Artificial Intelligence & Machine Learning

Better together: harnessing the power of machines and humans with AI and machine learning
The meaning of artificial intelligence (AI) can be easily confused and misconstrued — from the idea of human-like robots to the simple digital assistants already living in our mobile device. So what is AI?
At a high level, you have two major categories: weak AI and strong AI. The former, also known as “Narrow AI,” is an array of technologies that rely on algorithms and programmed responses that stimulate intelligence, usually in respect to a specific task. Weak AI can be seen in action every time you ask Alexa to set an alarm for you or turn on the lights. There’s no actual thinking, just seemingly smart responses based on prior training. Conversely, strong AI is supposed to mimic the human brain. It’s supposed to “think,” be aware of context and make decisions based on that understanding.

Most AI we encounter today is of the weak variety, but we’re making strides toward smart technology that learns and grows over time. This is where machine learning (ML), a subset of AI, comes into play. ML algorithms are reaching a level where they are successfully learning and executing based on the data around them, some (e.g. unsupervised ML) without the need to be explicitly programmed. From sorting mountains of data to finding useful patterns in it, tedious data-heavy tasks that could take humans hundreds of hours can now be done with an ML algorithm in a fraction of the time. This technology can already be seen in action in things like autonomous vehicles, cybersecurity technology and more.  It’s just the beginning, especially at the rate data is being generated every second.


The Importance

Why AI and ML?

AI and machine learning are coming into their own amid a data explosion. Machine learning is contingent on huge amounts of data to train the algorithms so that they can adjust and improve. Organizations today have a wealth of data — and will continue to generate more and more. With AI and ML, it’s possible to use that data to get value not possible with human analysis alone, leading to improved effectiveness and productivity with new insights and automation.
The Difficulty

The Barriers to the Human + Tech Connection

By applying AI and machine learning to business problems it becomes possible to augment and amplify the strengths that we have as humans, allowing us to derive new insights and automate existing processes.
But there are barriers:


Data Volume and Complexity

As data continues to surge, much of it unstructured or semistructured, making it much more difficult to make sense of and discover patterns than with the structured data from relational databases.




Data Prep

Data prep & ensuring good data quality is one of the key challenges to realizing value from AI & ML. Before any analysis can happen, it’s critical to explore and understand and then prepare it, formatting and cleaning the data. This accounts for the most intensive, manual steps for data scientists, often said to take 80% of their time.




Many Tools and Technologies

Typically to get value from AI & ML, it requires many different steps, tools and technologies. Organizations are slowed by having to move, aggregate and correlate data from disparate tools and systems. They need the ability to easily manage and apply machine learning to their real-time production environments using a single platform


Get the Value Out of Smart Technology

AI and machine learning are aptly suited for use cases like anomaly detection, predictive analytics and clustering, among other more tailored use cases.


Finding Anomalies in Your Data.

By creating a baseline through intensive training, AI and ML can pinpoint deviations from past behavior or from peer groups, like similar hosts or users, to quickly find unusual changes, behaviors or issues that could go unnoticed otherwise.

Anomaly Detection

  • Deviation from past behavior

  • Deviation from peers (Multivariateor Cohesive Anomoly Detection)

  • Unusual change in features



Forecast or Predict the Future.

You can make highly accurate, proactive decisions based on real-time data being generated by processes ranging from business to IT and security operations. You can only take preventative action if you have an idea of what’s in store. It’s now possible to predict service health scores, capacity planning and even predict maintenance requirements.

Predictive Analytics

  • Predict service health score

  • Predicting churn

  • Capacity planning

  • Trend forecasting

  • Detecting influencing entities

  • Early warning - predictive maintenance



Don’t Miss the Forest.

Finding trends is integral for proactive operations. It pays to find behaviors and trends in your data and understand how the compare to each other. Clustering allows you to identify and group similar data points, helping to reduce alert noise and, in turn, make better decisions..

Clustering

  • Identify peer groups

  • Event correlation

  • Reduce alert noise

  • Behavioral analytics

The Solution

Combine Human and Machine

Splunk allows you to complement your expertise of your organization and your data with AI and machine learning for enhanced effectiveness and productivity, across industries, use cases and skill set.


Use Cases
Drive better and smarter insights, decisions and actions, regardless of use case. Elevate your IT operations analytics to ensure high-performing IT environment. And better protect your organization by preventing the unknown. Learn more about how to get started with AI & ML across use cases.


Industries
With the right data, AI and machine learning can benefit any organization, in any industry. From preventing utility outages to identifying fraud, smart technology makes for more accurate and faster decisions. Learn more about how to get started with AI & ML across verticals.

The Solution That's Right For You
Embedded AI and ML with Splunk’s premium solutions, allowing you to select data sets and adjust models -- no need for data science expertise.