
Hey there! I'm an SRE, not your average suit-and-tie techie.  Based in Seattle, I've spent 17+ years navigating the intricate world of BFSI tech. Think of me as a problem-solving ninja, wielding my expertise in production support, AWS, and cloud computing to tame even the most complex technical beasts. ⚔️

I'm not just all code and algorithms, though. I believe in leading by doing, collaborating seamlessly, and building strong relationships across the board.  Whether it's crafting innovative solutions or strategizing for efficiency, I'm passionate about making things tick and helping others do the same.

**So, why am I here?** Well, I love sharing my knowledge and experiences, and I hope this blog becomes a space for open discussions, valuable insights, and maybe even a few laughs along the way.  Stay tuned for tech talk, industry insights, and a peek into the life of an SRE who doesn't take himself too seriously.


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Uma Shankar Shanmugam


Working in cybersecurity means you’re constantly playing a game of catch-up. Every day you learn something new, but your work is also never finished.

Whether you are a security researcher developing threat intelligence to power IDS / IPS systems or someone trying to plug holes in an area like application security, you will constantly be forced to find new solutions for unknown problems.

To Dutch security engineer Ferry (he prefers us not to share his last name), it’s this ever-evolving part of infosec that’s kept him in the field for over a decade. He currently works for Rijks ICT Gilde, a group of tech consultants who work on a variety of Dutch government IT projects. 

> “The thing I love most about my job is that every day comes with new challenges and opportunities. There are always new vulnerabilities to expose, new detection systems to set up, new hackers to catch. This job really is never done.”

Security engineers love working in the field of cybersecurity, also known as infosec or fosec, for a variety of reasons. One of the main reasons is the constant challenge and ever-evolving nature of the field. With new technologies and threats emerging all the time, security engineers are always learning and adapting to stay ahead of potential attacks.

Another reason security engineers enjoy working in fosec is the sense of purpose and importance of their work. With the increasing reliance on technology in nearly all aspects of our lives, the need for robust and effective cybersecurity measures is more critical than ever. Security engineers play a critical role in protecting sensitive information and ensuring the continuity of vital systems and services.

Additionally, the field of fosec offers many career opportunities and paths. From incident response and penetration testing to risk management and compliance, there are a wide range of specializations and roles to choose from. This variety allows security engineers to find a career that aligns with their interests and skills.

Finally, many security engineers find the field to be intellectually stimulating and rewarding. The field is full of complex problems and puzzles, and when a solution is found, it can be extremely satisfying.

Overall, security engineers appreciate the dynamic and challenging nature of fosec, the sense of purpose and importance of their work, the variety of career paths available, and the intellectual stimulation and rewards of the field.

Working in cybersecurity means you’re constantly playing a game of catch-up. Every day you learn something new, but your work is also never finished.

Why the security engineers loves working in fosec


A team of researchers at Jilin Engineering Normal University in China recently published a paper indicating they’d built an AI model capable of recognizing human facial expressions.

I’m going to save you some time here: they most certainly did not. Such a thing isn’t currently possible.

The ability to accurately recognize human emotions is what we here at Neural would refer to as a “deity-level” feat. The only people who truly know how you’re feeling at any given moment are you and any potential omnipotent beings out there.

Here are my three golden tips for realistic planning:

* Underestimate how much you can get done in a day
* Overestimate how much time a task will take
* Overestimate interruptions during your day

### The reason some bootcamp grads are set up to fail
* In my experience, not all bootcamps are created equal. They vary widely in acceptance processes, curriculum, program structure, and quality of instructors. They’re designed to push candidates through courses that will have them writing some code fairly quickly.

* As a result, programs are forced to strip away a lot of fundamentals — those basics that help developers understand the “why” behind the code they’re writing.

* When young software developers learn by copying and pasting, it can make troubleshooting difficult when they come across something that doesn’t fit the pattern they’re used to.

* Small businesses and startups are those most likely to hire developers without degrees, but I’ve seen too many bootcamp grads take jobs at startups only to find they haven’t learned enough to make any real impact.

### Up front:
The research is fundamentally flawed because it conflates facial expression with human emotion. You can falsify this premise by performing a simple experiment: assess your current emotional state then force yourself to make a facial expression that presents in diametric opposition to it.

If you’re feeling happy and you’re able to “act” sad, you’ve personally debunked the whole premise of the research. But, just for fun, let’s keep going.

### Background:
Don’t let the hype fool you. The researchers don’t train the AI to recognize expressions. They train the AI to beat a benchmark. There’s absolutely no conceptual difference between this system and one that tries to determine if an object is a hotdog or not.

What this means is the researchers built a machine that tries to guess labels. They’re basically showing their AI model 50,000 pictures, one at a time, and forcing it to choose from a set of labels.

### The problem:
All of this seems useful because, when it comes to outcomes that don’t affect humans, predictions models are awesome.

When AI models try to predict something objective, such as whether a particular animal is a cat or a dog, they’re aiding human cognition.

You and I don’t have the time to go through every single image on the internet when we’re trying to find pictures of a cat. But Google’s search algorithms do.

### Neural take:
This is a total scam. The researchers present their work as useful for “fields like human–computer interactions, safe driving … and medicine,” but there’s absolutely no evidence to support their assertion.

The truth is that “computer interactions” have nothing to do with human emotion, safe driving algorithms are more efficacious when they focus on attention instead of emotionality, and there’s no place in medicine for weak, prediction-based assessments concerning individual conditions.

A team of researchers at Jilin Engineering Normal University in China recently published a paper indicating they’d built an AI model capable of recognizing human facial expressions.

Tech

Why the security engineers loves working in fosec

Why ‘facial expression recognition’ AI is a total scam