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Nvidia Unveils New RTX Technology to Power AI Assistants and Digital Humans

Nvidia is once again pushing the boundaries of technology with its latest RTX advancements, designed to supercharge AI assistants and digital humans. These innovations are now integrated into the newest GeForce RTX AI laptops, setting a new standard for performance and capability.

Introducing Project G-Assist

At the forefront of Nvidia’s new technology is Project G-Assist, an RTX-powered AI assistant demo that provides context-aware assistance for PC games and applications. This innovative technology was showcased with ARK: Survival Ascended by Studio Wildcard, illustrating its potential to transform gaming and app experiences.

Nvidia NIM and the ACE Digital Human Platform

Nvidia also launched its first PC-based Nvidia NIM (Nvidia Inference Microservices) for the Nvidia ACE digital human platform. These announcements were made during CEO Jensen Huang’s keynote at the Computex trade show in Taiwan. Nvidia NIM enables developers to reduce deployment times from weeks to minutes, supporting natural language understanding, speech synthesis, and facial animation.

The Nvidia RTX AI Toolkit

These advancements are supported by the Nvidia RTX AI Toolkit, a comprehensive suite of tools and SDKs designed to help developers optimize and deploy large generative AI models on Windows PCs. This toolkit is part of Nvidia’s broader initiative to integrate AI across various platforms, from data centers to edge devices and home applications.

New RTX AI Laptops

Nvidia also unveiled new RTX AI laptops from ASUS and MSI, featuring up to GeForce RTX 4070 GPUs and energy-efficient systems-on-a-chip with Windows 11 AI PC capabilities. These laptops promise enhanced performance for both gaming and productivity applications.

Advancing AI-Powered Experiences

According to Jason Paul, Vice President of Consumer AI at Nvidia, the introduction of RTX Tensor Core GPUs and DLSS technology in 2018 marked the beginning of AI PCs. With Project G-Assist and Nvidia ACE, Nvidia is now pushing the boundaries of AI-powered experiences for over 100 million RTX AI PC users.

Project G-Assist in Action

AI assistants like Project G-Assist are set to revolutionize gaming and creative workflows. By leveraging generative AI, Project G-Assist provides real-time, context-aware assistance. For instance, in ARK: Survival Ascended, it can help players by answering questions about creatures, items, lore, objectives, and more. It can also optimize gaming performance by adjusting graphics settings and reducing power consumption while maintaining performance targets.

Nvidia ACE NIM: Powering Digital Humans

The Nvidia ACE technology for digital humans is now available for RTX AI PCs and workstations, significantly reducing deployment times and enhancing capabilities like natural language understanding and facial animation. At Computex, the Covert Protocol tech demo, developed in collaboration with Inworld AI, showcased Nvidia ACE NIM running locally on devices.

Collaboration with Microsoft: Windows Copilot Runtime

Nvidia and Microsoft are working together to enable new generative AI capabilities for Windows apps. This collaboration will allow developers to access GPU-accelerated small language models (SLMs) that enable retrieval-augmented generation (RAG) capabilities. These models can perform tasks such as content summarization, content generation, and task automation, all running efficiently on Nvidia RTX GPUs.

The RTX AI Toolkit: Faster and More Efficient Models

The Nvidia RTX AI Toolkit offers tools and SDKs for customizing, optimizing, and deploying AI models on RTX AI PCs. This includes the use of QLoRa tools for model customization and Nvidia TensorRT for model optimization, resulting in faster performance and reduced RAM usage. The Nvidia AI Inference Manager (AIM) SDK simplifies AI integration for PC applications, supporting various inference backends and processors.

AI Integration in Creative Applications

Nvidia’s AI acceleration is being integrated into popular creative apps from companies like Adobe, Blackmagic Design, and Topaz. For example, Adobe’s Creative Cloud tools are leveraging Nvidia TensorRT to enhance AI-powered capabilities, delivering unprecedented performance for creators and developers.

