Partnership with Camb.ai
IMAX has taken these trends into consideration and is now exploring localization using AI to attract more viewers. On Monday, the Canadian production theater company, renowned for its massive theaters and immersive movie experiences, announced its partnership with Dubai-based startup Camb.ai. This partnership aims to utilize Camb.ai's AI speech models for translating original content, including documentaries. Camb.ai has already deployed its AI dubbing and speech translation for various live sports events and leagues such as the Australian Open, Eurovision Sport, and Major League Soccer. It offers its Boli model for speech-to-text translation and Mars for speech emulation, available through its DubStudio platform that supports 140 languages, including low-resource ones.Camb.ai's co-founder and CTO, Akshat Prakash, who was a former Apple engineer working on AI and ML models for Siri, co-founded the company with his father Avneesh Prakash last year. He emphasized that Camb.ai takes a different approach compared to companies like OpenAI and Anthropic. "They're trying to build very horizontal models that can cover a wide range of tasks. We don't have to do that. Some of our models are less than 100 million parameters and are super specialized," he said. Prakash further explained that Camb.ai pretrained 70% of its models using academic-licensed datasets that are commercially usable, and the remaining 30% involves fine-tuning data from early partners who deploy its models for AI-based dubbing and translation.
He also asserted that Camb.ai is very careful and avoids potentially scraping the internet. "Some companies feel they can get away with it as they build consumer-facing apps or tools. But we don't do that. We have a 'three-layer' approach to provide AI-based translation, comprising the foundation layer of Boli and Mars models, the infrastructure layer hosting these models, and the DubStudio platform for the front end," Prakash said. Camb.ai's Boli takes input speech tokens and produces output text tokens in the translated language while retaining nuances. Once Boli generates the text, Mars translates it into speech using the same audio input signal to capture the actual audio performance, including ambient sounds like the background score of the audience cheering in sports events.
Deployment and Benefits
IMAX will roll out AI translations in stages, starting with high-resource languages. This deployment follows internal testing of Camb.ai's tech on its original content. Mark Welton, president of IMAX Global, said, "While we are only in the beginning stages of the partnership, we will continue to work together to better explore its potential and how it can best move us forward." Welton indicated that the AI deployment will help save on translation costs without disclosing specific details.Camb.ai currently has a team of 50 people. In February, it raised $4 million in a seed round led by Courtside Ventures. Prakash told TechCrunch that the startup is closing a bigger, pre-Series A round to expand its reach and headcount.
Unlock the Power of Context-Aware AI with MCP
How MCP Works
Anthropic's MCP allows models, not just their own but any models, to draw data from various sources such as business tools, software, content repositories, and app development environments. It acts as a protocol that enables developers to build two-way connections between data sources and AI-powered applications like chatbots. For instance, in the Claude desktop app, setting up MCP allows for direct connections to platforms like GitHub. Watch as Claude effortlessly creates a new repo and makes a PR through a simple MCP integration. Once configured, building such integrations takes just a matter of hours.This innovation solves a significant problem in the industry. As AI assistants gain mainstream adoption, they are often constrained by their isolation from data. Every new data source requires a custom implementation, making truly connected systems difficult to scale. But with MCP, developers can build against a standard protocol instead of maintaining separate connectors for each data source.Real-World Implementations
Companies like Block and Apollo have already integrated MCP into their systems, demonstrating its practicality. Dev tooling firms such as Replit, Codeium, and Sourcegraph are also adding MCP support to their platforms, further validating its potential. This shows that MCP is not just a theoretical concept but a viable solution in the real world.For subscribers to Anthropic's Claude Enterprise plan, they can connect the company's Claude chatbot to their internal systems via MCP servers. Anthropic has shared prebuilt MCP servers for enterprise systems like Google Drive, Slack, and GitHub, and is working on providing toolkits for deploying production MCP servers that can serve entire organizations.The Future of Context-Aware AI
Anthropic is committed to building MCP as a collaborative, open-source project and ecosystem. By inviting developers to join in, they aim to build the future of context-aware AI together. MCP holds the promise of enabling AI bots to better retrieve relevant information and understand the context around various tasks. However, it remains to be seen how widely it will be adopted and how effective it will be in practice.While MCP sounds like a good idea in theory, it faces competition from rivals like OpenAI. OpenAI recently brought a data-connecting feature to ChatGPT, similar to the use cases driven by MCP. But OpenAI is pursuing implementations with close partners rather than open sourcing the underlying tech. Only time will tell how MCP will fare in this competitive landscape and whether it will truly live up to Anthropic's claims.Uber's Leap into the World of Data Labeling
Why Uber is Venturing into Data Labeling
The rise of AI has made the data labeling market extremely hot, and Uber is well aware of this trend. As more and more companies rely on AI for their operations, the need for accurate and labeled data has skyrocketed. Uber recognizes the importance of having a reliable source of labeled data to enhance the performance and capabilities of its own AI systems. By entering the data labeling space, Uber is positioning itself at the forefront of the AI revolution. It allows the company to not only meet its internal needs but also tap into the growing demand from external customers.For instance, companies like Scale AI have witnessed a significant increase in demand for their data labeling services. Scale AI raised a whopping $1 billion in a recent round, with a valuation of $13.8 billion. This shows the immense potential and value that the data labeling market holds. Uber's entry into this market gives it a competitive edge and the opportunity to collaborate with leading players in the industry.
The Impact on Uber's Business
The creation of the Scaled Solutions division and the recruitment of gig workers for data labeling will have a profound impact on Uber's business. It will enable the company to improve the quality and accuracy of its AI-powered services, leading to better user experiences. With labeled data at their disposal, Uber can train its algorithms more effectively, resulting in more efficient ride matching, improved safety features, and enhanced overall performance.Moreover, by serving external customers, Uber can generate additional revenue streams. This diversification of income sources will make the company more resilient in the face of market fluctuations and competition. It also allows Uber to leverage its existing infrastructure and expertise in the transportation industry to expand into new areas such as autonomous vehicles and augmented reality through its collaborations with companies like Aurora Innovation and Niantic.
The Future of Uber in the Data Labeling Space
As Uber continues to expand its fleet of gig workers and strengthen its presence in the data labeling market, the future looks promising. The company has the potential to become a major player in this domain, providing high-quality data labeling services to a wide range of clients. With its vast resources and global reach, Uber can scale up its operations quickly and efficiently to meet the growing demand.Furthermore, Uber's foray into data labeling opens up new opportunities for innovation and collaboration. It can lead to the development of more advanced AI applications that can transform various industries. From transportation to gaming and beyond, the data labeled by Uber's gig workers could have a significant impact on the future of technology.