Why 42% of Companies are Failing AI
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Why 42% of Companies are Failing AI

Your AI pilot worked brilliantly. It exceeded expectations, impressed stakeholders, and demonstrated clear technical feasibility. Yet six months later, it's still running in isolation while production deployment remains frustratingly out of reach. You're not alone—42% of companies are now abandoning most of their AI initiatives—up from just 17% last year (S&P Global) while executives continue pouring millions into projects destined for "pilot purgatory".

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From RAGs to Riches: Multimodal Retrieval
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From RAGs to Riches: Multimodal Retrieval

While large language models (LLMs) have made remarkable strides in processing and generating text, they often struggle with visual information. This limitation has led to a text-only bias in many AI systems, where they struggle to incorporate or generate visual content effectively.

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Custom Chatbots by Delphi Intelligence
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Custom Chatbots by Delphi Intelligence

What if you could have a tireless, knowledgeable assistant that knows your business inside and out? Imagine having the power to provide instant, accurate information to your customers or employees 24/7, while seamlessly aligning with your brand voice and values. With your own custom chatbot, this isn't just a business dream—it's now a reality.

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From RAGs to Riches: Data Conflicts
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From RAGs to Riches: Data Conflicts

One of the main challenges in RAG is dealing with knowledge conflicts between the pre-trained language model and the external knowledge sources. These conflicts can arise when the information in the external sources contradicts or differs from the knowledge learned by the language model during pre-training. To address this issue, researchers have developed techniques for assessing conflicts and calibrating model confidence.

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From RAGs to Riches: Misinformation
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From RAGs to Riches: Misinformation

Imagine an AI assistant that not only understands natural language but also has instant access to the most up-to-date information from your company's databases and beyond. By retrieving relevant information from external sources and integrating it with the LLM's output, Retrieval Augmented Generation (RAG) ensures that generated text is not only fluent but also accurate and applicable to the user's specific needs.

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Anticipating Customer Needs with AI Personas
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Anticipating Customer Needs with AI Personas

In today's competitive business and political landscape, understanding your target audience is more critical than ever. Whether you're a business owner looking to break into new markets or a politician trying to connect with constituents, having a deep understanding of the people you're trying to reach is essential for success.

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Yes, we KAN!
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Yes, we KAN!

For years, a type of artificial neural network called Multi-Layer Perceptrons (MLPs) has been the backbone of many machine learning applications, from basic classification tasks to cutting-edge models like transformers and large language models. However, in April 2024, Liu et al. introduced a revolutionary new approach called Kolmogorov-Arnold Networks (KANs), drawing inspiration from a mathematical concept known as the Kolmogorov-Arnold representation theorem.

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