RTX Remix: Enhancing Classic Games

Nvidia RTX Remix is a platform for remastering classic DirectX 8 and 9 games with full ray tracing and DLSS 3.5. Since its launch, it has been used by thousands of modders to create stunning game remasters. Nvidia continues to expand RTX Remix’s capabilities, making it open source and integrating it with popular tools like Blender and Hammer.

AI for Video and Content Creation

Nvidia RTX Video, an AI-powered super-resolution feature, is now available as an SDK for developers, allowing them to integrate AI for upscaling, sharpening, and HDR conversion into their applications. This technology will soon be available in video editing software like DaVinci Resolve and Wondershare Filmora, enabling video editors to enhance video quality significantly.


Nvidia’s latest advancements in RTX technology are set to revolutionize AI assistants, digital humans, and content creation. By providing powerful tools and capabilities, Nvidia continues to push the boundaries of what AI can achieve, enhancing user experiences across gaming, creative applications, and beyond.

Stay updated with the latest in AI and RTX technology by subscribing to our blog and sharing this post on social media. Join the conversation and explore the future of AI with Nvidia!

What is Artificial Intelligence?

Artificial Intelligence (AI) has become a buzzword in recent years, but what does it really mean? This blog post will delve into the basics of AI, how it works, what it can and can’t do, potential pitfalls, and some of the most intriguing aspects of this technology.

Introduction to Artificial Intelligence (AI)

Artificial Intelligence, commonly referred to as AI, is the simulation of human intelligence in machines. These machines are programmed to think and learn like humans, capable of performing tasks that typically require human intelligence such as visual perception, speech recognition, decision-making, and language translation. AI can be found in various applications today, from self-driving cars to voice-activated assistants like Siri and Alexa.

The Inner Workings of AI and Its Comparison to a Hidden Octopus

AI systems work by using algorithms and large datasets to recognize patterns, make decisions, and improve over time. These systems are typically powered by machine learning, a subset of AI that enables machines to learn from experience. Here’s a simplified breakdown of how AI works: Data is collected from various sources, then processed to be clean and usable. Algorithms are applied to this data to identify patterns and make predictions. The AI system is trained using a training dataset, improving its accuracy over time through learning. Finally, the trained AI system is deployed and continues to learn and improve based on feedback.

Think of AI as a secret octopus with many tentacles, each representing a different capability. Just as an octopus uses its tentacles to explore and interact with its environment, AI uses its various functions (like vision, speech, and decision-making) to understand and influence the world around it. The “secret” part comes from the fact that, much like an octopus’s intricate movements can be hard to decipher, the inner workings of AI algorithms can be complex and opaque, often functioning in ways that are not immediately understandable to humans.

What AI Can (and Can’t) Do

AI can analyze vast amounts of data quickly and accurately, recognize patterns, and make predictions based on this data. It can automate repetitive tasks, improving efficiency and reducing errors. Through natural language processing (NLP), AI can understand and generate human language, enabling applications like chatbots and language translators. AI can also identify objects in images and understand spoken language, powering technologies like facial recognition and virtual assistants. However, AI lacks the ability to understand context in the way humans do and cannot genuinely understand or replicate human emotions. While AI can generate content, it does not possess true creativity or original thought. Additionally, AI cannot make ethical decisions as it does not understand morality.

How AI Can Go Wrong

AI systems are not infallible and can go wrong in several ways. AI can perpetuate and amplify biases present in training data, leading to unfair or discriminatory outcomes. Incorrect data or flawed algorithms can result in erroneous predictions or decisions. AI systems can also be susceptible to hacking and malicious manipulation. Over-reliance on AI can lead to the erosion of human skills and judgment.

The Importance (and Danger) of Training Data

Training data is crucial for AI systems as it forms the foundation upon which they learn and make decisions. High-quality, diverse training data helps create accurate and reliable AI systems. However, poor-quality or biased training data can lead to inaccurate, unfair, or harmful AI outcomes. Ensuring that training data is representative and free from bias is essential to developing fair and effective AI systems.

