Source: Tencent Technology 1. Huang Renxun emphasized that generative AI is growing at an exponential rate and that businesses need to adapt and utilize this technology quickly, rather than standing by and falling behind the pace of technological development. 2. Huang Renxun believes that open and closed source AI models will coexist and that companies need to leverage their respective strengths to promote the development and application of AI technology. 3. Huang Renxun proposed that the development of AI needs to consider energy efficiency and sustainability, reducing energy consumption by optimizing the use of computing resources and promoting the inference and generation capabilities of AI models to achieve more eco-friendly intelligent solutions. 4. With the constant accumulation of data and the continuous advancement of intelligent technology, customer service will become a key area for companies to achieve intelligent transformation. 5. According to foreign media reports, at the 2024 Databricks Data + AI Summit held recently, 6. Founder and CEO Huang Renxun had a fascinating conversation with Ali Ghodsi, co-founder and CEO of Databricks. The dialogue between the two parties demonstrated the importance and development trends of artificial intelligence and data processing technology in modern enterprises, emphasizing the key role of technological innovation, data processing capabilities and energy efficiency in promoting enterprise transformation and industry development. 7. Huang Renxun looked to the future of data processing and generative AI in the conversation. He pointed out that the business data of each company is like an untapped gold mine, with tremendous value but extracting deep insight and intelligence from it has always been a daunting task. 8. Huang Renxun also talked about open source models like Llama and DBRX are driving corporate transformation into AI companies, activating a global AI movement and promoting technological development and corporate innovation. Through the collaboration between NVIDIA and Databricks, the two companies will work together to leverage their respective strengths in accelerating computing and generative AI, bringing unprecedented benefits to users. 9. The following is the transcript of the conversation: 10. Moderator: I am very excited to introduce our next guest, a man who needs no introduction, the one and only global rock star CEO - NVIDIA CEO Huang Renxun. Please come to the stage. Thank you very much for coming! I want to start with NVIDIA's remarkable performance, with a market capitalization of up to 3 trillion US dollars. Did you ever think five years ago that the world would evolve so rapidly and present such a remarkable picture today? 11. Huang Renxun: Absolutely! I expected that from the beginning. 12. Moderator: That's really amazing. Can you offer some advice to the CEOs in the audience on how to achieve their goals? 13. Huang Renxun: Whatever you decide to do, my advice is not to get involved in the development of graphics processors (GPUs). 14. Moderator: I will tell the team that we are not going to get involved in that field. We spent a lot of time today discussing the profound significance of data intelligence. Enterprises have vast amounts of proprietary data that are critical for building customized artificial intelligence models. The deep mining and application of this data are crucial to us. Have you also noticed this industry trend? Do you think we should increase our investment in this area? Have you collected any feedback and insights from the industry on this issue? 15. Huang Renxun: Every company is like a gold mine with abundant business data. If your company offers a series of services or products and customers are satisfied with them while giving valuable feedback, you have accumulated a large amount of data. These data may involve customer information, market trends, or supply chain management. Over the years, we have been collecting these data and have a huge amount of data, but until now, we have just started to extract valuable insights from them, and even higher-level intelligence. 16. Currently, we are passionate about this. We use these data in chip design, defect databases, creation of new products and services, and supply chain management. This is our first time using engineering processes based on data processing and detailed analysis, building learning models, then deploying these models, and connecting them to the Flywheel platform for data collection. 17. Our company is moving towards the world's largest companies in this way. This is, of course, due to the extensive use of artificial intelligence technology in our company, which has helped us achieve many remarkable achievements. I believe that every company is experiencing such changes, so I think we are in an extraordinary era. The starting point of this era is data, and the accumulation and effective use of data. 18. The harmonious coexistence of open source and closed source 19. Moderator: This is truly amazing and very much appreciated. At present, the debate about closed-source and open-source models is gradually heating up. Can open-source models catch up? Can they coexist? Will they eventually be dominated by a single closed-source giant? What is your view of the entire open-source ecosystem? What role does it play in the development of large language models? And how will it develop in the future?

