AI & Machine Learning

Automation, from robotic process automation to artificial intelligence, is transforming every function of every business in every industry. Despite the many indicators of a transforming marketplace, almost all legacy leaders and board members still hesitate to apply artificial intelligence to corporate strategy. Leaders of businesses that don’t move quickly to capitalize on the power of AI will be left behind. Adopting an AI powered strategy is the natural next step. No matter the application, the process is similar. Here are the four steps of AI powered strategy.

Why exactly are more and more businesses embracing RPA technology and how can they ensure a successful transformation? What has quickly become clear is that RPA has the power to modernise how businesses operate. Deploying a virtual workforce can enable organisations to drive a whole host of workforce advancements, with robots taking over many of the more mundane, rules-based processes. For example, RPA robots can complete tasks such as processing transactions or filling out forms faster, meaning employees will no longer have to make repetitive, transactional decisions.

Linear regression is one of the most popular and best understood algorithms in the machine learning landscape. Since regression tasks belong to the most common machine learning problems in supervised learning, every Machine Learning Engineer should have a thorough understanding of how it works. This blog post covers how the linear regression algorithm works, where it is used, how you can evaluate its performance and which tools & techniques should be used along with it.

There are many ethical controversies surrounding artificial intelligence algorithms in the past few years. In tandem with advances in artificial intelligence, there is growing interest in establishing criteria and standards to weigh the robustness and trustworthiness of the AI algorithms that are helping or replacing humans in making important and critical decisions. With the field being nascent, there’s little consensus over the definition of ethical and trustworthy AI, and the topic has become the focus of many organizations, tech companies and government institutions.

The Chimp Paradox uses a simple analogy to explain functional brain types, dubbing one the ‘Human’ and the other ‘Chimp’. We all have them both, the Human side is logical, calm and emotionally assured brain, whereas the Chimp brain can be fiery, spontaneous and alert to help you avoid trouble. Same is the case with data chimp in Energy Management. In a vast and growing array of data feeds from energy meters, data can be hugely distracting and create emotional unease should it highlight unexpected results.

Transfer learning is all the rage in the machine learning community these days. It serves as the basis for many of the managed AutoML services and now figures prominently in the latest NLP research. We’re also starting to see examples of neural networks that can handle multiple tasks using transfer learning across domains. The main question at hand is: could transfer learning have applications within reinforcement learning? Compared to other machine learning methods, deep reinforcement learning has a reputation for being data hungry, subject to instability in its learning process.

Machine learning/Deep Learning/AI are fancy number crunchers and they can have some amazing results given good data, however, the first step is to properly understand your data so you can make informed decisions about what algorithms and data cleaning methods to use. One of the first things in understanding your data is to know what kind of data you have! Here are the 4 most common types of data that you will come across.

Better encoding of categorical data can mean better model performance. In this series, I’ll introduce you to a wide range of encoding options from the Category Encoders package for use with scikit-learn in Python. Use Category Encoders to improve model performance when you have nominal or ordinal data that may provide value. In this article we’ll discuss terms, general usage and five classic encoding options: Ordinal, One Hot, Binary, BaseN, and Hashing.

Artificial Intelligence techniques have traditionally been divided into two categories; Symbolic A.I. and Connectionist A.I. The latter kind have gained significant popularity with recent success stories and media hype, and no one could be blamed for thinking that they are what A.I. is all about. There have even been cases of people spreading false information to diverge attention and funding from more classic A.I. research and development. The truth of the matter is that each set of techniques has its place. Each has its own strengths and weaknesses, and choosing the right tools for the job is key.

Descriptive Statistical Analysis helps you to understand your data and is a very important part of Machine Learning. This is due to Machine Learning is all about making predictions. On the other hand, statistics is all about drawing conclusions from data, which is a necessary initial step. In this post, you will learn about the most important descriptive statistical concepts. They will help you understand better what your data is trying to tell you, which will result in an overall better machine learning model and understanding.

Robotic process automation (RPA), machine learning and artificial intelligence (AI) will continue to significantly impact the legal profession, and professionals will need to adapt to and embrace these new technologies to future-proof their careers. Some of the new technology removes the need for lawyers to perform the process-driven and repetitive tasks, like drafting and checking documents, and allows them to focus on more strategic and high impact activities for their clients. Some tools in the AI space make contract review much quicker, with less human errors.