How a ‘Language Model’ Makes Images

Language models, like OpenAI’s GPT-3, are primarily designed to process and generate text. However, they can also be used to create images when integrated with other AI models. The language model receives a text prompt describing the desired image. The model interprets the text and generates a detailed description of the image. A connected image-generating AI, such as DALL-E, uses the description to create an image. This process involves complex neural networks and vast datasets to accurately translate textual descriptions into visual representations.

What About AGI Taking Over the World?

Artificial General Intelligence (AGI) refers to a level of AI that can understand, learn, and apply knowledge across a wide range of tasks at a human-like level. While AGI is a fascinating concept, it remains largely theoretical. AGI does not yet exist and is a long way from being realized. The idea of AGI taking over the world is a popular theme in science fiction, but it raises legitimate concerns about control, ethics, and safety. Ensuring that AGI, if developed, is aligned with human values and controlled appropriately is crucial to preventing potential risks.


AI is a powerful technology with the potential to revolutionize various aspects of our lives. Understanding how it works, its capabilities and limitations, and the importance of training data is crucial to harnessing its benefits while mitigating its risks. As AI continues to evolve, it is essential to stay informed and engaged with its development to ensure it serves humanity positively and ethically.

OpenAI’s Model Sora unveiled First Music Video Generated

OpenAI sent shockwaves through the tech community and the arts scene earlier this year with the unveiling of their groundbreaking AI model, Sora. This innovative technology promises to revolutionize the creation of videos by producing realistic, high-resolution, and seamlessly smooth clips lasting up to 60 seconds each. Sora unveiled First Music Video, however, Sora’s debut has not been without controversy, stirring up concerns among traditional videographers and artists.

The Unveiling of Sora

In February 2024, OpenAI made waves by introducing Sora to a select audience. Although the technology remains unreleased to the public, OpenAI granted access to a small group of “red teamers” for risk assessment and a handpicked selection of visual artists, designers, and filmmakers. Despite this limited release, some early users have already begun experimenting with Sora, producing and sharing innovative projects.

The First Official Music Video with Sora

Among OpenAI’s chosen early access users is writer/director Paul Trillo, who recently made headlines by creating what is being hailed as the “first official music video made with OpenAI’s Sora.” Collaborating with indie chillwave musician Washed Out, Trillo crafted a mesmerizing 4-minute video for the single “The Hardest Part.” The video comprises a series of quick zoom shots seamlessly stitched together, creating the illusion of a continuous zoom effect.

Behind the Scenes

Trillo revealed that the concept for the video had been brewing in his mind for a decade before finally coming to fruition. He disclosed that the video consists of 55 separate clips generated by Sora from a pool of 700, meticulously edited together using Adobe Premiere.

Integration with Premiere Pro

Meanwhile, Adobe has expressed interest in incorporating Sora and other third-party AI video generator models into its Premiere Pro software. However, no timeline has been provided for this integration. Until then, users seeking to replicate Trillo’s workflow may need to generate AI video clips using third-party software like Runway or Pika before importing them into Premiere.

The Artist’s Perspective

In an interview with the Los Angeles Times, Washed Out expressed excitement about incorporating cutting-edge technology like Sora into his creative process. He highlighted the importance of exploring new tools and techniques to push the boundaries of artistic expression.

Power of Sora

Trillo’s use of Sora’s text-to-video capabilities underscores the technology’s potential in the creative landscape. By relying solely on Sora’s abilities, Trillo bypassed the need for traditional image inputs, showcasing the model’s versatility and power.

Embracing AI in Creativity

Trillo’s groundbreaking music video serves as a testament to the growing interest among creatives in harnessing AI tools to tell compelling stories. Despite criticisms of AI technology’s potential exploitation and copyright issues, many artists continue to explore its possibilities for innovation and expression.


As OpenAI continues to push the boundaries of AI technology with Sora, the creative community eagerly anticipates the evolution of storytelling and artistic expression in the digital age. Trillo’s pioneering work with Sora exemplifies the transformative potential of AI in the realm of media creation, paving the way for a new era of innovation and creativity.