Highlights:

1. Huang Renxun emphasized that generative AI is growing at an exponential rate and that businesses need to adapt and utilize this technology quickly, rather than standing by and falling behind the pace of technological development. 2. Huang Renxun believes that open and closed source AI models will coexist and that companies need to leverage their respective strengths to promote the development and application of AI technology. 3. Huang Renxun proposed that the development of AI needs to consider energy efficiency and sustainability, reducing energy consumption by optimizing the use of computing resources and promoting the inference and generation capabilities of AI models to achieve more eco-friendly intelligent solutions. 4. With the constant accumulation of data and the continuous advancement of intelligent technology, customer service will become a key area for companies to achieve intelligent transformation. 5. According to foreign media reports, at the 2024 Databricks Data + AI Summit held recently, 6. Founder and CEO Huang Renxun had a fascinating conversation with Ali Ghodsi, co-founder and CEO of Databricks. The dialogue between the two parties demonstrated the importance and development trends of artificial intelligence and data processing technology in modern enterprises, emphasizing the key role of technological innovation, data processing capabilities and energy efficiency in promoting enterprise transformation and industry development. 7. Huang Renxun looked to the future of data processing and generative AI in the conversation. He pointed out that the business data of each company is like an untapped gold mine, with tremendous value but extracting deep insight and intelligence from it has always been a daunting task. 8. Huang Renxun also talked about open source models like Llama and DBRX are driving corporate transformation into AI companies, activating a global AI movement and promoting technological development and corporate innovation. Through the collaboration between NVIDIA and Databricks, the two companies will work together to leverage their respective strengths in accelerating computing and generative AI, bringing unprecedented benefits to users. 9. The following is the transcript of the conversation: 10. Moderator: I am very excited to introduce our next guest, a man who needs no introduction, the one and only global rock star CEO - NVIDIA CEO Huang Renxun. Please come to the stage. Thank you very much for coming! I want to start with NVIDIA's remarkable performance, with a market capitalization of up to 3 trillion US dollars. Did you ever think five years ago that the world would evolve so rapidly and present such a remarkable picture today? 11. Huang Renxun: Absolutely! I expected that from the beginning. 12. Moderator: That's really amazing. Can you offer some advice to the CEOs in the audience on how to achieve their goals? 13. Huang Renxun: Whatever you decide to do, my advice is not to get involved in the development of graphics processors (GPUs). 14. Moderator: I will tell the team that we are not going to get involved in that field. We spent a lot of time today discussing the profound significance of data intelligence. Enterprises have vast amounts of proprietary data that are critical for building customized artificial intelligence models. The deep mining and application of this data are crucial to us. Have you also noticed this industry trend? Do you think we should increase our investment in this area? Have you collected any feedback and insights from the industry on this issue? 15. Huang Renxun: Every company is like a gold mine with abundant business data. If your company offers a series of services or products and customers are satisfied with them while giving valuable feedback, you have accumulated a large amount of data. These data may involve customer information, market trends, or supply chain management. Over the years, we have been collecting these data and have a huge amount of data, but until now, we have just started to extract valuable insights from them, and even higher-level intelligence. 16. Currently, we are passionate about this. We use these data in chip design, defect databases, creation of new products and services, and supply chain management. This is our first time using engineering processes based on data processing and detailed analysis, building learning models, then deploying these models, and connecting them to the Flywheel platform for data collection. 17. Our company is moving towards the world's largest companies in this way. This is, of course, due to the extensive use of artificial intelligence technology in our company, which has helped us achieve many remarkable achievements. I believe that every company is experiencing such changes, so I think we are in an extraordinary era. The starting point of this era is data, and the accumulation and effective use of data. 18. The harmonious coexistence of open source and closed source 19. Moderator: This is truly amazing and very much appreciated. At present, the debate about closed-source and open-source models is gradually heating up. Can open-source models catch up? Can they coexist? Will they eventually be dominated by a single closed-source giant? What is your view of the entire open-source ecosystem? What role does it play in the development of large language models? And how will it develop in the future?

Please use your Futubull account to access the feature.

