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The integration of Meta AI into WhatsApp is transforming our mobile experience. Meta has launched its virtual assistant across its various platforms: Facebook, Instagram, WhatsApp, and Messenger. This advanced chatbot uses the company’s most powerful language model, which is currently Llama 3.2, to offer context-aware interactions that boost productivity and engagement.
Last Updated on November 3, 2024 by Editorial Team Author(s): Devi Originally published on Towards AI. Part 1 of a 2-part beginner series exploring fun generative AI use cases with Gemini to enhance your photography skills! In this blog post, I’ll show you how to build a Photo Critique and Enhancement App using Google’s Gemini-1.5-Flash-8B API and Streamlit (all for free!).
In today’s fast-paced business world, a strong brand name is more crucial than ever. It’s the first impression you make on potential customers, and it can significantly impact your business’s success. But coming up with a unique and memorable name can be a daunting task. This is where AI business name generators come to the rescue.
Author(s): Devi Originally published on Towards AI. Part 2 of a 2-part beginner series exploring fun generative AI use cases with Gemini to enhance your photography skills! A beginner friendly introduction and application of RAG As an amateur photographer, I am experimenting with ways I can use generative AI to get better at my craft. In this blog post, I’ll walk you through the process of creating a simple interactive question-answering application using Python, Gemini Flash Pro API, LangChain,
Start building the AI workforce of the future with our comprehensive guide to creating an AI-first contact center. Learn how Conversational and Generative AI can transform traditional operations into scalable, efficient, and customer-centric experiences. What is AI-First? Transition from outdated, human-first strategies to an AI-driven approach that enhances customer engagement and operational efficiency.
Created Using Ideogram Next Week in The Sequence: Edge 445: We start a new series about one of the most exciting topics in generative AI: model distillation. The Sequence Chat: We discuss some coontroversial points on the debate between small vs. large foundation models. Edge 446: We dive into OpenAI’s MLE-Bench, one of the craziest benchmarks ever created.
Summary: Gamma AI is an innovative presentation tool that leverages Artificial Intelligence to simplify content creation. Users can quickly generate structured presentations, collaborate in real-time with team members, and utilise customizable templates for professional design. Its intuitive interface ensures that anyone can create engaging presentations without extensive design experience.
Summary: Gamma AI is an innovative presentation tool that leverages Artificial Intelligence to simplify content creation. Users can quickly generate structured presentations, collaborate in real-time with team members, and utilise customizable templates for professional design. Its intuitive interface ensures that anyone can create engaging presentations without extensive design experience.
Large pretrained vision-language models like CLIP have shown promising generalization capability, but may struggle in specialized domains (e.g., satellite imagery) or fine-grained classification (e.g., car models) where the visual concepts are unseen or under-represented during pretraining. Prompt learning offers a parameter-efficient finetuning framework that can adapt CLIP to downstream tasks even when limited annotation data are available.
Summary: Pattern matching in SQL enables users to identify specific sequences of data within databases using various techniques such as the LIKE operator and regular expressions. This powerful feature enhances data analysis, allowing for complex queries that can uncover trends and insights across datasets. Understanding pattern matching is essential for effective data manipulation.
With the introduction of Google's AI-powered Gemini technology, the maps are now being set up to become entertainment guides in addition to navigational tools.
Today’s buyers expect more than generic outreach–they want relevant, personalized interactions that address their specific needs. For sales teams managing hundreds or thousands of prospects, however, delivering this level of personalization without automation is nearly impossible. The key is integrating AI in a way that enhances customer engagement rather than making it feel robotic.
Promising — But Not Perfect ChatGPT’s latest hip-check to Google — its own search engine — is delighting untold numbers across the Web. Accessible with a simple click on a new globe icon in the ChatGPT message box, the new tool brings back summaries of searches for you — complete with hotlinks to the sources of the summaries.
China - November 04, 2024 Learning-capable robots set to enter Chinese industries (Voice_over) China is exploring wide-ranging applications for Embodied Artificial Intelligence, a technology that integrates AI into physical entities, like robots. Beijing recently issued the first catering business license to an embodied AI robot, paving the way for similar robots to serve food in the capital city.
Author(s): Get The Gist Originally published on Towards AI. Is global security at risk? This member-only story is on us. Upgrade to access all of Medium. Brookings Institute Open-source AI has changed the world for developers, researchers and enthusiasts. When companies release open-source models they want to democratize innovation — give people the tools to create, explore and even dream up entirely new applications.
