If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
There is a lot of research being done on the implementation of AI in medicine. In fact, healthcare chatbots are becoming more and more common. As chatbot technology improves our experiences with self-driving cars and virtual help desks, it's also improving health services through improved data entry, more detailed analytics, and better self-diagnosis. But exactly how can a chatbot improve your workplace? And what role does machine learning play in the process?
Natural language processing (NLP) is one of the most important technologies to arise in recent years. Specifically, 2019 has been a big year for NLP with the introduction of the revolutionary BERT language representation model. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. Convolutional Neural Network (CNNs) are typically associated with computer vision, but more recently CNNs have been applied to problems in NLP.
In this special guest feature, Claus Jepsen, Deputy CTO at Unit4, AI has risen as a main focus point for its ability to accelerate processes and help organizations remain competitive in today's unpredictable landscape. However, the benefits of AI go far beyond improved business operations and its potential to improve the internal workplace experience is often overlooked. Claus is a technology expert who has been fascinated by the micro-computer revolution ever since he received a Tandy TRS model 1 at the age of 14. Since then, Claus has spent the last few decades developing and architecting software solutions, most recently at Unit4, where he is leading the ERP vendor's focus on enabling the post-modern enterprise. At Unit4, Claus is building cloud-based, super-scalable solutions and bringing innovative technologies such as AI, chatbots, and predictive analytics to ERP.
Voice technology in education is taking over the academic sphere for both developers and learners. Marissa, from Alexa Education sheds more light on how the phenomenon is revolutionizing experiences in the education sector. Marissa has gathered massive passion for voice technology in education during her early days when she was producing CD-ROM educational content for'edutainment'. Her interest grew as she migrated to Microsoft where she worked on several projects that impacted the education industry such as Xbox and Encarta. Now, during her tenure in Amazon's Alexa Education, Marissa explains how her team is developing a solid connection between institutions and their stakeholders by providing efficient ways in which learners access educational content powered by technology.
Over the past few decades, technology-based innovations have added a new meaning to business conversations, thus changing the way we live and work in the digital age. With increased focus on improving business agility and performance, to sail through the challenging age of digital transformation, it is in the interest of modern businesses to leverage these cutting-edge technologies to maximize operational efficiencies and sustain profitability. The journey so far and how new technologies will impact key verticals in the coming years. The age of transformation will witness the emergence of varied possibilities and use-cases with the intermingling of new age technologies like Artificial Intelligence, IoT, automation and analytics. While Artificial Intelligence has many use cases, practically in every field, we would see that the best end-to-end application of Artificial Intelligence will happen in conjunction with automation.
A Japanese medical advice app provider is making a limited time offer of a free app that allows users to seek advice from doctors about the coronavirus. The free service, in Japanese only, is provided by Agree, a company based in Tsukuba, Ibaraki Prefecture. It also operates a medical advice app called Leber. Users are asked to send information such as whether they have traveled to any places where COVID-19 has been confirmed or whether they have developed a fever. With about 120 doctors registered for the service, users receive advice in about 30 minutes about the urgency of their condition, such as if they are suspected of having pneumonia and if they should seek advice from a public health center.
Every Marvel fan must have at some point of time in his fandom read or watched Ironman and wish if he had Jarvis at his disposal. I went through the same crisis once and that is where it all began. I started exploring how feasible developing my own Virtual Assistant was, and that is how MEERA was born. MEERA stands for Multifunctional Event-driven Expert in Real-time Assistance. It started as a general purpose scalable virtual assistant backed by the mystic power of machine learning and artificial intelligence.
Natural language models typically have to solve two tough problems: mapping sentence prefixes to fixed-sized representations and using the representations to predict the next word in the text. In a recent paper, researchers at Facebook AI Research assert that the first problem -- the mapping problem -- might be easier than the prediction problem, a hypothesis they build upon to augment language models with a "nearest neighbors" retrieval mechanism. They say it allows rare patterns to be memorized and that it achieves a state-of-the-art complexity score (a measure of vocabulary and syntax variety) with no additional training. As the researchers explain, language models assign probabilities to sequences of words, such that from a context sequence of tokens (e.g., words) they estimate the distribution (the probabilities of occurrence of different possible outcomes) over target tokens. The proposed approach -- kNN-LM -- maps a context to a fixed-length mathematical representation computed by the pre-trained language model.
Automated translation, including translating one programming language into another one (for instance, SQL to Python - the converse is not possible) Spell checks, especially for people writing in multiple languages - lot's of progress to be made here, including automatically recognizing the language when you type, and stop trying to correct the same word every single time (some browsers have tried to change Ning to Nong hundreds of times, and I have no idea why after 50 failures they continue to try - I call this machine unlearning) Detection of earth-like planets - focus on planetary systems with many planets to increase odds of finding inhabitable planets, rather than stars and planets matching our Sun and Earth Distinguishing between noise and signal on millions of NASA pictures or videos, to identify patterns Automated piloting (drones, cars without pilots) Customized, patient-specific medications and diets Predicting and legally manipulating elections Predicting oil demand, oil ...
As we have seen before, the Information Extraction step consists mainly of classifying words (tagging), the output can be stored as key-value pairs in a computer-friendly file format (e.g.: JSON). The data extracted can then be efficiently archived, indexed and used for analytics. If we compare OCR to young children training themselves to recognize characters and words, then Information Extraction would be like children learning to make sense of the words. An example of IE would be when you stare at your credit card bill trying to find the amount due and the due date. Suppose you want to build an AI application to do it automatically; OCR could be applied to extract the text from the image, converting pixels into bytes or Unicode characters, and the output would be every single character printed in the bill.