Published on January 20th, 2022 | by Christopher Porteus0
What Can Memory Computing Do for AI
Artificial intelligence helps machines learn, experience, adjust, and perform tasks the way a human would perform or better. It does so by processing large volumes of data generated mostly by humans. The amount of data produced for many years has remained limited but lately, the volumes have rapidly increased to several trillions of bytes.
It is not easy for AI to perform such enormous tasks manually but with in-memory processing, AI can perform tasks 1000 times faster than ordinary computing. The biggest e-commerce sites, retailers, and entertainment industries are soaring high in profits due to the adoption of AI technology and in-memory computing.
Solutions provided by memory computing
In memory computing helps businesses manage big data in several ways. Its architecture allows huge data to be stored in the memory instead of hard disks. To the business, it gains a huge advantage in terms of volumes of data stored and processing speed. Instead of storing big data in a single place, businesses divide it into smaller portions of terabytes then store it into several computers located remotely. The solution helps a business to store data of whatever scale.
Thankfully, in-memory computing has managed to offer the solution of data storage, and not only that, but it has also provided the required speed to data migration to and from. Storing and retrieving data is not enough for an enterprise if it does not benefit it. The company must make use of it to the maximum and this must be done fast, bearing in mind the cost implications, flexibility, speed, and efficiency. Another technology is required that can learn like humans to help interpret that data fast and take actions or advise humans on what actions to take. This is where AI and in-memory computing merges.
AI and in-memory computing for business acceleration
Every data generated by a business must be processed and acted upon fast. When businesses understand the importance of customer feedback, they cannot afford to work on historical data in a bid to respond to customer queries. However, a business must process current data and respond to millions of customer queries or feedback in real-time. In a situation where a business is serving millions of customers, human beings might take forever to respond to customer feedback on a specific day. Something might go wrong with its products in a given country and the only way to know is to process data gathered and stored in the main memory.
Some products rely on current data to function, without which the product would fail terribly. For example, an autonomous car self-driving on the road relies on data generated at that spot to move on. If it’s not processed promptly, the car will cause fatal accidents. In-memory computing receives and stores data but it requires a human-like mind to process it fast and issue a command for the next action. AI technology provides a human-like mind that can learn fast, process data, and advise on action.
How in-memory computing helps AI
AI can perform tasks that the main memory cannot perform and vice versa. AI can recognize speech, achieve a visual perception, translate a language, make a decision, and execute a task naturally. These are tasks that in-memory computing cannot perform. However, it will store enormous volumes of data from multiple sources and process it at super speeds. The data stored in the main memory could be voice data, videos, texts, audios, PDF, word documents, JPEG, etc.
The data requires interpretation, analysis, and reporting, which a human being can do. Unfortunately, human beings don’t have the speed of a machine to process terabytes of data within minutes or seconds. To help him, he created AI and coded it with sets of instructions to perform multiple tasks better than himself. These instructions are the AI algorithms that help it interpret data fast. AI uses a combination of several other technologies to accomplish its purpose. These are technologies such as machine learning, deep learning, natural language learning, and computer vision. In-memory computing handles the data and supplies it to AI at the required speeds.
Supplying the right data volume
For AI to learn, it requires big volumes of data. For example, for AI to learn NLP, it took developers several years to gather enough data for AI to learn speech. AI required big volumes of data to learn to operate self-driving cars. A higher level of data scalability is required and memory computing can supply the right volumes of data required by AI.
Supplying the right data speed
For AI to process data that adds value to a business, speed is critical. A self-driving car that is already on the road cannot wait for 30 seconds for AI to complete processing data. It has to be a matter of milliseconds to avoid collisions. If the required data is stored in hard disks, the time taken to store and retrieve it would be longer, leading to mistakes on the road. Memory computing solves this challenge and helps provide AI with the right speed for storage and availing data for fast processing.
Efficiency involves different things like cost, productivity, reliability, agility, and scalability. If any of these is lacking, AI will not deliver as programmed. A certain data architecture might supply at the right speed and the right volume but then make the cost escalate. AI will perform its tasks well, but the company will begin to count losses. AI might take more time to receive the required data due to latency from the source, thus affecting efficiency. System efficiency is something that cannot be compromised in a business situation that relies on AI to deliver better services and products.
In-memory computing is an advanced technology for providing storage solutions. It is a system with high scalability that can be used for different functions and can be enlarged as a business records growth. Businesses leverage RAM and parallelization to achieve a highly efficient storage solution. AI can depend on memory computing to efficiently supply the required data without fail. When a business combines AI with memory computing, it achieves a system it can rely on.