AI & Machine Learning

Creating a great machine learning system is an art. There are a lot of things to consider while building a great machine learning system. But often it happens that we as data scientists only worry about certain parts of the project.But do we ever think about how we will deploy our models once we have

Machine learning problems can generally be divided into three types. Classification and regression, which are known as supervised learning, and unsupervised learning which in the context of machine learning applications often refers to clustering.In the following article, I am going to give a brief introduction to each of these three problems and will include a walkthrough

In a recent McKinsey survey, only 13 percent of CFOs and other senior business executives polled said their finance organizations use automation technologies, such as robotic process automation (RPA) and machine learning. What’s more, when asked how much return on investment the finance organization has generated from digitization and automation in the past 12 months, only 5

The original vision of the pioneers of artificial intelligence was to create human-level AI, machines that could understand, reason, and act like humans. But six decades of research have proven that artificial general intelligence (AGI) is a very tough nut to crack.The field has seen tremendous progress, especially with an explosion of innovation in deep learning and neural

For simplicity, let’s assume there are three customers (c1, c2, c3) in this batch, and one vehicle (v1) information is provided as a sale. P(C=c1) represents the likelihood of c1 to buy any car. Assuming no prior knowledge about each customer, their likelihood of buying any car should be the same: P(C=c1) = P(C=c2) =

Bringing together knowledge from the business, a function or process, data and AI technology, and developing a governance structure where everyone works in unison, will help foster successful AI initiatives. Approaching AI as a shared capability will enable you to develop an ecosystem of partnerships, generate common best practices and create reusability. AI is a two-part journey: getting ready and applying. Organizations that prepare for the AI evolution will be able to access and embrace the future and surpass their competition in the process.

Artificial intelligence seems to be popping up everywhere. However, many of the boldest claims for artificial intelligence have not yet come to fruition.  And therein lies a key takeaway for anyone wanting to find success with AI: artificial intelligence can’t yet do things that humans can’t do. In twenty years, we will be having an entirely different conversation about AI—and hopefully reflecting on the many new insights and innovations this technology has contributed to the world.

By 2021, some of the capabilities offered by RPA platforms may well be commoditized, driving the technology costs to the floor. Companies will each create thousands of digital workers in the coming years. These digital workforces will be anchored into the RPA platforms where they run.  Businesses face some important choices around RPA. Navigating in the RPA realm is a daunting journey for many. This article explores some practical questions to make your RPA journey easier.

Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert. Such anomalies can usually be translated into problems such as structural defects, errors or frauds. Anomaly detection alone or coupled with the prediction functionality can be an effective means to catch the fraud and discover strange activity in large and complex datasets.

What about AI that benefits humans’ morality, our own capacity to behave virtuously? Designing technologies to encourage ethical behavior raises the question of which behaviors are ethical. To what degree should we focus on helping people develop a moral compass and fortitude, and to what degree should we focus on nudges and social platforms that make morality easy? What’s clear is that just as we define our technologies, they define us. AI systems could invite us to reflect privately upon the sort of person we think we are or want to be.

In the 2010s, automation got better, cheaper, and way less avoidable. It’s still mysterious, but no longer foreign. On the other hand, each of us now sees a personalized version of the world that is curated by an AI to maximize engagement with the platform. Humans and tech have always coexisted and coevolved, but this decade brought us closer together—and closer to the future—than ever. So here’s how we changed our bots this decade, how they changed us, and where our strange relationship is going as we enter the 2020s.