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OpenAI has taken a bold step in making artificial intelligence more accessible by integrating ChatGPT with WhatsApp and launching a phone service. This move brings the popular AI chatbot to billions of users through familiar communication channels. WhatsApp users can now chat with ChatGPT by simply adding a designated phone number to their contacts. To get started, open WhatsApp, add a new contact with the number +1 800-242-8478 (which spells out 800-CHATGPT). Save the contact as "ChatGPT" or any preferred name, and you're ready to start a conversation. For those in the United States, OpenAI has introduced a toll-free number (1-800-ChatGPT) that allows voice conversations with the AI assistant. This service offers 15 minutes of free interaction per month for anyone dialing from a US number, making AI engagement as simple as making a phone call. While the WhatsApp version comes with some limitations - it only supports text conversations, has a knowledge cutoff date of September 2021, and can't be added to group chats - it provides a straightforward way for users to experience AI without needing to create an OpenAI account. To use ChatGPT on WhatsApp effectively, keep your messages clear and concise for better responses. You can ask follow-up questions or request clarifications in the same chat thread. Remember that there may be daily message limits, although the service is free to use. This expansion is part of OpenAI's broader strategy to reduce barriers to AI adoption and showcase its potential to a wider audience. By leveraging familiar platforms like WhatsApp and traditional phone calls, OpenAI aims to make AI technology more approachable and less intimidating to the average user. As AI continues to evolve and integrate into our daily lives, initiatives like this play a crucial role in democratizing access to advanced technologies. While the current offering is described as experimental, it represents a significant step towards making AI a more common and accessible tool for people around the world.---Join the Future of Venture Capital Investing with Newnex <a href="https://www.newnex.io/edit/blogs/www.newnex.io" rel="noopener noreferrer" target="_blank">Newnex</a> is redefining venture capital by fostering collaborative and private investing and fundraising between VCs (GPs), Limited Partners (LPs), and Startups. Newnex simplifies syndication and co-investment for institutional investors, with a mission to connect 5,000+ VC and LP firms and 100,000 venture scale startups on a trusted platform for seamless, reference-only private deal-making. Join Newnex for free <a href="http://www.newnex.io/" rel="noopener noreferrer" target="_blank">www.newnex.io</a>

ChatGPT comes to WhatsApp and Phone calls - Add +1 1800 242 8478 to your contacts

