Technological revolutions have become a norm in this era of innovation. Big data and Analytics have been a crucial part of this decade’s advancements. So, how is big data affecting the ever-so-important field of Artificial Intelligence (AI)?
Big Data: Big Data is a collection of large data sets. This data may be structured or unstructured and can be used to analyze different patterns and trends. Such as, human interactions and behavior.
AI: Artificial Intelligence is technology that has been around for decades. It’s basically a machine capable of thinking for itself. Doing so, by analyzing the situation and taking necessary steps to achieve an end goal without any human intervention.
So, the question at hand is: How does an autonomous, self-thinking system work?
AI involves analysis of large amounts of data by a machine. Several complex algorithms that are modified by the system work with this data. The system is then pushed towards the necessary steps to achieve a given result. It’s not that different from Natural Intelligence, seen in sentient beings if given a thought.
Machines Learning from Actual Datasets, not Sample Data
As of last year, most machine learning systems employed a sample dataset which was tried and tested by the developers of the machine. While this approach got great results, machines were limited to very little data when compared to what the scenarios demanded.
With big data in the game, there’s no need to devote time to collect or generate this sample data. This is because big data almost always accounts for all the data you are working with. Bringing in sets that would have otherwise been discarded because of limited resources. Also, machines will not be limited to a fixed dataset. You can instead use real-time datasets to “teach” the machine how it can achieve the given goals.
A cloud computing approach is where the data is processed at the edge of the architecture near the data source. Edge computing is making headlines across the industry. Although this approach hasn’t yet gone mainstream, it’s being used by businesses working on the cutting edge of technology.
The idea of edge computing is to use Internet of Things (IoT) which will allow a system to collect data, process, and analyze it directly at the source. At its core, edge computing is just a small-scale representation of AI. It uses big data as most of these devices constitute only a few sensors and microprocessors that work in an autonomous, decentralized manner. This approach has several advantages over a traditional cloud computing system. These include:
- Better predictive maintenance: As edge computing utilizes big data, there are more than enough trends in the data set for the machine to analyze and use.
- Higher processing and computational power: Even though edge computing employs basic sensors and microcontrollers, the abundant amount of data enables predictive computing. This in turn, makes processing quicker and more effective.
- Better quality of customer service: As there aren’t any complicated architectures or software to work with, these systems are easy to deploy and maintain.
- The system is more energy efficient: Edge computing doesn’t require high-end processors on-premise. This reduces the power consumption significantly.
Exponential Increase in Computing Power
Processing power is one of the few parameters that has been increasing exponentially with the advent of new approaches. The same holds true when we apply AI in computing processes.
Processing huge chunks of data has become faster than ever with CPUs taking nanoseconds to perform these operations. Additionally, parallel running processing systems like GPUs push computation capabilities off the charts. Using this approach, it is now possible to derive trends and rules from real-time data to generate machine learning algorithms. This is true even for big data which, when coupled with the cutting-edge processors of this era, can be employed to make smarter-than-ever machines and faster-than-ever processing beasts.
We’ve all seen and experienced first-generation computer chatbots. It might’ve been the bundle of apps that are available on smartphones, or a customer care bot on Facebook. While these AI-driven systems get most jobs done to an extent, they still don’t have the “wow factor” that most people have come to expect from them. The difference is noticeable when chatting with a human or an “intelligent” bot.
This year, we might see bots which hare powered by this huge chunk of data. A technological development that’s been in the backseat for quite a while. This can massively affect the way they interact and respond to queries and comments. All of this considered, big data will essentially play a crucial role in the advent of next-generation chatbots.
Even though most of this technology’s current applications are limited to cryptocurrency and finance, there are several lesser known applications of Blockchain. Applications that could change the way we work with data.
Blockchain is essentially a decentralized, distributed file ledger, or a management system. This system is very versatile and can store any kind of digital data regardless of its format, size, or any other property. Many leading data scientists and analysts are envisioning how this technology applied in other processes like big data analytics and artificial intelligence.
Looking at the current market trends, a combination of big data analytics, AI, and Blockchain is inevitable. This combination could be the future of networking, file storage, and even security systems. Several independent researchers have predicted that we’ll see prototypes of enhanced artificially intelligent systems over Blockchain. In result, employing Big Data by the end of this year.
Big data and artificial intelligence are two major fields in IT that are going to join forces and change many trends in the industry. Combining the two of them together will start a new era in the technological world.