Leveraging Curated Data for Strategic Decision Making

Sure, it was laughable when X’s AI chatbot Grok accused NBA star Klay Thompson of a vandalism spree after users described him as “shooting bricks” during a recent game, but it was no joke when iTutorGroup paid $365,000 to job applicants rejected by its AI in a first-of-its-kind bias case. On a larger scale, multiple healthcare companies—including UnitedHealth Group, Cigna Healthcare and Humana—face class-action lawsuits based on their AI algorithms that are alleged to have improperly denied hundreds of thousand of patient claims.

So, while AI—driven by large language models (LLMs)—has emerged as a groundbreaking innovation for streamlining workflows, its current limitations are becoming more apparent, including inaccurate responses and weaknesses in logical and mathematical reasoning.

To address these challenges, Wolfram Research has developed a suite of tools and technologies to enhance the capabilities of LLMs. Wolfram’s technology stack, including the Wolfram Enterprise Private Cloud (EPC) and Wolfram|Alpha, increases the productivity of AI applications in multiple enterprise environments. By leveraging Wolfram’s extensive experience in computational intelligence and data curation, organizations can overcome LLM limitations to achieve greater accuracy and efficiency in AI-driven workflows.

At the same time, Wolfram Consulting Group is not confined to one specific LLM. Instead, we can enhance the capabilities of any sophisticated LLM that utilizes tools and writes computer code, including OpenAI’s GPT-4 (where Wolfram GPT is now available), Anthropic’s Claude 3 and Google’s Gemini Pro. We can also incorporate these tools in a privately hosted LLM within your infrastructure or via public LLM services.

Wolfram’s Integrated Technology Stack

Wolfram has a well-developed tech stack available to modern LLMs: data science tools, machine learning algorithms and visualizations. It also allows the LLM to write code to access your various data sources and store intermediate results in cloud memory, without consuming LLM context-window bandwidth. The Wolfram Language evaluation engine provides correct and deterministic results in complex computational areas where an unassisted LLM would tend to hallucinate.

When your organization is equipped with the Wolfram technology stack for tool-assisted AIs, the productivity of your existing experts is enhanced with methods that support exploratory data analysis, machine learning, data science, instant reporting and more:

• The LLM can interpret expert user instructions to generate Wolfram code and tool requests performing a wide variety of computational tasks, with instant feedback and expert verification of the intermediate results.
• Custom tools for accessing corporate/proprietary structured and unstructured data, models and digital twins, and business logic feed problems to the Wolfram Language algorithms implementing your analytic workflows.
• Working sessions create a documented workflow of thought processes, prompts, tool use and code that can be reused on future problems or reviewed for audit purposes.

Designed for system integration flexibility, use the platform as a fully integrated system or as a component in an existing one. In the full-system integration, the Wolfram tech stack seamlessly manages all communications between the LLM and other system components. Alternatively, use it as a set of callable tools integrated into your existing LLM stack as our modular and extensible design readily adapts to your changing needs. Also access the integrated Wolfram tech stack through a variety of user interfaces, including a traditional chat experience, a custom Wolfram Chat Notebook, REST APIs and other web-deployed custom user interfaces.

Wolfram Enterprise Private Cloud (EPC)

Wolfram’s EPC serves as a private, centralized hub for accessing Wolfram’s collection of LLM tools and works in commercial cloud environments such as Microsoft Azure, Amazon Web Services (AWS) and Google Cloud. For organizations preferring in-house solutions, EPC can also operate on dedicated hardware within your data center.

Once deployed, EPC can connect to various structured and unstructured data sources. These include SQL databases, graph databases, vector databases and even expansive data lakes. Applications deployed on EPC are accessible via instant web service APIs or through web-deployed user interfaces, including Chat Notebooks. As Wolfram continues to innovate, the capabilities of EPC also grow.

Wolfram|Alpha Infrastructure

Wolfram|Alpha can also be a valuable asset for your suite of tools. With a vast database of curated data across diverse realms of human knowledge, Wolfram|Alpha can augment your existing resources.