Nvidia has unveiled its latest offering in the world of compact AI computing: the Jetson Orin Nano Super Developer Kit. This palm-sized supercomputer, priced at an accessible $249, is set to revolutionize the landscape of AI development for hobbyists, students, and small businesses. Specifications and Performance The Jetson Orin Nano Super boasts impressive specifications that punch well above its weight class. It delivers up to 67 INT8 TOPS (Trillion Operations Per Second) of AI performance, marking a 70% increase over its predecessor. The device also features a 50% boost in memory bandwidth, reaching 102GB/s. These improvements translate to a 1.7x leap in generative AI inference performance compared to the previous model. At the heart of the Jetson Orin Nano Super is a system-on-module (SoM) featuring an NVIDIA Ampere architecture GPU with tensor cores and a 6-core Arm Cortex-A78AE CPU. This powerful combination allows for multiple concurrent AI application pipelines and high-performance inference. The device supports up to four cameras, offering higher resolution and frame rates than earlier versions. <iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://www.youtube.com/embed/S9L2WGf1KrM?showinfo=0"></iframe> Use Cases and Applications The Jetson Orin Nano Super is designed to handle a wide array of AI tasks locally. It's particularly well-suited for: 1. Running large language models (LLMs) and creating chatbots2. Building visual AI agents3. Deploying AI-powered robots4. Developing smart surveillance systems5. Creating AI-enhanced smart home devices and wearables6. Advancing autonomous vehicle technology7. Facilitating research and education in AI Its versatility makes it an ideal platform for those looking to develop skills in generative AI, robotics, or computer vision. Configuration and Software Ecosystem The Jetson Orin Nano Super comes with NVIDIA's comprehensive AI software stack, including popular frameworks and tools. It supports CUDA, CuDNN, and TensorRT, allowing developers to create sophisticated AI applications. The device runs on Linux, providing a familiar environment for many developers. NVIDIA also offers the Isaac platform for robotics and Metropolis for vision AI, expanding the potential applications of the Jetson Orin Nano Super. The company has emphasized the device's ability to handle foundation models, reflecting the AI industry's shift towards more generalized, multi-purpose AI systems. Competitors and Market Position While the Jetson Orin Nano Super stands out in its price-to-performance ratio, it does face competition in the compact AI computing space. Devices like the Radxa Fogwise Airbox and Firefly AIO-1684XQ motherboard offer similar capabilities for edge AI applications. However, NVIDIA's strong software ecosystem, extensive developer community, and competitive pricing give the Jetson Orin Nano Super a significant edge. The introduction of this affordable, high-performance AI computer could spark a new wave of innovation in AI development, much like how the Raspberry Pi revolutionized DIY computing projects. By lowering the barriers to entry for advanced AI tools, NVIDIA is positioning itself at the forefront of democratizing AI technology. As AI continues to permeate various aspects of technology and daily life, the Jetson Orin Nano Super Developer Kit represents a significant step towards making powerful AI capabilities accessible to a broader audience. Whether for hobbyist projects, educational purposes, or small-scale commercial applications, this palm-sized supercomputer opens up new possibilities in the realm of AI development and deployment.--Join the Future of Venture Capital Investing with Newnex <a href="https://www.newnex.io/edit/blogs/www.newnex.io" rel="noopener noreferrer" target="_blank">Newnex</a> is redefining venture capital by fostering collaborative and private investing and fundraising between VCs (GPs), Limited Partners (LPs), and Startups. Newnex simplifies syndication and co-investment for institutional investors, with a mission to connect 5,000+ VC and LP firms and 100,000 venture scale startups on a trusted platform for seamless, reference-only private deal-making. Join Newnex for free <a href="http://www.newnex.io/" rel="noopener noreferrer" target="_blank">www.newnex.io</a>

NVIDIA's Jetson $249 Orin Nano Super: Revolutionizing Edge AI for Developers and Everyday Users