Huang Renxun proposed that the development of AI needs to consider energy efficiency and sustainability, reduce energy consumption by optimizing the use of computing resources and promoting the inference and generation capabilities of AI models, and implement more eco-friendly intelligent solutions.

With the constant accumulation of data and the continuous advancement of intelligent technology, customer service will become a key area for companies to achieve intelligent transformation.

According to foreign media reports, at the 2024 Databricks Data + AI Summit held recently,$NVIDIA (NVDA.US)$Founder and CEO Huang Renxun had a fascinating conversation with Ali Ghodsi, co-founder and CEO of Databricks. The dialogue between the two parties demonstrated the importance and development trends of artificial intelligence and data processing technology in modern enterprises, emphasizing the key role of technological innovation, data processing capabilities and energy efficiency in promoting enterprise transformation and industry development.

Huang Renxun looked to the future of data processing and generative AI in the conversation. He pointed out that the business data of each company is like an untapped gold mine, with tremendous value but extracting deep insight and intelligence from it has always been a daunting task.

Huang Renxun also talked about open source models like Llama and DBRX are driving corporate transformation into AI companies, activating a global AI movement and promoting technological development and corporate innovation. Through the collaboration between NVIDIA and Databricks, the two companies will work together to leverage their respective strengths in accelerating computing and generative AI, bringing unprecedented benefits to users.

The following is the transcript of the conversation:

Moderator: I am very excited to introduce our next guest, a man who needs no introduction, the one and only global rock star CEO - NVIDIA CEO Huang Renxun. Please come to the stage. Thank you very much for coming! I want to start with NVIDIA's remarkable performance, with a market capitalization of up to 3 trillion US dollars. Did you ever think five years ago that the world would evolve so rapidly and present such a remarkable picture today?

Huang Renxun: Absolutely! I expected that from the beginning.

Moderator: That's really amazing. Can you offer some advice to the CEOs in the audience on how to achieve their goals?

Huang Renxun: Whatever you decide to do, my advice is not to get involved in the development of graphics processors (GPUs).

Moderator: I will tell the team that we are not going to get involved in that field. We spent a lot of time today discussing the profound significance of data intelligence. Enterprises have vast amounts of proprietary data that are critical for building customized artificial intelligence models. The deep mining and application of this data are crucial to us. Have you also noticed this industry trend? Do you think we should increase our investment in this area? Have you collected any feedback and insights from the industry on this issue?

Huang Renxun: Every company is like a gold mine with abundant business data. If your company offers a series of services or products and customers are satisfied with them while giving valuable feedback, you have accumulated a large amount of data. These data may involve customer information, market trends, or supply chain management. Over the years, we have been collecting these data and have a huge amount of data, but until now, we have just started to extract valuable insights from them, and even higher-level intelligence.

Currently, we are passionate about this. We use these data in chip design, defect databases, creation of new products and services, and supply chain management. This is our first time using engineering processes based on data processing and detailed analysis, building learning models, then deploying these models, and connecting them to the Flywheel platform for data collection.

Our company is moving towards the world's largest companies in this way. This is, of course, due to the extensive use of artificial intelligence technology in our company, which has helped us achieve many remarkable achievements. I believe that every company is experiencing such changes, so I think we are in an extraordinary era. The starting point of this era is data, and the accumulation and effective use of data.

The harmonious coexistence of open source and closed source

Moderator: This is truly amazing and very much appreciated. At present, the debate about closed-source and open-source models is gradually heating up. Can open-source models catch up? Can they coexist? Will they eventually be dominated by a single closed-source giant? What is your view of the entire open-source ecosystem? What role does it play in the development of large language models? And how will it develop in the future?

Huang Renxun: We need cutting-edge models, especially advanced models that can expand our horizons. OpenAI and Google's work in this area is critical, as they not only expand the technical boundaries, but also help us explore new possibilities. However, if we look at this year's situation, perhaps the most important events are closely related to open source, such as Llama 2, Llama 3, Mistral, and the DBRX project conducted by the Databricks team.

