VAIBHAV SHANKAR SHARMA’s Post

Discover the future of AI assistants in 2024 and how they're set to become even smarter and more personalized. Dive into this comprehensive guide: https://buff.ly/4gkJfes @[Top Apps AI](urn:li:organization:90548736)

Discover the future of AI assistants in 2024 and how they're set to become even smarter and more personalized. Dive into this comprehensive guide: https://buff.ly/3VKEXEI @[Top Apps AI](urn:li:organization:90548736)

Introducing Nexa: MIBT’s New AI Assistant Dear MIBT Team, We’re thrilled to introduce Nexa, our AI assistant, built to enhance user engagement, streamline support, and elevate the experience we deliver across the MIBT community. Nexa will be made available on the MIBT Official Website and other platforms, including StartShield, providing users an interactive way to access information, navigate resources, and stay connected with MIBT’s offerings. How Nexa Enhances Our Operations 1. 24/7 Support for Users: Nexa is ready to assist with frequently asked questions, provide guidance on MIBT resources, and support users with platform navigation, offering continuous assistance across our platforms. 2. Empowering Our Community: By handling immediate inquiries, Nexa enables our team to focus on strategic initiatives while keeping users informed, engaged, and well-supported. 3. Future Growth and Flexibility: Nexa’s capabilities are adaptable, allowing us to introduce new features like event reminders, personalized support, and deeper integrations with our platforms, including StartShield, as we gather insights from user interactions. 4. Voice Capabilities: Looking ahead, we’re planning to add voice functionality for a more intuitive experience. This will allow users to interact with Nexa through voice commands, expanding accessibility and enhancing personalized support across platforms. What’s Next? We’ll begin rolling out Nexa gradually, starting with FAQs and site navigation guidance. This initial phase will allow us to gather valuable feedback to refine Nexa’s responses and explore additional features, such as proactive notifications, personalized recommendations, and voice integration. How You Can Help Your feedback is crucial to ensure Nexa serves our community effectively. We encourage you to test Nexa’s features, share your insights, and suggest improvements. Together, we’ll shape Nexa into an essential part of MIBT’s digital ecosystem, enhancing both our website and StartShield. Thank you for your support and innovation as we bring MIBT’s vision to life. Best regards, MIBT CEO

Last week saw continued AI innovations to enhance accessibility, efficiency, and safety. Google’s Gemini Live and its deeper AI integration into Android and Pixel devices are bringing AI closer to more people’s daily lives. xAI’s Grok-2 offers a promising new model with enhanced features, while Runway AI’s Gen-3 Alpha Turbo makes video creation faster and cheaper. Anthropic’s new prompt caching reduces AI costs, and MIT’s AI Risk Repository provides valuable insights into managing AI risks. Here is a quick overview: 1. Google’s Gemini Live – Launched Gemini Live, conversational experience allowing users to interact with Gemini via real-time voice chat on mobile devices, with context-aware capabilities and integration into various Google apps. This could revolutionize how users manage tasks and interact with their devices, embedding AI more deeply into daily life. 2. Google’s AI device integration – Integrated Gemini and other AI capabilities into Android OS and new Pixel devices, including features like Gemini Live, Magic Editor, and AI-driven health monitoring. This integration could expose billions of users globally to AI, driving widespread adoption. 3. xAI’s Grok-2 – Unveiled Grok-2 and Grok-2 mini, featuring enhanced chat, coding, and reasoning abilities, along with real-time information integration on X and new image generation capabilities. If open-sourced like its predecessor, Grok-2 could offer businesses a cost-effective alternative to frontier models. 4. Runway AI’s Gen-3 Alpha Turbo – Introduced Gen-3 Alpha Turbo, cost-efficient model that creates videos at 7x speed and half the cost of its predecessor, with support for SD resolution and customizable text prompts. This model increases the accessibility and affordability of AI-powered video creation, streamlining creative tasks across industries. 5. Anthropic’s prompt caching – Released prompt caching feature on its API, allowing developers to cache frequently used contexts between API calls, significantly reducing costs and latency. This can make AI adoption more financially viable and enhance the efficiency and performance of AI applications. 6. MIT’s AI Risk Repository – Published the AI Risk Repository, publicly accessible database categorizing over 700 AI risks, designed as a living resource with customizable checklists. This repository can help businesses identify and prepare to address AI-related risks while driving collective thinking among AI leaders on managing these risks. These developments signal AI’s growing role in both business and personal settings, lowering adoption barriers and opening doors to new opportunities. As AI becomes more capable, accessible and embedded in everyday technology, how is your business preparing to leverage these innovations?

