A chatbot is a computer program designed to simulate conversation with human users, through auditory or textual methods. Chatbots are designed to handle inquiries through voice commands and text, and they are often integrated into messaging applications. Chatbots are often utilized with virtual assistants which are computer programs that are designed to manage tasks and conversations. Virtual assistants are built to understand natural language and context, and their main purpose is to complete tasks for the user. Chatbots and virtual assistants are predicted to be in high demand in the near future, due to the rise of AI and machine learning.

This article is a brief look at the relationship between artificial intelligence (AI), sensors and robotics. It is not meant to be comprehensive, rather it explores rudimentary concepts. Many human-like operations may require some degree of artificial intelligence or machine learning to operate in the manner needed. Yet many robots are only programmed for a set task and given a few different eventualities. Yet to help a machine with an understanding of its spacial environment -- in terms of the perception, speech recognition or learning otherwise it can happen through sensory inputs.

Note: all the links below take you to other articles by the same author. With all the gratuitous anthropomorphization infecting the machine learning (ML) and artificial intelligence (AI) space, many businessfolk are tricked into thinking of AI as an objective, impartial colleague that knows all the right answers. Here's a quick demo that shows you why that's a terrible misconception. A task that practically every AI student has to suffer through is building a system that classifies images as "cat" (photo contains a cat) or "not-cat" (no cat to be seen). The reason this is a classic AI task is that recognizing objects is a task that's relatively easy for humans to perform, but it's really hard for us to say how we do it (so it's difficult to code explicit rules that describe "catness").

Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom's family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness. This is caused in part by the fact that Machine Learning has adopted many of Statistics' methods, but was never intended to replace statistics, or even to have a statistical basis originally. Nevertheless, Statisticians and ML practitioners have often ended up working together, or working on similar tasks, and wondering what each was about. The question, "What's the difference between Machine Learning and Statistics?" has been asked now for decades. Machine Learning is largely a hybrid field, taking its inspiration and techniques from all manner of sources. It has changed directions throughout its history and often seemed like an enigma to those outside of it.1

Even the most trivial of terms can be the source of grave misunderstandings. So, an open discussion around the terms used can be crucial for the success of the project. So, how do we avoid confusing the terminology? In machine learning, we use a lot of data for different purposes as we've discussed. One way to avoid misunderstandings is to gather everybody involved in a workshop.

Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI decisions to various stakeholders. State-of-the-art explainable AI systems mostly serve AI engineers and offer little to no value to business decision makers, customers, and other stakeholders. Towards addressing this gap, in this work we consider the scenario of explaining loan denials. We build the first-of-its-kind dataset that is representative of loan-applicant friendly explanations. We design a novel Generative Adversarial Network (GAN) that can accommodate smaller datasets, to generate user-friendly textual explanations. We demonstrate how our system can also generate explanations serving different purposes: those that help educate the loan applicants, or help them take appropriate action towards a future approval. We hope that our contributions will aid the deployment of AI in financial services by serving the needs of the wider community of users seeking explanations.

We had the opportunity to sit down with Raullen Chai, the CEO of IoTeX, to discuss the state of the collaboration between IoT and blockchain.IoTeX tackles the key roadblocks of IoT: privacy, scalability and decentralization. IoTeX walks us through their novel consensus algorithm, randomized-DPoS (Delegated Proof of Stake), which takes traditional DPoS and combines it with random functions popularized by projects like Dfinity and Algorand. I want to start by asking you to paint a picture of the future of IoT. What will my home look like in ten years? In the future, all devices will be connected to the Internet.

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters -- at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others. Media often portrays Deep Learning as a magical recipe to the end of the world or the solution to all life's problems. In reality, it is anything but. Moreover, while DL has its fair share of strange behaviour and unexplained results, it is ultimately meritocratically driven.

Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom's family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness. This is caused in part by the fact that Machine Learning has adopted many of Statistics' methods, but was never intended to replace statistics, or even to have a statistical basis originally. Nevertheless, Statisticians and ML practitioners have often ended up working together, or working on similar tasks, and wondering what each was about. The question, "What's the difference between Machine Learning and Statistics?" has been asked now for decades. Machine Learning is largely a hybrid field, taking its inspiration and techniques from all manner of sources. It has changed directions throughout its history and often seemed like an enigma to those outside of it.1

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters -- at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others. Media often portrays Deep Learning as a magical recipe to the end of the world or the solution to all life's problems. In reality, it is anything but. Moreover, while DL has its fair share of strange behaviour and unexplained results, it is ultimately meritocratically driven.