An overview on why Artificial Intelligence is a game-changer in the 21^st century

Being the most common and vibrant technical term in the 21^st century, Artificial Intelligence has started reigning the world stage with its smart functionalities. There is no doubt that Artificial Intelligence is transforming the productivity and workflow of multiple industries across the world. But there is also a constant concern related to human job opportunities in the upcoming years. Gradually organizations have realized that Artificial Intelligence needs to collaborate with human employees and ample job opportunities are coming up for that.

This cutting-edge technology is providing a competitive edge to organizations in the cut-throat competitive tech-driven market. Industries are reaping so many benefits of the most popular disruptive technology that were unbelievable just a decade ago. The tech-driven future is here with Artificial Intelligence. Let’s explore some aspects to have a better understanding of why Artificial Intelligence is a game-changer in the 21^st century.

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Benefits of Artificial Intelligence

Smart decision-making process: The AI models collect enormous volumes of real-time complex data and transform into meaningful business in-depth insights for better productivity

Enhances customer engagement: The AI models help organizations to enhance customer engagement by providing a better understanding of the needs and wants of customers in the current market scenario

Eliminates human errors: AI models transform data efficiently and work back-end duties without any human intervention and potential human errors

Provides smart solutions: AI models can solve extremely complicated problems that organizations have to face to gain revenue efficiently

Improves cybersecurity: AI models can improve cybersecurity and protect confidential data and information from malicious hackers and ransomware

Automation services: AI models provide automation services and perform repetitive boring tasks within a short period of time by helping employees to focus on other priority work

Protects human employees: AI models perform activities in the high risky environment while protecting human employees from hazardous limitations or disasters

Available 24*7: AI models are active and available throughout a year to perform their duties for organizations or industries to protect data from cyberattacks and detect any unusual activity on-premises

Interactive Chatbots: Artificial Intelligence has produced interactive Chatbots for faster response to multiple different types of queries from customers to meet their satisfaction for brand loyalty

Recent Applications of Artificial Intelligence in Real-world

Marketing: AI models are helping to deliver highly personalized promotions to attract potential customers through facial recognition, content marketing, campaigns, Chatbots, and understanding customer behavior

Automotive: AI models are driving automotive companies to develop autonomous vehicles with the integration of Augmented Reality and Virtual Reality to enhance the in-vehicle experience to reach destinations without any hindrance

Agriculture: AI models provide farmers with sufficient information on analyzing weather forecasts, defects, and deficiencies in crops and soils as well as robots to help them during harvesting

Gaming: AI models are assisting game developers to use game-playing agents to create smart and actionable games while predicting human player actions

Human Resource: AI models have started helping human resource departments in organizations to interview candidates with appropriate questions and recruit the right candidate by filtering all the profiles

Healthcare: There is immense assistance from Artificial Intelligence in the healthcare sector, especially post-pandemic with virtual assistants, robotic surgeons, detecting early symptoms of diseases, alerting patients to have medicines at the right time, and many more

Manufacturing & Retail: AI models are helping tremendously to manufacturing and retail post-pandemic with a better understanding of customer behavior through social media accounts, websites, browsing history, virtual shopping assistants, Chatbots, cybersecurity, and so on

Future Market Scope of Artificial Intelligence

Artificial Intelligence will be thriving more in the nearby future with two aspects— job opportunities as well as innovating cutting-edge technologies. Artificial Intelligence provides multiple job opportunities with lucrative salary packages such as Big Data engineer, business intelligent developer, data scientist, machine learning engineer, data analyst, AI engineer, software engineer, and many more fields to kickstart professional careers. According to MarketsandMarkets, the global Artificial Intelligence market size is US$58.3 billion in 2021 and is expected to hit US$309.6 billion in 2026 with a CAGR of 39.7%.

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Analytics Insight explains the unexpected challenges from Explainable AI in 2021.

The emergence of cutting-edge technologies has introduced another form of AI known as Explainable AI or XAI in the global market. It is a set of frameworks that help human users understand and trust the interpreted predictions and solutions from machine learning algorithms. The advancements of AI technologies are creating challenges for humans to comprehend the entire process of receiving specific outcomes from these machine learning algorithms. The black box models are created from real-time data that are making it impossible for humans to understand the calculation process. Sometimes the functionalities of ML models or neural networks are difficult to comprehend due to the complicated process. But it is essential for companies and start-ups to have a complete understanding of the rapid decision-making process. It is not often suggestible to blindly trust the AI models because their performance can change if there is a shift in the type of data or biased results based on the demographic and geographic segments. Thus, Explainable AI is the key requirement to promote end-users trust in large-scale implementation of AI models with appropriate explainability and accountability.

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Explainable AI helps organizations to make the stakeholders understand the types of behaviors of AI models through monitoring model insights. There are multiple benefits of Explainable AI such as simplifying the complicated process of model evaluation, continuous monitoring and managing AI models to optimize business insights, and mitigating risks of unintended bias by keeping the models explainable and transparent. That being said, certain concerns with Explainable AI are rising too.