Unleash the Power of AI with the Latest Update for Nvidia ChatRTX

Exciting news for AI enthusiasts! Nvidia ChatRTX introduces its latest update, now available for download. This update, showcased at GTC 2024 in March, expands the capabilities of this cutting-edge tech demo and introduces support for additional LLM models for RTX-enabled AI applications.

What’s New in the Update?

  • Expanded LLM Support: ChatRTX now boasts a larger roster of supported LLMs, including Gemma, Google’s latest LLM, and ChatGLM3, an open, bilingual LLM supporting both English and Chinese. This expansion offers users greater flexibility and choice.
  • Photo Support: With the introduction of photo support, users can seamlessly interact with their own photo data without the hassle of complex metadata labeling. Thanks to OpenAI’s Contrastive Language-Image Pre-training (CLIP), searching and interacting with personal photo collections has never been easier.
  • Verbal Speech Recognition: Say hello to Whisper, an AI automatic speech recognition system integrated into ChatRTX. Now, users can converse with their own data, as Whisper enables ChatRTX to understand verbal speech, enhancing the user experience.

Why Choose ChatRTX?

ChatRTX empowers users to harness the full potential of AI on their RTX-powered PCs. Leveraging the accelerated performance of TensorRT-LLM software and NVIDIA RTX, ChatRTX processes data locally on your PC, ensuring data security. Plus, it’s available on GitHub as a free reference project, allowing developers to explore and expand AI applications using RAG technology for diverse use cases.

Explore Further

For more details, check out the embargoed AI Decoded blog, where you’ll find additional information on the latest ChatRTX update. Additionally, don’t miss the new update for the RTX Remix beta, featuring DLSS 3.5 with Ray Reconstruction.

Don’t wait any longer—experience the future of AI with Nvidia ChatRTX today!

GitHub Copilot Workspace: Revolutionizing Developer Environments with AI

GitHub has unveiled Copilot Workspace, an AI-native developer environment that promises to streamline coding processes, enhance productivity, and empower developers with cutting-edge tools. This innovative platform, initially teased at GitHub’s user conference in 2023, is now available in technical preview, inviting interested developers to join the waitlist for early access.

Copilot versus Copilot Workspace: Understanding the Evolution

While GitHub introduced a coding assistant named Copilot in 2021, the launch of Copilot Workspace marks a significant evolution in AI-driven development tools. Jonathan Carter, head of GitHub’s GitHub Next applied research and development team, distinguishes between the two offerings. Copilot assists in completing code snippets and synthesizing code within a single file, whereas Copilot Workspace operates at a higher level of complexity, focusing on task-centric workflows and reducing friction in starting tasks.

The Evolution of Copilot: From AI Assistant to Workspace

Since its inception, GitHub has continually refined Copilot, enhancing its code suggestions and adopting a multi-model approach. With support for OpenAI’s GPT-4 model and the introduction of an enterprise plan, Copilot has evolved into a versatile tool for developers. However, Copilot Workspace takes the concept further by providing a comprehensive AI-native environment aimed at empowering developers to be more creative and expressive.

Empowering Enterprise Developers: A Paradigm Shift in Development

GitHub anticipates that Copilot Workspace will significantly impact enterprise developers, offering greater productivity and job satisfaction. By facilitating experimentation and reducing implementation time, GitHub believes organizations will adopt more agile approaches, resembling smaller, more innovative companies. Moreover, standardization of workflows and skills across teams will streamline collaboration and reduce resource allocation for upskilling.

Key Features of Copilot Workspace: Enhancing Developer Experience

Copilot Workspace offers several key features designed to simplify common development tasks. These include:

  • Editability at All Levels: Developers maintain control over AI-generated suggestions, enabling them to modify and iterate on code seamlessly.
  • Integrated Terminal: Developers can access a terminal within the workspace, facilitating code testing and verification without context-switching.
  • Collaborative Functionality: Copilot Workspace supports collaboration, allowing multiple developers to work together on projects efficiently.
  • Optimized Mobile Experience: The platform can be accessed on mobile devices, enabling developers to code from anywhere, anytime.

