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Choose your Champion! Task-Specific vs. General Models

Should AI models be like Swiss Army knives, versatile and handy in a variety of scenarios? Or do we prefer them as precision tools, finely tuned for specific tasks? In the world of artificial intelligence, and natural language processing specifically, this is an ongoing debate. The question boils down to whether models trained for specific tasks are more effective at these tasks than general models. Task-specific models: specialization and customization In my last blog post , we looked at the rise of personalized LLMs, customized for specific users. Personalized LLMs can be seen as an extreme form of task-specific model. Fans of task-specific models stress that these kinds of models are better suited for tasks involving confidential or proprietary data. This is obviously true. But some people also believe that specialized models necessarily perform better in their specific domains. It may sound logical, but the ans...
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Liquid Networks: Unleashing the Potential of Continuous Time AI in Machine Learning

In the ever-expanding realm of Artificial Intelligence (AI), a surprising source has led to a new solution. MIT researchers, seeking innovation, found inspiration in an unlikely place: the neural network of a simple worm. This led to the creation of so-called "liquid neural networks," an approach now poised to transform the AI landscape. Artificial Intelligence (AI) holds tremendous potential across various fields, including healthcare, finance, and education. However, the technology faces various challenges. Liquid networks provide answers to many of these. These liquid neural networks have the ability to adapt and learn from new data inputs beyond their initial training phase. This has significant potential for various applications, especially in dynamic and real-time environments like medical diagnosis and autonomous driving. The strengths of scaling traditional neural networks While traditional n...
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Recruitment: Balancing AI Efficiency and Human Connection

Introduction Artificial intelligence (AI) is transforming various industries in today’s fast-paced digital world. Recruitment is no exception. The adoption of AI technology in the hiring process has revolutionized how candidates are evaluated. AI in recruitment can assess large amounts of data quickly. However, it is essential to balance AI's capabilities and the valuable role of human recruiters. According to a recent report by the World Economic Forum, AI is not poised to completely replace HR professionals soon. While AI systems offer strengths in certain areas, they also have limitations. Most AI tools are designed to help with specific parts of HR tasks. They are not meant to replace human involvement. This post will explore some strengths and drawbacks of using AI in recruitment. We will see how combining AI and human intelligence is an optimal solution in the near term. The strengths of AI in recrui...
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Don't Look Now, but the Bots Are Designing New Proteins...

Picture tiny protein architects effortlessly combining like pieces of an intricate puzzle to build nanoscale structures with mind-boggling precision. Dream or nightmare? These self-assembled protein structures hold the promise of creating entirely new materials with properties that defy our current imagination. But there are those who fear they also hold the key to the annihilation of all humankind… Welcome to the fusion of machine learning (ML) and protein synthesis. It’s not so far away as you might think. Say the words “artificial intelligence,” and most people today will probably think of the large language models like ChatGPT or any of the AI art generators . But many other ML techniques are used in various fields with equally exciting applications. Protein prediction and synthesis is one such area. ML is making remarkable advancements with implications for biotechnology and materials science. It works like t...
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Why the Bots Hallucinate – and Why It's Not an Easy Fix

It’s a common lament: “I asked ChatGPT for scientific references, and it returned the names of non-existent papers.” How and why does this happen? Why would large language models (LLMs) such as ChatGPT create fake information rather than admitting they don’t know the answer? And why is this such a complex problem to solve? LLMs are an increasingly common presence in our digital lives. (Less sophisticated chatbots do exist, but for simplification, I’ll refer to LLMs as “chatbots” in the rest of the post.) These AI-driven entities rely on complex algorithms to generate responses based on their training data. In this blog post, we will explore the world of chatbot responses and their constraints. Hopefully, this will shed some light on why they sometimes "hallucinate." How do chatbots work? Chatbots such as ChatGPT are designed to engage in conversational interactions with users. They are trained on large ...
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How the Robots Learned to Speak: A Look at the Evolution of NLP

Large language models (LLMs) like ChatGPT are designed to generate human-like text based on the input they receive. For this, they use various natural language processing (NLP) techniques. In recent years, NLP has undergone remarkable advancements. It has revolutionised the way machines understand and generate human language. NLP has become a cornerstone of modern AI systems, enabling applications such as translation, sentiment analysis, text summarization, chatbots, and more. In this blog post, we will take a trip through history to explore the foundation of NLP through information retrieval, the evolution to the Vector Space Model, and the subsequent advancements that have shaped the field. We will also discuss the challenges and future directions in NLP as researchers continue to push the boundaries of language understanding and processing. Foundation of Natural Language Processing: Information Retrieval At the he...
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