DBRX is really a cool achievement. Its coolness lies in that it ignites the vitality of every enterprise, making it possible for any company to transform into an artificial intelligence company. You must have noticed this too, we have seen this trend globally. We have recently turned Llama 3 into an inference microservice, which can now be downloaded and used. You can visit Hugging Face, and of course Databricks, which has now been adopted by hundreds of companies worldwide.

This fully illustrates that open source has stimulated the potential of every company, making them have the opportunity to become part of the artificial intelligence field. At Nvidia, we use open source models extensively, and combine them with our own data and skills for fine-tuning and training. Without open source, there would be no movement to motivate every company in the world to transform into artificial intelligence. I think this is undoubtedly something of great significance.

Host: Indeed, this is a commendable development. Open source and closed source models will coexist, and we do need these two modes. The Nim framework you mentioned, namely Nims, is exactly what we are concerned about. I am very excited to announce here that we will integrate DBRX into Nims and provide services on the Databricks platform. In fact, all new models we develop in the future will adopt this method. We are looking forward to the prospect of Nims.

Huang Renxun: It is indeed a technical challenge to create large-scale language model APIs. Although these models may not look very large at present, they are still computationally complex, and the technology stack involves many dependencies. To this end, we have developed Nvidia's inference microservice Nim, which integrates and optimizes all necessary dependencies. Nvidia has a team of professional engineers who focus on this field and encapsulate complex technology into easy-to-use microservices.

Users can easily use this service on the Databricks platform, or download it and customize it as needed. Nvidia NeMo (updated version of neural module) microservice provides this flexibility, ensuring that it can run in any cloud or local environment, truly achieving ubiquitous artificial intelligence capabilities.

Host: This is indeed an admirable technology. The ability to deploy and run locally is particularly prominent, which means that we no longer rely entirely on cloud services, which is undoubtedly a huge progress. In our conversations with customers, we found that they are committed to cultivating internal expertise to customize models and gain competitive advantages. What do you think of this phenomenon?

Huang Renxun: I think the trend in the future is, as we have witnessed today, that we can tag and process almost all types of information and data. We can extract their structure, understand their connotation, and learn their representation. Whether it is sound, language, images, videos, chemical substances, proteins, or even robot action control or driving operations, we can tag them.

Because cloud data centers are producing these tags, we are actually creating some unique products called artificial intelligence supercomputers. For the first time, we have tools called artificial intelligence supercomputers, which are designed in factories dedicated to this purpose, and we mass produce intelligence on a large scale. This is also one of the reasons why I believe we are at the beginning of a new industrial revolution, which is not about producing electricity, but about producing intelligence.

Of course, every company is intelligent about its core specific field. When it comes to data, data processing, artificial intelligence and its infrastructure, few companies have a deeper understanding than Databricks. We focus on our professional fields, and our foundation is the intelligence of this specific field, whether it is in finance, healthcare or other fields. Ultimately, we will all become intelligent manufacturers.

If you want to become an intelligent manufacturer today, you will have human resources in the field of artificial intelligence, which we call the artificial intelligence factory. Therefore, every company must start this process. We are doing this, and you will do it too. We have observed that regardless of the size of the company, they are all working towards this direction. Therefore, in the future, we will all participate in this process. You will start with your specific field data, which is stored somewhere in Databricks. You will process this data, refine and extract intelligence from it, and then put it into the Flywheel platform. You will have an artificial intelligence factory.

Accelerating the integration of computing and generative AI.

Host: Indeed, this is a commendable achievement, and I firmly believe in it. We are passionate about this, especially in the field of data processing. The amount of data we process at Databricks every day is extremely huge, about 40 quadrillion bytes per day.

Huang Renxun: This is undoubtedly one of the largest computing requirements on the earth, namely data processing. In fact, almost every enterprise is doing this work.

Host: Indeed, the high parallelism of data processing makes it an ideal field for us to repeat the same operation. We are committed to introducing GPU acceleration technology into data processing. We aim to achieve revolutionary progress in the core data processing field that is comparable to AI models. We are very excited to work with you to optimize our Photon engine using GPU acceleration technology to usher in a new era of applying GPU to core data processing. Currently, these huge workflow processes have to rely on CPUs to execute, and we hope they can also run efficiently on Nvidia GPUs.

