Cognitive computing

Take the cognitive controls

Share this post:

The first airplane, “Flyer,” had two flight controls: a hip cradle and a wooden lever. The pilot could feel wind, navigate by sight and react by managing simple tools to manipulate the wings, the rudder and the elevator. A commercial airliner today has hundreds of controls for reacting to numerous systems in flight including hydraulics, fuel, the engine and navigation. In short, the dashboard of a 747 looks like… the dashboard of a 747. Pilots cannot feel the wind or sense the plane’s mechanical abilities, cannot fly by sight and cannot easily manage the tools to react. But, today, planes do a lot more than they did in 1903.

Computing controls today have a lot in common with flying a plane.

The more advanced that computing gets, the further away we are from being able to understand each and every problem that comes up and to see the solutions that are possible. We can no longer take the machine apart and fix it in our own garage–or in our own data center.

Cognitive computing is a tool that allows you to run your systems better based on rules you have set up. You don’t have to feel the wind to know what’s wrong. With cognitive computing we take subject-matter expertise and encapsulate it so that the machine can act on it without external input. The infrastructure runs itself most of the time based on policies set by the administrator, just like a plane can fly itself most of the time based on a route set by the pilot.

We are instructing the machine as to our desires. We are now giving the machine information so that it can meet those desires in an accelerated fashion.

In practical terms, this is how our systems work in a cognitive capacity to help IT leaders provide more innovative services for their enterprise. Watch IT leaders discuss how data is used for analytics services:

At IBM z Systems we have a number of tools that emit data and others that consume data.

For example, imagine those tools are both producing and collecting data related to failed login attempts. As an IT administrator, I have a few hunches about how failed logins need to be dealt with. Based on my subject-matter expertise, I assume that most of the time the errors are made by employees who have gone away for a three-day weekend and spaced out on what their password is. There are 30,000 employees in my company. It’s a waste of my time to personally evaluate each one. So, I create instructions on how failed login attempts following certain patterns indicate a forgetful employee is attempting to login. I automate a response for that error. However, I also assume that there is a possibility non-employees might be trying to log in. But I am not sure what the criteria would be to differentiate those login attempts from those by forgetful employees.
Fortunately, a cognitive system can build on what I have described as a pattern and extract more insight from emitted and consumed data than I can, to better recognize what the attempts by employees and non-employees look like. This also allows the system to differentiate its reaction when a non-employee tries and fails to login.

That’s cognitive computing. It saves me time by keeping me from having to personally evaluate login errors. Not only that, it can also capture data about those non-employee failed logins. This will help me improve my data center security. I don’t have to feel the wind, or look at the error on every failed login attempt.

Airplanes can fly further, faster and with more capacity than ever before today. The same goes for computing. Cognitive computing is the next step in our advancing search for tools able to expand our reach and our capacity for growth.

Cognitive computing allows us to amplify the experiences of our best IT architects and free up time spent on problem solving and maintenance so we can be more creative with our systems and data. Cognitive literally makes room for enterprise growth and innovation.

See what you can amplify in your data center with cognitive computing for IBM z Systems by visiting IBM z/OS Platform for Apache Spark or IBM Operations Analytics for z Systems.

 

Chief Architect and Distinguished Engineer - z Systems Management and IT Optimization Software

More stories

5 top takeaways from Think 2018

The first Think conference is in the rear-view mirror. There, visionary speakers and experts shared their solutions for some of the world’s most daunting challenges, and IBM revealed some of our big bets on infrastructure innovation in the coming years. Now that I’ve had some time to reflect on the conference, I’ve assembled my five […]

Continue reading

Taking the AI training wheels off: moving from PoC to production

In helping dozens of organizations build on-premises AI initiatives, we have seen three fundamental stages organizations go through on their journey to enterprise-scale AI. First, individual data scientists experiment on proof of concept projects which may be promising. These PoCs then often hit knowledge, data management and infrastructure performance obstacles that keep them from proceeding […]

Continue reading

Announcing PowerAI Enterprise: Bringing data science into production

Today I am tremendously excited to announce IBM Cognitive Systems’ newest applied artificial intelligence (AI) offering, PowerAI Enterprise. This new platform extends all the capability we have been packing into our distribution of deep learning and machine learning frameworks, PowerAI, by adding tools which span the entire model development workflow. With these capabilities, customers are […]

Continue reading