AppWorld: Advancing AI Agents for Real-World App Interactions As the founder of QuantaLogic, I'm always on the lookout for advancements that bridge the gap between AI's theoretical potential and its practical applications. Today, I share insights from a research paper that's set to reshape how we evaluate AI agents in real-world scenarios. 👉 Introducing AppWorld: A New Frontier in AI Benchmarking Researchers have unveiled "AppWorld," a novel benchmark for testing AI agents' ability to interact with multiple apps via APIs. This isn't just another theoretical test – it's a leap towards creating AI assistants that can truly navigate our complex digital lives. Why AppWorld Matters 1. Realistic Simulation:    - Mimics 9 real-world apps with 457 APIs   - Populated with data from ~100 fictitious users   - Replicates typical digital activities we all engage in daily 2. Complex Task Suite:    - 750 diverse, challenging tasks   - Requires using multiple apps (avg. 1.8, max 6) per task   - Involves intricate API flows (avg. 9.5, max 26 API calls) 3. Robust Evaluation:    - Uses state-based programmatic evaluation   - Allows for different valid solutions   - Detects unintended changes or "collateral damage" 👉 The Digital Sandbox: How AppWorld Works Imagine a digital sandbox where AI agents can play with real-world apps without real-world consequences. That's AppWorld. It's like giving an AI a set of digital LEGO blocks – apps, APIs, and user data – and challenging it to build complex structures (complete tasks) that mirror our everyday digital interactions. Examples of AppWorld Tasks: - Scheduling a meeting by checking multiple calendars and sending invites - Splitting expenses among roommates using finance apps and messaging platforms - Planning a trip by coordinating flights, accommodations, and itineraries across various apps 👉 Beyond Simple Metrics: A New Way to Evaluate AI AppWorld's evaluation method is like a meticulous inspector checking not just if the job is done, but how it's done: 1. State-Based Checks: Examines the final state of the digital environment 2. Multiple Solutions: Recognizes that there's often more than one way to complete a task 3. Collateral Damage Detection: Ensures the AI didn't accidentally mess up other parts of the digital environment 👉 Current AI Performance: A Reality Check Even the most advanced AI models struggle with AppWorld's challenges: - Best performer (GPT4O with ReAct):   - 48.8% task completion on normal tasks  - 30.2% on more challenging tasks This reveals a significant gap between current AI capabilities and the complexity of real-world digital interactions. 👉 Why This Matters for Businesses and Developers 1. Realistic Expectations: Helps set accurate expectations for AI assistant capabilities 2. Development Focus: Highlights specific areas where AI needs improvement for practical use 3. Integration Challenges: Illustrates the complexities of integrating AI into multi-app ecosystems