The Road Ahead: General Availability and Beyond

While Copilot Workspace is currently available in technical preview, GitHub has not provided a timeline for general availability. Feedback from developers will inform the platform’s Go-to-Market strategy, with a focus on optimizing the user experience and addressing specific needs. Access to Copilot Workspace is prioritized on a first-come, first-served basis, with potential expansion to startups and small- to medium-sized businesses for rapid feedback collection.

In summary, GitHub Copilot Workspace represents a significant leap forward in AI-driven development environments, promising to revolutionize the way developers code and collaborate. As the platform continues to evolve, it holds the potential to reshape the future of software development, empowering developers to unleash their creativity and innovation.

How To Use New ChatGPT’s Memory Feature

OpenAI continues to evolve its renowned ChatGPT, introducing a slew of new features aimed at enhancing user experience and control. From memory management to temporary chats, here’s a comprehensive guide to making the most of ChatGPT’s Memory Feature latest offerings.

Unlocking ChatGPT’s Memory Feature:

ChatGPT Plus subscribers ($20 per month) can now leverage the expanded persistent memory feature, allowing them to store and recall vital information effortlessly. Learn how to utilize this feature to enhance your interactions with ChatGPT and streamline your workflow.

How to Use ChatGPT’s Memory Feature:

Discover the step-by-step process for storing information using ChatGPT’s memory feature. From inputting details to managing stored memories, we’ll walk you through the process to ensure seamless integration into your ChatGPT experience.

Important Limitations and Workarounds:

While ChatGPT’s memory feature offers enhanced functionality, it’s essential to understand its limitations. Explore the current restrictions and discover potential workarounds to maximize the utility of this feature.

Optimizing Temporary Chats for Temporary Projects:

For temporary projects or sensitive discussions, ChatGPT offers the option of starting a “temporary chat.” Learn how to initiate and manage temporary chats, ensuring privacy and security without compromising on functionality.

Accessing and Managing Chat History:

ChatGPT users now have more control over their chat history, with enhanced accessibility and management options. Explore how to access previous chats, retain chat history, and navigate through archived conversations with ease.

Empowering User Control with Data Controls:

OpenAI prioritizes user control and privacy with enhanced data controls. Discover how to manage data sharing preferences, opt-in or out of model training, and delete chat history to tailor your ChatGPT experience to your preferences.


With these latest updates, OpenAI continues to empower users with greater control and functionality within ChatGPT. Whether you’re a seasoned user or new to the platform, these features offer enhanced capabilities and customization options for a seamless AI-powered interaction experience. Stay tuned for further advancements as OpenAI remains at the forefront of AI innovation.

OpenAI Partnership with Financial Times to Elevate ChatGPT’s Journalism Capabilities

OpenAI latest move involves a strategic partnership with the esteemed British news daily, Financial Times (FT), aimed at enriching the journalistic content available through ChatGPT. This collaboration signifies a concerted effort to provide users with high-quality news articles directly sourced from FT, along with relevant summaries, quotes, and links—all properly attributed, as emphasized by both parties in a recent press release.

Driving Forces Behind the Partnership

In light of recent debates surrounding AI companies’ ethical use of training data, particularly in relation to web scraping practices, OpenAI’s decision to forge partnerships with reputable publications like FT reflects a strategic pivot towards responsible data sourcing. This move comes amidst regulatory scrutiny, such as the recent fine imposed on Google by France’s competition watchdog for unauthorized use of publishers’ content in training AI models.

By partnering with FT, OpenAI aims to bolster ChatGPT’s standing as a leading AI chatbot while ensuring compliance with ethical data usage standards. Beyond content aggregation, the collaboration entails joint efforts to develop innovative AI products and features tailored to FT’s audience, potentially signaling a new era of symbiotic relationships between AI research labs and media organizations.