Huang Renxun: By the way, this is a major news: Nvidia and Databricks will join forces to bring our professional skills in these areas and the cutting-edge technologies of accelerated computing and generative artificial intelligence to every user. Although accelerated computing for data processing is highly challenging, we have invested five years of unrelenting efforts and have finally developed the library capable of significantly improving Photon performance. This is the result of our long-term efforts, and now we will accelerate Photon to make data processing more rapid and cost-effective, and significantly reduce energy consumption.

Moderator: This is indeed a significant development and it makes perfect sense logically. Although the data processing process is complex and full of special cases, we do not actually need general computing power because of its high parallelism. We are dealing with highly repetitive operations and large datasets, not unique data. Therefore, I am optimistic about this technology as it not only has the ability to subvert the status quo but also will greatly improve performance and reduce costs, which will undoubtedly bring amazing changes.

Huang Renxun: When we can process massive amounts of data quickly, researchers can wake up one morning with a sudden inspiration to say: "Let's collect all the data on the Internet to train a large model because now it is no longer a time-consuming and laborious task." If it were not for the development of accelerated computing technology, people would not consider such an idea because it would be expensive and time-consuming. But now, this is possible, and we can process an unprecedented amount of data at lower cost and higher efficiency. This will inspire endless innovative thinking, such as "Let's use all of our company's data to train our super AI." Those days are coming soon.

Opening a new chapter of intelligent services.

Moderator: Indeed, processing all the data on the Internet was once a concept only existing in science fiction novels. We thought it was impossible until the hardware and infrastructure developed to a sufficiently advanced level that allowed us to specialize in technology. Now, this has become a reality and everyone is participating. Let's turn to another topic. The booming development of generative artificial intelligence is truly eye-catching.

Initially, many companies started with chatbots and were committed to developing and customizing chatbots based on their own data. However, we now see people gradually expanding to more cutting-edge applications. Looking to the future, which new applications of artificial intelligence excite you the most?

Huang Renxun: Among all the potential impacts, customer service may be the most far-reaching area. For every company present, customer service involves costs as high as trillions of dollars, spanning every industry and every enterprise. The significance of chatbots in customer service not only lies in their automation capabilities but also in their contribution to the data flywheel. Enterprises need to capture conversations and incorporate customer interactions into their data systems, which undoubtedly generates a significant amount of data.

Currently, the growth rate of data is about ten times every five years. Considering the promotion of customer service, I anticipate that the growth rate of data volume in the future may reach a hundred times every five years. We will integrate all the elements into the data flywheel, which will collect more data, refine deeper insights, extract more accurate intelligent information, provide better services, and even achieve proactive prevention and resolution before problems occur, similar to preventive maintenance. We will achieve active customer support, which will further promote data generation and the rotation of the flywheel. Therefore, I believe that customer service will be the key for most companies to achieve super acceleration, especially considering the amount of data it will collect.

We have achieved a digital label of everything, and I am excited about our progress in chemistry, proteins, carbon capture materials, enzymes, innovative batteries, etc. We have also used generative artificial intelligence to achieve significant precision in regional weather forecasts, which previously required the computing power of supercomputers. Logistics, insurance, and the ability to protect people from harm will also be enhanced by this. In addition, generative artificial intelligence has shown enormous potential in the physical and biological fields, as well as in 3D graphics, digital twins, video game virtual world construction, and other areas. If your company has not yet ventured into generative artificial intelligence, it may be because it has not been sufficiently focused on it. In fact, it has permeated every industry.

Moderator: I completely agree with your views. The application of artificial intelligence will undoubtedly spread across various fields, which is not only reasonable but also full of infinite possibilities, making people full of expectations. Facing these emerging cutting-edge fields, our demand for data is increasing day by day. What is your opinion on how to help companies achieve more sustainable development of artificial intelligence?

Huang Renxun: Sustainability can be considered from multiple perspectives, especially those related to energy. It is worth noting that artificial intelligence itself is not picky about the location of its "learning." We do not need to set up artificial intelligence training data centers in densely populated areas where the electric grid is under pressure. On the contrary, we can place them in energy-abundant and evenly distributed areas.

