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) …
The Australian government has gone to market seeking for help to design, develop, and roll out an online digital tool that farmers can use to assess the risk and impact caused by climate change. According to the request for tender (RFT), the drought resilience self-assessment tool (DR SAT) would be used to provide data and online drought resilience assessment capability to give farmers insight to help improve their decision-making capabilities, help them better understand and manage risk and uncertainty, as well as help identify options to improve their business resilience and drought preparedness. Additionally, the capability's architecture, when delivered, is expected to be designed for the potential of a national rollout, as well facilitate individual data entry, analysis, and feature data visualisation and user dashboards, the tender said. The initial phase of work would involve delivering a proof of concept through four pilots across the country. The DR SAT would be delivered as part of the federal government's AU$5 billion Future Drought Fund managed by the Department of Agriculture, Water and the Environment.
In an era of constant change, companies' data and analytics capabilities must rapidly adapt to ensure that the business survives, never mind competes. Organizations seek insights from their data to inform strategic priorities in real time, yet much of the historical data and modeling formerly applied to predict future behavior and guide actions are proving to be far less predictive, or even irrelevant, in our current normal with COVID-19. In order to survive through crises, proactively detect trends, and respond to new challenges, companies need to develop greater analytical agility. This agility comes from three areas: improving the quality and connections of the data itself, augmenting analytical "horsepower" at the organization level, and leveraging talent that is capable of bridging business needs with analytics to find opportunity in the data. Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations. The quest for better data is not new, but the cost of not having it is easier to substantiate and understand in a time of crisis.
Today Juniper Networks announced it was acquiring smart wide area networking startup 128 Technology for $450 million. This marks the second AI-fueled networking company Juniper has acquired in the last year and a half after purchasing Mist Systems in March 2019 for $405 million. With 128 Technology, the company gets more AI SD-WAN technology. SD-WAN is short for software-defined wide area networks, which means networks that cover a wide geographical area such as satellite offices, rather than a network in a defined space. Today, instead of having simply software-defined networking, the newer systems use artificial intelligence to help automate session and policy details as needed, rather than dealing with static policies, which might not fit every situation perfectly.
Always worried about the potential for embarrassing background noises at home during video meetings? Microsoft is working on an update that could save you from future videoconferencing faux pas. The company's Microsoft 365 roadmap lists as in development "AI-based real-time noise suppression," which is scheduled for release in November 2020. The feature, spotted by news site Windows Latest, "will automatically remove unwelcome background noise during your meetings." Artificial intelligence technology is used to analyze a user's audio and "specially trained deep neural networks" will filter out noises and keep the person's voice, the software giant's planning document says.
In the age of big data, the challenge is no longer accessing enough data; the challenge is figuring out the right data to use. In a past article, I focused on the value of alternative data, which is a vital business asset. Even with the benefits of alternative data, however, the wrong data granularity can undermine the ROI of data-driven management. "We're so obsessed with data, we forget how to interpret it". So how closely should you be looking at your data?
Rapid advances in technology, connectivity and telecommunications are conspiring to make Africa's large, rapidly growing population a valuable asset for the automation revolution. It is imperative that Africa quickly develop agency in data and artificial intelligence and it will be lucrative for investors who support them by financing Africa's telecom and data backbone. Africa must urgently develop cogent digital strategy. This at first seems fanciful, or even superfluous, given the continent's relative lack of more basic development. Indeed, there are myriad other challenges to which most would assign primacy.
Using AI to manage COVID-19 risks and applying predictive models for multiple kinds of retail. U.K. retailers are applying AI to track customer feedback and manage new risks caused by the COVID-19 pandemic. Nike is using predictive models to optimize warehouse inventory. A U.K. retail group is increasing its investment in AI and predictive analytics after a trial run reports great results.
A look at how Zulily is using the latest tools in artificial intelligence, machine learning, and cloud computing to innovate and serve its customers with purpose. Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it's by design. We've built a supply chain where we hold only some goods: most of the time, we don't purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience.
As an industry, we've gotten exceptionally good at building large, complex software systems. We're now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. In fact, many of today's fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond.
Data mining isn't a new invention that came with the digital age. The concept has been around for over a century but came into greater public focus in the 1930s. According to Hacker Bits, one of the first modern moments of data mining occurred in 1936, when Alan Turing introduced the idea of a universal machine that could perform computations similar to those of modern-day computers. Forbes also reported on Turing's development of the "Turing Test" in 1950 to determine if a computer has real intelligence or not. To pass his test, a computer needed to fool a human into believing it was also human. Just two years later, Arthur Samuel created The Samuel Checkers-playing Program that appears to be the world's first self-learning program.