Recently, Gartner, the world’s most authoritative IT market research and consulting firm, released the 2019 Technology Maturity Curve for New Technologies (“Gartner Curve”). Simply put, this is a curve that describes the changes in social expectations after the emergence of new technologies, showing the market heat of new technologies and the degree of deviation from the actual development, thus helping companies to make better use of mature technologies and look for potential opportunities.
Gartner designed this analysis tool as early as 1995. Combining the judgment of analysts, experts and industry figures, they draw a up-and-down curve to list the most high-profile and promising new technologies of the year and predict how long it will take them to mature. The Gartner curve consists of two curveoverlays, one “hype level” curve that reflects the public’s false expectations of technology, and the maturity curve for engineering or business. Basically, the new technology of throwing concepts and “telling stories” will initially be hotly sought after by the media, but once the market is validated, the bubble previously advertised by the new technology will slowly be blown away, and then enter the “high and low” stage, and then gradually climb to maturity.
The Gartner curve consists of two curve overlays. Gartner
In terms of coordinates, the Y-axis represents expectations of new technologies, while the X-axis corresponds to time, with five stages, “trigger period,” “expected expansion period,” “low period,” “recovery period, and maturity period” from left to right. In addition, each technology is marked with the number of years required to reach the maturity of production.
2019 New Technology Maturity Curve Gartner
This year, Gartner selected 29 of the 2,000 technologies, which identified five innovative technology trends that business decision makers should take into account. “Artificial intelligence permeates all other trends,” Brian Burke, vice president of research at Gartner, told CIO Dive. ”
Five prominent trends
Sensing and Mobility
With the development of sensing technology and AI, autonomous robots will be able to better understand their surroundings. The trend towards sensing and movement is characterized by the increasing ability of machines to move and manipulate objects, such as 3D sensor cameras , which collect large amounts of data, which AI can gain insight into and apply to a variety of scenarios. Light cargo drones, for example, will be better able to navigate and manipulate goods.
In addition, companies betting on this technology trend should consider the enhanced reality cloud, the flying car, and the L4 and L5 levels of autonomous driving 4 and 5)。
Human Capability Enhancement (Augmented Human)
” (Human capacity enhancement) is not a substitute for human decision-making, but rather a guide in the performance of their tasks. This enhancement is like fitting human limbs, both physically and cognitively. Burke said. This enhancement includes biochips and emotion AI. Among them, emotional AI is being used in insurance fraud detection, unlike the past need to combine claimanalysis, computer programs, and manual detection, with emotional AI, insurance companies can complete the test through the caller’s statement.
The trend also includes personalityization, augmented intelligence, immersive workspaces, and biotechnology (cultivating or artificial organizations).
Postclassical Compute and Comms
Over the decades, classic core computing, communications, and integration technologies have evolved rapidly through improvements to traditional architectures, resulting in faster CPUs, higher storage density, and increasing throughput. Classic computing and communication will then use a new architecture. Next-generation cellular standard 5G, for example, will rely on core network slices and wireless edge, and these new architectures will drive a series of incremental technological improvements that allow the Low-Earth orbit satellite complex to Satellite Systems) can operate at altitudes within 1200 miles of the earth. They will radiate to 48% of users who are not currently connected to the network, with significant social implications and economic impact.
In addition, post-classical computing and communication include technologies such as next-generation storage and nanoscale 3D printing.
Enterprises should break through the limitations of their own industrial chain, and more enterprises, people and things to carry out cross-industry sharing and cooperation. The digital ecosystem is breaking down this traditional value chain to develop more seamless and flexible connections. As a result, enterprises will look for solutions on the blockchain.
Key technologies that businesses can consider include digital ops, knowledge graphs, synthetic data, decentralized networks web) and distributed autonomous organizations.
Advanced AI and Analytics
Advanced analytics uses more complex tools to automatically or semi-automatically verify data or content, often beyond traditional business intelligence (BI). For example, Migration Learning focuses on storing existing problem-solving models and using them on other different but related issues. Models used to identify cars, for example, can also be used to enhance the ability to identify trucks. This advanced analysis provides deeper insights, predictions, and recommendations.
New technologies on the Gartner curve also include adaptive machine learning(adaptive ML), edge artificial intelligence (edge AI), edge analytics, interpretable artificial intelligence (explainable AI), and artificial intelligence platform as a service (AI) PaaS), the generative anti-network, and graph analytics.
AI continues to penetrate all industries
On this year’s Gartner curve, autopilot, L4 and L5-level autopilot, biotechnology, biochips, knowledge maps, edge artificial intelligence, artificial intelligence PaaS and 5G coincided with last year. Of these, 5G has entered the expected expansion period this year, with 2-5 years to go before the technology matures. Also entering this range are Edge Artificial Intelligence, Near-Earth Orbital Systems, L5-level Autonomous Driving, Edge Analysis, AI PaaS, Biochip, and Chart Analysis. The downturn is marked with next-generation storage, 3D sensor cameras, and L4-class autonomous driving, which are marked as more than 10 years away from technological maturity.
2018 New Technology Maturity Curve Gartner
In addition, this year Gartner put 21 new technologies on the curve, “phasing out” technologies such as the Internet of Things and blockchain, which fell into the expansion period last year. “This year Gartner is focusing on the embodiment of new technologies,” commented Computerworld, a foreign media outlet. “For example, the Internet of Things is “decomposed” into branched 3D sensor cameras, blockchain technology is embodied by technologies such as distributed autonomous organizations.
Unchanged, this year’s Gartner curve continues to emphasize the remodeling of AI businesses.
The vision of the future, as Gartner describes, is that by 2029, leading companies across industries will be using advanced analytics and using automation to add labor to their workforces. These companies will also differentiate themselves from the competition by leveraging blockchain to collaborate with multiple parties in a complex digital ecosystem and maximizing efficiency with sensing technology and next-generation computing.
“Artificial intelligence permeates all other trends,” Brian Burke, vice president of research at Gartner, told CIO Dive. “AI is able to pick and choose massive amounts of data more finely, provide decision support, replace some of the workforce, and drive other technologies.” There is no doubt that AI will have a huge impact on business development.
But when it comes to leveraging AI, organizations first need to overcome the following barriers: “The key to making any AI solution work is computing, algorithms, massive amounts of data, and the employees who skilled in using AI.” “It’s the executive vice president of the IT company L?amp;T Infotech, Soumendra Mohanty. According to Gartner, AI will create 2.3 million jobs by 2020. At the same time, 50% of organizations are short of relevant AI and data analytics talent.