Enhancing Customer Support on WhatsApp: Building a WhatsApp Chatbot with Human Handover using…

This post was originally published on this site Published in · 7 min read · Nov 9, 2023 In today’s digital age, WhatsApp has become a preferred communication channel for businesses and customers. WhatsApp Business API once limited to a select few, is now within reach of a broader range of businesses, offering new opportunities […]

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Unlocking the Recall Power of Large Language Models: Insights from Needle-in-a-Haystack Testing

The rise of Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), enabling significant progress in text generation and machine translation. A crucial aspect of these models is their ability to retrieve and process information from text inputs to provide contextually relevant responses. Recent advancements have seen a trend towards increasing the size of context windows, with models like Llama 2 operating at 4,096 tokens, while GPT-4 Turbo and Gemini 1.5 handle 128,000 and an impressive 10M tokens, respectively. However, realizing the benefits of a longer context window hinges on the LLM’s ability to recall information from it reliably.

With the proliferation of LLMs, evaluating their capabilities is crucial for selecting the most appropriate model. New tools and methods, such as benchmark leaderboards, evaluation software, and innovative evaluation techniques, have emerged to address this issue. “Recall” in LLM evaluation assesses a model’s ability to retrieve factoids from prompts at different locations, measured through the needle-in-a-haystack method. Unlike traditional Natural Language Processing metrics for Information Retrieval systems, LLM recall evaluates multiple needles for comprehensive assessment.

The researchers from VMware NLP Lab explore the recall performance of different LLMs using the needle-in-a-haystack method. Factoids (needles) are hidden in filler text (haystacks) for retrieval. Recall performance is evaluated across haystack lengths and needle placements to identify patterns. The study reveals that recall capability depends on prompt content and may be influenced by training data biases. Adjustments to architecture, training, or fine-tuning can enhance performance, offering insights for LLM applications.

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2 Popular Artificial Intelligence (AI) Stocks to Sell Before They Drop 29% and 77%, According to Certain Wall Street … – Yahoo Finance

This post was originally published on this site The artificial intelligence (AI) gold rush is in full swing. The technology-heavy Nasdaq Composite advanced 31% over the past year as investors piled into the market, hoping to strike it rich. Sentiment is still running hot, but it’s important to be choosy when buying stocks. Not every

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Improving Speech Recognition on Augmented Reality Glasses with Hybrid Datasets Using Deep Learning: A Simulation-Based Approach

Google AI researchers showed how a joint model combining sound separation and ASR could benefit from hybrid datasets, including large amounts of simulated audio and small amounts of real recordings. This approach achieves accurate speech recognition on augmented reality (AR) glasses, particularly in noisy and reverberant environments. This is an important step for enhancing communication experiences, especially for individuals with hearing impairments or those conversing in non-native languages. Traditional methods face difficulties in separating speech from background noise and other speakers, necessitating innovative approaches to improve speech recognition performance on AR glasses.

Traditional methods rely on recorded impulse responses (IRs) from actual environments, which are time-consuming and challenging to collect at scale. In contrast, using simulated data allows for the quick and cost-effective generation of large amounts of diverse acoustics data. GoogleAI’s researchers propose leveraging a room simulator to build simulated training data for sound separation models, complementing real-world data collected from AR glasses. By combining a small amount of real-world data with simulated data, the proposed method aims to capture the unique acoustic properties of the AR glasses while enhancing model performance.

The proposed method involves several key steps. Firstly, real-world IRs are collected using AR glasses in different environments, capturing the specific acoustic properties relevant to the device. Then, a room simulator is extended to generate simulated IRs with frequency-dependent reflections and microphone directivity, enhancing the realism of the simulated data. The researchers develop a data generation pipeline to synthesize training datasets, mixing reverberant speech and noise sources with controlled distributions. 

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History Says the Nasdaq Will Continue to Soar in 2024: My Top 5 Artificial Intelligence (AI) Growth Stocks to Buy Before … – Yahoo Finance

This post was originally published on this siteAfter enduring one of its worst performances since 2008, the Nasdaq Composite has turned things around, gaining 50% since the beginning of last year. After such a sharp move higher, some investors wonder if much upside remains. Yet a review of the data suggests the current market rally

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create-tsi: A Generative AI RAG Toolkit that Generates AI Applications using LlamaIndex with Low Code

In the digital age, creating AI applications can be complex and time-consuming. Many businesses struggle to develop AI bots or agents that can efficiently handle tasks and answer questions. Existing solutions often require extensive coding knowledge and lack flexibility, making them inaccessible to many.

