Accelerating the integration of HPC simulations and AI with SambaNova Systems

SambaNova will be exhibiting at ISC High Performance 2023, May 21-25 in Hamburg, Germany. There we will showcase how generative AI is already being used to accelerate scientific discovery, including the progress we have been making with major research centres around the world. We continue to advance our solutions and capabilities, alongside more and more customer success from integrating HPC simulations and AI.

Below are a couple of links to recent news, as we have scaled up our deployment at Argonne National Laboratory and are now installed in the RIKEN Fugaku system.

SambaNova delivers next-generation DataScale system to Argonne National Laboratory

SambaNova Systems’ SambaNova DataScale Adopted – Accelerating the Integration of Fugaku and AI for research in the Society 5.0 era.

We can show significant advantages for deploying LLMs with large sequence lengths, also for high resolution imaging results, including greater than 5123, without the need for complex model partitioning and parallelisation. Our architecture and software stack has the potential to revolutionise scientific research across a variety of domains, enabling a deeper understanding of complex processes, more accurate predictions, and accelerated discovery for our customers.

Come and meet any of our engineers at Booth G703 on the exhibition floor. Marshall Choy, SVP of Product at SambaNova Systems, will also be speaking on Generative AI Driven Scientific Discovery as part of the HPC Forum session on Tuesday, May 23rd from 1:20 PM to 1:40 PM in Hall H, Booth K1001. If you would like to book time to meet with any of our team during the conference, come by the booth and we will make that happen, or just reply to this note.

You could also be in with a chance to WIN a Lego® Robot Inventor Set valued at €359.99! Visit the SambaNova Booth between Monday, May 22nd at 3:00 PM and Tuesday, May 23rd at 3:00 PM CET to enter. The drawing will take place on Tuesday, May 23rd at 3:00 PM CET. Must be present to win!

Accenture and SambaNova are now delivering powerful, generative AI solutions that have been optimized for enterprise and government organizations. Capable of analyzing massively complex documents, understanding volumes of data of varying types, creating net new content, and more, it is hard to overstate the potential impact of generative AI. The solutions from Accenture and SambaNova are designed to meet the demanding needs of these organizations in ways that consumer grade generative AI does not. 

Overcoming the Challenges with Consumer Generative AI

While consumer grade generative AI, such as ChatGPT, has captured the public’s and the media’s imagination, business leaders are struggling with how to take advantage of the massive opportunities this transformative technology presents. 

The challenges with incorporating this technology into business applications are significant. Some of these include:  

• The models are trained on generic, internet data
• Models refined using an organization’s data may become available to competitors
• Models fine tuned with organizational data remain the property of the vendor
• Governance and auditability may not be possible

Building solutions optimized for the enterprise

Recognizing these challenges, Accenture and SambaNova are partnering to co-develop generative AI solutions, optimized for enterprise and government organizations, that unlock the potential of this technology, while meeting the demanding requirements of these organizations. The partnership between the two companies has resulted in the development of solutions that drive line of business efficiency and productivity, with security features that protect the privacy and integrity of the data used by generative AI solutions. It can automate user and employee experiences, streamline operations, improve efficiency, and unlock insights trapped in unstructured data.

Bringing generative AI to the business

The Accenture and SambaNova solutions seamlessly integrate with existing workflows through simple APIs, so there is no need to replace existing tools or processes. Examples of these solutions include Contact Center Intelligence, which enables enterprises to assist agents with customer calls, discover information about customer interactions, and better meet compliance requirements. Document Intelligence extracts information from massive volumes of documents, derives insights from extremely complex documents, and more. 

Organizational data, fine-tuning, and governance

These solutions utilize models that are pre-trained with domain specific data on the latest open source models. This means that in addition to always having the latest and most powerful models, organizations are able to take advantage of models pre-trained, out-of-the box. 

Models are then further adapted using an organization’s own data for even higher accuracy. Once a model has been trained using internal data, that model becomes a critical asset and is the property of the organization in perpetuity.  

These solutions deliver significant benefits to the enterprise, including:

Governance: Customers control all the layers of the model, not just the last layer.

Auditability: Get full visibility on the model weights and datasets it was trained on. 

Control: Export the model at any point and maintain ownership of the model.

