AI & Machine Learning

Most of the AI in use now is narrow AI, meaning it is only capable of performing individual tasks. Narrow AI does a good job at executing tasks, but it comes with limitations, including the possibility of introducing biases. AI bias may come from incomplete datasets or incorrect values. Bias may also emerge through interactions overtime, skewing the machine’s learning. Moreover, a sudden business change, such as a new law or business rule, or ineffective training algorithms can also cause bias. 

You’ve probably heard of machine learning and artificial intelligence, but are you sure you know what they are? If you’re struggling to make sense of them, you’re not alone. There’s a lot of buzz that makes it hard to tell what science is and what’s science fiction. Starting with the names themselves…machine learning is just a thing-labeler, taking your description of something and telling you what label it should get.

Artificial Intelligence (AI) is finally making headways in the broader Customer Interaction Management space. Customer service departments have a lot of technology options to choose from to better their productivity and the customer experience. It incentivizes them to invest in software allowing incremental improvement of performance indicators. This has led to a conservative approach with breakthrough technologies such as AI. This is changing through the state of AI adoption. 

As technology advances, marketers are looking at providing unique and more relevant experiences for their prospects and customers. In the age of millennials, no one likes to be marketed to or sold to. A simple yardstick would be to check how many of us have ad blocks on our browsers. Millennials like to be engaged with. This is where we as marketers, can leverage data and machine learning. The right and intelligent augmentation of humans and technology is the future of millennial marketing. And that mix will vary with every enterprise.

It is a function that measures the performance of a Machine Learning model for given data. Cost Function quantifies the error between predicted values and expected values and presents it in the form of a single real number. Depending on the problem Cost Function can be formed in many different ways. The purpose of Cost Function is to be either: Minimized – then returned value is usually called cost, loss or error. The goal is to find the values of model parameters for which Cost Function return as small number as possible.

Robotic process automation allows organizations to immediately find opportunities to increase productivity and accuracy in the back office. This is, however, just the first stage of a process that can increase operational efficiency and create even more value across the organization. This stage is defined by technologies that build on RPA: intelligent automation. These technologies provide a structured output, which is exactly what RPA bots require in order to be at their most efficient. Soon it will become apparent where intelligent automation technologies can work together to create an eco-system of capabilities.

 In this article, we show how the simple sorting algorithm is at the heart of solving an important problem in computational geometry and how that relates to a widely used machine learning technique. Although there are many discrete optimization based algorithms to solve the SVM problem, this approach demonstrates the importance of using fundamentally efficient algorithms at the core to build complex learning model for AI. A dizzying array of clever algorithms are being developed continuously for solving ML problems to learn patterns from streams of data and build AI infrastructure.

Here are some useful advice and questions and answers for machine learning/data science ‘starters’. We cover key books, foundation knowledge, mathematics, and programming tools needed to kickstart the journey. A curiosity to learn new things and a passion to work hard for it is necessary. You have to acquire knowledge, practice, and internalize concepts as you go. Do your own reading, understand what it is and what it is not, where it might go, and what possibilities it can open up. Then sit back and think about how you can apply machine learning or imbue data science principles into your daily work.

The level of complexity, speed and detail in modern manufacturing processes has become almost impossible to manage via manual or human effort alone. Assistance from technology and engineering have been prevalent since the introduction of the steam engine, but as we navigate through the Fourth Industrial Revolution (Industry 4.0), artificial intelligence (AI) is becoming a common theme. When we apply artificial intelligence or machine learning to the manufacturing process, what do we mean? This is about understanding data, extracting insight and learning from the outputs.

Linear Regression is a very plain algorithm so the reader can grasp an understanding of fundamental Machine Learning concepts such as Supervised Learning, Cost Function, and Gradient Descent. Additionally, after learning Linear Regression it is quite easy to understand Logistic Regression algorithm and believe or not — it is possible to categorise that one as small Neural Network.  Linear Regression is a Supervised Learning algorithm which goal is to predict continuous, numerical values based on given data input. 

Getting into Machine Learning isn’t an easy thing. The names like Linear Regression, Logistic Regression, and Decision Trees etc. are just the names of the algorithms. Those are just theoretical concepts that describe what to do in order to achieve the specific effect. Model is a mathematical formula which is a result of Machine Learning algorithm implementation. It has measurable parameters that can be used for prediction. Models can be trained by modifying their parameters in order to achieve better results. It is possible to say that models are representations of what a Machine Learning system has learned from the training data.