Meta has recently unveiled its latest AI-powered tool, [Movie Gen](https://ai.meta.com/research/movie-gen/), designed to create short videos and audio tracks from text prompts. This technology marks Meta's entry into the competitive AI video and audio generation space, alongside major players like OpenAI and Google.

Movie Gen is capable of generating videos up to 16 seconds long at 16 frames per second (FPS), which, while lower than the standard Hollywood frame rate of 24 FPS, still approaches professional-quality visuals. Users can provide text prompts to either generate entirely new content or edit existing videos by changing objects and backgrounds. In addition, the tool can generate synchronized audio, including background music and sound effects, which plays in harmony with the video footage.

Although Movie Gen is currently only accessible to internal employees and select partners, Meta has plans to integrate it into its broader suite of apps, such as Instagram and WhatsApp, sometime next year. This move is expected to encourage more user-generated video content, driving engagement across the platform.

The underlying AI models behind Movie Gen are still in a research phase, and Meta is being cautious about its public release, citing the need for further safety testing. The company plans to implement safety measures to prevent misuse, including the ability to reject inappropriate prompts or content.

This development intensifies the race in AI video generation, with competitors like OpenAI's Sora and Google's Veo also entering the scene with their own video generation tools. However, Meta is positioning Movie Gen as a potential game-changer due to its integration of audio and video, promising a seamless creative experience for users.

Meta Announces Movie Gen, an AI Video and And Gen Model

[OpenAI recently introduced a new feature for ChatGPT called Canvas](https://openai.com/index/introducing-canvas/), a workspace designed to enhance the AI model’s usability for tasks like writing and coding. Canvas opens a side window, allowing users to interact with the AI in a more visual and organized manner. This setup is especially useful for placing prompts next to the generated content, facilitating easier real-time editing and revisions.

The interface includes features tailored to both writing and coding tasks. Users can generate and edit content directly within the Canvas, with added functionality for highlighting specific sections to request targeted improvements or explanations. For writers, there are shortcuts for suggesting edits, adjusting document length, refining tone, and even adding final polish to the content. Coding tasks, on the other hand, benefit from features like inline code reviews, debugging suggestions, and code porting across different programming languages like Python and JavaScript.

![alt](https://firebasestorage.googleapis.com/v0/b/saks-test.appspot.com/o/uploads%2F3pSAcKD69pYhIdjDD4hooYQZIx13%2F20241004144640576.jpeg?alt=media&token=5070f590-aa78-4d93-9cfd-315aa823a0c6)

Canvas aims to streamline workflows and improve productivity. It fosters a more intuitive and collaborative interaction with ChatGPT, allowing users to refine their work more effectively. Available to ChatGPT Plus and Team users, the feature is currently in beta and will be extended to Enterprise and educational users soon. OpenAI also plans to roll out Canvas to free users once the beta phase is complete

This update reflects OpenAI’s continued efforts to make ChatGPT more versatile and user-friendly, helping users handle complex tasks with greater ease and precision. The new interface not only improves productivity but also opens up new possibilities for collaboration between users and the AI, especially in creative and technical projects.

OpenAI Introduces Canvas: A New Way to Interact with ChatGPT

OpenAI, a pioneer in the artificial intelligence space, has just closed a record-breaking [$6.6 billion fundraising round](https://openai.com/index/scale-the-benefits-of-ai/), solidifying its position as the world's most highly-valued AI startup at $157 billion. This recent funding, led by Thrive Capital and supported by investors like Microsoft, Nvidia, SoftBank, and others, highlights the sustained interest in AI technology despite ongoing concerns around its safety and effectiveness.

Since launching ChatGPT in 2022, OpenAI has fueled a global AI boom, prompting significant investments in AI startups. However, investments in smaller startups had slowed earlier this year, as the dominance of tech giants like Google, Amazon, and Microsoft led to fears that smaller companies couldn’t compete. OpenAI’s successful fundraising offers hope for the broader AI startup ecosystem, proving that investor excitement around AI remains strong.

OpenAI’s growth has been remarkable. The company now has around 1,700 employees, having added over 1,000 in the last nine months. Financial projections suggest that OpenAI’s annual revenue will reach $3.7 billion in 2023, with ChatGPT alone expected to contribute $2.7 billion. However, this success comes with steep operational costs. The company spends billions annually on training and developing its AI models, including the cutting-edge GPT-4. At one point, running ChatGPT was reported to cost $700,000 per day. Consequently, OpenAI expects to lose about $5 billion this year, largely due to the high costs of maintaining its services.

