Big Data & AI (How Do They Work Together?)

Wondering about big data and AI? We explain what big data is, how it relates to artificial intelligence, and its use cases for modern businesses.

How do big data and AI work together? Artificial intelligence is the ability of computer systems to learn to make decisions and predictions from observations and data. Big data refers to large amounts of data that can be mined for information.

What Is Big Data?

“Big data” denotes the evolution of data-driven technologies as they have changed over time. Computing technologies like high-performance storage, networks devices, and cloud platforms have fundamentally altered how we think about data storage and processing as a practical set of operations on massive scales.

Consider how data storage has been implemented over time. Originally, long-term storage was only feasible in small quantities. Magnetic tape storage was the norm, and larger, rewritable storage in the form of hard drives came decades later in the era of personal computers.

As hard drives grew in capacity, so too did the ability of computers to handle larger volumes of data. By 1989, top-of-the-line enterprise or hobbyist hard drives could reach max capacities of three gigabytes. Over the 1990s and 2000s, hard drive capacity changed by orders of magnitude, so much so that by 2020, it’s not uncommon for consumers to have hard drives that can hold terabytes of data.

On the enterprise side of computing and IT infrastructure, we saw huge boosts in several parallel innovations:

  • Hard Drive Capacity: As hard drives expanded in size, data centers increased in capacity simultaneously. Server centers housing petabytes of data became common, and businesses used this readily available storage to collect data on their users in droves.
  • Online Communities and Commerce: The increased normalization of online communication, social media, and eCommerce has driven more and more people to perform daily activities online. This includes banking, shopping, sharing information with friends, and simply locating useful or entertaining information. This gives enterprises unparalleled opportunities to collect data related to these activities.
  • High-Performance Cloud Computing: Over the 2000s, heavy-duty computing applications moved away from traditional supercomputers into networked processing clusters. Combining ingestion, processing, and storage functions, cloud platforms leveraged dozens or hundreds of computers into interconnected nodes acting as one super-powerful system.

The combination of HPC, an increasingly online population, and expanded storage capacities have led to the evolution of data collection and processing known as “big data.”

It’s important to note that big data doesn’t simply refer to the capacity to collect and store large amounts of information. Rather, big data refers to IT configurations where immense quantities of data can be collected, cleaned, processed, used in applications, and stored for later use. The collected IT systems work together to operationalize petabytes of data toward solving some of the most complicated computational problems.

What Is Artificial Intelligence?

One of the more prominent applications of big data is the development of artificial intelligence.

Development of AI started shortly after World War II and rose to prominence in mathematics alongside disciplines like computational theory, informatics, and computer science.

In these earliest days, the general question facing theorists and researchers was complex yet simple to articulate: can a machine think and act like a human? Thought experiments and mathematical proofs were structured around what it would mean for a machine to think like a human.

As interest in AI waxed and waned over the years, one of the major walls that development would run into was resource availability. It simply wasn’t feasible to properly provide enough data, processing power, or hardware resources to support machine learning and decision-making in machines. And, as AI splintered into related disciplines like robotics, natural language processing, and expert systems, that remained the case.

With the advent of big data cloud systems, however, this changed. Big data and machine learning provided critical resources to power AI. These resources include the following:

  • Massive Data Sources: Training machines requires immense amounts of data—and big data platforms can supply that data readily and in whatever structure or form needed. Furthermore, the types of data sources available have radically increased, from legacy data stores to online eCommerce platforms and social media providers.
  • High-Performance Processing: Developments in field-programmable gate arrays, Non-Volatile Memory Express, and parallel processing through GPUs greatly increased the capacity for computers to perform the types of computations needed to support machine learning and AI systems.
  • Hybrid Cloud Systems: Scalable, flexible, and manageable cloud systems that could combine the data sources, computing resources, and user interfaces necessary to support AI as a solution.

This isn’t to say that HPC, big data, and the cloud have created machine intelligence. However, they have refined our understanding of AI as a context-specific intelligence that can be trained and implemented to do specific tasks, typically better than humans.The following application areas utilize big data-driven AI:

  • Life Sciences
  • Healthcare
  • Financial Services and Investments
  • Insurance and Risk Modeling
  • Genomic Sequencing
  • Manufacturing and Supply Chain Management
  • Retail
  • Chemical and Mechanical Engineering
  • Resource and Utility Management
  • Customer Service
  • Self-Driving Cars

How Are Big Data and AI Used in Modern Technology?

To get into more complex applications of AI and big data, several major examples tie into the areas listed above:

  • Tesla, Ford, and Self-Driving Cars: Both Ford and Tesla are pushing for the development of self-driving cars. These cars are powered by AI “brains” taught through simulations and big data cloud platforms using information gathered from digitized maps and real-time information gathered through sensors installed on the vehicles.
  • The Human Genome: Cloud platforms have supported genomic sequencing research led by several organizations. Many of these organizations are turning to AI applications to help researchers locate patterns in genome sequences that might aid in discovering new ways to diagnose and treat disease.
  • Project Hanover: Microsoft’s AI platform is a healthcare-specific project that emphasizes using intelligent machines to power machine reading. These systems can read patient files, charts, and scans to create comprehensive medical pictures that fuel “precision medicine,” where informed doctors can make targeted diagnoses based on AI analytics.
  • Gaming: The chess computer Deep Blue made a huge splash in the 1996 when it defeated Gary Kasparov in a six-game match. In some form of development since 1985, this machine was one of the first demonstrations that machines could think like humans in creative, strategic contexts like gaming. Its descendants, notably DeepMind, have extended AI gaming experiences into classical games like Go and modern computer games like StarCraft.

These examples are notable in their impact on popular culture. The reality is that dozens of AI applications operate every day, embedded in unassuming places like insurance actuaries, support chatbots, and backroom retail analytics.

Power AI and Machine Learning with WEKA Big Data Cloud Architecture

The core of big data and AI development combines high-performance computing, cloud systems, and rapid-access storage on a massive scale. With this combination of tools, everything from predictive big data analytics to self-driving cars and chess-playing supercomputers is possible and even available to more people.

WEKA is the foundation of machine learning and AI platforms, providing high-performance hardware and cloud storage to power heavy AI workloads. Features included with WEKA solutions are as follows:

  • Streamlined and fast cloud file systems to combine multiple sources into a single high-performance computing system
  • Industry-best GPUDirect performance (113 Gbps for a single DGX-2 and 162 Gbps for a single DGX A100)
  • In-flight and at-rest encryption for governance, risk, and compliance requirements
  • Agile access and management for edge, core, and cloud development
  • Scalability up to exabytes of storage across billions of files

Contact our team today to learn more about how WEKA can fuel your big data AI solution.

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