Some existing solutions attempt to streamline the AI application creation process but often have limitations. These solutions may offer pre-built templates or drag-and-drop interfaces, but they still require technical expertise from users. 

Meet create-tsi, a generative AI toolkit that simplifies the process of building AI applications. With create-tsi, users can easily generate full-stack, enterprise-grade AI applications, all through a simple command-line interface. This toolkit is designed to be user-friendly, flexible, and fast, making it accessible to a wide range of businesses and developers.

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Megalodon: A Deep Learning Architecture for Efficient Sequence Modeling with Unlimited Context Length

Developing and enhancing models capable of efficiently managing extensive sequential data is paramount in modern computational fields. This necessity is particularly critical in natural language processing, where models must process long text streams seamlessly, retaining context without compromising processing speed or accuracy. One of the key challenges within this scope is the traditional reliance on Transformer architectures, which, despite their broad adoption, suffer from quadratic computational complexity. 

Existing research includes the Transformer architecture, which, despite its efficacy, suffers from high computational costs with longer sequences. Alternatives like linear attention mechanisms and state space models have been developed to reduce this cost, though often at the expense of performance. With its gated attention mechanism and exponential moving average, the LLAMA model and the MEGA architecture aim to address these limitations. However, these models still face challenges in scaling and efficiency, particularly in large-scale pretraining and handling extended data sequences.

Researchers from Meta, the University of Southern California, Carnegie Mellon University, and the University of California San Diego have introduced MEGALODON, a model designed to efficiently handle sequences of unlimited length—a capability that existing models struggle with. By integrating a Complex Exponential Moving Average (CEMA) and timestep normalization, MEGALODON offers reduced computational load and improved scalability, distinguishing itself from traditional Transformer models exhibiting quadratic computational growth with sequence length.

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Google DeepMind’s SIMA Project Enhances Agent Performance in Dynamic 3D Environments Across Various Platforms

The exploration of artificial intelligence within dynamic 3D environments has emerged as a critical area of research, aiming to bridge the gap between static AI applications and their real-world usability. Researchers at Google DeepMind have pioneered this realm, developing sophisticated agents capable of interpreting and acting on complex instructions within various simulated settings. This new wave of AI research extends beyond conventional paradigms, focusing on integrating visual perception and language processing to enable AI systems to perform human-like tasks across diverse virtual landscapes.

A fundamental issue in this field is the limited capability of AI agents to interact dynamically in three-dimensional spaces. Traditional AI models excel in environments where tasks and responses are clearly defined and static. However, they falter when required to engage in environments characterized by continuous change and multifaceted objectives. This gap highlights the need for a robust system that adapts and responds to unpredictable scenarios akin to real-world interactions.

Previous methodologies have often relied on rigid command-response frameworks, which confine AI agents to a narrow range of predictable, controlled actions. These agents operate under constrained conditions and cannot generalize their learned behaviors to new or evolving contexts. Such approaches are less effective in scenarios that demand real-time decision-making and adaptability, underscoring the necessity for more versatile and dynamic AI capabilities.

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The Future of Finance: How AI is Transforming Credit Card Companies

The finance industry, particularly the credit card sector, is undergoing a rapid transformation driven by artificial intelligence (AI) advancements. As technology evolves, AI is reshaping how credit card companies operate, from enhancing customer service to redefining security protocols. Let’s explore the multifaceted impacts of AI on the credit card industry, envisioning a future where finance meets innovation at every corner.

Enhanced Customer Experience

One of AI’s most visible transformations to credit card companies is the enhanced customer experience. AI-driven chatbots and virtual assistants are now commonplace, providing 24/7 customer service that can handle inquiries, resolve disputes, and offer personalized advice much faster than human counterparts. For instance, predictive analytics enables these AI systems to provide customized spending tips and financial management advice based on the user’s spending habits and financial history.

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