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) …
In 2016, the enthusiasm for chatbots knew no limits. For example, according to a survey by Oracle, around 80 percent of all decision-makers in companies assumed that they would be using chatbots in sales, marketing, and customer service by 2020. And according to another prediction, more than two-thirds of all office workers should be interacting with chatbots on a daily basis by 2020. So where are we at right now and what can we expect after the chatbot hype? Download our free e-book to learn everything you need to know about chatbots for your business.
Summary: If you're planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources. Read the research, then consult with your own data scientists for a better evaluation of risk. It's likely not as bad as you think. Supposing you're a business leader and supposing you're trying to make an intelligent decision about prioritizing your AI adoption plans. It's likely that like many of us the first thing you'd reach for would be one of Gartner's many hype cycle or magic quadrant analyses.
According to Gartner, artificial intelligence is still in the early phases of the hype cycle. Among the 37 types of AI on its chart, only speech recognition and GPU accelerators have reached the plateau of productivity. Despite the fact that many AI technologies are too new for mainstream adoption, manufacturing leaders are already stuck in a rut with these projects, according to a new survey from Plutoshift. Plutoshift found that manufacturing professionals are having trouble with almost every aspect of AI projects from defining realistic outcomes and collecting data to getting internal buy-in. The Gartner Hype Cycle for Artificial Intelligence 2019 examines the stream of innovations and trends in the AI sector and scopes AI plans.
Natural language technology has fueled a boom in AI adoption, as everyone from small businesses to large corporations seek to introduce streamlined, automated language functions into their customer service and back-end systems. But it's also an area of confusion, owing to plenty of hype--and industries need to get through this confusion in order to bring the sophisticated natural language solutions of tomorrow to fruition. To gain a better understanding of what natural language AI will look like in 2020, we sat down with Alex Poulis. Alex is the senior director of AI at Transperfect, where he founded their Dataforce division, which focuses on training data for machine learning. He's been involved in language technologies since 2002--long before the world entered its current AI hype cycle--and previously worked with Lionbridge on their data collection efforts.
The global rush forward of AI development continues at a breakneck pace and shows no signs of stopping. Stanford University recently called on the U.S. government to make a $120 billion investment in the nation's AI ecosystem over the course of the next 10 years, and reports from France show 38% more AI startups in 2019 with government and investor backing. The U.S. Department of Energy (DOE) is planning a major initiative to use AI to speed up scientific discoveries and will soon ask for an additional $10 billion in funding. Dozens of countries have acknowledged that AI is going to be increasingly important for their citizens and the growth of their economies, resulting in widespread country-level investment and strategies around AI. This trend supports arguments that AI is entering a "golden age."
Machine learning and artificial intelligence are coming close to the peak inflated expectations stage in a hype cycle. In a webinar, Gartner director analyst, Peter Krensky, outlined the current state of machine learning and artificial intelligence, the next five years, and some of the challenges likely to impact adoption, development, and deployment. According to Krensky, ML and AI are coming close to the peak inflated expectations stage in a hype cycle. Augmented and virtual reality have already hit the'trough of disillusionment', which follows the peak, and autonomous vehicles and drones were past the peak, but have yet to hit the bottom of the cycle. That said, there is still a large amount of untapped industries for AI and ML.
Hype cycles are interesting to me. These are the cycles as described by Gartner where new innovation undergoes predictable phases of inflated expectations, disillusionment, enlightenment and finally adoption. Big Data and machine learning have been undergoing a hype cycle for the past decade and organizations are finally figuring it out. Tools have matured and data science teams are learning how to perfect building predictive and classification models to inform better decisions. You can recognize when we're nearing the end of a hype cycle by the explosion of vendors competing to deliver largely similar solutions designed to accelerate adoption of the technology.
Understanding and deploying the right technologies at the right time is pivotal to being successful in business today. From AI (artificial intelligence) and automation to line of business applications, there is no shortage of technologies that could have a profound, positive impact on an organization. Deciphering which ones are right for your business and when to deploy them is no easy task. Conversely, move too late and you may have missed out on a competitive edge. Few know the challenge of getting in at the right point of a technology hype cycle better than those in the IoT (Internet of Things) sector--where the buzz outpaced the reality of early applications, yet it is already plateauing in many markets.
The customer engagement centre (CEC) and contact centre (CC) have been integrating in silos for decades, with limited sharing of customer interaction channel functionality and data. This has resulted in a fragmented customer experience (CX), leaving customers to guess which channel will yield the best and fastest answer, reports Gartner. The analyst firm says its latest Gartner Hype Cycle for Customer Service and Support Technologies describes the most critical technologies for supporting customers as they seek answers, advice and resolutions to problems, either through a variety of interaction channels or by enabling customer-facing employees to deliver resolution and advice. "Combining the formerly separate yet closely related Hype Cycle for CRM customer service and customer engagement and Hype Cycle for contact centre infrastructure, this new Hype Cycle encourages customer service and support leaders to combine CEC and CC systems to create a broader technology ecosystem," says Drew Kraus, vice president in Gartner's Customer Service & Support practice. "In doing so, they can leverage consistent analytics and knowledge tools for gathering, analysing and sharing critical information and recommendations to both customers and employees."
Regardless of the industry you work in, you've no doubt heard about artificial intelligence (AI) and its potential in changing the world around us. The technology has been a source of debate in the private and public sectors for more than 50 years, and yet it has only been in the last decade that we've begun to really see momentum build in the AI space. But what actually is AI? Leaving the jargon to one side, it should simply be understood as the use of computer systems to perform tasks that would normally require human intelligence. To date, there has been some significant progress made in the adoption of AI technologies, with industries from financial services to healthcare demonstrating a keen willingness to use AI to their advantage. At the same time, investment has been pouring in at unprecedented levels; investment into UK AI businesses alone now exceeds £3.8 billion according to Big Innovation Centre and Deep Knowledge Analytics.