AI is finding it's way in a wide variety of applications pertaining to almost every industry. This AI driven rapidly evolving landscape has created a demand for a unique blend of technical, creative, and interpersonal skills highly sought-after by employers. Listed below are some specialized AI-related skills that are becoming increasingly valuable in the modern times. 1. AI Models Development Understanding how AI and ML work including the underlying algorithms, and learning to develop ML powered apps using tools like TensorFlow or PyTorch is a highly desirable skill to master in the age of AI. Furthermore, the skills in fine-tuning and adapting large pre-trained models (like GPT, BERT, or Vision Transformers) to specific use cases are also useful, allowing you to create specialized applications without starting from scratch. Leveraging pre-trained models and adapting them to new tasks with limited data is particularly useful in NLP and computer vision. 2. AI Models Deployme...
When it comes to safety-critical software—whether in aircraft avionics, medical devices, nuclear control systems, or automotive braking—failure isn’t just an inconvenience; it can be catastrophic. That’s why safety-critical software engineers don’t just focus on functionality—they focus on fault detection, error prevention, and fail-safe design . Over decades of practice and research, engineers have developed a range of techniques to make sure that when something goes wrong, it is either detected, mitigated, or rendered harmless . Below are some of the most widely used safety-related implementation techniques that help ensure such software performs reliably under all conditions. 1. Checksums and Cyclic Redundancy Checks (CRC) Data corruption during transmission or storage can cause unpredictable behavior in safety-critical systems. To guard against this, developers use checksums and CRC (Cyclic Redundancy Checks) to verify the integrity of data. A checksum works by computing a ...