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) …
Editor's note: Dave Currie joined Lob's Atlas team in June 2020 as a remote contractor. Working as the team's Machine Learning Engineer, he has helped to improve the accuracy of the Address Verification product by developing microservices that utilize machine learning. This article was written about one of these microservices. When I tell people that my work is focused on improving an address verification product, I sometimes receive confused looks. If you think about a friend's address, you might picture something like "1600 Pennsylvania Avenue, Washington, DC 20500".
Think of all the data sources which include your personal information within the public administration services; be it bank account details, financial or medical records, tax information, etc. We often take it for granted that our data is safe and protected. However, what happens when this information is shared among different public administration entities? In reality, the General Data Protection Regulation (GDPR) laws safeguard the general public by limiting what data can be shared among entities, requiring that the data be anonymised before it is shared among different entities, including those within the public administration. The Multilingual Anonymisation for Public Administration (MAPA) Project is a European-funded project which is developing an open-source toolkit that enables effective and reliable text anonymisation, focusing on the medical and legal domains.
It may seem like something out of a sci-fi novel, bots playing a role in helping you. But, is that truly the case? Its actually become a growing reality with various industries utilizing these artificial intelligence-powered chatbots to automate tedious processes and seamlessly provide consumers with round-the-clock attention. Chatbots were limited to marketing, banking, and customer service earlier, but they established themselves in healthcare during the pandemic. The genuine interest in adopting chatbots in the healthcare sector is clear since more than $800 million has been spent by startups on developing healthcare chatbots.
Chatbots have a checkered past of often not delivering the performance their providers have promised. This is especially true in the IT service management (ITSM) and multilingual NLP spaces, where service desks found support teams deluged with complaints -- yes, about the support chatbots. Just getting English language nuance right and how enterprises communicate often require chatbots to be custom programmed with constraint and logic workflows supported with natural language processing (NLP) and machine learning. If that sounds like a science project, it is, and IT users are the test subjects. Because of their complexity, chatbots were contributing to already overflowing trouble-ticket queues.
Jack Clark, OpenAI's policy director, calls this trend of copying GPT-3, "model diffusion." Yet, among all the copies, Wu Dao 2.0 holds the record of being the largest of all with a striking 1.75 trillion parameters (10x GPT-3). Coco Feng reported for South China Morning Post that Wu Dao 2.0 was trained on 4.9TB of high-quality text and image data, which makes GPT-3's training dataset (570GB) pale in comparison. Yet, it's worth noting OpenAI researchers curated 45TB of data to extract clean those 570GB. It can learn from text and images and tackle tasks that include both types of data (something GPT-3 can't do).
If your business is driven by data, Optical Character Recognition (OCR) -- as most of us know it -- is not the answer. For those of you who view OCR as an industry staple for document processing, let me explain. OCR as a technology has been around for ages and it still has its place in processing unstructured document formats like PDFs, images, and other text formats that cannot be edited digitally. Users can quickly convert those files into editable documents. In short, it's a terrific technology for enabling you to edit and search for files that may have been "frozen."
Artificial intelligence is a branch of computer science that deals with making intelligent machines and computer programs. It is a broad branch that includes machine learning and deep learning. John McCarthy, a professor emeritus at Stanford University, coined the term artificial intelligence in 1956. The applications of artificial intelligence include voice assistants like Alexa, Siri, and Google Assistant. It is also applied to deep learning models like Luther AI.
In many cases, for an enterprise to build its digital business technology platform, it must modernize its traditional data and analytics architecture. A modern data and analytics platform should be built on a services-based principles and architecture. This part, provides a conceptual-level reference architecture of a modern D&A platform. Parts 3 and 4, will explain how these two reference architectures can be used to modernize an existing traditional D&A platform. This will be illustrated by providing an example of a Transmission System Operator (TSO) that modernizes its existing traditional D&A platform in order to build a cyber-physical grid for Energy Transition. However, the approaches used in this example can be leveraged as a toolkit to implement similar work in other vertical industries such as transportation, defence, petroleum, water utilities, and so forth.
Artificial intelligence and machine learning pioneers are rapidly expanding on techniques that were originally designed for natural language processing and translation to other domains, including critical infrastructure and the genetic language of life. This was reported in the 2021 edition of the State of AI Report by investors Nathan Benaich of Air Street Capital and Ian Hogarth, an angel investor. Started in 2018, their report aims to be a comprehensive survey of trends in research, talent, industry, and politics, with predictions mixed in. The authors are tracking "182 active AI unicorns totaling $1.3 trillion of combined enterprise value" and estimate that exits by AI companies have created $2.3 trillion in enterprise value since 2010. One of their 2020 predictions was that we would see the attention-based transformers architecture for machine learning models branch out from natural language processing to computer vision applications.