The AI Moment
Solving the Challenges of AI Applications using Palantir Foundry and AIP
TL;DR
The pace of innovation in the open-source community is quickly outpacing large organizations in a race to discover the most compelling use cases and products that will dominate future AI applications. Choosing a platform like Palantir Foundry and Palantir AIP will allow you to manage complexity at scale leading to faster cycle times that leave your competitors in the dust.
Origins
The success of FAANG companies (Facebook, Apple, Amazon, Netflix, and Google) can be largely attributed to their adoption of data-driven decision-making. By leveraging vast amounts of data collected from users and various sources, these companies have been able to personalize user experiences, optimize business processes, and identify new growth opportunities. This data-driven approach has enabled them to stay ahead of their competition and continually innovate in their respective markets. FAANG companies have recognized data's value and invested heavily in big data technologies, machine learning, and artificial intelligence to extract meaningful insights and make informed decisions.
One prominent example of a successful open-source project originating from a FAANG company is Presto, a distributed SQL query engine developed by Facebook. Presto was designed to enable interactive analytics on large-scale data sets, allowing analysts and engineers to make data-driven decisions more efficiently. Facebook open-sourced Presto in 2013, and since then, it has been widely adopted by various organizations, including other FAANG companies, for its ability to query data across multiple data sources in real time. This high-performance, scalable query engine has significantly democratized access to big data analytics and empowered organizations to adopt data-driven decision-making strategies. Similarly, other FAANG companies have also contributed to the open-source community with projects like TensorFlow (Google), Apache Cassandra (Facebook), and Apache Flink (Amazon), all of which have greatly advanced the field of data processing and analysis, enabling more businesses to harness the power of data-driven decision-making.
The Modern Data Stack
We’ve come a long way since Presto. The modern data stack is a complex and interconnected ecosystem of tools that enables organizations to effectively manage, process, and analyze vast amounts of data. It’s also the cornerstone of AI applications allowing large language models (LLMs) to perform in-context learning and to help generate training sets and prompts. Architectures like retriever-reader, which help solve problems related to model hallucination and staleness of content, heavily rely on the modern data stack to preprocess data for use in AI applications.
The process begins with data ingestion, where tools like Airbyte, Fivetran, and Steampipe facilitate data importation from various sources, preparing it for further processing. Data orchestration solutions, such as Apache Airflow and Dagster, automate and streamline data ingestion, ensuring seamless integration between data sources and storage. Next, data warehouses and data lakes, powered by industry-leading platforms like Amazon Redshift, Google BigQuery, Databricks, and Snowflake, store and manage the ingested data.
Data transformation and machine learning operations are then performed using tools like dbt Labs, DataRobot, Tecton, and Hopsworks, optimizing the data for analysis. Data observability solutions such as Atlan, Amundsen, Monte Carlo Data, and Anomalo come into play to maintain data quality and governance. Finally, the data is visualized and analyzed using general business intelligence capabilities provided by platforms like PowerBI, Sisense, Tableau, and Streamlit.
The complexity of the modern data stack stems from the distributed nature of these tools and the challenges associated with managing different products and dependencies. As Rich Hickey once said, “Simplicity is hard work. But, there’s a huge payoff. The person who has a genuinely simpler system — a system made out of genuinely simple parts, is going to be able to affect the greatest change with the least work. He’s going to kick your ass. He’s gonna spend more time simplifying things up front and in the long haul he’s gonna wipe the plate with you because he’ll have that ability to change things when you’re struggling to push elephants around.” Ensuring smooth data flow between various components and maintaining compatibility across platforms can be daunting for organizations. Moreover, constant updates to individual tools can introduce additional challenges in maintaining a stable and efficient data infrastructure.
This complexity is further compounded by things like Lehman’s laws of software evolution, which state that the more code we add, the harder it becomes to add more code. Complexity in software emerges quickly and becomes extremely hard to manage. Distributed computing systems, like the modern data stack, are inherently complex due to their interconnected components. Therefore, it is essential for organizations to have a deep understanding of their data stack and invest in robust strategies for managing and maintaining these complex systems. Pursuing simplicity and mastering the intricate interplay of tools within the modern data stack will enable organizations to fully harness the power of data-driven decision-making while remaining agile and adaptive in an ever-changing technological landscape.
Foundation Models
Foundation models, which have emerged as a cornerstone in the artificial intelligence (AI) landscape, are large-scale, pre-trained models that serve as the basis for various downstream tasks across diverse domains. The advent of transformer architecture by Vaswani et al. in 2017 further revolutionized the field, creating models such as BERT, GPT, and RoBERTa. Foundation models have since expanded beyond NLP, finding applications in computer vision, protein modeling, and other areas. In essence, a foundation model is a versatile and adaptable AI model that can be fine-tuned for specific tasks, empowering researchers and developers to build upon its base to create highly specialized and efficient solutions. Below is a summary of some foundation models that are being heavily adopted by consumers and developers alike.
OpenAI’s Generative Pre-trained Transformer (GPT) series is a family of state-of-the-art large language models (LLMs) designed to advance natural language understanding and generation. GPT models, which have evolved through multiple iterations, employ a transformer architecture and are pre-trained on vast amounts of text data, enabling them to generate human-like text and perform a wide range of tasks such as machine translation, summarization, and question-answering. Notable for their impressive capabilities and scalability, GPT models have significantly influenced the development of AI research and applications, driving the adoption of LLMs across various industries.