Global energy resources are very abundant, and the key is how to allocate and utilize them reasonably. Therefore, I believe this is our first opportunity to capture and utilize the surplus energy and convert it into the power of artificial intelligence models, and ultimately feed back these intelligent achievements to our society to serve our practical needs.

Global energy resources are abundant, and the key lies in how to allocate and utilize them properly. Therefore, I believe that this is our first opportunity to capture and utilize surplus energy, convert it into power for artificial intelligence models, and ultimately feed back these intelligent results to society, serving our actual needs.

Another important perspective is that the core of AI is not just in model training, but also in its reasoning and generation capabilities. Our ultimate goal in training models is to apply them. When we focus on the long-term benefits of AI, taking using AI for weather forecasting which I mentioned earlier as an example, we no longer need to simulate physical laws from scratch every time, but can generate prediction results through AI. This method not only shortens prediction time and improves prediction accuracy, but also achieves a thousands-fold reduction in energy consumption.

In addition, the vertical benefits of AI are also reflected in other aspects, such as designing mobile phone chips by training models only once, thus saving energy for all users. I believe that over time, AI will demonstrate its potential in energy saving.

Finally, regarding generative AI, today's computing experience is mostly based on retrieval. Every time we click on our phones, although it seems that the energy consumed is not much, in fact it activates the global APIs, retrieves information, lights up the Internet, and then collects small amounts of information from different data centers to present to us via recommendation systems. In the future, as the small language models running on devices become more contextual and generative, internet traffic will be greatly reduced, and computing will generate more instantly, which will greatly save energy and fundamentally transform computing models.

Through this way, we can not only save a lot of energy, but also obtain answers more efficiently. This will completely change our way of computing, make us able to ask and get answers faster, and thus stimulate more interesting questions. This future of collaborating with AI will be a new era full of hints and inspirations.

Host: Yes, the future is very exciting. Okay, my last question is, how can we help customers, that is, everyone here today, start taking action today? What is the best way to do it?

Huang Renxun: As I mentioned earlier, I think Databricks' transformation from data processing to data governance, then to data storage, and then vertically extended to extracting intelligence from data is very visionary. I couldn't remember her name, but there is no doubt that Miss "Cookie" did an excellent job. Is it Casey? Please don't let her be poached by other companies. Her demonstration backstage was really impressive. I was deeply attracted by her demonstration and, even though I had many opportunities to communicate with her backstage, I personally preferred to watch her demonstration wholeheartedly.

Her mastery of the data intelligence platform and presentation skills are undoubtedly worthy of our high praise and respect. I think this platform is very impressive. You make it easier for people to manage data, extract information, and process data. Data organization is still a very important part of model training. People talk about model training, but before training models, you must figure out which data is correct. This concerns data quality, data format, and data preparation. So, I think the way to start is to come to Databricks and use Databricks' data intelligence platform. Am I right?

Host: Absolutely.

Huang Renxun: Indeed, no one would object to naming their platform DIP, the Data Intelligence Platform. This name is both sonorous and meaningful. It is as impressive as NIMS, and both are impressive names. You can use them both at the same time without making a choice. Getting a NIMS plus DIP, I completely agree with this combination of use, which is a wise strategy.

No matter what you plan to do, the key is to start taking action immediately. You must actively participate and immerse yourself in this rapidly developing train. Remember, generative AI is growing at an exponential rate, and you should not just watch or wait. The development speed of exponential trends is amazing, and within a few years, laggards will be far behind. Therefore, join this technological revolution immediately, and as technology continues to advance, you will also learn and grow with it. This is exactly how we take action.

This is a process that should not be learned by observation alone. You cannot master it by just reading. Real learning comes from hands-on practice. Like what we do, we fully immerse ourselves in it.

Host: Thank you very much. This is a valuable suggestion. The past decade of cooperation is unforgettable. Thank you for everything you have done. We have always been outstanding partners and look forward to welcoming the next glorious decade with Databricks.

Editor/tolk