In an era where constant connectivity is essential, Direct-to-Cell (DTC) satellite technology has emerged as a groundbreaking solution to bridge the digital divide. Unlike traditional cellular networks reliant on terrestrial infrastructure like cell towers, DTC technology enables standard mobile devices to connect directly to satellites in low Earth orbit (LEO). This innovation promises to eliminate dead zones, providing seamless communication across even the most remote locations, from mountains and oceans to deserts and disaster-hit regions. Starlink: Leading the ChargeSpaceX’s Starlink has been a trailblazer in satellite communication, and its move into Direct-to-Cell technology marks a significant leap. By equipping its satellite constellation with DTC capabilities, Starlink aims to deliver texting, calling, and data browsing services directly to unmodified mobile phones.Recent launches have further strengthened this initiative; on November 23, 2024, SpaceX took a significant step forward by deploying 13 DTC-capable satellites as part of a broader network expansion. This launch, executed from the Vandenberg Space Force Base in California using a Falcon 9 rocket, highlights Starlink’s aggressive efforts to enhance connectivity. These satellites, part of the second-generation (Gen2) constellation, bring enhanced transceivers and larger antennas specifically designed for DTC capabilities. What These Satellites Bring to the TableThe 13 DTC-capable satellites are engineered to support:<ul><li>Text Messaging: Sending and receiving SMS messages directly from a satellite, even in areas with no cell towers.</li><li>Voice Calls: Supporting clear and reliable voice communication in remote regions.</li><li>Data Services: Providing basic web browsing and email capabilities, with plans to expand as the technology matures.</li></ul>These features are crucial for connecting remote areas and providing a reliable backup during emergencies when terrestrial networks fail. This milestone reinforces Starlink's commitment to bridging the global digital divide. Integration with Existing ServicesStarlink’s collaboration with T-Mobile plays a pivotal role in this initiative. Their partnership promises free emergency texting via satellite in the coming months, enabling lifesaving communication in critical scenarios. By integrating these services into existing mobile plans, Starlink ensures that users can access satellite connectivity without additional hardware or complex configurations. A Milestone in Satellite DeploymentThe November 23 launch utilized a reusable Falcon 9 rocket, reducing costs and enabling rapid deployment. This launch is part of Starlink’s larger plan to deploy thousands of DTC-capable satellites, creating a dense network that minimizes latency and provides continuous global coverage. Alternative Offerings: The Growing CompetitionWhile Starlink has garnered much attention, other companies are stepping up with their own innovations in Direct-to-Cell technology:<ul><li>AST SpaceMobile: This company is building a space-based cellular broadband network that directly connects to regular smartphones. With successful trials like a two-way call using their BlueWalker 3 satellite, AST SpaceMobile has formed partnerships with major telecom providers, including AT&amp;T and Verizon, to extend coverage to underserved regions.</li><li>Lynk Global: Known for being the first to send a text message from space to a standard mobile phone, Lynk Global is planning continuous global coverage by 2025. Its "cell tower in space" concept aims to provide reliable connectivity even in the most remote areas.</li><li>Iridium Communications: A veteran in satellite communication, Iridium is developing "Project Stardust," set to launch in 2026. This initiative focuses on 5G messaging, IoT applications, and emergency communications, leveraging non-terrestrial networks (NTN) standards for greater compatibility.</li><li>Apple and Globalstar: Apple’s collaboration with Globalstar brings satellite-based emergency messaging to iPhones, a feature already active in the iPhone 14 series and newer models. While currently limited to emergencies, this partnership highlights the potential for consumer devices to integrate satellite connectivity.</li><li>Google and Skylo: Google has joined the fray with satellite messaging capabilities on its Pixel 9 series, in partnership with Skylo. This feature ensures communication remains possible in off-grid scenarios, targeting adventurers and emergency use cases.</li></ul> The Future of Direct-to-Cell TechnologyAs the technology matures, several trends and developments are likely to shape the future:<ol><li>Enhanced Bandwidth and Scalability: Continued advancements in satellite technology will improve data capacity and reliability, allowing DTC networks to serve larger populations and support data-intensive applications.</li><li>Integration with 5G and IoT: The synergy between DTC satellites and terrestrial 5G networks could create a seamless global communication ecosystem. Applications in IoT—such as smart agriculture, logistics, and disaster management—will benefit immensely.</li><li>Affordability and Accessibility: As competition increases, the cost of satellite connectivity is expected to decrease, making it more accessible to consumers and enterprises alike.</li><li>Global Regulatory Alignment: Overcoming regulatory challenges will be critical for the widespread adoption of DTC services. Collaboration among countries and telecom providers will play a key role.</li><li>Hybrid Networks: Future connectivity solutions are likely to integrate satellites with terrestrial infrastructure, leveraging the strengths of both systems to provide uninterrupted service.</li></ol> Direct-to-Cell technology is set to redefine global connectivity, ensuring that no location is too remote or underserved. As companies like Starlink, AST SpaceMobile, Lynk Global, and others push the boundaries of innovation, the dream of universal connectivity is becoming an achievable reality. The next decade promises to be transformative, connecting humanity in ways never before imagined.

Goodbye Dead Zones: How Direct-to-Cell Satellites Are Redefining Global Connectivity

