Beyond the Hype: Providing Computational Superpowers for Enterprise AI

Navigating today’s volatile business landscape without top-tier data is like trying to predict a hurricane with last month’s weather report. It’s not just reckless; it’s downright dangerous. Quality, up-to-date information is the Doppler radar for your business, helping you see through the unpredictable market conditions to make decisions that aren’t just reactive guesses but proactive strategies. After all, facts are as unyielding as the laws of nature: they don’t bend to our wishes or fears.

Cautionary tales underscore the critical importance of data accuracy in our interconnected, information-driven world. The 1999 NASA Mars Climate Orbiter failure serves as a stark reminder of how a simple unit conversion error, undetected due to inadequate data validation, led to the spacecraft’s destruction. Similarly, the 2008 financial crisis highlighted the dangers of relying on flawed historical data models that failed to account for the risks of subprime mortgages, resulting in a global economic downturn. More recently, the 2020 UK A-Level grading algorithm controversy demonstrated the pitfalls of not thoroughly validating the impact of data models on diverse student groups, leading to widespread perceived unfairness and policy reversal.

So what’s the solution? It’s not just one thing. Instead, multiple factors come into play: data breadth and curation, as well as its integration into a traditional programming environment or high-level AI/LLM analysis.

Enter the Wolfram Knowledgebase, a comprehensive data system that underpins Wolfram Language, with vast, curated datasets that are immediately computable—no data cleanup or excessive tagging/categorization is required because that’s already been done for you.

Data Scope

The Wolfram Knowledgebase is a comprehensive repository of curated data across multiple areas: from physical sciences (elements and molecules), medical data (gene properties) and astronomical information to economic indicators (stock prices), country statistics and cultural data, including historical events and notable personalities, along with internet-based metrics.

As Alan Joyce, Director of Content Development at Wolfram, describes it, “Basically, the Wolfram Knowledgebase covers pretty much every type of known information about the world that you can think of.”

This data is then organized into hundreds of different “entities” that represent either physical “things,” including types of animals (dogs and cats), movies (Casablanca to Barbie) and countries (the US and France), or mathematical and other scientific concepts such as space curves, mathematical functions and periodic tilings.

Plus, you’re not limited to only the data in the Wolfram Knowledgebase. You can also add entities for your own datasets and import that information to use with what’s already in the Wolfram entity framework.

Data Management

The breadth of available datasets is the first thing that sets the Wolfram Knowledgebase apart from other online databases. But this information is not just a hodgepodge collection of information from different sources. Instead, as detailed by CEO Stephen Wolfram, it’s carefully evaluated, selected and processed for integration within the Wolfram tech stack.

The process of transforming raw data into coherent computable data begins with collecting it from records, surveys and experiments, followed by converting it into digital formats such as .txt, .pdf and .xls files. The data is then systematically stored in cloud repositories with metadata and organized in structured formats using JSON and XML. Quantitative elements like quantities, dates and geolocations are represented in canonical symbolic form, and standard entities such as countries, species and chemicals are also given canonical symbolic representations. All entities, including custom ones, are uniformly consistent through normalization and manual or automated analysis. Computations for derived properties like interpolations, formulas and models are added, along with natural language mappings and access via tools like Wolfram|Alpha. The final goal is to ensure the data is suitable for repeated, systematic computations in Wolfram Language.

In contrast, a resource such as the Google-curated Dataset Search is more indiscriminate about indexing available online datasets, which can have limited (or no) manual curation, unlike the Wolfram Knowledgebase. And other information sources such as Quandl and OpenWeatherMap are more narrowly focused, which can make cross-referencing multiple dataset points more labor intensive due to the need for data alignment across multiple file types and categories.

Programming Integration

The final piece is the Wolfram Knowledgebase’s integration within Wolfram Language with sophisticated semantic representations such as Quantity and DateObject. This makes data analysis and visualization part of a unified framework as opposed to juggling data libraries from varied sources, multiple pieces of software and different programming languages.

“The Wolfram Knowledgebase isn’t just a big database that you can go and pluck data out of to put somewhere else,” says Joyce. “Its power comes from the fact that it’s all integrated with the Wolfram tech stack for analysis, visualization and deployment to the web.”

For example, with just a few lines of Wolfram Language code, you can easily generate an up-to-date UK air temperature map.

But the possibilities of data analysis and visualization with Wolfram Language extend even further. As George Danner, president of Business Laboratory, says about the software, “It can work on very large, very diverse datasets, including unstructured datasets, [and] can do just about whatever we ask and perform any kind of function. We’ve used statistical functions. We’ve used the optimization functions… to put together very fine-grained visualizations of the systems at hand, and these visualizations are critical for allowing our complicated models to communicate in non-mathematical ways to our audience, which are often company executives.”

Sustaining Success through Data-Driven Insights

Utilizing the Wolfram technology stack for data analysis can drive significant advancements and innovative solutions across multiple fields. For example, perhaps you need to cross-reference geographic, seismic exploration and imaging data to find new oil reservoirs. Or if you’re in power generation, maybe you want to use thousands of different inputs—everything from weather forecasts to plant repair time—to create predictive energy load models for power plants. And in finance, you may have to pull together data such as asset prices, cost of goods and inflation to analyze and predict economic trends.

One fact remains clear: in today’s digital age, quality data is the currency of success, and that requires multiple information streams to stay ahead of the curve. As you navigate the business landscape, remember that it’s not about weathering the storm—it’s about harnessing the power of data to create your own blue sky opportunities.

Contact the Wolfram Consulting Group to learn more about using the Wolfram tech stack, curated data and custom AI tools to generate actionable business insights.

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.