A chatbot or robot is a computer program that simulates or provides human-like answers to questions engaging a conversation via auditory or textual input, or both. Chatbots can perform tasks such as data entry and providing information, saving time for users. In recent times, there has been an increased focus on the potential for chatbots to better serve as interfaces between humans and businesses identifying it as a service marketed at solving conversational problems. A chatbot is a computer program that simulates human conversation. It can be used to create automated customer service agents, marketing assistants, and other similar systems.

Fremont, CA: Conversational AI is an umbrella phrase that refers to numerous approaches to allowing computers to converse with humans. This technology extends from simple natural language processing (NLP) models to more powerful machine learning (ML) models capable of interpreting various inputs and carrying on more intricate conversations. Chatbots, which employ NLP to read user inputs and carry on a conversation, is one of the most frequent uses of conversational AI. Examples of such uses are virtual assistants, customer service chatbots, and voice assistants. Well-informed consumers expect to connect via mobile apps, the web, interactive voice response (IVR), chat, or messaging channels. In addition, they want a consistent and engaging experience that is quick, simple, and personalized.

A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs. One LSTM will carry forward pass information, Another LSTM will carry backward pass information. In bidirectional LSTM, Input flows in forward and backward directions to preserve information from the past and future. This fundamental modification makes biLSTM different than LSTM where input flow either forward or in a backward direction. With the above basic understanding, let's try to implement biLSTM on the IMDB dataset.

An important step in developing machine learning models is to evaluate the performance. Depending on the type of machine learning problem that you are dealing with, there is generally a choice of metrics to choose from to perform this step. However, simply looking at one or two numbers in isolation cannot always enable us to make the right choice for model selection. For example, a single error metric doesn't give us any information about the distribution of the errors. It does not answer questions like is the model wrong in a big way a small number of times, or is it producing lots of smaller errors?

The Yellowbrick library also contains a set of visualisation tools for analysing clustering algorithms. A common way to evaluate the performance of clustering models is with an intercluster distance map. The intercluster distance map plots an embedding of each cluster centre and visualises both the distance between the clusters and the relative size of each cluster based on membership. We can turn the diabetes dataset into a clustering problem by only using the features (X). Before we cluster the data we can use the popular elbow method to find the optimal number of clusters.

We took a look at the three types of region-based CNN (R-CNN) in the last article and we now know how they operate together with their downsides. With that in mind, let's explore two other object detection algorithms that are far more superior than region-based networks, so much so that they are widely used in real-time detection tasks. In case you forgot what the downside of Faster R-CNN is, it is a relatively slow detector which is unable to match the requirements of real-time detection. It does, however, have a small accuracy advantage if real-time detection is not required. Check out this article to compare the mAP and inference speed of all the detectors mentioned in this article.

Intent marketing aims to affect prospect purchase decision by analyzing consumer behavior. By leveraging intent marketing, 64% of companies reported improvement in return on investment and conversion rates. Intent marketing is a marketing practice relying on identifying and meeting the intent of the customer. Intent refers to what customers want or need in a given period. Intent-based marketing helps marketers determine a brand's real audience and their purchase behavior at any particular time.

As you can see from the plots above, the relative sample efficiencies of the described algorithms follow their theoretical estimations. This project is also available on my GitHub.

Unlike any other time, the past decade has seen unprecedented development in the field of Artificial Intelligence (AI). There are a lot of talks on machine learning doing things humans currently do in our workplace. Deep learning is leading in some of the fronts of machine learning making practical changes. Deep learning is an artificial intelligence function that imitates the working of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural network (ANN).

You hear a lot about wildly ambitious AI initiatives these days -- from curing diseases and solving world hunger to reversing climate change. While ambition is great and all, the problem with AI moonshots is that they generally crash and burn. Who can forget when the MD Anderson Cancer Center blew $62 million on a project to use IBM Watson to treat cancer that was later shelved. It's because of this harsh reality that Tom Davenport, distinguished professor of information technology and management at Babson College, believes that if enterprises ever want to see the benefits of AI, they must embrace the mundane. While many CTOs might want to aim for the moon with their AI projects, speaking with Information Age at IPsoft's Digital Workforce Summit 2019, Davenport argued the best results come to those who opt to tackle a series of smaller projects first.