Top-tier intelligent assistants, websites, knowledge-based apps and various partners have trusted Wolfram|Alpha APIs for over a decade. These APIs have answered billions of queries across hundreds of knowledge domains. Designed for use by LLMs, Wolfram|Alpha’s public LLM-specific API endpoint is tailored to enable smooth communication and data consumption.

If your LLM platform requires a customized version of Wolfram|Alpha, our sales and engineering teams will work with you to optimize your access to its extensive capabilities. This ensures that you have the right setup to harness the full potential of Wolfram|Alpha in your specific context.

Preparing Knowledge for Computation

While many platforms give an LLM access to data retrieval tools, what sets Wolfram apart is extensive experience in preparing knowledge for computation. For over a decade, Wolfram has provided knowledge curation services and custom versions of Wolfram|Alpha to diverse industries and government institutions with sophisticated data curation workflows and exposed ontologies and schemas to AI systems. Direct access to vast amounts of data alone is not enough; an LLM requires context for data and an understanding of the user’s intent.

Wolfram consultants can establish workflows and services to equip your team with tools for programmatic data curation through an LLM. This process involves creating a list of questions and identifying the subjects or entities to which these questions apply. The LLM, with the aid of the appropriate retrieval tools, then finds the answers and cites its sources. These workflows alleviate the workload of extensive curation tasks, and the enhanced curation capabilities then operate within the EPC infrastructure.

At the same time, you’ll retain ownership of any intellectual property created for your funded project, including custom plugins or tools Wolfram develops, ensuring you have full control over the solutions created for your organization.

Enterprise AI the Wolfram Way

When you decide you need a custom LLM solution, let Wolfram Consulting Group build one tailored to your specific needs. From developing runtime environments that help your teams integrate Wolfram technology into existing platforms to creating application architecture, preparing data for computation and performing modeling and digital twin implementation, Wolfram has the unique experience across all areas of computation for the right balance of approaches to achieve optimal results.

By working with Wolfram, you get the best people and the best tools to keep up with developments in the rapidly changing AI landscape. The result? You will capture the full potential of the new generation of LLMs.

Contact Wolfram Consulting Group to learn more about using Wolfram’s tech stack and LLM tools to generate actionable business intelligence.

It’s no secret: quantum computing has been poised to be “the next big thing” for years. But recent developments in the quantum ecosystem, including major investments by companies such as IBM, Google, Microsoft and others, are the best indicators that now is the time to begin preparing for potentially viable quantum applications—and to identify where and when to most effectively use them.

“Classical” computers operate in a binary fashion, processing information as either zeros or ones. But quantum computers? They leverage quantum mechanics so data can exist simultaneously in multiple states—zeros, ones or both. The result is a supercomputer that can explore countless possibilities at once and produce results in minutes or seconds instead of hours, weeks or years.

Projected use-case areas span multiple industries and market sectors. In finance, for example, quantum computing could be utilized for portfolio optimization, risk management and algorithmic trading. In healthcare, it could enable advancements in drug discovery, personalized medicine and disease modeling. In logistics, it could optimize supply chain management, route optimization and scheduling.

At the same time, there’s a gap between what quantum can do at the current moment versus anticipated capabilities in the next three to five years.

“In each real-world application, the first step in solving the problem is translating it to a computational problem. After that’s done, you need to choose—or develop—an algorithm for solving that problem. And because these applications are things people do every day, there are clearly a number of ‘classical’ computing algorithms that are known—and still being explored—for solving the associated problems,” says John McNally, Wolfram Academic Innovation Solutions Developer. “The catch is that the scale of certain real applications can become too great for even the best classical computing resources to successfully carry out any known classical algorithm to reach a full solution.”

At this point, quantum algorithms and the hardware needed to run them become interesting. And there are important questions to address, in particular how to scale up quantum systems following practical fault-tolerant approaches.

And this is where the Wolfram Quantum Framework comes in.

