For the majority of organizations, continuously delivering and integrating AI solutions within enterprise applications and business workflows is a complex afterthought. On average, it takes about eight months to get an AI-based model integrated within a business workflow and for it to deliver tangible value. However, to reduce AI project failures, organizations must efficiently operationalize their AI architectures. Gartner expects that by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives. Organizations should consider model operationalization (ModelOps) for operationalizing AI solutions.

Earlier this year, Christie's announced two major firsts for an auction house: It would accept cryptocurrency, and it would offer for sale a purely digital work backed by a unique nonfungible token (NFT). The work, by digital artist Beeple, sold for more than $69 million. It forms part of an entirely new means of monetization -- and potentially a whole new digital ecosystem. An NFT is a unique blockchain-based digital asset that links to real-world assets like digital art or music and tokenized physical assets like houses or cars. NFTs use immutable public blockchains and, as of last month, had a market valuation greater than $300 million.

"As soon as it works, nobody calls it AI anymore." Those were the words of John McCarthy, a computer scientist who is considered one of the founding fathers of artificial intelligence. It makes you wonder when artificial intelligence (AI) will stop being a disruptive technology and just become something everyone uses to do things more efficiently. One way to gauge the maturity of any given technology is to see where it sits on the Gartner Hype Cycle. As it turns out, artificial intelligence has spawned its own Gartner Hype Cycle.

There are few bigger targets for cyber criminals than credit card companies. Which is why the U.S. alone had over 270,000 reports of credit card fraud in 2019, double the 2017 rate. So what's a credit card company to do? Use artificial intelligence to sniff out fraud and block it. "We believe at American Express that we have the world's largest and most advanced machine learning system in the financial services industry," American Express' VP of risk management Anjali Dewan told me recently on the TechFirst podcast. "And these models are ... monitoring 100% of these transactions and returning 8 billion credit and fraud risk decisions in real time."

Despite the global impact of COVID-19, 47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations actually planned to increase such investments, according to a Gartner poll. Only 16% had temporarily suspended AI investments, and just 7% had decreased them. During the pandemic, for example, AI came to the rescue. Chatbots helped answer the flood of pandemic-related questions, computer vision helped maintain social distancing and machine learning (ML) models were indispensable for modeling the effects of reopening economies. "If AI as a general concept was positioned on this year's Gartner Hype Cycle, it would be rolling off the Peak of Inflated Expectations. By that we mean that AI is starting to deliver on its potential and its benefits for businesses are becoming a reality," says Svetlana Sicular, VP Analyst, Gartner.

Last week, Gartner released a unique hype cycle; for emerging technologies. It includes technologies that can completely change the direction of human civilization. "This Hype Cycle highlights technologies that will significantly affect business, society and people over the next five to 10 years," said Brian Burke, Research VP, Gartner. From algorithmic trust to advanced AI, the Gartner's hype cycle features many new technologies. In this article, we will focus exclusively on the AI segment of this survey.

In most cities in China, citizens and visitors must download Health Code -- an app that indicates COVID-19 status -- to access many public and private spaces and services. A green screen means the person is free to travel, yellow indicates required quarantine and red means a confirmed infection. In India, the Aarogya Setu app indicates which travelers are "safe" to use rail and air travel. The United Arab Emirates recently launched ALHOSN UAE, which also indicates via color if a person is okay, infected or need to be quarantined, but also has an option for "hasn't been tested." ALHOSN UAE is currently being used to grant access to air travel.

In the Hype Cycle for Data Science and Machine Learning 2019 (available to Gartner subscribers), Gartner states, "much of advanced anomaly …

Insights are from Gartner Hype Cycle For AI Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019 Gartner's latest Hype Cycle for AI reflects the growing popularity of: - AutoML - Intelligent applications - AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. Speech Recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits. Speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows. Eight new AI-based technologies are included in this year's Hype Cycle. The latest technologies to be included in the Hype Cycle for AI reflect how enterprises are trying to demystify AI to improve adoption while at the same time, fuel new business models.

These and many other new insights are from Gartner Hype Cycle For AI, 2019 published earlier this year and summarised in the recent Gartner blog post, Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019. Gartner's definition of Hype Cycles includes five phases of a technology's lifecycle and is explained here. Gartner's latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. Gartner advises its clients to consider including speech recognition on their short-term AI technology roadmaps. Gartner observes, unlike other technologies within the natural-language processing area, speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows.