How Big Data will influence AI in 2018?

Big Data AI

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Technological revolutions have become a norm in this era of innovation. Big data and Analytics have been a crucial part of many of this decade’s advancements. But how is big data affecting the ever-so-important field of Artificial Intelligence (AI)?

To understand this better, let’s first look into what big data and AI are at their roots and why you should learn big data and AI this year.

Big Data: It is nothing but a collection of large data sets. This data may be structured or unstructured and can be used to analyze different patterns and trends like human interactions and behavior.

AI: Artificial Intelligence is a technology which has been around for decades that is basically a machine thinking for itself – analyzing the situation and taking necessary steps to achieve an end goal without any human intervention.

So, the question at hand here is: How does an autonomous, self-thinking system work?

AI involves analysis of a large amount of data by a machine. Several complex algorithms modified by the system over time work with this data and push the system towards the necessary steps to achieve a given result. It is not that different from Natural Intelligence seen in sentient beings if you give it a thought.

Now that you have a basic understanding of what is AI, let’s see how this extensive technological field is being influenced by big data and why you should learn big data and AI this year.

Machine 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, you don’t really have to devote time to collect or generate this sample data. This is because big data almost always account for all the data you are working with, bringing in sets that would have otherwise been discarded because of limited resources. Moreover, machines will not be limited to a fixed dataset. Instead, you can use real-life, real-time datasets to “teach” the machine as to how it can achieve the given goals.

Edge Computing

A cloud computing approach 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.

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The idea of edge computing is to use the 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 which 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:

1.  Better predictive maintenance: As edge computing utilizes big data at its core, there are more than enough trends in the data set for the machine to analyze and use.

2. 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.

3. Better quality of customer service: As there are no complicated architectures or software to work with, these systems are easy to deploy and maintain.

4. 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 have now 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.

Chatbots

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. You can always tell if you are chatting with a human or an “intelligent” bot.

A technological development that has been in the backseat for quite a while, this year we might see bots which are powered by this huge chunk of data. This can massively affect the way they interact and respond to queries and comments. In conclusion, big data will essentially play a crucial role in the advent of next-generation chatbots.

Blockchain Technology

We all know what Blockchain is, and even though most of this technology’s current applications are limited to cryptocurrency and finance, there are several lesser known applications of Blockchain that could change the way we work with data.

Blockchain is essentially a decentralized, distributed file ledger or management system. This system is very versatile and can store any kind of digital data, regardless of its format, size or any other property. With that in play, many leading data scientists and analysts are envisioning how this technology applied to other processes like big data analytics and artificial intelligence.

Looking at the current market trends, a combination of big data analytics, AI and Blockchain are 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 which employ 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. Their individual applications are ginormous and coupling the two of them together will start a new era in the technological world.

Convinced to start your journey down this new technological lane? Then there is no point in waiting. You should aim to learn big data and artificial intelligence now.

How Big Data will influence AI in 2018?