The most asked questions of Data Science are: “When will Skynet go live”?
“When will the cyborgs take over”?
“How dangerous is AI”?
“Will the robots destroy us or just take over all of the jobs humans do?”
Maybe we need to ask what AI means to “you,” then “you” can weigh the benefits for “you” against the risks “you” are willing to take.
According to William Vorhies, “AI is Simply the Sum of its Data Science Parts.”
Table of Contents
What Is AI Made Of?
The data science ‘parts’ that make up AI fall into the following categories. There is overlap here, but these are the detailed topics you’ll see in the press.
- Deep Learning
- Natural Language Processing
- Image Recognition
- Reinforcement Learning
- Question Answering Machines
- Adversarial Training
- Robotics
Each of these disciplines is quite complex. When they work well together, as in Alexa, or as they are starting to do in self-driving vehicles, then they may appear to be more than the sum of their parts. They are not. Integration of these different technologies is still one of AI‘s biggest challenges. This integration of these disciplines with the massive amounts of diverse data sets available can yield game-changing results.
The rate of information complexity is growing exponentially. Data volume and growth rates combined with the different types of data, reliability of that data, and real-time analysis of it define success in today’s digital universe.
We have exceeded the human capacity to absorb the volume and complexity of information fast enough to make the optimum business decisions we need to succeed. Enter our hero, “AI.” There seems to be no end to the applications and opportunities to use AI. Today, I have selected AI Operations (AIOps) to show the effects of AI. AIOps can simply be defined as the method of using big data, machine learning, artificial intelligence, and analytics to solve issues in information technology. It is basically the convergence of AI into traditional IT operations.
As Steve Jobs said so well. “Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”
Managing ITOps Complexity and Expectations
IT Operations must avoid downtime and outages. It must demonstrate business value (e.g., preserving revenue, protecting brand reputation, improving customer satisfaction) and reduce friction between IT and the business. This requires a platform that brings together data and performance metrics between the business and IT into one operational place. So your organization can move forward with the speed and agility necessary to meet modern business demands.
Currently, there are too many alerts for operators to process, with limited visibility into which alerts are business-impacting. Each day, we are falling further and further behind due to the vast number of new devices being added and the growing list of services being deployed by organizations. This growing complexity is negatively impacting detection and resolution times. Getting through this noise manually is no longer possible. Instead, it leads to a massive loss in productivity when resources are misallocated.
The Primary Benefits of AI Operations
As defined by Gartner: “AIOps platforms are software systems that combine big data and artificial intelligence (AI) or machine learning functionality to enhance and partially replace a broad range of IT Operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.”
The primary benefits of AI Ops. Can be defined in these categories:
- Improved user experience: Using machine learning to help predict an outage and the service impacted before it happens so that your customer experience and revenue are not negatively impacted.
- Proactive support and maintenance: Using AI to go beyond reactive IT to predictive and preventive IT. At Splunk, ITSI uses machine learning to help foresee outages and predict how services will be impacted before they happen. Avoid costly downtime and improve customer satisfaction.
- Predictive operational analytics: Use event data and apply analytics to reduce event noise, false alerts, and rule maintenance so you can easily identify the business-impacting problem that needs to be prioritized and addressed. Better predict sources of downtime to proactively fix problems.
- Efficient utilization of resources: In June 2016, the DeepMind team demonstrated the power of AI in an enterprise setting. The team used AlphaGo technology to manage a Google data center. They turned over control of the data center infrastructure to the AlphaGo system, and data center performance improved significantly. Overall energy efficiency, expressed in the power usage effectiveness (PUE) metric, decreased by 15%, and the cooling bill fell by 40%.
- Dissolving IT silos: Gaining value from data that’s trapped in silos to reduce downtime through accelerated root-cause analysis and remediation.
- Eliminate tedious manual tasks: Use automation to reduce inconsistency in response, eradicate errors that are hard to troubleshoot, and enable IT teams to focus more time and energy on analysis and optimization.
- Collaborate with your business peers: Work together to demonstrate the business value of strategic organizational initiatives.
All of these benefits can be gained with the help of an AI & ML development service.
Some of the additional benefits available with the adoption of AIOps:
- Improved storage management
- Threat detection and analysis
- Capacity planning
- Optimize IT processes and reduce cost
- Increase end-to-end business application assurance and uptime
- Removes noise and distraction
Conclusion
As the ability to integrate the different AI elements with the new flows of data grows, the number of business solutions that are able to utilize AI will increase at an ever higher rate. These solutions will evolve into AI platforms. When the different AI platforms start to communicate freely with each other, they will evolve into complete business solutions. Currently, these solutions are the marketing hype used to drive today’s AI initiatives. Buried in all of this hype, there are tremendous opportunities and risks. While much work needs to be done to achieve these goals, there is good news. All of these AI elements are evolving rapidly, and new glue technologies are being developed daily to bind them together.