Perspectives from OpenAI and FT

Brad Lightcap, OpenAI’s COO, underscores the collaborative nature of the partnership, emphasizing the mutual goal of leveraging AI to enhance news delivery and reader experiences globally. Meanwhile, FT Group CEO John Ridding reaffirms the publication’s commitment to upholding journalistic integrity amidst technological advancements, emphasizing the importance of safeguarding content and brand reputation in the digital age.

Previous Partnerships and Challenges

OpenAI’s collaboration with FT follows similar partnerships with renowned media entities like Associated Press (AP), Axel Springer, and the American Journalism Project (AJP), underscoring the research lab’s ongoing efforts to diversify its training datasets responsibly. However, the journey hasn’t been without its hurdles, as evidenced by legal challenges from entities like the New York Times and multiple American publications alleging copyright infringement—a reminder of the complex legal and ethical considerations inherent in AI development.

In summary, OpenAI’s alliance with FT represents a significant step towards fostering synergy between AI technology and journalism, with the potential to shape the future of news consumption and content creation in the digital era. As both parties navigate this evolving landscape, their collaboration underscores the pivotal role of responsible data partnerships in driving AI innovation while upholding journalistic integrity.

Oracle Fusion Cloud CX: Elevating Customer Engagement with Next-Level AI Solutions

In the ever-evolving landscape of global enterprises, the integration of generative AI has become paramount for driving efficiencies and maintaining a competitive edge. Oracle, a leader in the tech industry, has recognized this trend and is spearheading advancements in AI technology, particularly within its Fusion Cloud CX suite. Let’s delve into how Oracle’s latest AI features are revolutionizing customer service, sales, and marketing workflows.

Oracle Fusion Cloud CX: Empowering Business Engagement

Oracle Fusion Cloud CX serves as a centralized hub for businesses to consolidate data from various touchpoints and leverage a suite of cloud-based tools. The platform aims to enhance customer engagement across both physical and digital channels, ultimately boosting customer retention, up-selling opportunities, and brand advocacy.

AI-Powered Automation: Streamlining Workflows

With Oracle’s latest update, Cloud CX users can bid farewell to tedious manual tasks. The introduction of AI smarts enables service agents to respond to customer queries more efficiently. Through contextually-aware responses generated by AI, agents can swiftly handle cases, allowing them to focus on more complex requests. Additionally, AI algorithms optimize schedules for field service agents, ensuring timely and efficient customer service.

Enhanced Marketing and Sales Capabilities

Oracle’s generative AI features extend beyond customer service, empowering marketers and sellers to deliver targeted content and drive engagement. AI-driven content creation facilitates the production of personalized emails and landing page content, expediting workflows and accelerating deal closures. Moreover, AI-based modeling assists in identifying reachable contacts and provides valuable insights into buyer interests, enhancing engagement and driving purchase decisions.

Expansive AI Ecosystem

Oracle boasts over 50 generative AI features across its Fusion Cloud applications, catering to diverse business functions. These capabilities span Customer Experience (CX), Human Capital Management (HCM), and Enterprise Resource Planning (ERP) applications, driving productivity and cost savings for organizations. Notably, Oracle collaborates with customers to optimize AI utilization, whether for enhancing productivity, reducing costs, or generating revenue streams.

Future Outlook: Innovations in AI

While Oracle’s current AI efforts leverage partnerships with external providers like Cohere, the possibility of proprietary AI models remains open. The company’s commitment to advancing AI functionality within its products reflects broader industry trends, with competitors also exploring partner-driven approaches. The potential of generative AI within enterprise functions, as highlighted by McKinsey, underscores the immense opportunities for organizations to drive profitability through AI-driven efficiencies.


Oracle’s integration of generative AI within its Fusion Cloud CX suite marks a significant milestone in enhancing customer experiences and driving operational efficiencies. By automating critical workflows and delivering personalized insights, Oracle empowers businesses to stay ahead in an increasingly competitive landscape. As the realm of AI continues to evolve, Oracle remains at the forefront, poised to deliver innovative solutions that redefine the future of enterprise operations.

