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) …
I wake up in the middle of the night. "Hey, Google, what's the temperature in Zone 2," I say into the darkness. A disembodied voice responds: "The temperature in Zone 2 is 52 degrees." "Set the heat to 68," I say, and then I ask the gods of artificial intelligence to turn on the light. Many of us already live with A.I., an array of unseen algorithms that control our Internet-connected devices, from smartphones to security cameras and cars that heat the seats before you've even stepped out of the house on a frigid morning.
When it comes to artificial intelligence (AI) and machine learning (ML) in testing, much of the interest and innovation today revolves around the concept of using these technologies to improve and accelerate the practice of testing. The more interesting problem lies in how you should go about testing the AI/ML applications themselves. In particular, how can you tell whether or not a response is correct? Part of the answer involves new ways to look at functional testing, but testers face an even bigger problem: cognitive bias, the possibility that an application returns an incorrect or non-optimal result because of systematic inflection in processing that produces results that are inconsistent with reality. This is very different from a bug, which you can define as an identifiable and measurable error in a process or result.
To navigate through the worldwide Covid-19 health crisis, organisations from every sector have needed to adapt enterprise and customer-facing operations using AI, the cloud and other technologies, to stay relevant for employee and consumer behaviours. The shift to remote working has meant that plans for digital innovation needed to be accelerated, and AI has been key to easing transitions and improving customer experience. Initially, fears were frequently expressed about the evolution of AI meaning that human workers would no longer be needed. But with flaws such as bias and inaccuracies remaining common, the contrary has proven to be the case. "If you've watched any sci-fi film or TV programme, you'll be aware of the fears around AI.," said Samantha Humphries, senior security specialist at Exabeam.
Whether your current experience with chatbots allows you to believe me or not, better customer experiences are possible with human-intelligence-powered AI chatbots. Research by Gartner indicates that within a few years from now, 89% of businesses will compete mostly on customer experience versus 36% four years ago. That means that businesses need to be deploying all the help they can get in CX. A well-trained AI-powered knowledge management system can help to provide more accessible information to your employees, helping them to be more efficient. It can also bring consistency to your CX experience, granted that you provide the AI with the intelligence it needs from your human agents.
Businesses across the globe are going through rapid digital transformation and automation. Cutting-edge technologies like AI, robotics, and IoT are enabling this transformation by enhancing business efficiency and agility. Robotic Process Automation (RPA) and Cognitive Automation are two components of redefining and automating industry-wide business operations. According to a Statista report, the expenditure on Cognitive Robotic Process Automation is expected to reach about 3.62 billion USD globally at a CAGR of 60.9% from 2017 to 2026. Robotic Process Automation (RPA) enables the automation of mundane and repetitive tasks in an organization with maximum accuracy and minimum labour.
AI, ML, and NLP are making it far more feasible to automate many data analytics processes. It hasn't taken long for smart technologies such as Google Home and Amazon Alexa to become embedded in everyday life. In the process, millions of us have become accustomed to the idea of holding something approaching a natural conversation with a machine. Natural language processing (NLP) is one of the key enablers of this voice-controlled revolution. Going forward, we can expect NLP to play a similarly central role in transforming the way we interact with data analytics tools.
In the era of social distancing, using robots for some health care interactions is a promising way to reduce in-person contact between health care workers and sick patients. However, a key question that needs to be answered is how patients will react to a robot entering the exam room. Researchers from MIT and Brigham and Women's Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women's, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker. "We're actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts," says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and the senior author of the study.
Researchers from MIT and Brigham and Women's Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women's, the team found that a large majority of patients reported that interacting with a health care provider via a video screen mounted on a robot was similar to an in-person interaction with a health care worker. "We're actively working on robots that can help provide care to maximize the safety of both the patient and the health care workforce. The results of this study give us some confidence that people are ready and willing to engage with us on those fronts," says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women's Hospital, and the senior author of the study. In a larger online survey conducted nationwide, the researchers also found that a majority of respondents were open to having robots not only assist with patient triage but also perform minor procedures such as taking a nose swab.
Not surprisingly, the COVID-19 pandemic sparked a permanent shift in how businesses in every industry view artificial intelligence (AI) and automation. In the past, many saw these technologies as a nice-to-have; and therefore, pushed them further out on their roadmaps. Today, companies are realizing how imperative these technologies are as a means to be more productive in an all-digital, work-from-anywhere world. Plus, they're starting to question why employees should be trapped by repetitive processes that hinder their ability to move fast and engage customers with empathy at a time when people need it most. Throughout this past year, my conversations with our customers and other business leaders have shifted from casual inquiries about automation, to the immediate need for more efficient and informed teams.
In today's digital age, it is impossible to ignore Artificial Intelligence (AI) and its impacts. AI is important in understanding how businesses operate. AI services and programs have the capacity to transform everything about the business. AI and automation are touted to be the biggest game-changers in the century. Latest companies are now moving to machine learning and artificial intelligence to transform interactions, relationships, revenues, and services.