Conclusion

While consumer tools, such as ChatGPT, have captured the attention of the media, Accenture and SambaNova are delivering systems that are optimized for and can meet the specific requirements of  banks and other large enterprises. These solutions deliver the data governance, auditability, and control that these types of organizations demand, on a fully integrated platform, that is available as an on-premises solution or delivered anywhere as a cloud service. 

To learn more about this transformative technology, click here. 

Solving Enterprise Data Privacy and Security Concerns with Generative AI

As we near the 6 month mark of generative AI dominating the headlines, the conversation has quickly shifted from amazement to more practical considerations and risks. In response to leaks of confidential and private data, one of the biggest topics of discussion related to generative AI has quickly become data privacy and security, particularly for enterprises and government organizations.

One of the biggest security and privacy risks is caused by what is known as a ‘shared model backbone’, referring to when a generative AI tool uses a single model across all of its users and customers. The implication of this is that any data that is used to interact with a generative AI tool, such as ChatGPT, becomes part of the model, improving it over time. However, it also means that this data can be accessed by other users.  Unsurprisingly, for enterprises and government organizations this poses serious security and privacy concerns

In one high profile example, employees at Samsung inadvertently leaked confidential information by sharing meeting minutes and source code in a ChatGPT prompt.

In another example, it was revealed that a bug resulted in leaked sensitive information about ChatGPT user data.

Unsurprisingly, the market is already reacting to these security concerns, with both large enterprises (such as JP Morgan and Verizon) and even entire countries taking steps to ban ChatGPT.

Overcoming these issues requires a fundamentally different approach to enable generative AI for enterprises and government organizations. Generative AI must be deployed within a customer’s firewall, and provide the organization with its own ‘dedicated model backbone’. This means that the organization has its own unique generative AI model that is not shared with any other customers, and can use its own data to adapt and interact with the model without risk of that information being leaked. It also enables these organizations to retain ownership of the models built in this way.

Click here to learn more about how SambaNova Suite delivers generative AI optimized for the enterprise.

Last week, SambaNova’s Co-founder and CEO Rodrigo Liang caught up with Ed Abbo, President and Chief Technologist of C3 AI to discuss how generative AI is transforming the enterprise, including how generative AI delivers a new human computer interface for enterprise AI, the importance of verifiability, and why enterprises will require generative AI solutions to be securely deployed within their own environment.

Below are three of my favorite insights from Rodrigo’s conversation with Ed. You can also watch the full video above.

Generative AI has fundamentally changed the human computer interface for enterprise AI

Historically, enterprise AI tools have had different interfaces that required users to learn and be trained on these systems, often requiring learning complex coding languages. Generative AI removes the need for training on these systems by enabling users to both ask a question and receive an answer in natural language. This means that business users can simply ask a question through a ‘search bar’ experience, such as “Where will my supply chain break down?” and receive a detailed answer based on the inputs across different enterprise systems.

Enterprises need verifiability

One of the main differences between consumer and enterprise generative AI is the importance of verifiability. In the enterprise, generative AI not only needs to be highly accurate, but when retrieving an answer to a question it needs to provide references to where that answer came from. This is particularly important in complex enterprise technology environments with hundreds or even thousands of different tools and systems. 

Generative AI needs to be deployed securely in an enterprise’s own environment

Just as with any new technology, generative AI needs to meet information security requirements for enterprises and government organizations. That means generative AI models and infrastructure need to be deployed and managed within a company’s own environment.

Be sure to check out our other blog post

Three takeaways from SambaNova’s conversation on generative AI with Alex Ratner, CEO of Snorkel

There is no question that generative AI, and the impressive potential it represents, is 2023’s hottest tech trend. The buzz around generative AI has consumed everything from technology press, to mainstream media outlets, and even late night talk shows. Much of this virality has resulted from the public’s ability to engage with consumer generative AI products, such as ChatGPT, and experience first hand some of the most exciting, creative, and surprising outputs from these tools. 

Despite all of this buzz about consumer generative AI, the reality is that consumer use cases for generative AI are vastly different than for enterprises and government organizations. As often happens with exciting consumer technologies, enterprise and government leaders have already started asking “How can we start applying generative AI to solve our business and operational challenges?”

Introducing SambaNova Suite for generative AI

It is with this question in mind that I am excited to announce the SambaNova Suite for generative AI, the first generative AI platform specifically optimized for enterprises and government organizations. 

