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5 Amazing Examples of Artificial Intelligence in Action

As scientists and researchers strive harder to make Artificial Intelligence (AI) mainstream, this ingenious technology is already making its way to our day to day lives and continues ushering across several industry verticals. From voice-powered personal assistants like Siri and Alexa to autonomously-powered self-driving vehicles, AI has been rearing itself as a force to be reckoned with. Many tech giants such as Apple, Google, Facebook, and Microsoft have been making huge bets on the long-term growth potential of Artificial Intelligence.

According to a report published by the research firm Markets and Markets, the AI market is expected to grow to a $190 billion industry by 2025. More and more businesses are looking to boost their ROI by leveraging the capabilities of AI. In this blog post, we are going to list out the applications of AI in use today. 

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2020 AI, Big Data, and Analytics Predictions

Data is the key to the future. It's (allegedly) the source of every tech company's decisions, and it's become an essential component not only of the software industry, but verticals around the world. The relationship between Big Data, AI, and analytics is well-known at this point, so let's look at where industry experts see these technologies going in 2020.

  • Sara Faatz, Sr. Product Marketing Manager, Progress: AI and ML get top billing: The increased popularity of artificial intelligence (AI) and machine learning (ML) and its promise to automate everyday tasks means developers with these skills and expertise are in high demand. AI and ML are the brains of the smarter applications, and through these technologies the apps learn from patterns of behavior and are able to more intelligently respond or produce an action. There is so much that can be done with the data that is collected by modern organizations.

    Since organizations are beginning to prioritize the implementation of AI and ML across the entire business, developers who understand how to build, implement and use AI and ML effectively – and understand that these are powerful tools – will be in high demand in 2020.

  • Haoyuan Li, founder and CTO, Alluxio: Hadoop storage (HDFS) is dead. Hadoop compute (Spark) lives strong. There is a lot of talk about Hadoop being dead... but the Hadoop ecosystem also had many rising stars. These were the compute frameworks like Spark that extracted more value from data. Others like Presto have also been adopted into the broader compute ecosystem. So today’s Hadoop has been broken up. Hadoop storage (HDFS) is dead because of its complexity and cost and because compute fundamentally cannot scale elastically if it stays tied to HDFS. To glean immediate, real-time insights, users need immediate and elastic compute capacity that’s plenty available in the cloud. Data in HDFS will move to the most optimal and cost-efficient system be it cloud storage or on-prem object storage. HDFS will die but Hadoop compute will live on and live strong.

  • Tomer Shiran, CEO and co-founder, Dremio: The rise of data microservices for bulk analytics. Traditional operational microservices have been designed and optimized for processing small numbers of records, primarily due to bandwidth constraints with existing protocols and transports. But now this long-standing bottleneck issue has been solved with the arrival of Apache Arrow Flight, which provides a high performance, massively parallel protocol for big data transfer across different applications and platforms. We predict that in 2020 Arrow Flight will unleash a new category of data microservices focused on bulk analytical operations with high volumes of records, and in turn, these data microservices will enable loosely coupled analytical architectures that can evolve much faster than traditional monolithic analytical architectures.

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Future Trends in Data Analytics

The future of business is data

Data is exploding: the IDC says data is growing at 40% annually. By 2025, there will be 175 zettabytes —that’s 175 sextillions bytes-of data floating around the world.

To harness that data and use it to create a competitive advantage can be quite daunting. One way forward-thinking organizations have responded to the challenge is by focusing on streaming data.

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Anomaly Detection Using the Bag-of-Words Model

I am going to show in detail one use case of unsupervised learning: behavioral-based anomaly detection. Imagine you are collecting daily activity from people. In this example, there are six people (S1-S6). When all the data are sorted and pre-processed, the result may look like this list:

  • S1 = eat, read book, ride bicycle, eat, play computer games, write homework, read book, eat, brush teeth, sleep
  • S2 = read book, eat, walk, eat, play tennis, go shopping, eat snack, write homework, eat, brush teeth, sleep
  • S3 = wake up, walk, eat, sleep, read book, eat, write homework, wash bicycle, eat, listen music, brush teeth, sleep
  • S4 = eat, ride bicycle, read book, eat, play piano, write homework, eat, exercise, sleep
  • S5 = wake up, eat, walk, read book, eat, write homework, watch television, eat, dance, brush teeth, sleep
  • S6 = eat, hang out, date girl, skating, use mother's CC, steal clothes, talk, cheating on taxes, fighting, sleep

S1 is the set of the daily activity of the first person, S2 of the second, and so on. If you look at this list, then you can pretty easily recognize that activity of S6 is somehow different from the others. That's because there are only six people. What if there were six thousand? Or six million? Unfortunately, there is no way you could recognize the anomalies. But machines can. Once a machine can solve a problem on a small scale, it can usually handle the large scale relatively easily. Therefore, the goal here is to build an unsupervised learning model that will identify S6 as an anomaly.