Large Language Models (LLMs) have revolutionized natural language processing, offering impressive capabilities in text generation, translation, and various other linguistic tasks. While proprietary models like GPT-3 and GPT-4 have garnered significant attention, open source LLMs have emerged as powerful alternatives, providing accessibility, transparency, and flexibility to researchers and developers worldwide. Understanding LLMsLarge Language Models are artificial intelligence systems trained on vast amounts of text data to understand and generate human-like text. These models use deep learning techniques, particularly transformer architectures, to process and generate language. LLMs can perform a wide range of tasks, from answering questions and writing content to assisting with code generation and language translation. The Rise of Open Source LLMs Open source LLMs have gained popularity due to several advantages:1. Transparency: Users can inspect the code and understand the model's architecture and training methodology.2. Customization: Developers can fine-tune these models for specific use cases or domains.3. Cost-effectiveness: While initial setup costs may be significant, open source LLMs often prove more economical in the long run compared to proprietary alternatives.4. Community support: A vibrant ecosystem of developers contributes to the improvement and maintenance of these models. Notable Open Source LLMs Here's a list of some prominent open source Large Language Models: 1. LLaMA 2: Developed by Meta, LLaMA 2 comes in 7B, 13B, and 70B parameter versions, offering scalability and performance comparable to proprietary models 2. Mistral 7B: Created by Mistral AI, this model outperforms many larger models despite its relatively small size 3. Falcon: Available in 7B, 40B, and 180B parameter versions, Falcon is known for its excellent performance and multilingual capabilities 4. BLOOM: With 176 billion parameters, BLOOM is a multilingual model that supports 46 languages and 13 programming languages 5. GPT-NeoX-20B: Developed by EleutherAI, this model is architecturally similar to GPT-3 and is suitable for advanced content generation tasks 6. MPT: MosaicML's MPT comes in 7B and 30B parameter versions, offering efficiency improvements and commercial usage rights 7. BERT: One of the earliest transformer-based models, BERT remains widely used for various NLP tasks and has spawned numerous variants 8. GPT-J: Another EleutherAI creation, GPT-J is a 6B parameter model that offers good performance for its size 9. Dolly 2.0: Developed by Databricks, Dolly 2.0 is a fine-tuned version of GPT-J, demonstrating the potential of smaller models with targeted training 10. XGen-7B: Salesforce's entry into the open source LLM space, XGen-7B focuses on handling longer context windows efficiently 11. Mixtral 8x7B: A cutting-edge sparse mixture-of-experts model that offers impressive performance across various tasks These open source LLMs cater to different needs, from research and experimentation to commercial applications. Their availability has democratized access to advanced language AI, fostering innovation and pushing the boundaries of what's possible in natural language processing. As the field of AI continues to evolve rapidly, open source LLMs are likely to play an increasingly important role, offering alternatives to proprietary models and driving progress in language understanding and generation technologies.

Open Source Large Language Models: An Overview

In the rapidly evolving landscape of artificial intelligence, particularly with the rise of generative AI, organizations are increasingly turning to AI guardrails to ensure their AI systems operate safely, ethically, and in alignment with company values. Much like guardrails on highways protect vehicles from veering off course, AI guardrails are designed to prevent AI systems from producing harmful, inaccurate, or inappropriate content. These guardrails serve multiple crucial functions. They help maintain privacy and security by protecting against malicious attacks that could manipulate AI-generated outcomes. They also ensure regulatory compliance, which is particularly important as government scrutiny of AI intensifies. Perhaps most importantly, AI guardrails help build and maintain trust with customers and the public by continuously monitoring and reviewing AI-generated outputs. There are several types of AI guardrails, each addressing specific risks:1. Appropriateness guardrails filter out toxic, harmful, or biased content.2. Hallucination guardrails prevent the generation of factually incorrect or misleading information.3. Regulatory-compliance guardrails ensure adherence to relevant laws and standards.4. Alignment guardrails keep generated content in line with user expectations and brand consistency.5. Validation guardrails check that content meets specific criteria. AI guardrails typically consist of four main components: a checker that scans for issues, a corrector that refines and improves outputs, a rail that manages the interaction between the checker and corrector, and a guard that oversees the entire process. Implementing AI guardrails at scale requires a multidisciplinary approach. Organizations should design guardrails with input from diverse stakeholders, define clear content quality metrics, and adopt a modular approach for easy integration and scalability. It's also crucial to develop new capabilities and roles within the organization to manage these systems effectively. While AI guardrails don't guarantee completely risk-free AI systems, they are a critical tool in creating a safer environment for AI innovation and transformation. As AI continues to advance, we can expect to see not only new types of AI systems but also evolving standards for their development and operation, with guardrails playing a central role in responsible AI implementation.

AI Guardrails: Ensuring Responsible AI Implementation

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