SambaNova Suite is a collection of the highest accuracy generative AI models which can be deployed directly in a customer’s own environment, and can be further adapted using their data for even greater accuracy

You can learn more about SambaNova Suite by watching the overview below:

Our thesis for SambaNova Suite is based on four key principles.

1. Enterprises and government organizations require the highest accuracy: SambaNova Suite delivers a collection of the highest accuracy generative AI models, including both state-of-the-art open source models, as well as models that have been pre-trained by SambaNova including GPT and Bloom.
2. “Your data, your models”: SambaNova Suite empowers customers to optimize these models with their own data to further increase accuracy, while also allowing them to retain ownership of models that have been adapted with their data. 
3. An open approach to generative AI: SambaNova Suite has been developed and will continue to evolve as an open platform integrating innovations from ecosystem partners at every layer of the stack, including model development, data, and enterprise integration.
4. Deploy Anywhere: SambaNova Suite is a full stack AI offering which can be deployed on-premises or in the cloud, so no data ever needs to leave the customer’s environment. Further, unlike consumer generative AI cloud offerings, SambaNova Suite is delivered on a dedicated model backbone for every customer.

With SambaNova Suite, we are empowering enterprises and government organizations to take advantage of the full potential of generative AI to solve their biggest business and operational challenges, while delivering the flexibility, privacy, and security required of modern technologies and tools. At SambaNova, we think that the generative AI revolution is just beginning, and we are excited to work together with our customers and partners to discover the full exciting potential of this transformational technology.

To see for yourself how SambaNova Suite is empowering enterprises and government organizations to take advantage of the full potential of generative AI to solve their biggest business and operational challenges, watch these product demo videos.

You can learn more about SambaNova Suite for generative AI and our other exciting announcements by visiting our official launch announcement page.

Today SambaNova announced SambaNova Suite for generative AI, the first generative AI platform specifically optimized for enterprises and government organizations. SambaNova Suite is a collection of the highest accuracy generative AI models which can be deployed directly in a customer’s own environment, and can be further adapted using their data for even greater accuracy.

So what do we mean when we say generative AI “specifically optimized for enterprise and government organizations”? The answer starts with the fundamental differences between consumer and enterprise generative AI.

The reality is that consumer use cases for generative AI are vastly different than for enterprises and government organizations. While much of the viral applications for generative AI in the consumer space have involved fun content or discussions about the impact on school work, enterprises and government organizations are interested in optimizing business use cases, such as their contact center and customer experience, or analyzing and processing complex documents. 

These are complex use cases that pose several fundamental challenges for the consumer generative AI tools that have been dominating the headlines.

The first challenge is the high level of accuracy required by enterprises and government use cases. For example, a frequent area of discussion related to consumer generative AI is when models ‘hallucinate’, or make up an answer to a question because it thinks that is the correct answer. In consumer applications, this can be good for a laugh. In the enterprise, hallucinations could have a catastrophic impact on customer trust and a company’s brand.

The second challenge is the strict security and privacy requirements of both enterprises and government agencies. Because many consumer generative AI tools are delivered through a cloud API on a shared ‘model backbone’ there are significant data privacy concerns about sending sensitive data outside of an organization’s firewall. Additionally, this data is often sent to a generative AI model which is shared with other companies, including potential competitors.

The third challenge is that generative AI will need to evolve according to open standards to find long term success in the enterprise and with government organizations. This is required both so these tools can be integrated with the existing technology infrastructure of these organizations, and also to help them avoid vendor lock-in. However, consumer generative AI tools are often closed and fully proprietary systems.

With SambaNova Suite, we are empowering enterprises and government organizations to take advantage of the full potential of generative AI to solve their biggest business and operational challenges, while delivering the flexibility, privacy, and security required of modern technologies and tools. At SambaNova, we think that the generative AI revolution is just beginning, and we are excited to work together with our customers and partners to discover the full exciting potential of this transformational technology.

You can learn more about how SambaNova is addressing these challenges and delivering the highest accuracy generative AI, specifically optimized for enterprise and government, by visiting our official SambaNova Suite launch announcement page.

Over the past several weeks, I have had the chance to talk with numerous current and future customers about the SambaNova Suite. In almost every conversation, these customers have been most excited by the product demo, where they could directly see how fast and easy it is to train and deploy these transformational generative AI capabilities. 

