Cheetah Mobile Inc.

Good day and welcome to the Cheetah Mobile third quarter 2024 earnings conference call. All participants will be in listen-only mode. Should you need assistance, please signal a conference specialist by pressing the star key followed by zero. After today's presentation, there will be an opportunity to ask questions. To ask a question, you may press star then one on your telephone keypad, and to withdraw your question, please press star then two. Please note today's event is being recorded. I would now like to turn the conference over to Helen Jingzhu, IR for Cheetah Mobile. Please go ahead.

Thank you, operator. Welcome to Cheetah Mobile's third quarter 2024 earnings conference call. With us today are our company's chairman and CEO, Mr. Fu Shun, and our director and CFO, Mr. Thomas Jin. Following management's prepared remarks, we will conduct the Q&A section. please note that the CEO script will be presented by an AI agent. Before we begin, I refer you to the Safe Harbor statement in our earnings release, which also applies to our earnings conference call today, as well as make forward-looking statements. At this time, I will now turn the call over to our CEO, Mr. Fusheng. Please go ahead, Fusheng.

Hello, everyone. Thank you for joining us today. Cheetah Mobile once again achieved accelerated revenue growth in Q3, driven by our service robotics and internet businesses. This consistent growth results from our strategies to expand the use cases of our wheeled service robotics and expand into overseas markets, as well as the resilience of our legacy internet business. Industry demand for service robots continues to rise, especially in the overseas markets and in restaurants, hotels, factories, and offices. Business owners are using robots more often to help their staff and improve efficiency. In the past weeks, I have visited many customers and partners in Europe and associations Asia. In fact, I am still in Europe today, meeting with our local partners to further strengthen our presence Building a strong local distribution network is very important for our global expansion, as it will set us apart from our peers. That's why I have spent significant effort on this initiative. During my conversations with local partners, I learned that our robots are helping them solve labor shortages. Some customers in Europe share that using Cheetos robots have reduced employee absences and turnover. Meanwhile, some Japanese customers told us our robots are much more reliable than other players' offerings and are switching to our products. In September, we launched a new robot for factory and fulfillment century use. This robot can autonomously deliver goods to move low-payload items from transit warehouses to assembly lines. We are currently optimizing the product to better meet the needs of customers in overseas markets. This highlights the importance of receiving feedback and inputs from local partners. We believe this product will become an important part of our service robotics business in 2025. While the robotics industry is still in its early stages, it will be a trillion-dollar market. Robots are becoming essential helpers for humans, and it will happen in the bell markets first. And LLMs will speed up this growth by enabling robots to do more tasks. and making them easier to deploy than ever before. Through our conversations with investors, we've noticed a lot of interest in how LMs are making our robots smarter and driving steady revenue growth. Today, I will share what we've achieved so far with LMs in our products and what's coming next. First, we are using LMs to improve the way our service robots interact through voice. Thanks to our strong far-field voice recognition, our robots already hear users well. Now with LMs, they understand users' questions more clearly and respond better, making the overall experience much smoother. For instance, in restaurants, our robots don't just deliver food during busy hours. They can also help attract customers, boosting the return on investment for restaurant owners by taking on more roles. And because LMS break down language barriers. We are expanding these voice-enabled robots to international markets. We are also working on agentless, a system that lets customers set up tasks for robots using voice prompts. For example, you can tell a robot to check each table at 2 p.m. to see if anyone wants to order more food before the kitchen closes at 2.30 p.m. The robot will go to each table. skip the ones without customers, and even allow people to place orders if the ordering system is linked without LMs. This kind of functionality would be nearly impossible or would require writing a lot of complicated code. Secondly, we are using multimodality models to improve our robots' indoor autonomous driving. One area that we are working is to enable our robots to map out the large factory as they move and look around once the map is ready. Our MV team can mark key locations, and the robots can then navigate the factory on their own to deliver goods. If factory owners want to change these key locations, they can easily update the map whenever needed. Based on our initial testing, thanks to LLMS, the time it takes to set up our robots can dramatically reduce from about two days to just two hours. We are already using vision-based autonomous driving technology in some cases and plan to expand it further. For instance, our robots use vision-based autonomous driving technology to avoid people and obstacles and understand their surroundings. Over time, we aim to achieve an end-to-end navigation system. This will allow our robots to handle more complex environments entirely on their own. Third, we're adding robotic arms to our robots to help them do specific jobs. Some of these arms can press buttons, which is useful for delivering things between floors, in particular in the overseas markets where business owners are reluctant to adjust the elevator access control systems due to security concerns. Others can pick up and sort items, which is great for use in factories. These arms are powered by on-device multimodality models, making it easier to automate routine tasks. When it comes to LMs, we use advanced models through API calls to support some of the features we have discussed. At the same time, we are also developing our own models. In November, we launched an 8x7 billion mixture of experts model, covering many languages, including Chinese, English, Korean, and Japanese. We made it open source and use it to power our robots, especially the agent OS features. Additionally, we have trained smaller on-device models to support indoor autonomous driving and control robotic arms, turning to LM-based applications. We recently introduced AirDS, an high-based data service platform to assisting enterprises in data and building prompts for their LM-based application. AirTips was built on top of our insights into developing LMs and LM-based apps. So far, we have received positive customer feedback on our LM-based application, and we will continue to enrich our portfolio. Our goal is to offer relatively standardized SaaS products, allowing businesses to use LMs gain efficiency. Before handing the call over to Thomas for the financial highlights, I want to stress this. Cheetah Mobile is in a good position to tap into the growing markets for service robots and LM-based apps. We've been years of experience on the PC and mobile phone areas, as well as expanding into international markets. And we have strong LM expertise We've already made solid progress in growing our revenue and cutting losses. This is just the beginning of Cheeto Mobile's turnaround.