The Machine Mindset

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Showing posts with the label ChatGPT

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...

Beyond ChatGPT: The Future of Language Models and Personalized AI

Introduction The rise of Large Language Models (LLMs) such as ChatGPT has been revolutionary and is poised to radically change society as we know it. Over the last few months, many companies have started looked into creating their own “personalized LLMs”, tailored with insights derived from their company's specific documentation and data and fine-tuned for specific tasks. It is anticipated that these so-called Leveraged Pre-trained Language Models (LPLMs) will revolutionize various domains like healthcare, finance, and customer service by enabling more intuitive and personalized interactions, enhanced data analysis, and streamlined decision-making processes. While the rest of the early 2020s are poised for a significant integration of LPLMs, we can, in the near future, also look forward to Individualized Language Models (ILMs), tailored to suit individual preferences, needs, and purposes. In an interview with ...

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...

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 ...

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...

Unlocking Profit Potential: How to Make Money with ChatGPT

In recent years, artificial intelligence has revolutionized various industries. One of the exciting developments is the rise of conversational AI models like ChatGPT. ChatGPT is powered by OpenAI's GPT-3.5 architecture. A paid version uses the even more advanced GPT-4. ChatGPT has tremendous potential in enhancing user experiences and creating lucrative opportunities. This post will explore strategies and avenues through which you can leverage ChatGPT to generate income. These ideas were suggested by ChatGPT itself! Then the copy was rewritten and expanded on by myself. (This is a real-world example of how ChatGPT can help you create content!) Providing Virtual Assistance and Customer Support One of the most practical ways to monetize ChatGPT is by offering virtual assistance and customer support services . Many businesses struggle to handle large volumes of customer queries efficiently. This is where ChatGPT can be...

Awaiting the Shoggoth: Why AI Emergence is Uncertain – for Now

“It is absolutely necessary, for the peace and safety of mankind, that some of earth’s dark, dead corners and unplumbed depths be let alone; lest sleeping abnormalities wake to resurgent life, and blasphemously surviving nightmares squirm and splash out of their black lairs to newer and wider conquests.” ― H.P. Lovecraft, At the Mountains of Madness Horror fans might be familiar with author H.P. Lovecraft's fictional “shoggoths”, the shape-shifting and amorphous entities that he wrote about in his Cthulhu Mythos. In the context of AI emergence, the term "shoggoth" is sometimes used to refer to a possible futuristic advanced form of artificial intelligence. It highlights the idea of an AI system that can rapidly learn, evolve, and assimilate new information and skills, much like how Lovecraft's shoggoths can change their forms and abilities. Much has been made of so-called emergent abilities in AI....