The first concern is the primary function of Explainable AI – explanation with transparency. This policy is becoming a threat for organisations that are continuously innovating new AI models or technologies with machine learning algorithms. The reason is that the creators have to explain and be transparent about the whole process and performance of the whole model to the stakeholders for a better understanding. The firms do not want to disclose all types of confidential information, trade secrets, and source codes to the public for security concerns. Then what will happen to the intellectual property rights that distinguish each company from one another? This is one of the unexpected challenges from the Explainable AI to innovators and entrepreneurs.

The second concern is that machine learning algorithms are highly complex and intangible in nature. Software developers or machine learning engineers can make common people understand the process of creating algorithms but the inner tangible process is very difficult to explain. Customers use these AI products subconsciously in their daily life such as face recognition locks, voice assistants, virtual reality headsets, and so on. But do they really need to know the complicated process in this fast-paced life? This information tends to become a little uninteresting and time-consuming to some stakeholders.

The third concern is for organizations to tackle different forms of explanation for different users with different contexts. Even if any company wants to follow the Explainable AI policy of making people understand the algorithms, different stakeholders can ask about different explanations such as technical details, functionalities, data management, factors affecting the result, and so on. The explanation should reflect the needs and wants of the stakeholders effectively for better stakeholder engagement. But sometimes it is impossible for organizations to answer so many questions at one time.

The fourth concern is receiving unreliable outcomes from these black boxes. Users should trust business insights from AI models, but it consists of potential risks. The system can generate misleading explanations due to a change in data. Then, the users will trust the error with utmost confidence that can lead to a massive failure in the market. These explanations are useful for the short term but not for the long-term plans.

That being said, despite the unexpected challenges from Explainable AI, companies can consider these five essential points to drive appropriate insights from AI models— monitor fairness and debiasing, analyze the models to drift mitigation, apply model risk management, explain the dependencies of machine learning algorithms as well as deploy the projects across different types of clouds.

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No second thoughts to the fact that today, the world runs on data. Irrespective of whether you want to create something or come up with better decisions, the dependence on data is insanely high. Well, data doesn’t stop there. It continues to rise, way beyond one’s imagination. Simply put, this data is humungous – thanks to the technology rich world that we are living in. gone are the days when data in petabytes was addressed. Now is the era of zettabytes. Just about a decade ago, the data across the world was limited to a few zettabytes. But, today we’ve crossed a century already!

Data forms the backbone of every possible field that you can think of. Companies and industries rely on data to formulate their business strategies. Following this, they carry on with their day-to-day operations. Where to invest, how much to invest and why to invest – all of this revolves around data. It is only by virtue of data that companies are able to apply the right marketing strategies that makes it possible for them to align with what the customers expect. Doing so, not only increases their customer base but also helps them in creating value and making profits at the same time. Healthcare, finance, education, defence, you name it and you learn how each and every sector relies on data to draw meaningful insights. Since, it is impossible to think of a life without data and how critical it is to lead our lives, the scope that this field has is immense, without a doubt. The rising demand for data-related jobs is hence not surprising.

This is where ‘data scientist’ as a job comes into picture. When Harvard Business Review itself has termed the role of a data scientist as “the sexiest job of the 21st century”, is there any other validation required?

Data scientists are concerned with transforming the raw, unstructured data into a form that it is possible to draw meaningful insights from the same. The data obtained should be such that the company is in a position to take better decisions and take the business to new heights. But, what is observed across the globe is that there is a huge gap between what the organizations demand and what the candidates are capable of delivering. It is because of this that the number of vacancies in these jobs is huge.

All this throws light on how can one get skilled to be able to fit in the role of a data scientist. Yes, it does require some effort to be put in. But, all this would be worth it, for sure.

Here’s everything that could help you grab the sexiest job of the century

• Usually, graduation in the field of engineering, computer science, statistics are given importance to. But, on the bitter side, a degree alone might not be enough.

• You need to understand what sets a data scientist apart from the rest, what are those unique skills that are required.

• Companies are more interested in the skill-set that the candidates possess. A clarity on the skills desired and working on the same is the key here.

• Yes, coding skills are critical but what you need to understand here is that data scientists typically need to be adept at programming languages such as Java, Python, SQL, R, and SAS.

• Techniques such as decision trees, neural networks, clustering, etc. play a pivotal role in the day-to-day activities of a data scientist. Hence, a sound knowledge about them helps.

• How can one not talk about Big Data frameworks while talking about data scientists? A good knowledge about Big Data frameworks such as Hadoop, Spark, and Pig, to name a few would help you land a job faster.

• Data scientists should also be familiar with technologies like deep learning, machine learning, etc.

• Since, technology keeps improving daily, it is expected that data scientists are in line with what’s happening on the technological front.

Since, it is evidently seen that data is growing exponentially, the dependence of the companies on data will continue till eternity. Data science is meant to stay and this is why the requirement of the right people who’d manage this humungous data in the best possible manner is the need of the hour. All that needs to be done now is to stay updated and skilled in the relevant areas that’d pave the way for the sexiest job of the 21^st century.

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