Accelerating AI to the next level for enterprises
From time to time, we invite industry thought leaders to share their opinions and insights on current technology trends on the IBM Systems IT Infrastructure blog. These opinions are their own, and do not necessarily reflect the views of IBM.
For a major part of the last decade we have been talking about the future, with artificial intelligence (AI) and all the endless opportunities that it holds. Now that we have AI placed in the center of everything around us, it is time that data scientists, managers and thought leaders lead the way forward into the era of unprecedented opportunities.
Developments in the field of AI can be seen across numerous industries. Enterprise AI is thus the calling of the hour, as it gives a well-rounded AI solution to avoid being crushed by the competition around you. Industrial AI will transform the business scenario around us, and usher in a new era of AI-powered innovation. In short, enterprise AI is designed to make your operations superior and effective.
In these times of change it is necessary for CEOs, CDOs, CIOs, CTOs and other C-suite employees to realize the need of AI for their industries. As customer expectations grow, every industry needs to diversify its offerings to meet these growing demands.
The need for better customer experience and performing algorithms
Considering the changing characterizations of AI, it is imperative that stakeholders and executives recognize the need to develop their systems over time.
Deep learning (as part of AI) can improve the customer experience. It is the use of deep learning that improved the accuracy of speech recognition, fraud detection and anomaly detection among many others. The fact that deep learning can do this without coding is one of the big reasons that it is revolutionary. With machine learning, we had to hard code for feature classification and identification. With deep learning, this is automatic.
An example of a recent AI project that required extensive system capabilities is a power company in South Korea. This company wanted to check power lines in an attempt to find out any anomaly such as broken lines or poor connections. Having humans check the lines was an extensive and dangerous job and required a lot of attention to detail. The company tried to reduce the hassle by using drones to video the power lines and deliver footage to survey rooms, where humans would monitor the footage. This process was very exhausting, because humans cannot be expected to capture every aspect of a video. In a bid to reduce operational inefficiencies, the company now has a deep learning model in place that takes data from all of the cameras and drones on a regular basis and updates the system. If there is an anomaly, it is detected at an early stage, saving the company valuable resources.
The requirements for accelerated AI
Old legacy system, like traditional data warehouses, do not have the proficiency required to handle the influx of data and other tools that these new AI and deep learning demands brings with them.
The first challenge comes in adjusting to the raw format of the data, and building concrete steps based on it. The second challenge is posed by the looming threat of below average performance due to poor real-time settings.
With the changing requirements of AI, there is now a need to change infrastructure as well. The infrastructure that was used to manage previous forms of innovation would now barely suffice for AI. Hardware and software go hand in hand, so physical computers need to have the necessary adjustments made inside them.
Organizations need to have a hybrid setup in place for a seamless transition to AI. An open-sourced AI platform will take the following things into consideration:
- Simplicity: The system should be simple enough to integrate efficient software that works. The AI platform should be easy to develop and implement for starters.
- Ease of use: The data should be easy to use for the AI platform. The right AI platform will clear the environment and make way for easy data storage and working methods.
- Faster model: An AI server should have faster training times: Machine learning and deep learning shouldn’t take as much time as they currently take.
- Open AI platform: A platform that partners can build upon. This program will make the shift to AI easier for many organizations.
How to move to the next level
It is time now to change the traditional thinking that is holding organizations back from success in AI. The traditional approach can only work for a period of time and the way forward is the right software and hardware mix.
This process starts from augmenting the right software and data for deep learning. The perfect software for deep learning should not only be easy to use but should also be quick to deploy and optimized with your hardware. Preparing the data is the next step and deployment.
The AI infrastructure setup aims to revolutionize the use of AI and gives users a single system for managing their AI needs. At the heart of IBM’s AI infrastructure sits the accelerated AC922 Power System and PowerAI software. Enterprises can now accelerate their journey towards AI with fast deployment and optimal performance. The pastures on the other side are not only greener, but better suited for organizations across industries.