Thrive Capital’s $1.3 billion investment, with an option to invest up to $1 billion more by 2025, was crucial to the round. Microsoft, OpenAI's long-time partner, also contributed heavily, with a total investment exceeding $13 billion. The new funds will enable OpenAI to scale its research, expand compute capacity, and enhance its product offerings. OpenAI aims to maintain its leadership in AI by continuing to develop frontier technologies that solve complex problems.

Yet, this latest funding round has not come without challenges. OpenAI's board of directors fired and then reinstated CEO Sam Altman in 2023, leading to the departure of several key executives, including co-founder and chief scientist Ilya Sutskever and CTO Mira Murati. Additionally, investors are pressuring OpenAI to restructure itself from a nonprofit-capped entity to a fully for-profit company. Under the terms of the new funding, OpenAI has two years to make this transition or risk its funding converting into debt.

Competition in the AI space is fierce. Rival startups like Anthropic, xAI, and Cohere, as well as established giants like Google and Meta, are racing to develop next-generation AI systems. Runway and Luma Labs have already released advanced video generation models ahead of OpenAI, putting pressure on the company to debut its own model, Sora, later this year. OpenAI may need to adjust its business model to compete effectively, potentially raising the price of its premium ChatGPT Plus subscription and securing exclusive licensing deals with data providers to protect itself from intellectual property disputes.

OpenAI’s ambitious growth targets include reaching $100 billion in revenue by 2029, but the company must overcome internal challenges, competitive pressures, and operational hurdles to achieve these goals. The new funding provides critical support, but the coming years will test OpenAI’s ability to remain at the forefront of AI innovation.

OpenAI Raises $6.6 Billion at $157 Billion Valuation

OpenAI’s recent announcements at its 2024 DevDay mark a pivotal moment for developers and the AI community. The unveiling of the Realtime API, a powerful tool designed to facilitate low-latency, AI-generated voice interactions, promises to reshape how applications handle voice-driven experiences. This advancement, along with updates to the Chat Completions API, brings AI closer to delivering natural, real-time conversations with reduced complexity.

Previously, developing a voice assistant required piecing together multiple models. Developers had to transcribe audio using speech recognition, process the text through a reasoning model, and generate speech using text-to-speech technology. This multi-step process not only introduced delays but often disrupted the natural flow of conversations.

With the Realtime API, OpenAI has simplified this workflow significantly. Now, developers can integrate speech-to-speech experiences using just one API call. Both audio input and output are processed in real-time, drastically reducing latency and creating more lifelike responses. This streamlined integration allows for more dynamic, immersive interactions, as evidenced by early adopters like Healthify and Speak, who use the API for nutrition coaching and language learning, respectively.

The Realtime API operates through a persistent WebSocket connection, enabling continuous, uninterrupted communication. It also supports function calling, allowing AI-powered voice assistants to take real actions based on user input—such as placing orders or retrieving information—creating a smooth, human-like conversational experience.

In addition to voice interaction capabilities, the Realtime API comes equipped with six distinct voices, separate from those used in ChatGPT, helping developers customize the experience without worrying about copyright issues. OpenAI demonstrated this versatility with an app that helps users plan trips by conversing with an AI assistant about travel plans and restaurant recommendations. While the API doesn’t directly make phone calls, it integrates seamlessly with calling APIs like Twilio, adding another layer of functionality to the applications built with it.

OpenAI’s head of developer experience, Romain Huet, showcased how the API could facilitate smooth phone conversations, such as ordering food for an event. However, there remains a responsibility on developers to ensure AI-generated voices identify themselves in calls, particularly in regions like California, where laws might mandate such disclosures.

Another significant update revealed during DevDay is the introduction of vision fine-tuning in OpenAI’s API, allowing developers to use images alongside text for tasks requiring visual understanding. This enhancement will make GPT-4o more versatile for applications that rely on image data, although strict guidelines remain in place to prevent the use of copyrighted or inappropriate images.

OpenAI also announced a model distillation feature, which enables developers to fine-tune smaller models like GPT-4o mini using larger models such as GPT-4o. This provides a cost-effective way to improve the performance of smaller AI models, as running smaller models typically reduces costs. OpenAI’s beta evaluation tool further allows developers to assess the effectiveness of these fine-tunes within the API.