The Pythia suite, introduced by EleutherAI, is a collection of 16 large language models (LLMs) designed to facilitate research on training dynamics and scaling in natural language processing. These models, ranging from 70 million to 12 billion parameters, are trained on public data in the same order and come with 154 checkpoints for each model. Researchers can also access tools to download and reconstruct the exact training data loaders. The Pythia suite enables various research areas, including memorization, term frequency effects on few-shot performance, and reducing gender bias. It offers novel insights into LLMs and their training dynamics, addressing the research gap in scaling laws and learning dynamics due to the lack of access to appropriate model suites. The Pythia suite is unique as it provides models spanning several orders of magnitude in scale, trained on the same data in the same order, and has publicly available data and intermediate checkpoints for study.
Meta’s LLaMA (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to democratize access to AI research and facilitate advancements in natural language processing (NLP). As a smaller and more performant model, LLaMA enables researchers with limited infrastructure to study, validate, and explore new use cases in this rapidly evolving field. Available in various sizes (7B, 13B, 33B, and 65B parameters), LLaMA is trained on massive sets of unlabeled data, making it ideal for fine-tuning across a range of tasks. Despite the impressive capabilities of large language models, access to them has been restricted due to the resources required for training and deployment. LLaMA addresses this issue by providing a more accessible model that researchers can retrain and fine-tune for specific applications. While LLaMA shares the challenges of bias, toxicity, and hallucinations commonly found in large language models, its public release allows the research community to test new approaches to mitigating these problems, thereby fostering collaboration and driving responsible AI development.
Open Source Outpacing Enterprise
The rapid advancements in open source technologies are transforming the AI landscape, presenting crucial considerations for organizations deciding on their AI strategy. Open-source models, like LLaMA and Dolly, are increasingly becoming faster and more adaptable, offering enhanced functionality compared to their proprietary counterparts. This democratization of AI technology empowers a broader audience to access, train, and experiment with these models, leading to accelerated innovation and improvements in quality.
Further open-source projects like LangChain provide application developers with the tools and frameworks required to chain together prompts and improve the LLM's performance by leveraging agents. Agents allow the LLM to interact with the outside world, from solving math equations to ordering pizza. This is leading to the creation of AI applications that are months ahead of their enterprise counterparts.
As your organization contemplates investing in AI, recognizing the growing potential of open-source alternatives is crucial. These options offer a practical substitute for collaborating with large corporations like Microsoft or Google and can act as a safeguard for existing investments. By embracing open-source models and frameworks, your company can benefit from rapid innovation, tailored solutions, and active involvement in a vibrant, cooperative ecosystem.
Palantir‘s AI Moment
Some historical moments are tailor-made for an organization, and the AI moment was made for Palantir. Palantir has two powerful platforms for solving the problem of open-source complexity and the ethical questions surrounding AI applications: Palantir Foundry and Palantir Artificial Intelligence Platform (AIP). Companies partnering with Palantir saw a 60% reduction in production costs and eye-popping $200 million in savings in 24 months. Gartner has also named them the leading AI platform.
Palantir Foundry
Foundry is a comprehensive big data operating system designed to encompass all aspects of the modern data stack. This all-encompassing platform features data integration, data transformation, ontological modeling, application creation, as well as model creation and operations. Palantir utilizes well-known open-source technologies such as Apache Spark and Flink while implementing an orchestration layer to effectively manage all dependencies and associated complexities. Further, Palantir’s exceptional data access and governance (Palantir is one of only three companies with IL6 clearance) governs the use of data in the platform from the moment of ingestion to application usage. The power of this solution can not be understated. Palantir is also deeply committed to data privacy (Courtney Bowman, head of Palantir’s Privacy and Civil Liberties, wrote “The Architecture of Privacy”) and open source.
Foundry enables businesses to concentrate on extracting value from their data, instead of struggling with the intricacies of open-source solutions. Moreover, they can leverage the power of the open-source community. Additionally, Foundry seamlessly integrates with pre-existing systems and data products, augmenting the returns on your prior investments.
Palantir AIP
As artificial intelligence continues to revolutionize industries, Palantir’s AI platform offers a transformative solution for organizations looking to harness the power of AI. The platform allows for deploying various AI models on private networks, including large language models (LLMs), whether commercial, homegrown, or open-source. Built on a robust AI-optimized data foundation, AI Core provides a natural language interface into Palantir Foundry, effectively making everyone using the system a Foundry power user.
AIP can directly produce data integrations, transformations, visualizations, and applications from natural language. This is made possible by a domain-specific language that Palantir used to train the LLM. This allows human operators to gain confidence in the model's recommendations through data exploration and actions like publishing a remediation strategy for review. AIP also comes with an audit trail to ensure the data used by the LLM was securely accessed and used. This ensures your organization’s private data stays private and is only accessed for authorized purposes by authorized personnel. These capabilities are unmatched and light years ahead of products like ChatGPT, where basic visualizations from Excel data are considered cutting-edge. For a demo of AIP, check out this video.
Conclusion
The rapid advancements in open-source AI technologies, coupled with the growing capabilities of platforms like Palantir Foundry and Palantir AIP, are redefining the landscape of AI applications and decision-making. As businesses seek to harness the power of AI, it is essential to recognize the potential of open-source alternatives and platforms that enable efficient management of complexity while promoting data privacy and ethical AI practices. By investing in solutions that balance innovation, accessibility, and responsible AI development, organizations can stay ahead of the curve, driving growth and success in an ever-evolving technological environment. The AI moment is upon us, and those who embrace it with the right strategy will undoubtedly emerge as the leaders of tomorrow.
