9 Benefits of Artificial Intelligence
Artificial Intelligence (AI) is a concept which has been a fly in everyone’s ears for quite some time now. Be it the quick suggestions on search engines like Google or Bing, or even Bing’s very own Chatbot, or the auto-focus in the smartphone camera application. AI arose as an idea associated with robots and has now permeated every process available online. It hasn’t spared even email in the form of auto-completions.
Table of Contents
1) Benefits of Artificial Intelligence
a) Smart Decision Making
b) Medical Innovations
c) Data Analysis
d) Business Efficiency
e) Risk Taking
f) Available 24/7
g) Digital Assistance
h) Unbiased decision making
i) Daily Applications
Benefits of Artificial Intelligence
Artificial Intelligence has a vast range of meaningful applications to its name, which we will discuss in the list below:
Smart Decision Making
Artificial Intelligence has been utilised in business for a very long time now. This means that it has led to an increase in efficiency and smartness in business decisions. AI has been programmed to perform a diverse range of tasks, including data delivery coordination, maintaining data consistency and providing forecasts for companies. AI has been made capable of even quantifying any uncertainties to boost decision-making for a company.
The distinction between AI and human beings is human emotion. Probably the biggest line is drawn between AI and humans. Humans possess both logical reasoning abilities and emotional intelligence, which are in varying balance across all human beings.
Through Artificial Intelligence, organisations have capitalised on AI datasets to make more precise, quicker, and consistent decisions without error. Artificial Intelligence is much less prone to a human being’s cognitive bias and is more comfortable with millions of data groupings. It is even capable of detecting variance at granular levels, which would be rather baffling to the perception of humans.
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One of the booming sectors for AI applications is the Medical or Healthcare industry. The implementations of AI in the Medical domain have proven to cross the boundaries of human expectation and even imagination. For example, Remote patient monitoring technology enables healthcare providers to carry out clinical diagnoses followed by suggested treatment procedures.
This process does not require the patient's presence at the hospital. Additionally, the benefits of AI have also permeated to monitoring contagious diseases and predicting their future effects.
The leverage of AI in healthcare leads to the deployment of precise and efficient innovations designed to care for patients who suffer from these diseases, hopefully also finding a cure for them. This also includes Virtual health assistants designed for responding to routine patients' calls, emails and medical information. Not to forget even covering confidential data, scheduling appointments and clinical reminders, etc.
This system is designed by integrating cognitive computing with augmented reality along with body gestures and speech processing. Therefore, the frequency of hospital visits is significantly reduced, helping medical experts and their patients in the long term.
Data analysis methods and techniques have evolved considerably over the years, and the involvement of AI and Machine Learning technology has sent efficiency skyrocketing. To go into detail, AI is designed to generate predictive models and algorithms to execute the data. After which, the AI proceeds to understand the possible outcomes of various scenarios.
With AI's involvement in the data industry, automation is carried out on the work which would generally be performed by a human data analyst. The impression may come across as the data analyst being replaced by AI. However, AI's existence is meant to improve data analysts' abilities. These improvements involve increased speed, the scale of the data being analysed, and the data's granularity which is monitored.
Since the advent of AI, companies have pounced at the opportunity to evolve their business forecasting efficiencies, where critical decisions are made more promptly and prepared with a contingency plan in the case of any emergency. Furthermore, risk management typically relies on data analysis and management. Using AI-supported tools would aid companies in their risk management processes and respective outcomes in the case of crises. What's more, integrating AI with Machine Learning can generate scenarios to guide businesses in planning a strategy for rapid disaster recovery.
Now concerning the marketing side of Business, retailers have been known to use AI to direct their marketing efforts better and generate a better supply chain and pricing for optimising returns.
Data analysis generally generates observations of the audience or the economic trends that may impact marketing efforts. This is followed by tailor-made messages being scheduled at the right times without any involvement or intervention from the marketing team. This implies that AI is applied where speed is of the essence. A few of the best examples of AI marketing are Natural Language Processing (NLP), Data Analysis, Content Generation and Automated Decision-making.
This can probably be counted as one of the prime benefits of using AI technology. Risk is a term that describes any situation potentially exposing someone or something to danger. Moreover, humans are generally prone to taking risks followed by either successful outcomes or errors. Inadvertently, emotions also come into play during decision-making for humans.
At this point, AI takes over by significantly decreasing errors and increasing precision. This is because every instance of a decision made in AI is based on information gathered previously and a particular set of algorithms. Since humans design AI, once programmed precisely, even the errors can potentially reduce to null.
The exponential increase in interest in accepting AI is driven by evolving data regulations and old methods of data inspection becoming unreliable. The unreliability arises from the enormous volumes of data that organisations are compelled to handle, which puts them in a desperate position to garner resources for analysis, evaluation and risk monitoring. These are all carried out while staying up to date with the pressures of compliance.
This benefit would be quite the no-brainer. It implies the precedence of AI over humans regarding productivity over long periods. Even the adjective 'long' is a massive understatement for AI's so-called 'stamina'. Artificial Intelligence can operate endlessly, as it is designed as a machine to run as long as it is programmed to do so. It learns non-stop based on a model which trains input and produces the respective outputs.
AI can, without a doubt, think much faster than human beings and carry out many tasks simultaneously with high-accuracy results. This implies that AI can work tirelessly around the clock at even repetitive jobs as long as AI algorithms support them.
Using Artificial Intelligence, scientists and designers came up with a technology called a 'Chatbot'. More precisely, a 'predictive chatbot' is an advanced computer application simulating a conversation with the people interacting with it. Digital assistants generally utilise NLP and Natural Language Understanding (NLU) to learn a human language on the go and offer a human-like conversational experience.
These chatbots are supported by algorithms that process the incoming data in the form of purchase preferences, home possession, family size and location. After this, data models are generated to recognise behaviour patterns and the refinement of patterns is carried out as the data is added with each conversation.
It is to be noted that a Digital Assistant is an advanced version of a chatbot and is quite distinct from a normal chatbot. When complex human requests require access to multiple sources, digital assistants arise, and an ordinary chatbot cannot handle such complex requests.
An excellent example of a chatbot is ChatGPT or 'Chat Generative Pre-Trained Transformer', an Artificial Intelligence chatbot designed by OpenAI. This Chatbot has been the talk of the tech town since its release in November 2022.
To explain it briefly, ChatGPT was designed and fine-tuned using both reinforcement and supervised learning methods. Human trainers improved the model's performance, where the trainers would enact both the user and the AI assistant. The models were trained on Microsoft Azure's supercomputing infrastructure.
Unbiased decision making
Concerning the 5th benefit described earlier in this blog, Artificial Intelligence technology is void of emotions and very rational in its approaches to decision-making. These reasons contribute to precise and accurate outcomes from decisions because they are not interfered with by biased views or emotions. Now a counter to this apparent strength of AI in decision-making is that if humans program AI, can it also be biased?
Two types of biases exist in AI, namely 'data bias' or 'algorithmic bias', and 'societal bias'. The former involves algorithms trained with biased data, and the latter involves data containing societal assumptions and norms. These assumptions and norms arise from humans having certain blind spots or even particular expectations in our thought processes.
Artificial Intelligence should ideally stand as a bias-free decision-making machine. However, the involvement of humans as designers and programmers of this very machine may cause one to question its so-called utopian image. In essence, Artificial Intelligence machines or models will think the way their creators have taught them.
Reading until this point in the blog, you have been introduced to the complex and large-scale applications of Artificial Intelligence. There also exist regular applications commonly well-known to most users of technology.
A few notable examples include:
1) Revolutionised navigation systems: Predictive traffic conditions and immersive city view, like in Google Maps.
2) Facial Recognition: Virtual facial filters in the camera and facial detection to unlock phones.
3) Text Editors: Use of NLP to recognise improper language and suggestions for corrections. AI editors are designed through the collaborative effort between linguists and computer scientists teaching them grammar.
4) Recommendation Algorithms: Learned behaviour by online services supported by smart systems and tracked online user activity. This is followed by data collection, storage and analysis using machine and deep learning techniques.
5) Social Media: Social Media applications designed with AI monitor content and provide advertisements to targeted end-users to ensure they remain invested and plugged in 'online' regularly.
So far, until this point, you have been briefly introduced to the idea of Artificial Intelligence along with its benefits. You may have realised how deeply technology has permeated nearly every aspect of your digital life, and this can come across as both a boon and a bane. The boon here would be AI supplementing human capabilities in the long term. The bane would be humans smartly figuring out a workaround for being employed and relevant in any industry.
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