AI & Machine Learning

The AI market specifically for oil and gas is expected to reach USD 2.85 billion by 2022. It’s growing fast as more companies realize the potential of the technology. Artificial intelligence is being used to discover new gas and crude oil sources, optimize various industrial processes such as the transport of raw oil and even make more positive environmental decisions. How are oil and gas companies putting AI technologies to use in today’s market? To break it down, we’re going to take a look at the three most important sectors in oil and gas: upstream, midstream and downstream applications.

It is heady days for deep learning with the stellar advances and infinite promises. But, to translate this unbridled power into business benefits on the ground, one must watch out for these five pitfalls. Ensure availability of data, feasibility of labeling them for training, and validate the total cost of ownership for business. You may wonder when deep learning must be used vis-a-vis other techniques. Always start with simple analysis, then probe deeper with statistics, and apply machine learning only when relevant. When all these fall short, and the ground is ripe for some alternate, expert toolsets, dial in deep learning.

Artificial intelligence (AI) is giving customer experience a shot in the arm. There are some clear front runners – organisations embracing the power of AI to solve consumer pain points such as in banking and financial services. These front runners differ in many ways; they discuss a set of key practices they should follow to differentiate themselves while building a customer experience strategy for an AI-driven environment. They aim for a holistic approach to deploying AI in customer experience. 

Well, the biggest advantage of deep learning is really its shortcoming. The very fact that humans don’t have to identify distinguishing features means that the machine defines what it deems important. Interpretability of deep learning algorithms and visual explanation of results is a rapidly evolving field, and research is fast catching up. And yes, it needs tons of data to even get started. So yes, there are some hiccups in this area, but the stellar and stable results clearly outweigh the cons, for now.

With robots becoming more and more competent and intelligent, there are more applications for them than ever in a variety of industries. That’s got a lot of people worried that robots are getting ready to take over their jobs. It’s understandable, of course, to fear that you might become obsolete in the workplace—your ability to earn an income depends on being able to find a job. In the debate over robots in the workplace, there’s a lot to think about. Will robots eventually take over most jobs? Is your job really in jeopardy? Let’s take a look.

You probably knew that there are two types of machine learning. Supervised and unsupervised. Well, there is a third one, called Reinforcement Learning. RL is arguably the most difficult area of ML to understand because there are so many things going on at the same time. It is a really astonishing area and you should definitely know about it. It involves complex thinking and 100% focus to grasp it, and some math. 

Now that you know what machine learning is, let’s meet the easiest kind. My goal here is to get humans of all stripes and (almost) all ages comfy with its basic jargon: instance, label, feature, model, algorithm, and supervised learning. You’re dealing with supervised learning if the algorithm has the correct label handy for every instance. Later, it will use the model, or recipe, to label new instances.

Machine learning uses patterns in data to label things. Sounds magical? The core concepts are actually embarrassingly simple. How does it actually work? If you were expecting magic, well, the sooner you’re disappointed, the better. Machine learning may be prosaic, but what you can do with it is incredible! It lets helps you write the code you couldn’t come up with yourself, allowing you to automate the ineffable. Don’t hate it for being simple. Levers are simple too, but they can move the world.

Disruptive AI technologies are significantly boosting creative productivity, and it’s not just happening in the movie business. All companies from big to boutique shops need to create content to connect with their clients. Business owners, content marketers, investors, anyone with a story to tell but little time to tell it, will soon have AI-powered tools to create high-quality content at a much faster rate. AI systems are still a long way from encoding the visceral and emotional knowledge of humans.

Ecommerce businesses often struggle to convert online browsers into actual shoppers due to their inability to replicate the traditional “physical shopping” experience that most buyers are accustomed to. Artificial intelligence is, however, rewriting this script by helping ecommerce businesses to not only attract but also retain customers. Businesses cannot, therefore, ignore the powerhouse that AI is growing to become. That is why, e-commerce websites must considerer seriously implementing AI into their businesses and enjoy its benefits. Here are five ways through which AI is achieving this.

There is an explosion of interest in data science today. One just needs to insert the tag-line ‘Powered-by-AI’, and anything sells. But, that’s where the problems begin. Here we’ll talk about the 8 most common myths I’ve seen in machine learning projects, and why they annoy data scientists. If you’re getting into data science, or are already mainstream, these are potential grenades that might be hurled at you. Hence, it would be handy knowing how to handle them.