The guide for revolutionizing the customer experience and operational efficiency This eBook serves as your comprehensive guide to: AI Agents for your Business: Discover how AI Agents can handle high-volume, low-complexity tasks, reducing the workload on human agents while providing 24/7 multilingual support. Enhanced Customer Interaction: Learn how the combination of Conversational AI and Generative AI enables AI Agents to offer natural, contextually relevant interactions to improve customer exp
The current design of causal language models, such as GPTs, is intrinsically burdened with the challenge of semantic coherence over longer stretches because of their one-token-ahead prediction design. This has enabled significant generative AI development but often leads to “topic drift” when longer sequences are produced since each token predicted depends only on the presence of mere preceding tokens, not from a broader perspective.
Articles Uber has written their Open-Source and In-House LLM training stack in a very comprehensive blog post. They talk about how their training tech stack has evolved over time, which showed a good timeline on their adoption of deep learning, and now GenAI technologies. Of course, each timeline brings their own separate stack in terms of software and hardware.
Recent advancements in Large Language Models (LLMs) have demonstrated exceptional natural language understanding and generation capabilities. Research has explored the unexpected abilities of LLMs beyond their primary training task of text prediction. These models have shown promise in function calling for software APIs, supported by the launch of GPT-4 plugin features.
Transformers have transformed artificial intelligence, offering unmatched performance in NLP, computer vision, and multi-modal data integration. These models excel at identifying patterns within data through their attention mechanisms, making them ideal for complex tasks. However, the rapid scaling of transformer models needs to be improved because of the high computational cost associated with their traditional structure.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Predicting protein conformational changes remains a crucial challenge in computational biology and artificial intelligence. Breakthroughs achieved by deep learning, such as AlphaFold2, have moved the goalpost for predicting static structures but do not address the dynamic conformational change most proteins undertake to exercise their biological roles.
Mathematical reasoning within artificial intelligence has emerged as a focal area in developing advanced problem-solving capabilities. AI can revolutionize scientific discovery and engineering fields by enabling machines to approach high-stakes logical challenges. However, complex tasks, especially Olympiad-level mathematical reasoning, continue to stretch AI’s limits, demanding advanced search methods to navigate solution spaces effectively.
Knowledge distillation (KD) is a machine learning technique focused on transferring knowledge from a large, complex model (teacher) to a smaller, more efficient one (student). This approach is used extensively to reduce large language models’ computational load and resource requirements while retaining as much of their performance as possible.
Generative diffusion models have revolutionized image and video generation, becoming the foundation of state-of-the-art generation software. While these models excel at handling complex high-dimensional data distributions, they face a critical challenge: the risk of complete training set memorization in low-data scenarios. This memorization capability raises legal concerns like copyright laws, as these models might reproduce exact copies of training data rather than generate novel content.
The DHS compliance audit clock is ticking on Zero Trust. Government agencies can no longer ignore or delay their Zero Trust initiatives. During this virtual panel discussion—featuring Kelly Fuller Gordon, Founder and CEO of RisX, Chris Wild, Zero Trust subject matter expert at Zermount, Inc., and Principal of Cybersecurity Practice at Eliassen Group, Trey Gannon—you’ll gain a detailed understanding of the Federal Zero Trust mandate, its requirements, milestones, and deadlines.
In healthcare, time series data is extensively used to track patient metrics like vital signs, lab results, and treatment responses over time. This data is critical in monitoring disease progression, predicting healthcare risks, and personalizing treatments. However, due to high dimensionality, irregularly sampled trajectories, and dynamic nature, time series data in clinical settings demands a nuanced approach for rigorous analysis.
Designing autonomous agents that can navigate complex web environments raises many challenges, in particular when such agents incorporate both textual and visual information. More classically, agents have limited capability since they are confined to synthetic, text-based environments with well-engineered reward signals, which restricts their applications to real-world web navigation tasks.
Conversational AI is now a cornerstone of technology, but achieving fast, efficient, and real-time interaction remains challenging. Latency—the delay between input and response—limits applications like customer service bots and virtual assistants, making interactions feel sluggish. Existing models often require significant computational power, putting real-time AI out of reach for smaller setups and independent developers.
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