AI & Machine Learning

Artificial Intelligence in its purest benchmark is referred to as a technology that strives to mimic human intelligence. So, at the dawn of a new era (will that be the 5th industrial revolution/evolution?) let’s stay pragmatic and try to learn more about what is going on, what Pragmatic AI can actually do for us today, what are the basic algorithms that allow machines to learn, where we stand with BMIs technologies and much more…

RPA works best when application interfaces are static, processes don’t change, and data formats also remain stable – a combination that is increasingly rare in today’s dynamic, digital environments. The problems with RPA, however, aren’t that the tools aren’t ‘smart’ enough. Instead, the challenge is more about resilience – dealing with largely unexpected changes in the IT environment. Adding cognitive capabilities to RPA doesn’t solve these resilience issues – you simply end up with smarter technology that is still just as brittle as before.

If you’re a developer or sys-admin you probably already use a lot of libraries and frameworks that you know little about. You don’t have to understand the inner workings of web-scraping to use curl. The same is true with AI. There are a number of frameworks and projects that make it easy to get going fast without needing a data science Ph.D. The math helps you feel confident about what’s going on behind the scenes. If you want to start using AI, you can do that today. Let’s get started with some practical projects.

There’s much confusion surrounding artificial intelligence and machine learning. Some people refer to AI and machine learning as synonyms and use them interchangeably, while other use them as separate, parallel technologies. In many cases, the people speaking and writing about the technology don’t know the difference between AI and ML. In others, they intentionally ignore those differences to create hype and excitement for marketing and sales purposes. This post disambiguates the differences between artificial intelligence and machine learning to help you distinguish fact from fiction where AI is concerned.

You’re fascinated with AI. Maybe you’d love to dig deeper and get an image recognition program running in TensorFlow or Theano? Perhaps you’re a developer or systems architect and you know computers incredibly well but there’s just one little problem: You suck at math. That’s all right! Here are some little secrets that help you get rolling fast. No matter how you cut it, AI solves big, intractable problems that have eluded us for decades. AI is hot for good reasons. 

As artificial intelligence (AI) works its way into mainstream business practices, various different applications are coming up in conversations about how to best leverage the technology. In observing these conversations, I notice some writers using the terms machine learning (ML) and deep learning (DL) interchangeably. The two are actually different concepts in terms of the business problems they solve and the resources they require, and confusing them could lead to unwanted — and costly — results. Let’s take a moment to set the record straight.

When someone asks you to guess a person’s age, your mind starts to answer all sorts of questions regarding the person’s demographics, and then finally you take a guess. Without even being asked, the human mind inter-relates different abstract concepts that add up together to answer a specific question. That is the essence of learning, and in Machine Learning, we call this a Multi-Task Learning Problem. In Multi-Task Learning, we train our machine to answer multiple questions simultaneously, and in the process, features useful for multiple tasks are shared in our model. 

Considering the many, significant achievements, problems that have less to do with categorization and more to do with commonsense reasoning essentially lie outside the scope of what deep learning is appropriate for. Humans integrate knowledge across vastly disparate sources and as such, are a long way from the sweet spot of deep learning-style perceptual classification. What we are missing is a sort of operating system that brings together all the different techniques, orchestrating the deriving abilities to deliver something closer to ‘Pure AI’. A Cognitive Operating System for a brand-new generation of Artificial Intelligence.

Manufacturing and Distribution, the supply chain, does not always get as much publicity as the latest technology trend or the newest financial vehicle threatening to bring the global economy to its knees. Nonetheless, it remains the backbone of the economy. Manufacturing and distribution is also a hotbed of AI development. There are innumerable points along the supply chain where Artificial Intelligence can add value. Here are a few examples.

There are four major ways to train deep learning networks: supervised, unsupervised, semi-supervised, and reinforcement learning. We’ll explain the intuitions behind each of these methods. Along the way, we’ll share terms you’ll read in the literature in parentheses and point to more resources for the mathematically inclined. By the way, these categories span both traditional machine learning algorithms and the newer, fancier deep learning algorithms.

Financial Services is all about data- it’s one of the most data-rich industries out there. Banks and Insurance companies are always processing and analyzing data in the hopes of providing better service and making better decisions. With all this data, there are an inordinate number of potential AI applications, including Customer Service, Trading, Risk Management, and Regulatory. From banking to brokerage to insurance, AI offers new solutions to improve customer satisfaction, reduce costs, increase revenue, and ultimately increase profitability in the financial services industry.