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How a Data Science Company Uses Comics to Make Data Less Complicated

You could be the best writer in the world, but no one will receive your message if they don't read your work. Writers often struggle to write stories about complex, data-driven topics in a way that engages an audience who won't read a report, long article, or deep-dive analysis. One way to communicate with less effort is graphics, pictorials, and cartoons.

I spoke to Naveen Gattu, Chief Operations Officer at Gramener about their efforts to tell insightful stories from data. Gramener was founded in 2010 by a team of ex-IBMers. The founding team had mostly worked together in the 1990s and then went their own ways — mostly into IT services and consulting firms, such as Birlasoft, Infosys, Accenture, BCG, etc. In 2010, the team decided to venture into their own enterprise. Gattu explained:

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What Happened to Hadoop? What Should You Do Now?

Apache Hadoop emerged on the IT scene in 2006 with the promise to provide organizations with the capability to store an unprecedented volume of data using commodity hardware. This promise not only addressed the size of the data sets, but also the type of data, such as data generated by IoT devices, sensors, servers, and social media that businesses were increasingly interested in analyzing. The combination of data volume, velocity, and variety was popularly known as Big Data.

Schema-on-read played a vital role in the popularity of Hadoop. Businesses thought they no longer had to worry about the tedious process of defining which tables contained what data and how are they connected to each other — a process that took months and not a single data warehouse query could be executed before it was complete. In this brave new world, businesses could store as much data as they could get their hands on in Hadoop-based repositories known as data lakes and worry about how it is going to be analyzed later.

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Comparing 11 IoT Development Platforms

1. Abstract

This article presents a general survey of the current IoT software platform landscape based on a detailed analysis we conducted on IoT vendors. We first create a list of key features which are important for any IoT software platform. Next, we compare the extent to which those key features have been implemented in the current IoT software platforms. Finally, we list down the desired features of an IoT software platform based on our observations.

2. Introduction

The Internet of Things (IoT) has undergone rapid transformation since the term was first coined in 1999 by Kevin Ashton. Since the variety — and the number — of devices connected to the Internet has increased exponentially in recent years, IoT has become a mainstream technology with a significant potential for advancing the lifestyle of modern societies.

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Waking Up the World of Big Data

The term "Big Data" has lost its relevance. The fact remains, though: every dataset is becoming a big data set, whether its owners and users know (and understand) that or not. Big data isn't just something that happens to other people or giant companies like Google and Amazon. It's happening, right now, to companies like yours.

Recently, at Eureka!, our annual client conference, I presented on the evolution of Big Data technologies including the different approaches that support the complex and vast amount of data organizations are now dealing with. In this post, I'll break down some of my presentation and dig into the current state of Big Data, the trends driving its evolution, and one major shift that'll deliver up massive value for companies in the next wave of Big Data's growth.

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Improving Analytics With a Hybrid Cloud Workflow

The public cloud has changed computing forever. It moves information technology into a world of utility where compute and storage are available as needed — easy to implement and decommission. It provides a flexible infrastructure for a data-centric world increasingly based on analytics, where experimentation is the foundation of digital transformation.

Analytics are a complex workflow that relies on both large data sets to take advantage of historic data for analytic models, and also high performance for making timely decisions and generating more iterations to derive deeper insights to your data.

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Splunk Connected Experiences: The Power of Splunk Wherever You Are

With an always-on life—and one where your systems are also ‘always on’—you need to be able to act and even interact with your data to run your business day-to-day. We’ve already heard and continue to hear from customers the need to follow or stay plugged into their KPIs when they’re away from their desks. In response, Splunk’s engineering teams are doubling down on building new capabilities in mobile and augmented reality (AR) to bring your data to where you are—in the palm of your hands (that’s where you want it, right?). Through Splunk Connected Experiences (whose capabilities include the Splunk Cloud Gateway, Splunk Mobile, Splunk TV, and Splunk AR), we're making these game-changing advances available to apply to your most pressing business challenges.

Within the manufacturing industry, the AR revolution has already begun its early stages. There are low-hanging fruit opportunities to use AR to impact productivity gains on the factory floor, assist with visibility into the real-time health of machine assets, and coordination of field logistics. And as we continue to hear the refrain of the challenges associated with collecting and merging data from different systems, that’s where Splunk’s original secret sauce comes in to make all these new capabilities a seamless addition to your analysis.

