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) …
How do companies like Amazon and Netflix know precisely what you want? Whether it's that new set of speakers that you've been eyeballing, or the next Black Mirror episode -- their use of predictive algorithms has made the job of selling you stuff ridiculously efficient. But as much as we'd all like a juicy conspiracy theory, no, they don't employ psychics. They use something far more magical -- mathematics. Today, we'll look at an approach called collaborative filtering.
Amazon recently announced its plan to develop AI-enhanced homes through Plant Prefab, a company that specializes in high-quality, sustainable prefabricated homes, by integrating Alexa devices into what are being touted as affordable and easy-to-assemble houses. But is it possible to have high-tech homes that are also ecologically sustainable? Amazon's introduction of AI voice technology into home design and architecture is not new. Many companies have been marketing electronic domestic products with AI for years. At the beginning of 2018, LG decided to put AI into all its products, in fact.
The Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Data61 has developed a tool to track infectious diseases and how they specifically might spread to Australia, using Bayesian inference, a statistical machine learning method, for understanding the propensity of a region to spread disease to other regions. Using data from dengue virus outbreaks in Queensland as a case study, the tool identifies and tracks new cases of infection to their original source in Australia, and links how the disease has transferred between people. According to Data61 computer scientist Raja Jurdak, traditional methods of tracking infection routes often depend on time-consuming site investigations or interviews relating to travel routes of infected patients. Data61 has partnered with Queensland Health to obtain fully anonymised records of the reported dengue cases over a 15-year period. Jurdak told ZDNet these records serve as the ground-truth used to train the models.
Bot-powered credential stuffing is a scourge on the modern Internet. These attacks attempt to log into and take over a user's account by assaulting password forms with a barrage of dictionary words and previously stolen account credentials, with the aim of performing fraudulent transactions, stealing sensitive data, and compromising personal information. At Cloudflare we've built a suite of technologies to combat bots, many of them grounded in Machine Learning. ML is a hot topic these days, but the literature tends to focus on improving the core technology -- and not how these learning machines are incorporated into real-world organizations. Given how much experience we have with ML (which we employ for many security and performance products, in addition to bot management), we wanted to share some lessons learned with regard to how this technology manifests in actual products.
The OpenAI research group has demonstrated artificial intelligence that can compose authentic-looking fake news articles from a few fragments of information. The OpenAI research group has demonstrated artificial intelligence (AI) that can compose authentic-looking fake news articles from a few fragments of information. After being fed a few sentences of sample text, the software successfully generates a persuasive, but completely false, seven-paragraph news story. The AI was trained to perform language modeling, or predicting the next word of a piece of text based on knowledge of all previous words. OpenAI's Jeff Wu suggested the software could have beneficial applications, like helping creative writers generate ideas or dialogue, or hunting for bugs in software code.
Join ID assistant professor Tom MacTavish and ID students Ye Jin Han (MDes 2019) and Zhongyang Li (MDes 2020) on Thursday, February 21 for exploreID: Interaction Design Healthcare to learn more about their work, interaction design, emerging technologies in healthcare, and other ways we examine and create digital experiences at ID. My journey into this problem area began five years ago when ID assembled a team of student research assistants to work on a government-funded research project under the auspices of the Chicago Trial Consortium, which is comprised of six clinical centers, four collaborating partners, three consultants, and a community healthcare worker coordinating center. The project was named the CHICAGO Plan (Coordinated Healthcare Interventions for Childhood Asthma Gaps in Outcomes). Funded by the Patient-Centered Outcomes Research Institute (PCORI), our multicenter comparative effectiveness trial tested strategies to improve the care and outcomes of African–American and Latino children with uncontrolled asthma presenting to the emergency department (ED) in Chicago. From our field research findings, my colleague Kim Erwin (MDes 1993) conducted additional research that led to the design of a paper-based communication tool that would help healthcare staff and caregivers more effectively record, track, and preserve information at the time of a child's discharge from the emergency department. This tool was called the CAPE (CHICAGO Action Plan after ED discharge) and a description of the tool, its underlying theory, and application was published in the Journal of Comparative Effectiveness Research2.
The global construction industry has grown by only one per cent per year over the past few decades. Compare this with a growth rate of 3.6% in manufacturing, and 2.8% for the whole world economy. Productivity, or the total economic output per worker, has remained flat in construction. In comparison, productivity has grown 1,500% in retail, manufacturing, and agriculture since 1945. One of the reasons for this is that construction is one of the most under-digitized industries in the world and is slow to adopt new technologies (McKinsey, 2017).
Every year, it seems, pundits and egg-heads (like me) write down our predictions. Most of the time they are self-serving. However, looking into my crystal ball, I think 2019 is going to be a defining year at the intersection of physical and cybersecurity. The Internet of Things (IoT) and advances in artificial intelligence are some of the main driving forces in this evolution. Further, with chaos happening throughout the world, and the lead-up to a turbulent US presidential election, there has never been a more perilous--and opportunistic--time for those in the IoT and security business.
To stay ahead of their competitors, companies need to stay up-to-date on current communication technologies and marketing trends. Recently the spotlight has been on artificial intelligence (AI) and its potential applications. There are plenty of predictions making the rounds on how AI will be used in brand marketing and communications in the near future. Seasoned experts in the communications and marketing industry know which way the wind is blowing--many are already laying the groundwork for AI applications in their own work. Bringing their expert insights right to your proverbial door, 12 members of Forbes Communications Council share their top AI predictions, as well as how savvy marketers can get ahead of the trend.
Artificial intelligence – also commonly known as AI – has revolutionized the technology world. Companies both inside and outside the tech circle are introducing AI into their work suite. AI takes the basic principles of computing and processing and applies intelligent environment analysis on top of it. For industries, AI analyzes the data they generate and provides them with insights based on its findings. AI can also apply machine learning to examine historical data in order to perform tasks without human input.