Elevating AI Video Creation: Synthesia Unveils Expressive Avatars

Synthesia, the groundbreaking startup revolutionizing AI video creation for enterprises, has unveiled its latest innovation: “expressive avatars.” This game-changing feature elevates digital avatars to a new level, allowing them to adjust tone, facial expressions, and body language based on the context of the content they deliver. Let’s explore how this advancement is reshaping the landscape of AI-generated videos.

Synthesia’s Next Step in AI Videos

Founded in 2017 by a team of AI experts from esteemed institutions like Stanford and Cambridge Universities, Synthesia has developed a comprehensive platform for creating custom AI voices and avatars. With over 200,000 users generating more than 18 million videos, Synthesia has been widely adopted at the enterprise level. However, the absence of sentiment understanding in digital avatars has been a significant limitation—until now.

Introducing Expressive Avatars

Synthesia’s expressive avatars mark a significant leap forward in AI video creation. These avatars possess the ability to comprehend the context and sentiment of text, adjusting their tone and expressions accordingly. By leveraging deep learning model EXPRESS-1, trained with extensive text and video data, these avatars deliver performances that blur the line between virtual and real. From subtle expressions to natural lip-sync, the realism of these avatars is unparalleled.

Implications of Expressive Avatars

While the potential for misuse exists, Synthesia is committed to promoting positive enterprise-centric use cases. Healthcare companies can create empathetic patient videos, while marketing teams can convey excitement about new products. To ensure safety, Synthesia has implemented updated usage policies and invests in technologies for detecting bad actors and verifying content authenticity.

Customer Success Stories

Synthesia boasts a clientele of over 55,000 businesses, including half of the Fortune 100. Zoom, a prominent customer, has reported a 90% increase in video creation efficiency with Synthesia. These success stories highlight the tangible benefits of Synthesia’s innovative AI solutions in driving business growth and efficiency.


With the launch of expressive avatars, Synthesia continues to push the boundaries of AI video creation, empowering enterprises to deliver engaging and authentic content at scale. As the demand for personalized and immersive experiences grows, Synthesia remains at the forefront, driving innovation and reshaping the future of digital communication. Join us in embracing the era of expressive avatars and redefining the possibilities of AI video creation.

Meta Unveils Llama 3: The Next Leap in Open Generative AI Models

Meta has launched the latest iteration of its renowned Llama series of open generative AI models: Llama 3. With two models already released and more to follow, Meta promises significant advancements in performance and capabilities compared to its predecessors, Llama 2 8B and Llama 2 70B.

Meta Llama 3

Meta introduces two models in the Llama 3 family: Llama 3 8B, boasting 8 billion parameters, and Llama 3 70B, with a staggering 70 billion parameters. These models represent a major leap forward in performance and are among the best-performing generative AI models available today.

Performance Benchmarks: Meta highlights Llama 3’s impressive performance on popular AI benchmarks such as MMLU, ARC, and DROP. The company claims superiority over comparable models like Mistral 7B and Gemma 7B, showcasing dominance in multiple benchmarks.

Meta Llama 3

Enhanced Capabilities: Llama 3 offers users more “steerability,” lower refusal rates, and higher accuracy across various tasks, including trivia, history, STEM fields, and coding recommendations. Llama 3’s larger dataset, comprising 15 trillion tokens, and advanced training techniques contribute to these improvements.

Meta Llama 3

Data Diversity and Safety Measures

Meta emphasizes the diversity of Llama 3’s training data, sourced from publicly available sources and including synthetic data to enhance performance across different languages and domains. The company also introduces new safety measures, including data filtering pipelines and generative AI safety suites, to address toxicity and bias concerns.

Meta Llama 3

Availability and Future Plans

Llama 3 models are available for download and will soon be hosted on various cloud platforms. Meta plans to expand Llama 3’s capabilities, aiming for multilingual and multimodal capabilities, longer context understanding, and improved performance in core areas like reasoning and coding.

Conclusion: With the release of Llama 3, Meta continues to push the boundaries of open generative AI models, offering researchers and developers powerful tools for innovation. While not entirely open source, Llama 3 promises groundbreaking advancements and sets the stage for future developments in AI technology.