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Augmented Analytics: The Future of Data and Analytics

With the rising need and importance for data, many next generation technologies and data processing tools are coming into the spotlight. Today, becoming data-driven is a key priority for many advanced organizations. In order to sustain a good position in the industry, organizations need to adopt an advanced data processing tool such as augmented analytics.

Augmented analytics uses Artificial Intelligence (AI) and machine learning to augment human efforts to evaluate data. It beats the traditional analysis tools by automating data insights and providing clearer information. According to Forbes, 89% of industry leaders believe that Big Data will transform business operations in the same way the Internet did. Also, enterprises that don’t implement a business intelligence (BI) strategy to gather, evaluate, and apply that information in a meaningful way will be left in the dust. Here’s where an advanced data analytical tool like augmented analytics comes into the picture. According to a report by Allied Analytics, due to the growing adoption of next-generation technologies, such as augmented analytics, the global augmented analytics market size is expected to reach $29 million by 2025.

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Tom’s Tech Notes: What You Need to Know About Big Data [Podcast]

Welcome to our latest episode of Tom's Tech Notes! In this episode, we'll hear advice from a host of industry experts on the most important things you need to know about big data. Learn some tips around data quality, big data app development, data governance, and more.

The Tom's Tech Notes podcast features conversations that our research analyst Tom Smith has had with software industry experts from around the world as part of his work on our research guides. We put out new episodes every Sunday at 11 AM EST.

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DataSecOps: Leveraging DevSecOps Principles for Secure Data Analytics

If you’re a developer, software tester, or IT Ops admin, you probably know all about DevSecOps. But what if you’re a data analyst? Do you feel left out of the DevSecOps revolution?

If so, you have not yet heard of DataSecOps. DataSecOps an approach to data analytics and storage that allows data engineers to benefit from the same principles and philosophies that the DevSecOps movement emphasizes.

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Google Colab: Create Predictive Models in No Time

To democratize data analytics and do all the data munging related heavy lifting, let's explore Google's Colaboratory, which is a Jupyter notebook environment that requires no setup and runs entirely on the cloud. Google's Colaboratory is a perfect solution for today's data analysts and engineers. In this article, we will see how we can use this amazing cloud-based platform and use a Random Forest model to predict customer churn in less than 200 lines of code.

Before we start, I would like to point out some great capabilities that Google Colab environment has in store for its users.

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The Growing Importance of Data Analytics in Sport

Sport is undoubtedly one sector that has taken data analytics to heart, with the success of the Oakland Athletics so beautifully illustrated in

This has been supercharged by the increase in wearable technology that has allowed so much more data to be generated about seemingly all aspects of athletic performance. A recent special issue of features a collection of articles highlighting the impact of data on various sports.

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Three Business Benefits of a Data-Driven Organization

Data is the new oil for forward-looking businesses to unlock the full potential of an entire organization, allowing all business units to make data-driven business decisions, and deliver personalized digital experiences to customers and stakeholders.

What Is Being a Data-Driven Enterprise All About?

Data-driven businesses rely on huge volumes of data – and smart analytics – to bolster and speed up business decision-making processes. By executing predictive data analytics, organizations can have superior data insights. Smarter and superior analytics technologies now empower businesses of all sizes to become more data-driven than ever before.

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IoT for Business Enterprises: Everything You Need to Know

Before digging out the details of IoT, let me explain what IoT actually means and how it has evolved over the years. To begin with, let's understand what IoT is. It is a device or software that connects the physical world to the digital. 

IoT allows access to things remotely; this could be any kind of gadgets or appliance, including things like TV, AC units, fans, doors, and other physical objects. At the same time, it not only helps people interact with things but also organizations and companies to maintain their business process. Importantly, as the potential of IoT enhances, a large number of business enterprises are realizing (some of them have already started using IoT applications) the need and are trying to learn how to implement this technology. According to research, more than 21 billion things will be connected through the IoT by 2020. In fact, IoT has the power to reincarnate your business. So, my aim here is to help you explore the benefits of IoT applications in business enterprises.

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What it Means to Be a Data-Driven Enterprise and How to Become One

What is a data-driven organizationIn today's digital economy, it is generally understood that businesses must become data-driven enterprises to improve business performance, create sustainable value for consumers, build and run more innovative and efficient businesses to deliver unprecedented levels of performance to remain competitive.

But How Exactly Does a Business Become Data-Driven Enterprise?

According to the McKinsey Global Institute report, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain those customers, and 19 times more likely to gain in profitability.