Collaborating Authors


Promise and problems: AI put patients at risk but that shouldn't prevent us developing it. How do we implement artificial intelligence in clinical settings?


In a classic case of finding a balance between costs and benefits of science, researchers are grappling with the question of how artificial intelligence in medicine can and should be applied to clinical patient care – despite knowing that there are examples where it puts patients' lives at risk. The question was central to a recent university of Adelaide seminar, part of the Research Tuesdays lecture series, titled "Antidote AI." As artificial intelligence grows in sophistication and usefulness, we have begun to see it appearing more and more in everyday life. From AI traffic control and ecological studies, to machine learning finding the origins of a Martian meteorite and reading Arnhem Land rock art, the possibilities seem endless for AI research. The genuine excitement clinicians and artificial intelligence researchers feel for the prospect of AI assisting in patient care is palpable and honourable. Medicine is, after all, about helping people and the ethical foundation is "do no harm."

Artificial Intelligence Markup Language (AIML)


AIML can be used to solve many real-life applications. The creation of Chatbot is one such application that is now widely used in the market. Also, AIML acts as the core of Machine Learning which is integration to Natural Language Processing. A category consists of (i) an user input, in the form of a sentence (assertion, question, exclamation, etc), (ii) a response to user input, presented by the chatterbot, and (iii) an optional context. A KB written in AIML is formed by a set of categories.

Artificial Intelligence Markup Language (AIML) - CouponED


Create your own chatbots using the world's most popular chatbot language. This course is designed for people with absolutely no knowledge of Artificial Intelligence Markup Language (AIML). It guides you step by step and teaches you how to create a chatbot using the world's most popular chatbot language. From the very beginning to more advanced features, take it at your own pace, practice and learn from Steve Worswick, the 5 times holder of the Loebner Prize.

Scaling Enterprise Machine Learning Through Governance & MLOps


In my roles as a customer success and business development executive covering Artificial Intelligence & Machine Learning (AIML) at leading tech companies, I've spoken with executives, data scientists and IT managers across startups, Fortune 500 and Global 1000 companies about their AIML needs. After discussing what is AIML, platform features or API services easiest to use for non-specialist, companies get stuck on an equally important component of enterprise AIML, governance of operations. Companies get caught up in the hype led by consultants and industry media outlets that promote AIML led digital transformation is happening across every industry, in companies of all sizes with millions of models being deployed to production weekly. AIML software vendors promise adoption of their solution enables instant production readiness enabling their customers to, "Build and deploy a machine learning model in 9 minutes," with limited or no expertise. The reality is not quite as advertised but I'll help you on your journey by discussing why deploying ML in production can be difficult, provide a way to assess your return on investment (ROI) with AIML, how to create a comprehensive ML platform and provide a framework for assessing your organization's AIML maturity to better determine the capabilities you need to acquire to improve your org's proficiency. There are many definitions for Machine Learning Operations (MLOps) and governance but to keep things simple, I'll define governance and MLOps as the best practices and policies for businesses to run AIML successfully.

Artificial intelligence revolution offers benefits and challenges


Australia could once again have a globally competitive manufacturing sector by using automation driven by artificial intelligence (AI). That's the view of University of Adelaide researchers who are aiming to play a major role in the development of AI which is poised to reshape the global economy, bringing challenges and opportunities. The authors of the latest Economic Issues paper--"The impact of AI on the future of work and workers"--published by the South Australian Centre for Economic Studies (SACES) and the Australian Institute of Machine Learning (AIML), both research centers at the University of Adelaide, maintain that AI "has reached a global tipping point and we need to plan for it." The authors, Professor Anton van den Hengel and Dr. Paul Dalby, Director and Business Development Manager, respectively, of the AIML, and SACES Research Associate, Dr. Andreas Cebulla, describe AI as "the automation of tasks normally requiring human intelligence." "AI has the potential to temper the impact of globalization which has seen industry leaving developed countries seeking lower cost manufacturing options offshore," the authors say.

Getting started with AIML to create Chatbots


AIML stands for Artificial Intelligence Markup Language and is used to create Chatbots. Chatbots are software applications with whom you can have normal conversations instead following an elaborate syntax or commands. Following article contains some basic utilities which can equip you to write a fulling functioning AIML bot. It derives itself from eXtensible Markup Language, and so every opening tag has a closing Tag, aiml shall be closed with a backward slash as follows \aiml . Below it the UDC file with nothing much incorporated.

How Artificial Intelligence is escalating in cybersecurity


When progressive technologies start to deliver on their potential, we can expect a wholesale shift of vendors looking to get on the bandwagon. First the technology enthusiasts and early adopters will come to validate the promises of the newest technology and hone its potential into something viable for the mainstream. Once that is done, the early majority, late adopters and finally, even the skeptics jump in as well. Finally the time is here for Artificial Intelligence and Machine Learning (AIML) in cyber. There is a widespread move out of the early adopter stage and into the early majority stage of adoption.

Democratizing AI - Build versus Buy for AIML is not Hobson's Choice John Snow Labs


The Internet was revolutionized by a book store named Amazon and search engines du jour. Google ushered in the storage paradigm Amazon Web Services unleashing the 16 digits-to freedom-to-compute era of 1,000 startups a day. Computing is now being pushed to the edge with decisions generated using artificial intelligence (AI). Google Search and Google Ads began using Hadoop as implemented by Yahoo to be consumable on a massive scale. Apache Spark has made the movement of data a tad more palatable from a latency standpoint.