"The new Gemini 1.5 Flash, optimized for speed and efficiency 1.5 Flash is the newest addition to the Gemini model family and the fastest Gemini model served in the API. It’s optimized for high-volume, high-frequency tasks at scale, is more cost-efficient to serve and features our breakthrough long context window. While it’s a lighter weight model than Gemini 1.5 Pro, it’s highly capable of multimodal reasoning across vast amounts of information and delivers impressive quality for its size. 1.5 Flash excels at summarization, chat applications, image and video captioning, data extraction from long documents and tables, and more. This is because it’s been trained by 1.5 Pro through a process called 'distillation,' where the most essential knowledge and skills from a larger model are transferred to a smaller, more efficient model. ... Significantly improving Gemini 1.5 Pro ... 1.5 Pro can now follow increasingly complex and nuanced instructions, including ones that specify product-level behavior involving role, format and style. We’ve improved control over the model’s responses for specific use cases, like crafting the persona and response style of a chat agent or automating workflows through multiple function calls. And we’ve enabled users to steer model behavior by setting system instructions. We added audio understanding in the Gemini API and Google AI Studio, so 1.5 Pro can now reason across image and audio for videos uploaded in Google AI Studio. And we’re now integrating 1.5 Pro into Google products, including Gemini Advanced and in Workspace apps. ... Next generation of open models ... We’re announcing Gemma 2, our next generation of open models for responsible AI innovation. Gemma 2 has a new architecture designed for breakthrough performance and efficiency, and will be available in new sizes. The Gemma family is also expanding with PaliGemma, our first vision-language model inspired by PaLI-3. And we’ve upgraded our Responsible Generative AI Toolkit with LLM Comparator for evaluating the quality of model responses. ... Progress developing universal AI agents As part of Google DeepMind’s mission to build AI responsibly to benefit humanity, we’ve always wanted to develop universal AI agents that can be helpful in everyday life. That’s why today, we’re sharing our progress in building the future of AI assistants Project Astra (advanced seeing and talking responsive agent), https://lnkd.in/deaUMi6g." Demis Hassabis, CEO of Google DeepMind, on behalf of the Gemini team, Gemini breaks new ground with a faster model, longer context, AI agents and more, 14 May 2024, https://lnkd.in/dNag94fK "𝐏𝐫𝐢𝐜𝐢𝐧𝐠 Gemini 1.5 Pro is US$7 per 1 million tokens. For prompts up to 128K, it will be 50% less at US$3.50 Gemini 1.5 flash is US$0.35 per 1 million tokens up to 128K." (Josh Woodward, Senior Director of Product Management at Google Labs)

If you are looking for real world examples of how to use AI with data analytics, this article includes a list of about 40 generative AI applications focused on data. https://lnkd.in/g-VYq9Xk #datascience #dataanalytics #genai #googlecloud

USER-LLM: The Secret Weapon for Hyper-Personalized AI Experiences Get ready for the next wave of AI that truly gets you! Google's research paper, "USER-LLM: Efficient LLM Contextualization with User Embeddings," could revolutionize the way we interact with technology. Under the Hood: How USER-LLM Works • User Embeddings: Your Digital DNA: USER-LLM takes a unique approach to understanding you. It leverages a Transformer-based encoder to distill your entire digital footprint – from browsing history and product purchases to social media interactions – into a concentrated "user embedding." Think of it as your digital DNA, a unique representation of your preferences and behaviors. • LLMs Get Personal: These user embeddings are then integrated with large language models (LLMs) through cross-attention mechanisms. This enables the LLM to dynamically adapt its responses based on your individual context, leading to truly personalized interactions. • Efficiency is Key: USER-LLM goes the extra mile for efficiency. It incorporates Perceiver layers, a powerful technique for compressing information, to streamline the integration of user embeddings with LLMs. This ensures that the personalization process is fast and doesn't bog down systems. Proven Performance & Efficiency Gains USER-LLM isn't just a theoretical concept. Google's researchers have demonstrated its effectiveness across a range of tasks and datasets. Here's what they found: • Superior Performance: USER-LLM consistently outperforms traditional text-prompt-based methods, especially in scenarios that require understanding nuanced user preferences or long interaction histories. • Scalability: Unlike text-prompt methods, which can become unwieldy with large amounts of input data, USER-LLM maintains its efficiency regardless of the size of your digital footprint. • Resource-Friendly: Thanks to its efficient design, USER-LLM can be implemented without a significant increase in computational resources. Hyper-Personalization: The Future of AI Experiences Imagine... • Search engines that surface results you didn't even know you were looking for. • Social media feeds that feel curated just for you. • Shopping experiences that anticipate your needs before you even have them. • Customer service chatbots that understand your context and provide tailored solutions. The possibilities are endless! The USER-LLM Opportunity for Companies Giants like Google, Amazon, and Facebook are sitting on a treasure trove of user data. With USER-LLM, they can unlock the full potential of this data to deliver hyper-personalized experiences that delight customers and drive engagement. Dig Deeper: Explore the Research Want to dive into the nitty-gritty of how USER-LLM works? Check out page 4 of the research paper for a visual overview of the architecture. 🔗 https://lnkd.in/dBtV_WBZ #AI #LLMs #MachineLearning #Personalization #UserExperience #GoogleResearch

Discover the future of AI assistants in 2024 and how they're set to become even smarter and more personalized. Dive into this comprehensive guide: https://buff.ly/3RRMUWl @TopAppsAI