Two years ago, if you had written “Agentic AI Engineer” on your resume, most hiring managers would have looked at you blankly.
Today, that same title is appearing in job postings from Bangalore to Berlin. Companies are advertising for it urgently, offering salaries that reflect genuine scarcity, and in many cases, not finding the people they need.
This is not a gradual skills gap. It is a sharp, sudden chasm between what the industry needs and what the talent pool can currently supply. And it opened so fast that even many experienced ML engineers are not yet on the right side of it.
It is really important for people who want to work with AI to learn these skills and understand why there is such a high demand for them.
In Natural Language Processing (NLP), embeddings transform human language into numerical vectors. These are usually arrays of multiple dimensions & have schematic meaning based on their previous training text corpus The quality of these embeddings directly affects the performance of search engines, recommendation systems, chatbots, and more.
But here’s the problem:
Not all embeddings are created equal.
So how do we measure their quality?
To Identify the quality of embeddings i conducted one experiment:
I took 3 leading (Free) Text → Embedding pretrained models which worked differently & provided a set of triplets and found the triplets loss to compare the contextual importance of each one.
Mental health care is an essential component of overall well-being, yet it remains one of the most underserved areas of medicine. The stigma surrounding mental health issues, coupled with limited access to qualified professionals, has created barriers to effective care for millions worldwide. AI-powered chatbots are emerging as a promising solution to bridge these gaps, providing accessible, scalable, and cost-effective mental health support. This blog explores how these innovative tools revolutionize mental health care, their challenges, and their potential future impact.
History of AI in Mental Health Care
The integration of artificial intelligence into mental health care has a rich and evolving history. The journey began in the mid-20th century with the development of early AI programs designed to simulate human conversation. One of the earliest examples was ELIZA, created in the 1960s by computer scientist Joseph Weizenbaum. ELIZA was a rudimentary chatbot that used pattern matching and substitution methodology to simulate a psychotherapist’s responses. While basic by today’s standards, ELIZA demonstrated the potential of conversational AI in providing mental health support.
The field of natural language processing has witnessed remarkable advancements over the years, with the development of cutting-edge language models such as GPT-3 and the recent release of GPT-4. These models have revolutionized the way we interact with language and have opened up new possibilities for applications in various domains, including chatbots, virtual assistants, and automated content creation.
What is GPT?
GPT is a natural language processing (NLP) model developed by OpenAI that utilizes the transformer model. Transformer is a type of Deep Learning model, best known for its ability to process sequential data, such as text, by attending to different parts of the input sequence and using this information to generate context-aware representations of the text.
What makes transformers special is that they can understand the meaning of the text, instead of just recognizing patterns in the words. They can do this by “attending” to different parts of the text and figuring out which parts are most important to understanding the meaning of the whole.
For example, imagine you’re reading a book and come across the sentence “The cat sat on the mat.” A transformer would be able to understand that this sentence is about a cat and a mat and that the cat is sitting on the mat. It would also be able to use this understanding to generate new sentences that are related to the original one.
GPT is pre-trained on a large dataset, which consists of:
Yes, it is Artificial Intelligence, Machine Learning, Data Science, and Data Engineering.
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Artificial intelligence (AI) is having a profound impact on many different industries and is transforming the way businesses and organizations operate and serve their customers. With the help of AI, organizations are able to automate complex processes, make better predictions and decisions, and provide more personalized and efficient services to their customers.
One of the key areas where AI is making a big difference is in the field of healthcare. AI algorithms are being used to analyze medical data, such as images, records, and biomarkers, and to make more accurate predictions about the likelihood of diseases and the effectiveness of treatments. This can help healthcare providers to diagnose and treat patients more effectively, and improve the overall quality of care.
In the telecom industry, the use of AI and data science is becoming increasingly important for companies that want to stay competitive and deliver the best possible services to their customers.
Only by leveraging the power of AI and data science, telecom companies can gain valuable insights into their operations and make data-driven decisions that can help them improve efficiency, reduce costs, and develop new products and services.
One key area where AI and data science can help telecom companies is in network optimization. By analyzing vast amounts of data from network sensors and other sources, AI algorithms can identify patterns and anomalies that can indicate where the network is underperforming or prone to failure. This can help telecom companies take proactive steps to improve network reliability and reduce downtime, leading to a better overall customer experience.
In today’s competitive manufacturing landscape, companies that want to stay ahead of the curve are turning to AI and data science to improve efficiency and drive innovation. By harnessing the power of AI and data science, manufacturing companies can gain valuable insights into their operations and make data-driven decisions that can help them improve productivity, reduce costs, and develop new products and services.
One key area where AI and data science can help manufacturing companies is in the realm of predictive maintenance. By analyzing vast amounts of data from sensors and other sources, AI algorithms can identify patterns and anomalies that can indicate when equipment is likely to fail. This can help companies schedule maintenance and repairs at the optimal time, reducing downtime and improving overall equipment reliability.
As AI and other technologies continue to advance, it is likely that many jobs that are currently considered essential will become obsolete, while new job opportunities will emerge in areas related to AI and other emerging technologies.
