companies are increasingly looking for proven results from generative AI, rather than early-stage prototypes. That's no easy feat for a technology that's often expensive, error-prone and vulnerable to misuse. And regulators will need to balance innovation and safety, while keeping up with a fast-moving tech environment.

Here are eight of the top AI trends to prepare for in 2025.

https://www.techtarget.com/searchenterpriseai/tip/9-top-AI-and-machine-learning-trends

Regards,

Shafagat

Ma'am Shafagat Mahmudova

Thank you for your insightful response and for sharing the AI trends article. I completely agree that AI is at a crucial turning point, with both exciting advancements and significant challenges ahead.

As a beginner in AI/ML, I find these evolving trends fascinating, especially the shift towards more practical and reliable AI solutions. My "AI Essay Feedback Tool" was a small step in exploring generative AI’s potential, and I’m eager to deepen my understanding under expert guidance.

Best regards, Shah Jahan

If you're interested in AI/ML research and seeking guidance, start by building a strong foundation in mathematics (linear algebra, calculus, probability) and programming (Python, TensorFlow, PyTorch). Engage with online courses, textbooks, and research papers to understand core concepts and stay updated on advancements. Join AI/ML communities, attend conferences, and collaborate on open-source projects to gain practical experience. Seek mentorship from professors, industry professionals, or online platforms, and consider pursuing internships or research opportunities to apply your knowledge. Focus on identifying a specific area of interest within AI/ML, such as NLP, computer vision, or reinforcement learning, and contribute to the field by publishing your findings or developing innovative solutions.

Hi Shah Jahan,

Since you've already built an "AI Essay Feedback Tool" the next step is to find gaps in existing solutions and build something that actually solves a real problem. AI is evolving fast, and keeping up with trends like Generative AI and Agentic AI can help you stay ahead. Look at where AI is lacking maybe in better personalization, explainability, or real-time feedback and work on improving that. Also, staying active in research communities and experimenting with new ideas will help you grow in this field. Keep building and refining your approach.