Big Data and Artificial Intelligence (AI): How Big Data and AI Work together

Fueling the rise of machine learning and deep learning is the availability of massive amounts of data, often referred to as?big data. If you wanted to create an AI program to identify pictures of cats, you could access millions of cat images online. The same is true, or more true, of other types of data. Various organizations have access to vast amounts of data, including charge card transactions, user behaviors on websites, data from online games, published medical studies, satellite images, online maps, census reports, voter records, economic data and machine-generated data (from machines equipped with sensors that report the status of their operation and any problems they detect). So what is the relationship between AI and big data?

This treasure trove of data has given machine learning a huge advantage over symbolic systems. Having a neural network chew on gigabytes of data and report on it is much easier and quicker than having an expert identify and input patterns and reasoning schemas to enable the computer to deliver accurate responses.

In some ways the evolution of machine learning is similar to how online search engines evolved. Early on, users would consult website directories such as Yahoo! to find what they were looking for — directories that were created and maintained by humans. Website owners would submit their sites to Yahoo! and suggest the categories in which to place them. Yahoo! personnel would then vet the sites and add them to the directory or deny the request. The process was time-consuming and labor-intensive, but it worked well when the web had relatively few websites. When the thousands of websites proliferated into millions and then crossed the one billion threshold, the system broke down fairly quickly. Human beings couldn’t work quickly enough to keep the Yahoo! directories current.

In the mid-1990s Yahoo! partnered with a smaller company called Google that had developed a search engine to locate and categorize web pages. Google’s first search engine examined backlinks (pages that linked to a given page) to determine the relevance and authority of the given page and rank it accordingly in its search results. Since then, Google has developed additional algorithms to determine a page’s rank (or relevance); for example, the more users who enter the same search phrase and click the same link, the higher the ranking that page receives. This approach is similar to the way neurons in an artificial neural network strengthen their connections.

The fact that Google is one of the companies most enthusiastic about AI is no coincidence. The entire business has been built on using machines to interpret massive amounts of data. Rosenblatt's preceptrons could look through only a couple grainy images. Now we have processors that are at least a million times faster sorting through massive amounts of data to find content that’s most likely to be relevant to whatever a user searches for.

Deep Learning Architecture

Deep learning architecture adds even more power, enabling machines to identify patterns in data that just a few decades ago would have been nearly imperceptible. With more layers in the neural network, it can perceive details that would go unnoticed by most humans. These deep learning artificial networks look at so much data and create so many new connections that it’s not even clear how these programs discover the patterns.

A deep learning neural network is like a black box swirling together computation and data to determine what it means to be a cat. No human knows how the network arrives at its decision. Is it the whiskers? Is it the ears? Or is it something about all cats that we humans are unable to see? In a sense, the deep learning network creates its own model for what it means to be a cat, a model that as of right now humans can only copy or read, but not understand or interpret.

In 2012, Google’s DeepMind project did just that. Developers fed 10 million random images from YouTube videos into a network that had over 1 billion neural connections running on 16,000 processors. They didn’t label any of the data. So the network didn’t know what it meant to be a cat, human or a car. Instead the network just looked through the images and came up with its own clusters. It found that many of the videos contained a very similar cluster. To the network this cluster looked like this.

A “cat” from “Building high-level features using large scale unsupervised learning”

Now as a human you might recognize this as the face of a cat. To the neural network this was just a very common something that it saw in many of the videos. In a sense it invented its own interpretation of a cat. A human might go through and tell the network that this is a cat, but this isn’t necessary for the network to find cats in these videos. In fact the network was able to identify a “cat” 74.8% of the time. In a nod to Alan Turing, the Cato Institute’s Julian Sanchez called this the “Purring Test.”

Final thoughts about Artificial Intelligence and Big Data

If you decide to start working with AI, accept the fact that your network might be sensing things that humans are unable to perceive. Artificial intelligence is not the same as human intelligence, and even though we may reach the same conclusions, we’re definitely not going through the same process.

Frequently Asked Questions

What is Big Data and how does it relate to AI?

Big Data refers to large volumes of data that can be analyzed for insights. AI, or artificial intelligence, uses big data to train models and improve data processing capabilities. The relationship between the two is fundamental as AI requires extensive data to learn and make accurate predictions.

How do Big Data and AI work together?

Big Data and AI work together by utilizing large data sets to train AI algorithms. AI uses data analytics and big data processing to uncover patterns and make informed decisions. Thus, the large quantities of data from various sources enable AI systems to become more accurate and efficient.

What are the main challenges of Big Data and AI integration?

The main challenges include data quality, data management, and data storage. Handling large volumes of data efficiently requires sophisticated technologies. Additionally, ensuring the accuracy of data and transforming unstructured data into useful formats are crucial for effective AI and big data analytics.

Can you provide examples of AI applications that use Big Data?

Examples of AI applications that use Big Data include predictive analytics in finance, generative AI in content creation, and personalized recommendation systems in e-commerce. These applications leverage historical data and real-time data to optimize performance and user satisfaction.

How is AI used in Big Data management?

AI is used in big data management through data analysis, data integration, and automation of data-related tasks. AI models can improve data collection and streamline data processing, allowing organizations to handle large data sets more effectively and make strategic decisions based on data-driven insights.

What role does Machine Learning play in Big Data and AI?

Machine Learning, a subset of AI, plays a pivotal role in big data and AI by enabling systems to learn from data patterns and improve over time. It involves training AI models on large data sets to develop predictive analytics capabilities and enhance the accuracy of AI algorithms.

What are common examples of Big Data technologies used in AI?

Common Big Data technologies used in AI include Hadoop for data storage and processing, Spark for real-time data analytics, and NoSQL databases like MongoDB for handling unstructured data. These technologies facilitate the scalable management and analysis of large data sets.

What is the significance of data quality in AI and Big Data initiatives?

Data quality is critical in AI and Big Data initiatives as it directly impacts the accuracy of AI models and the reliability of insights derived from data analysis. Poor data quality can lead to erroneous conclusions, making it essential to ensure the integrity and consistency of data used in AI applications.

How do organizations handle large data sets for AI training?

Organizations handle large data sets for AI training by using advanced data management and big data processing platforms. They employ data storage solutions, data cleansing processes, and scalable data integration techniques to prepare the data sets for effective training of AI models.

What are the benefits of using AI in Big Data analytics?

The benefits of using AI in Big Data analytics include improved data processing speed, enhanced predictive capabilities, and the ability to uncover hidden patterns within large data sets. AI techniques enable more efficient data analysis, leading to better decision-making and strategic insights for organizations.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and utilizing data science methods. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?

This newsletter is 100% human written ?? (* aside from a quick run through grammar and spell check).

More sources

1. https://online.maryville.edu/blog/big-data-is-too-big-without-ai/
2. https://proceedings.esri.com/library/userconf/petrol18/papers/petrol-14.pdf
3. https://www.techtarget.com/searchenterpriseai/tip/How-do-big-data-and-AI-work-together
4. https://blog.purestorage.com/perspectives/how-much-of-the-worlds-data-is-cat-content/
5. https://www.qlik.com/us/augmented-analytics/big-data-ai
6. https://www.wired.com/2012/06/google-x-neural-network/
7. https://indatalabs.com/blog/big-data-tech-and-ai
8. https://nexusintegra.io/how-big-data-and-ia-work-together/
9. https://swifterm.com/deep-learning-vs-machine-learning/
10. https://www.weka.io/learn/ai-ml/big-data-machine-learning/
11. https://binariks.com/blog/how-big-data-and-ai-work-together/
12. https://forbytes.com/blog/big-data-and-ai/