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The term artificial intelligence came into being in 1956, but its popularity has increased today, thanks to high data volumes, advanced algorithms, and efficiency in computing power and storage.

In the 1950s, early AI research worked on topics such as problem-solving and symbolic methods. However, the US Department of Defense took an interest in this type of work in the 1960s and started training computers to copy basic human reasoning.

For instance, in the 1970s, the Defense Advanced Research Projects Agency (DARPA) finished street mapping projects. And in 2003, DARPA developed intelligent personal assistants, long before Cortana, Alexa or Siri were household names.

These early breakthroughs lay the groundwork for the formal reasoning and automation that we see in computers today, along with smart search systems and decision support systems that can be designed to augment and complement human abilities. 

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Though science fiction novels and Hollywood movies reflect AI as human-like robots that will soon dominate the world, AI has advanced to provide particular benefits to almost every industry.

 

Artificial Intelligence Is On The Rise

Artificial Intelligence (AI) has received a lot of attention due to its hype in the startup sector. Besides this popularity, there are explicit technological advancements and new platforms that increase accessibility of AI to a broader audience, which are highly adaptable to different new use cases.

Artificial intelligence aims to refine and empower machines with cognitive capabilities to develop an intelligent agent that understands its environment and performs actions that heighten the chance of success to achieve the goal. 

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For this, AI makes use of big data as well as various machine learning concepts, natural language parsing, and other fields too. Machine learning is taking over the world, and a machine learning certification can make your career in it.

AI is described mainly in the context of big data by the BIA (Business Improvement Areas) community, as well as in the context of data discovery. Nevertheless, with a deep understanding, AI supports other areas like strengthening BIA acceptance by making self-service systems significantly “intelligent.”

Now, let's understand how artificial intelligence impacts businesses.

Artificial intelligence (AI) is usually defined as "a part of computer science involving the computer software study focused on helping machines make practical decisions, solve problems and perform complex reasoning.”

However, that definition inclines toward what experts consider "strong AI," which focuses on artificial intelligence systems that are as much as flexible as the human brain when it comes to performance. 

That AI version is likely to be at least three decades away from becoming a reality. Instead, what is arising in countless everyday applications today is what we call "Weak" AI. 

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Weak AI functions within a highly focused area of capability, efficiently doing a few simple tasks that humans can perform. 

Examples are: 

  • Various control systems of air-traffic towers ascertain flight plans and select the ideal landing gates for airplanes.
  • Logistics apps help businesses like UPS route their vehicles, saving time and fuel.
  • Loan-processing systems that evaluate the creditworthiness of applicants. 
  • Speech-recognition tools that manage incoming calls and offer automated customer service. 
  • Digital personal assistants that find different data sources and give answers in plain English, like Apple's Siri.

In all these cases, AI depends on a set of algorithms –a formula or set of rules that neural networks use to process information to help you get an answer.

 

Here’s why Business Intelligence needs Artificial Intelligence:

1. Big Data Volume Is High

Big Data is growing in different forms and at high speed. It has the power to provide substantial insight for enterprises. Companies are investing in Big Data tools to evaluate the data and develop ideas from it.

But how do you describe the analysis results in a way that everyone understands?  This is precisely why we need Artificial Intelligence to reshape data into easy-to-understand insights at scale. bp58en-5.jpg

2. Lack Of Expertise

According to McKinsey, the United States faces a shortage of 190,000 people with analytical data skills. Furthermore, there is a shortage of 1.5 million analysts to make data-based decisions. It costs companies a significant amount of money to assign data experts to every department in an enterprise.

Even if a business could afford to do this, the data analysts still would not be able to analyze and describe data fast enough.

 

3. Real-Time Insights

Due to the staggering success rate of Big Data and the speed at which the market shifts, it has become impossible to make decisions from old data. Natural Language Generation and Artificial intelligence, however, helps businesses do real-time data analysis with just one click of a button.

Fresh data has an added value when it can be acted upon in real time. bp58en-6.jpg

4. Dashboards Are Not Enough

An analyst helps with one dashboard (from one dataset), but what if you have various data points coming in real time from multiple data sources.

How can many dashboards be described all at once? As artificial intelligence applies reasoning to data, it can define what this information means at scale.

We’re confident that soon most companies will be forced to take the “AI way or the highway.” As a business owner, you don’t have to think about this as a negative situation.

The changes that AI will bring to your business are likely to be positive and embracing it sooner than later will help your firm gain the upper hand over your peers.

 

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Danish Wadhwa

By: Danish Wadhwa

Strategic thinker and an IT Pro. With more than six years of experience in the digital marketing industry, he is more than a results-driven individual. He is well-versed in providing high-end technical support, optimizing sales and automating tools to stimulate productivity for businesses.

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