While DevDay brought exciting advancements, there were notable absences from the announcements. Developers anticipating new AI models or updates to OpenAI’s much-awaited video generation model, Sora, were left waiting. Similarly, no updates were shared regarding the GPT Store, a platform teased at last year’s DevDay.

OpenAI’s 2024 DevDay: Launching the Realtime API to Transform AI Voice Experiences for Developers

Liquid AI, co-founded by renowned robotics expert Daniela Rus and a team of researchers from MIT, has developed a new AI model called the Liquid Foundation Model (LFM), powered by liquid neural networks. This architecture offers a more efficient and adaptable solution compared to traditional AI models.

Liquid AI recently emerged from stealth mode, securing an impressive $37.5 million in seed funding from investors such as OSS Capital, Automattic, and angel investors like GitHub’s Tom Preston Werner and Shopify’s Tobias Lütke. This backing values Liquid AI at $303 million. The founding team includes Ramin Hasani (CEO), Mathias Lechner (CTO), and Alexander Amini (Chief Scientific Officer), all of whom have made significant contributions to the development of liquid neural networks.

Unlike traditional models, liquid neural networks are designed with flexibility in mind. Inspired by the neural mechanisms of simple organisms like roundworms, these networks consist of neurons governed by equations that predict behaviour over time. The "liquid" aspect refers to the model's ability to adapt dynamically to new environments or changing conditions, offering more flexibility than static models like GPT-3 or GPT-4.

Liquid neural networks bring several key advantages over traditional AI models. One of the most compelling features is their smaller size and reduced computational requirements. For instance, while GPT-3 contains 175 billion parameters, a liquid neural network designed for drone navigation might only require 20,000 parameters and 20 neurons. This drastic reduction in complexity allows liquid neural networks to perform well on resource-constrained devices like Raspberry Pi, making them suitable for edge deployments, such as mobile applications, autonomous vehicles, and drones.

In addition to their compact size, liquid neural networks are highly interpretable. With fewer parameters, it’s easier to understand the function of individual neurons, which enhances transparency — a crucial aspect in industries requiring high accountability, such as healthcare and finance.

Perhaps the most significant advantage of liquid neural networks is their ability to learn and adapt in real time. While most AI models process static snapshots of data, liquid neural networks analyze sequences of data, allowing them to adjust to new information dynamically. This makes them particularly useful in unpredictable environments, such as changing weather conditions for autonomous driving or navigating unfamiliar terrains.

Liquid neural networks have already demonstrated impressive performance across various fields. In 2022, Rus and her team tested liquid neural networks on drones, training them using data from a professional drone pilot. The models outperformed traditional algorithms in navigating complex outdoor environments, such as dense forests and city neighborhoods, even in noisy and unpredictable conditions. Notably, the liquid neural network was the only model that could generalize to new, unseen scenarios without requiring additional fine-tuning.

These results highlight the potential applications of liquid neural networks in areas like search and rescue, wildlife monitoring, and autonomous deliveries. Beyond these use cases, liquid neural networks are poised to revolutionize industries that rely on analyzing fluctuating data over time, such as financial markets, electric power grids, and medical diagnostics.

#### Liquid AI has introduced three LFM variants at launch:

- LFM-1B: A dense model with 1.3 billion parameters optimized for resource-constrained environments.
- LFM-3B: A mid-tier model with 3.1 billion parameters, ideal for edge deployments like mobile devices and drones.
- LFM-40B: A high-capacity model with 40.3 billion parameters for complex applications on cloud servers.

These models offer state-of-the-art performance while requiring significantly less memory compared to transformer-based models. For example, the LFM-3B model uses only 16 GB of memory, whereas similar-sized models, such as Meta's Llama, require over 48 GB. This efficiency makes LFMs particularly attractive for businesses looking to integrate AI into applications that need to run on low-resource hardware.

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Meta Announces Movie Gen, an AI Video and And Gen Model

OpenAI Introduces Canvas: A New Way to Interact with ChatGPT

OpenAI Raises $6.6 Billion at $157 Billion Valuation

OpenAI’s 2024 DevDay: Launching the Realtime API to Transform AI Voice Experiences for Developers

Liquid AI: MIT Spinoff Pioneers Efficient and Adaptive AI with Liquid Neural Networks