The term “artificial intelligence” encompasses various aspects of technology. In general, AI aims to create machines that can mimic the behavior of humans by learning on their own — without having to be programmed. There have been many advances in AI development that bring this technology a step closer to understanding sights and actions in the same manner as the human brain.
Some forms of AI function to identify objects in photographs through the use of visual technologies that help them see. Other systems include technologies that work to process the human language and carry on normal conversations. But the ability to process big data quickly and efficiently will be one of the most important ways businesses harness the power of artificial intelligence.
As businesses are developing more “data first” strategies, they are accessing larger volumes of datasets and seeing exceptional results. There are trillions of gigabytes of data out there in the digital universe, and over 90 percent of it has been collected within the last two years. With the amount of data collected overall projected to exceed 44 zettabytes by 2024, it’s crucial for businesses to be able to process the information efficiently.
How AI and the cloud work together
By 2011, smartphone sales surpassed PCs, launching mobile devices to the forefront of the computing world. Smartphones are able to capture and process large amounts of unstructured data through photos, emails, and text messages, but analyzing all of this unstructured data takes a lot of time and processing power that most devices simply are not equipped with. As a result, phones send the data they receive to cloud servers, which drastically slows down the response time of AI.
In order to combat this issue, split that labor between the phone and the cloud. AI learns in the cloud, where there’s plenty of processing power for it to teach itself quickly. Ever wondered how the computer always manages to beat you when you compete against it in online games? That’s the power of AI at work.
For example, Google’s AI algorithm plays Go with itself millions of times until it masters the game and learns all the techniques required to beat humans. The AI learns quickly, and as a result, it’s able to play against humans on their devices using the techniques it learned during its practice sessions in the cloud.
Increased Big Data calls for quicker response
As AI becomes more complex, it’s more important than ever that devices work with the cloud to process all the data. Even the most advanced smartphones lack the ability to meet the computing demands of AI, but relying on the processing power of the cloud helps these types of operations run flawlessly.
Businesses around the world have already integrated various AI-driven technologies across the services they offer. In fact, research by Accenture demonstrates that 85 percent of IT executives and businesses expect to make massive investments in AI-related technologies within the next three years. Apple just recently acquired a company focusing on AI for end-user devices.
Cloud technologies are the foundation of AI as it works to provide storage for substantial amounts of data. This infrastructure ensures scalable processing power and embedded graphic processing units (GPUs) are always available to handle the large amount of data stores and algorithms required to run AI systems uninterrupted.
Selecting the Right Infrastructure Partner
When deciding between potential cloud infrastructure partners, it’s essential for companies to choose providers that are equally invested in the continual use, development, and implementation of artificial intelligence. There are a few tips you can follow when evaluating potential partners to ensure you’re selecting the best platform possible:
1. Determine Your Specific Needs
If possible, make a checklist of your minimum requirements and expectations for the provider. Creating a list that specifies what you need out of a service will better enable you to compare the servers against your list rather than against one another. With AI and machine learning you might have different requirements for different parts of the system. You might need GPU’s to train your models, or vast amounts of storage to keep your data sets safe. If you mainly deal with making decisions based on result data, your infrastructure might need high-memory systems with lots of compute power. Your infrastructure provider should be able to help with selecting the right environment for your needs.
2. Seek global availability
Although network optimizations can help WAN traffic, there is no real comparison between data centers located across the globe versus right next door. Companies, especially those with employees across the globe, expect consistent service for their employees. As a result, they should opt for global infrastructure providers to ensure the service wrap and technology stack are consistent in all regions.
3. Find a Partner You Can Trust
It is no secret that AI developments make users’ experiences with technology easier and more natural than ever before, and this technology is here to stay. AI and machine learning environments are typically big and specific – and will be evolving over a long period of time. It’s imperative to choose a partner that can help you grow and evolve that platform with a customer-centric and personalized approach.
The Main Takeaway
Overall, if companies are looking to harness the full power of AI, they need to find the right environments to handle the relevant processes for them. Infrastructure providers should be able to help select and deliver different environments depending on their needs such as GPU’s to train models or large amounts of storage for backing up data sets. As AI/machine learning are not widely adopted by companies yet, building such a setup will require personalization and flexibility from infrastructure providers. Make sure to choose a partner you trust as you should be in it for the long term with this powerful technology.