Wolfram Quantum Framework

On its own, the Wolfram Quantum Framework is not a quantum computer. Instead, it is a set of tools to model quantum circuits and design algorithms. Then, after you’ve analyzed a quantum system using classical means, the Framework also gives you tools to automatically translate your models into representations that can be run on quantum hardware through service connections with Amazon Braket and other providers:

• Classiq
• Infleqtion
• Q-CTRL
• QuEra
• Strangeworks

The Wolfram Quantum Framework’s key advantage, however, is its seamless integration with Wolfram Language. This includes optimized numerics and symbolics that offer a streamlined approach to quantum computation.

“Aside from working numerically, you can specify noise channels, gates or even elements of a Hamiltonian as symbolic parameters,” McNally says. “This allows you to get exact formulas out of your analysis rather than numerical simulations only. Plus, it seamlessly integrates with other quantum platforms, even across different programming languages.”

Wolfram’s symbolic computation offers distinct benefits over numerically based programming languages by enabling the calculation of “exact” solutions to complex problems. For academia, this means a deeper understanding of quantum principles and the ability to explore new frontiers in research. But for businesses, prospective bottom-line returns include potential cost savings through more efficient algorithms, improved accuracy in simulations and predictions, and faster innovation cycles.

The Wolfram Quantum Framework continues to evolve with new features to expand the Framework’s support for different computational models, such as tensor networks and stabilizer formalism, as well as to accommodate a wider range of quantum tasks. Additionally, plans are underway to introduce enhancements to improve usability, scalability and compatibility with emerging quantum hardware technologies.

The Future of Quantum

Navigating quantum is more than thinking about quantum algorithms alone: it also means understanding the world of classical algorithms to identify when practical problems make contact with the rapidly developing world of quantum hardware. This uniquely positions Wolfram Research to address challenging quantum problems and develop quantum utilities, particularly considering its longstanding development of classical algorithms and well-developed footprint in academia.

In a larger sense, the development of quantum capabilities is going to result in a great divide: the haves and the have-nots, or more importantly, the dids and the did-nots. While quantum hardware is not yet where it’s going to be for use on an industrial scale, the time is now to begin planning for its use when it is. And while this may seem a bit nebulous, vision is the art of seeing what is invisible to others, and those who understand how quantum will benefit them stand to reap the most significant rewards.

Contact the Wolfram Consulting Group to learn how the Wolfram Quantum Framework can provide insights and tools for innovation.

In an economic environment where costs are rising, businesses are searching for new ways to improve margins, ideally by increasing productivity while lowering costs at the same time. Generative AI is offering a quickly growing toolbox for enhancing efficiency and reducing operational expenses with relatively low targeted investments. For example, AI tools can be used to process large amounts of documents, images or video content as well as to automatically generate new content at high quality.

It is not difficult for organizations to develop a multitude of ideas of how to put generative AI to work—indeed, the potential seems almost unlimited. But developing a comprehensive AI strategy for a business is a big challenge at a time when foundational technologies appear to evolve on a weekly basis.

The generative AI ecosystem is moving at a breathtaking speed, with new players arriving daily and established players at risk of disappearing. Big, commercial large language models (LLMs) are leading the scoreboards, but smaller and open-source models, including those with commercially viable licenses, are catching up quickly. The cost structure of operating LLMs is currently dominated by a scarcity of specialized hardware for AI clusters, with delivery times of a year or more for large customers. Selecting the right set of tools from an avalanche of unproven and quickly changing open-source projects is another considerable challenge.

It seems hard to pick the right combination of tools, AI models and technology suppliers for long-term tech investments, especially for organizations (including large, established consulting firms and IT service providers) that lack the expertise to implement generative AI. So what is a safe approach to creating an AI strategy if you do not want to miss out on this exciting technology, while hedging your bets and minimize your risk?

Wolfram Consulting Group can help companies to navigate this quickly transforming landscape by beginning with carefully selected and sharply focused use cases, avoiding the pitfalls of premature and costly investments. By rapidly developing prototypes for the most promising application areas, clients can gain experience and build the expertise and confidence to develop a longer-term generative AI strategy in preparation for more profound and transformative changes.