Role of Data Science in Artificial Intelligence

Role of Data Science in Artificial IntelligenceKaren LinBlockedUnblockFollowFollowingFeb 18Steve Urkel and Urkelbot, whose intelligence doubled every 2 minutes.

Image Credit: ABC’s Family MattersThe age of spreadsheet is over.

A google search, a passport scan, your online shopping history, a tweet.

All of these contain data that can be collected, analyzed, and monetized.

Supercomputer and algorithms allow us to make sense of an increasingly large amount of information in real time.

In less than 10 years, CPUs are expected to reach the processing power of the human brain.

With the rise of big data and fast computing power, many CEOs, CTOs, and decision makers of organizations are thinking of ways to innovate their company.

When they want to launch a new product or service, they are looking to data analytics for insights on market, demand, the target demographic, etc.

Artificial Intelligence and machine learning are being adopted into the enterprise at a rapid pace.

This trend is only likely to surge upward.

Let’s look at some statistics:According to IDC, global spending on AI and cognitive technologies will reach $19.

1 billion in 2018, up 54.

2 percent compared to a year ago.

By 2021, AI and cognitive spending will hit $52.

2 billion.

AI skills are among the fastest-growing skills on LinkedIn, and saw a 190% increase from 2015 to 2017.

When we talk about “AI skills”, we’re referring to the skills needed to create artificial intelligence technologies, which include expertise in areas like neural networks, deep learning, and machine learning, as well as actual “tools” such as Weka and Scikit-Learn.

Wanted: Artificial intelligence expertsIn artificial intelligence, job openings are rising faster than job seekers.

AI-related jobs include machine learning engineer, predictive modeler, cmt analytics manager, data scientist, computer vision engineer, computational linguist, and information strategy manager.

 Source: Indeed.

comThere’s no slowing down the spread of AI.

Tech companies are heavily investing in it, and a PwC report estimates that artificial intelligence could add $15.

7 trillion to the global economy by 2030 — and boost North America’s GDP by 14% that year.

Perhaps the most compelling aspect about Machine Learning is its seemingly limitless applicability.

There are already so many fields being impacted by ML and now AI, including Education, Finance, and more.

Machine Learning techniques are already being applied to critical areas within the Healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis.

What is AI exactly?Artificial intelligence is the general field of “intelligent-seeming algorithms” of which machine learning is the leading frontier at the moment.

Our definition changed over time what AI exactly is.

We have come a long way from the 1939 Smart Fellow Robot as seen in this footage below.

Source: 1939 I AM A SMART FELLOW ROBOT TALKS Stock FootageAI is just a computer that is able to mimic or simulate human thought or behavior.

Within that, there’s a subset called machine learning that is now the underpinning of the most exciting part of AI.

By allowing computers to learn how to solve problems on their own, machine learning has made a series of breakthroughs that once seemed nearly impossible.

It’s the reason that computers can spot a friend’s face in a photo or steer a car.

It’s the reason people are actively talking about the arrival of human-like AI.

A simplified explanation of AI, Machine Learning, and Data Science.

Source: Suraj Jena, June 10, 2018So, how Does Machine Learning and Data Science Intersect?Machine learning is a branch of artificial intelligence where a class of data-driven algorithms enables software applications to become highly accurate in predicting outcomes without any need for explicit programming.

The basic premise here is to develop algorithms that can receive input data and leverage statistical models to predict an output while updating outputs as new data becomes available.

The processes involved have a lot in common with predictive modeling and data mining.

This is because both approaches demand one to search through the data to identify patterns and adjust the program accordingly.

Most of us have experienced machine learning in action in one form or another.

If you have shopped on Amazon or watched something on Netflix, those personalized (product or movie) recommendations are machine learning in action.

Data science, on the other hand, employs computer science disciplines like mathematics and statistics and incorporates techniques like data mining, cluster analysis, visualization, and — yes — machine learning.

So, the main difference between the two is that data science as a broader term not only focusses on algorithms and statistics but also takes care of the entire data processing methodology.

Machine learning is a subset of artificial intelligence.

While data science is an interdisciplinary field to extract knowledge or insights from dataVENN diagram of AI, Big Data and Data Science Fraunhofer FOKUSExamples of how the field of data science is used in AI technologiesIBM Watson is an AI technology that helps physicians quickly identify key information in a patient’s medical record to provide relevant evidence and explore treatment options.

It takes in a patient’s medical records then provides its evident-based and personalized recommendation fueled by information from a curated collection of 300 + journals, 200 textbooks, and 15+ pages of texts which give doctors instant access to a wealth of information personalized to the patient’s treatment plan.


This robot can perform improv comedy after being fed subtitles from hundreds of thousands of movies.

Kory Mathewson, an artificial intelligence researcher at the University of Alberta, Edmonton, created an algorithm designed to riff with him onstage.

He trained it to create lines of dialogue to be used in an improv performance by rewarding it when the dialogue makes sense and punishing it when it spits out gibberish.

While Blueberry will not be auditioning at The Second City anytime soon, this adorable robot does occasionally hit the right note with funny lines.

I’ll end this showing a short clip of Blueberry in action.

Kory Mathewson, inventor of Blueberry.

Video Credit: Bloomberg, Hello World, Is AI Ready for Improv ComedyReferences:“As Companies Embrace AI, It’s a Job-Seeker’s Market”, Ann Saphir, Oct 15, 2018“Can Artificial Intelligence Replace Data Scientists?”, Pedro Uria-Recio, Sept 14, 2018“Is AI Ready for Improv Comedy”, Bloomberg Business Week, Hello World Season 1 Episode 16, June 11, 2018“Expert Talk: Data Science vs.

Data Analytics vs.

Machine Learning”, Sarihari Sasikumar, Oct 18, 2018“I.

Putting the ‘G’ in ‘AI’: An Overview of Terms used in (Narrow/Applied) AI- and what they mean to each other”, Suraj Jena, June 10, 2018“Artificial Intelligence and Machine Learning in the Media Sector”, Fraunhoker Fokus“AI to Drive GDP Gains of $15.

7 Trillion with Productivity, Personalisation Improvements”, PWC Pressroom, June 27, 2017“Worldwide Spending on Cognitive and Artificial Intelligence Systems Will Grow to $19.

1 Billion in 2018, According to New IDC Spending Guide”, IDC, Mar 22, 2018“LinkedIn 2018 Emerging Jobs Report”, LinkedIn, Dec 13, 2018.

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