The era of spreadsheets is over. Google search, passport scan, your online shopping history, a tweet. All of these contain data that can be collected, analyzed, and monetized. Supercomputers and algorithms make it possible to make sense of ever-increasing amounts of information in real time. In less than 10 years, microprocessors will reach the processing capacity of the human brain.

With the rise of big data and rapid computing power, many CEOs, CTOs, and organizational decision makers are thinking about innovative ways for their businesses to grow and adapt to modern trends. When they want to launch a new product or service, they turn to data analytics to better understand the market, needs, target population, and more. Artificial intelligence and machine learning are being adopted in business at a rapid pace. This trend is only expected to accelerate.

Let’s look at some statistics:

According to IDC, global spending on AI and cognitive technology reached $19.1 billion in 2018, up 54.2% from the previous year. In 2021, spending on artificial and cognitive intelligence reached $52.2 billion and it will help in boosting the data management strategy of organizations. 

AI skills are among the fastest growing and the most in-demand skills on LinkedIn and have seen a huge growth between 2020 and 2021 and the interest in data science is steadily on the rise.

When we talk about “AI skills,” we are referring to the skills needed to create artificial intelligence technology, including expertise in areas such as neural networks, deep learning, data management, cloud computing, data storage, data security management. AI-related jobs include machine learning engineer, enterprise security solutions, cloud services provider, predictive modeler, analytics manager, data scientist, data engineer, computer vision engineer and computational linguist.

Cyber security companies in UAE are investing heavily in AI, and a PwC report estimates that artificial intelligence could add $15.7 trillion to the global economy by 2030 – and boost GDP. Perhaps the most compelling aspect of machine learning is its seemingly limitless applicability. There have been a lot of Use Cases affected by ML and now AI in education, finance, data science, data backup, entertainment,  manufacturing and more. Machine learning techniques are being applied to critical areas of the healthcare sector, impacting everything from efforts to reduce differences in care to analyzing medical scans. AI is also being used to decipher national language and understand customer sentiment in Contact Center environments.

What is AI exactly?

Artificial intelligence is the general domain of “intelligent search algorithms”, where machine learning is the main frontier today. Our definition has changed over time about what exactly AI is. We’ve come a long way since the Smart Fellow robot in 1939.

AI is just a computer that can imitate or simulate human thinking or behavior. Inside that is a subset known as machine learning, which is now the foundation for the most interesting part of AI. By allowing computers to learn how to solve problems on their own, machine learning has made a series of breakthroughs that previously seemed almost impossible. That’s why computers can detect a friend’s face in a photo or control a car or recommend a video on YouTUbe which you are most likely to watch. YouTube algorithm seems to know our taste better than we know it ourselves because it studies an extensive history of videos watched, topics searched on google along with other data that points to our tastes and preferences. It does this through machine learning. This is why people are actively talking about the arrival of human AI. Experts say that there will come a time when AI enabled robots will be better able to diagnose illnesses and make better recommendations than doctors themselves because their ability to sift through billions of data points in minutes and inform decisions which is simply impossible for humans to replicate. 

So, how Does Machine Learning and Data Science Intersect?

Machine learning is a branch of artificial intelligence where a class of data-driven algorithms allows software applications to become highly accurate in predicting outcomes without explicit programming. The basic principle here is to develop algorithms that can take input data and leverage statistical models to predict outputs while updating outputs as new data becomes available.   

So, the key difference between the two is that data science as a broader term focuses not only on algorithms and statistics but also supports the entire methodology of data processing. Machine learning is a subset of artificial intelligence while data science is an interdisciplinary field to extract knowledge or insights from data.

Examples of how the field of data science is used in AI technologies

IBM Watson is AI technology that helps doctors quickly identify important information in a patient’s medical record to provide relevant evidence and explore treatment options. It reviews the patient’s medical records and then provides personalized and evidence-based recommendations, powered by information from a curated collection of more than 300 journals, 200 textbooks and more than 15 pages of text giving physicians instant access to a wealth of personalized information.

In 1956, IBM introduced the first commercial computer with a magnetic hard drive, the 305 RAMAC. The entire device required 30 feet by 50 feet of physical space, weighed over a ton, and cost $3,200 per month. Businesses could rent it to store up to 5MB of data. It was a type of “cloud service” of that time. In the 60 years since, the price per gigabyte of DRAM fell from a whopping $2.64 billion in 1965 to $4.9 in 2017. In addition to being significantly more affordable, data storage has also become much denser/smaller.

This combination of greatly reduced cost and data storage size is what makes big data analytics possible today along with the availability of high speed parallel processors. With extremely low storage costs, building a data science infrastructure to collect and extract insights from massive amounts of data has become a cost-effective approach for businesses. And with an abundance of IoT devices constantly generating and transmitting user data, companies are collecting data on an ever-increasing amount of activity, creating a massive amount of information assets at speed,high volume, high velocity, and high variety (or the “three Vs of big data”). Most of these activities (e.g. email, video, audio, chat messages, social media posts) generate unstructured data, which today accounts for almost 80% of all business data and growing twice as fast as structured data over the past decade.

AI/ML is here to stay and will work in conjunction with other digital transformation drivers such as Cloud, Data Science, Robotic Process Automation and Business Analytics to drive innovation and competitiveness in business enterprises. After accumulating data over decades and most of it lying dormant “just-in-case” it is required for legal or auditing purposes, companies can finally mine it for the gold (business insights) which can give them a competitive edge in the market, invent new products or services and provide a level of tailored customer service that was never possible before the advent of AI/ML. AI/ML is now becoming almost ubiquitous and is being embedded in most technology products. Major cloud service providers are now offering AI/ML as a service for very affordable prices and this is very attractive to companies who might not have the resources to deploy and manage all the infrastructure required in their on-premise data center. By subscribing to a AI/ML service, companies can focus on Data Science rather than worrying about the underlying infrastructure. They can create, train and publish working models that deliver real value to their business consumers. Today, even enterprise data management vendors like Netapp are building features into their products to make them AI/ML friendly so customers can utilize their on-premise storage systems as well as Cloud Storage to facilitate Data Science and Business Analytics projects.