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) …
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.
It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".
With surmounting interest in data science and the fast-growing Data Scientist community, AI as a technology has come a long way crossing the chasm from Innovators and early adopters to the Early Majority. Along with all the hype that's there today around AI, there is still the unaddressed issue of less than 12% models reaching the production stage Data Scientists are creating models day in and day out but there are millions of models that are still waiting to see the light of the day in production. While the usual belief is that the deployment should need fewer days than building a model but it is becoming the most challenging issue of the industry today. Building the model is one thing, what's more, challenging is operationalizing AI. Analytics challenged leadership: This one serves as the major hurdle in operationalizing AI.
As part of its Hype Cycle for emerging technologies in 2020, Gartner has identified social distancing technologies, composable enterprise, AI-assisted design, differential privacy and biodegradable sensors as the five key emerging trends that will drive technology innovation over the next decade. "Emerging technologies are disruptive by nature, but the competitive advantage they provide is not yet well known or proven in the market. Most will take more than five years, and some more than 10 years, to reach the Plateau of Productivity. But some technologies on the Hype Cycle will mature in the near term and technology innovation leaders must understand the opportunities for these technologies, particularly those with transformational or high impact," said Brian Burke, research vice president at Gartner. For example, Gartner points to health passports and social distancing technologies, both related to the COVID-19 pandemic, that are taking the fast track through the Hype Cycle and having a high impact.
We are living in a time where everything is digital. Disruptive technologies like artificial intelligence (AI) has become central to this transformation. From retail to Fintech and cybersecurity to predictive analytics, tech pundits avow that AI now plays an essential cog in the future of these industries and disciplines. However, through some alarmists argue that AI is stealing jobs through automation and robotics, on the contrary, it has been observed that AI is also adding new job roles every day to the existing employment pool. Researchers have tracked down new job roles, occupations and emerging industries, in the AI landscape that can help us understand the job market better.
In a recent IDC survey, only about 30 percent of companies reported a 90 percent success rate for AI projects Most reported failure rates of 10 to 49 percent, while 3 percent said that more than half of their AI projects failed. That’s a pretty daunting success rate. But the benefits outweigh the risks. We see this time and again in our customer base. For a world-renowned department store chain, investing in AI-powered technology means a boost in conversions from 4% to 7.6%; for RedHat's customer support team it meant a 50K reduction in support tickets. The opportunity to boost
It has been already a year since I published a similar article on the same Gartner's report for 2019 that you can find here. Which AI related technologies have been excluded from the report? Which ones should be, according to Gartner, focus areas for companies AI leaders? First, a quick reminder of an important background to understand how Gartner's Hype Cycles are presented. As Gartner explains in its research, its Hype Cycle covers a very broad spectrum of topics, so if a specific technology is not featured it does not necessarily imply that they are not important, quite the opposite.
When automation moved to the cutting-edge of most ventures, numerous organizations felt hesitant to implement this new innovation. Instead of zeroing in on the massively expansive and verifiable advantages of using automation for business process improvement, business pioneers feared change and employees stressed over losing their jobs. Nonetheless, with the progression in automation technology, patterns like hyper-automation are developing, implying that organizations are presently moving their practices towards making "people-centric smart workplaces." This change has introduced another period for business operations that depend on technology and automation tools to keep up a competitive edge. This is particularly evident in the financial services industry.
If data is the gas in a car, then, analytics is the car itself. Currently, there are a few trends and topics in tech without which the talk around technology and innovation is incomplete -- analytics, artificial intelligence, blockchain to name a few. Augmented analytics is an extension of analytics that focuses on three main areas -- Machine Learning, Natural language generation (NLP) and, Insight automation. The basic premise of augmented analytics is the elimination of painstaking tasks in the process of data analysis and, replacing them by automation thus, refocusing human attention on modern analytics, business process, and business value generation. As per predictions made by Gartner, over 40% of tasks involved in data science will be automated thus, increasing productivity, quickening the process, and initiating broader usage of data and analytics.