Can you recall when machines first made their mark on human life?
Perhaps, it was in 1784 when steam engine power first mobilised the industrial revolution. Thereafter, electricity and information technology took over its domain in their successive revolutions in 1870 and 1996 respectively.
Today, we are in its fourth phase, where artificial intelligence has risen as the frontrunner for technology today.
Certainly, inventions and innovations have been changing human life. They have the power to alter the course of the future.
Amid such incredible transformations, humans have grown to be reliant on digital interaction. And, incredibly, both are consistently gelling up with each other.
This is all due to AI’s ability to cast human-like impressions.
In case you didn’t already know, here’s a refresher on what Artificial Intelligence is:
An alliance of tools and programs (functions and codes) that inserts cognitive thinking skills in the software which basically offers us a smarter way of making decisions and performing human tasks.
It would be good to note that many patrons have mistakenly thought that AI services were carried out by a human — and not by a machine.
Plainly speaking, AI penetrates self-learning tendencies in the form of a device. For example: Siri or Home. Data mining, pattern and speech recognition and natural language processing are part of this transformative technology.
Ultimately, the device starts imitating human instinct, logical reasoning, common sense and forming opinions.
Advantages of AI in business scenarios:
- Scalability, which significantly prevents a massive loss.
- Effortlessness and consistency in work
- Rule-based programmes perceive errors (omission and commission), which deny the requirement of frequency of verification and data validation.
- Continuous improvements.
- Playing a pivotal role in document processing.
The three stages of AI:
- Artificial Narrow Intelligence (ANI): When the intelligence is constrained to one functional area, it is considered artificial narrow intelligence. It represents the nascent stage for further developments to rely on.
- Artificial General Intelligence: This is a level up from ANI where the machines get the power of reasoning, problem-solving and abstract thinking.
- Artificial Super Intelligence: This represents the final stage, whereby devices will be ultra-intelligent, surpassing human excellence in almost all domains.
Thus far, the first two stages have been a success while the third stage has yet to be achieved.
Role of Artificial Intelligence (AI) in data science:
The permeation of AI through devices like Echo, and analysis technology like Facebook Insights and Google Analytics, is snowballing.
Now, any layman can utilise its efficiency to set up a smart office and render customer support or outsource accurate market research.
Amazon Go, for example, has injected its utility in its stores. The power of AI is evident when customers ‘buy today and pay later’ through its app. In the meantime, Amazon’s point-of-sale staff do not interfere with their transactions. The customers can pay from the comfort of their homes later.
This example epitomizes the rise of fast and extra-ordinary intelligence techniques. It’s a gift for data science, which then emerges in a leading role. It is all about evolving such patterns that give rise to humanlike brainstorming and innovation, like in Google Duplex.
A report from IDC states that the global expenditure on AI and cognitive technology exceeded US$19.1 billion in 2018 and is expected to surpass US$52.2 billion by 2021.
In a nutshell, machine learning and intelligence have a prosperous future ahead and together with their overwhelming utility, the demand for its decision makers is at a surge.
Various top players, such as Google, Apple and Microsoft are expending superfluously in this domain, owing to the surfeit of demand in this technology.
Demand in technology ripples over to products and supplies and we can expect this area to flourish.
The following processes are conducted to achieve AI:
This is a method of analysing unique patterns to define models through big data so that a condition can match it via software. An array of conditional data helps to check whether or not the speech or text or visual data matches the background.
This is how a machine gains intelligence. The method is modelled after the cognitive learning procedure, requires millions of datasets to make decisions.
For example, when you command ‘Home’ (a smart device) to play music, the machine learning algorithm starts analysing the request for a song. The machine is then triggered to sift through millions of datasets from the API library, playing your music after a few seconds.
Natural language processing
This determines the automatic manipulation of human speech or text. The machine intelligence is interwoven with natural language processing.
Gmail is its biggest example, where bots automatically sift through the incoming emails, segregating spam and important mail with the help of machine learning.
This gives machines eyes to see through. Various vehicles, for example, are likely to come with front, rear and side cameras to capture and analyse visual information. The process of machine learning interferes with the analysis, which results in recognition (of various interruptions, objects, U-turn and so on).
This is an advanced stage of AI that replicates human motion whereby the consistency of it performing a particular task is constrained to the strength and stamina in a man. The AI in robotics aims to eliminate this drawback.
The AI adapted in these vehicles is going to advance to such a level where it could match or surpass our human driving ability.
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