The release of the artificial intelligence technology maturity curve, what problems does it reflect?

At the 2019 World Artificial Intelligence Conference, the 2019 World Artificial Intelligence Technology Trend Analysis Report and Artificial Intelligence Technology Maturity Curve were officially released.

The report was jointly completed by Gartner Group, an internationally renowned information technology research and analysis consulting organization, and the research team of the World Artificial Intelligence Conference Organizing Committee. It aims to show the current status and challenges of the development of artificial intelligence technology through a comprehensive analysis of the global artificial intelligence technology route trends in 2019, as well as discussions on core technologies such as smart chips and smart software.

This Gartner Hype Cycle highlights the many different ways AI is impacting the enterprise

According to Gartner’s 2019 CIO Agenda Survey, organizations deploying artificial intelligence (AI) grew from 4% to 14% between 2018 and 2019.

Artificial intelligence is impacting organizations in many different ways compared to a few years ago, when there was no alternative to building your own solutions using machine learning (ML). While AutoML and intelligent applications have the most obvious momentum, other approaches are also worth paying attention to - namely artificial intelligence platform as a service (AIPaaS) or artificial intelligence cloud services.

Driven by the global success of Amazon Alexa, Google Assistant and others, conversational AI remains at the top of enterprise planning agendas, while new technologies such as augmented intelligence, edge AI, data labeling and explainable AI continue to emerge.

This year’s Hype Cycle features many new technologies, but few are widely known to have value or purpose.

The Gartner 2019 Hype Cycle for Artificial Intelligence examines the innovation and trends in the field of AI, as well as the scope of AI initiatives. Fast followers should first design a business case for AI. For early adopters, scalability of AI is the next challenge.

This year's Hype Cycle includes many new technologies, but few are known to have value or purpose, and even fewer are in mainstream use. "This doesn't mean AI is unavailable. It means it will change, and in order to assess the value and risks of AI, CIOs need to set realistic expectations for it," said Svetlana Sicular, vice president analyst at Gartner.

Here are the AI ​​technologies Sicular highlights that must be on CIOs’ radars to have a huge impact on business transformation over the next two to five years.

Augmented intelligence

Augmented intelligence is a human-centered collaborative model in which AI and humans collaborate to improve cognitive abilities. It focuses on the auxiliary role of artificial intelligence in improving human capabilities.

AI interacts with people, improving what they already know, can reduce everyday errors, and can improve customer interactions, citizen services, and patient care. The goal of augmented intelligence is to increase the efficiency of automation while supplementing it with a human touch and common sense to manage the risks of decision automation.

Complaints: We are no longer teasing Siri and artificial intelligence Ai-chan with whom we play in daily life. We are starting to pursue higher intelligence, such as AlphaGo. The more successful ones we have heard of so far are the black technology plug-ins for games. The black technology of StarCraft 1 and King of Fighters 97 can kill the world's top players in seconds. If such enhanced intelligent programs are developed in other fields, Skynet will not be far away.

Chatbots​

Chatbots are the face of AI, impacting all areas where people can communicate, such as car manufacturer KIA, which speaks to 115,000 users per week, or Lidl's Winebot Margot, which provides guidance on buying wine and advice on food pairings.

Chatbots can be text-based or voice-based, or a combination of both, and rely on scripted responses with minimal human intervention.

Common applications exist in HR, IT help desks, and self-service, and chatbots have already had a huge impact in the customer service space, especially changing the way customer service is performed. The shift from "users learning the interface" to "chatbots are learning what users want" means a greater impact on employee onboarding, productivity, and training.

Complaint: It seems like a good direction, but please don't use it for promotional spam messages. For example, on Taobao, any store that leaves a phone number will receive a steady stream of discount messages. If used in some standardized business processes, it may indeed save a lot of tedious steps.

Machine Learning (ML)

Machine learning can solve business problems such as personalized customer service, supply chain recommendations, dynamic pricing, medical diagnostics, and anti-money laundering. ML uses mathematical models to extract knowledge and patterns from data. As organizations face exponential growth in the amount of data and improvements in computing infrastructure, the use of ML is increasing.

Currently, ML is being used in multiple fields and industries to drive improvements and find new solutions to business problems. American Express uses data analytics and ML algorithms to help detect fraud in near real time, saving millions of dollars in losses. Volvo uses data to help predict when parts are likely to fail and when they need repairs, thereby improving the safety of its cars.

AI Governance

Organizations shouldn’t ignore AI governance. They need to be aware of potential regulatory and reputational risks. “AI governance is the process of developing policies to combat AI-related bias, discrimination, and other negative impacts of AI,” Sicular said.

Determine transparency requirements for data sources and algorithms to reduce risk and increase confidence

To develop AI governance, data and analytics leaders and CIOs should focus on three areas: trust, transparency, and diversity. They need to focus on trust in data sources and AI results to ensure the successful use of AI. They also need to determine transparency requirements for data sources and algorithms to reduce risks and increase confidence in AI. In addition, they should ensure diversity in data, algorithms, and perspectives to pursue ethics and accuracy in AI.

Complaint: Everyone is familiar with IT governance, but AI governance is still in the theoretical stage. In recent years, we can often see that ethical issues in the fields of security and AI may really be put on the agenda in the future. Why do I say this? I don’t know if you have watched the recently released "Child’s Play". It is really hard to say what AI will be like if there is no constraint on machine learning.

Smart Applications

Most organizations are changing their preference for acquiring AI capabilities, preferring to use them in enterprise applications. Intelligent applications are enterprise applications with embedded or integrated AI technologies that support or replace human-based activities through intelligent automation, data-driven analysis, and guidance recommendations to improve productivity and decision-making.

Today, enterprise application vendors are embedding AI technologies in their products and introducing AI platform capabilities—from enterprise resource planning to customer relationship management to human resource management to workforce productivity applications.

CIOs should demand from their outsourcing software vendors that they outline in their product roadmaps how they will incorporate AI to add business value in the form of advanced analytics, intelligent processes and superior user experience.

As an important driving force of the new round of scientific and technological revolution and industrial transformation, artificial intelligence is profoundly changing the world. The world of science and technology is changing with each passing day, and we must keep up with it.