Below we have included a few of these demos so you can see for yourself how SambaNova Suite is empowering enterprises and government organizations to take advantage of the full potential of generative AI to solve their biggest business and operational challenges, while delivering the flexibility, privacy, and security required of modern technologies and tools.

SambaNova Suite Demo

SambaNova Contact Center Demo

SambaNova Document Intelligence Demo

Last week SambaNova’s Co-founder and CEO Rodrigo Liang caught up with Vipul Prakash, Co-Founder and CEO of Together to discuss some of the most important topics facing generative AI, including why generative AI is so exciting, what needs to happen for generative AI to succeed in the enterprise, and how SambaNova and Together are partnering to build a thriving open source generative AI ecosystem.

Below are three of my favorite insights from Rodrigo’s conversation with Vipul. You can also watch the full video above.

Generative AI’s transformative potential is defined by an unprecedented combination of accuracy and versatility

Vipul noted that generative AI models have increased accuracy on standard benchmarks (such as the Stanford Question and Answer) by more than 23%, a larger increase than the last decade combined. On top of that, generative AI models are highly flexible, with the ability to not only write code and answer customer service requests, but to complete creative tasks, such as writing poetry. It is this unprecedented combination of accuracy and versatility that has captured the world’s attention with this new technology.

Enterprises want to control their own destiny with generative AI 

To find success in the enterprise, generative AI needs to evolve from consumer applications such as ChatGPT. This means not just higher accuracy and dependability, but transparency and reproducibility. Perhaps most important for enterprises is the ability to “control their own destiny” with generative AI: they need to be able use their data to adapt these models in a secure way. This means data provenance and data privacy, with the ability to deploy generative AI models on premises, and ultimately retain ownership of any model they adapt and develop in this way.

The best ideas can come from anywhere

One thing that SambaNova and Together strongly agree on is that the future of generative AI will be powered by the open source community. This means that the community not only needs access to the latest models, but also a platform to innovate, train, and experiment on. Enabling this access for the open source community is one of the core aspects of the SambaNova and Together partnership.

Be sure to check out our other blog post

Three takeaways from SambaNova’s conversation on generative AI with Alex Ratner, CEO of Snorkel

Last week SambaNova’s Co-founder and Chief Technologist Kunle Olukotun connected with Alex Ratner, CEO of Snorkel, on what he sees as the most exciting trends (and challenges) for generative AI in the enterprise. They talked about a number of interesting topics including how generative AI and foundation models have changed what it means to develop AI, why ‘good enough’ isn’t enough for the enterprise when it comes to generative AI, and how SambaNova and Snorkel are partnering to solve the challenge of adaptation and scalable deployment of generative AI in the enterprise.

Below are three of my favorite insights from Kunle’s conversation with Alex. You can also watch the full discussion in the video above.

Generative AI and foundation models have shifted AI development from being model-centric to becoming data-centric

During the conversation, Alex made the point that generative AI and foundation models have changed what it means to do AI development, shifting the focus from being model-centric to becoming data-centric. Because these models can be thought of as ‘foundations’ to build upon, data has shifted from being a blocker to get right in the AI development process, to becoming the most important way to develop a model. For enterprises, this means that they can use their own highly bespoke and complex data to adapt generative AI models to be highly optimized for their specific business needs and workflows. 

When it comes to generative AI in the enterprise, “good enough” just isn’t enough

Alex perfectly summarized the difference between consumer and enterprise AI: “When it is a research project on the internet you can get away with a lot more than you can when you are a bank or a hospital system or a government agency.” In the enterprise, generative AI needs to be accurate enough to be deployed in a business critical production workflow. For this to happen, these models need to be adapted with the company’s own data in their own environment, in a secure and private way. This is a central focus of SambaNova and Snorkel’s partnership to solve the challenge of adaptation and scalable deployment of generative AI in the enterprise.

Longer sequence length will be critical for applying generative AI and foundation models to solve real world challenges

In simple terms, sequence length refers to the amount of data that can be processed by a generative AI model at a time. Why is this important? With a longer sequence length, a generative AI model can analyze and process increasingly larger pieces of information. In an enterprise, this means the difference between analyzing a short Email and understanding a lengthy, complex report. Longer sequence length models are also increasingly showing promise for solving other types of applications, such as genome sequencing.

Be sure to check out our other blog post

Three takeaways from SambaNova’s conversation on generative AI with Vipul Ved Prakash, Co-Founder and CEO of Together