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 the last decade, Data Management personnel solved business problems with data; in the next decade, highly capable machines using Artificial Intelligence Applications will solve problems with available data in a scale unheard before. As the algorithm economy continues to gain momentum among global businesses, the challenges facing Deep Learning are still real. Big Data going mainstream may successfully help combat the Data Management issues making Big Data and Deep Learning the formidable combination for unlocking any complex data handling problem. How Artificial Intelligence is Revolutionizing IT Operation Analytics companies are already leveraging AI-powered Operations Analytics to optimize real-time business operations with "unprecedented granularity, preciseness, and impact."
In January this year, a Japanese insurance firm replaced 34 of its employees with an AI system based on IBM Watson. One of the reasons why IBM Watson is so important is because IBM has opened Watson up to businesses and developers. IBM opened up Watson application programming interfaces in 2015, allowing developers to use the cloud-based artificial intelligence system with their own programs. Cognitive intelligence, artificial intelligence, and virtual reality all present opportunities for businesses to serve their customers in new and exciting ways.
Artificial intelligence helps farmers, doctors and rescue workers make a positive impact on society. There is a growing number of AI applications actively improving people's lives and creating positive change in the world. "AI will deliver societal transformation on par with the industrial, digital and information revolutions," Bryant told the SXSW audience. One of AI's greatest impacts could be in food production -- an industry challenged by a rapidly growing world population, competition for natural resources and plateauing agricultural productivity.
Comedian TJ Miller, of HBO's Silicon Valley, performs a standup in which he tells of an entertaining, yet extremely terrifying time in which he suffered a life-threatening brain malformation. He was in the middle of pitching a movie idea when he collapsed to the floor while seizing, and was rushed to the hospital. His story continues, he explains that he suffered from an arteriovenous malformation (AVM) hemorrhage, which is essentially an abnormal connection between the veins and arteries. When Miller awoke from his coma in the Cedars-Sinai ICU neurology ward, he found a nurse standing over him saying, "Your doctor cannot be here, but a proxy will be here in just a bit." He then explains how he was given little to no information about his condition.
In an age of infinite connections, digital technology has opened our minds to what a networked world could mean for healthcare. For example, if Amazon[i] Prime can email us when we're about to run out of dog food, why can't our doctor's office text us when it's time for a physical, check our calendar, and offer available slots? If Fitbits can automatically upload data into our smartphones, why can't they upload data into our electronic health records, so software can assess the information for warning signs of health problems? And, if Facebook can recognize our friends' faces in seconds, why can't systems identify patients and their medical history nearly as quickly? Despite life-saving accomplishments, healthcare has also been characterized by fragmented care, inefficient workflows and waste.
As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of the year, the discussion has turned from what it is, to how to drive value from it, to the tactical: how to make it work. IoT will produce a treasure trove of big data – data that can help cities predict accidents and crimes, give doctors real-time insight into information from pacemakers or biochips, enable optimized productivity across industries through predictive maintenance on equipment and machinery, create truly smart homes with connected appliances and provide critical communication between self-driving cars. The possibilities that IoT brings to the table are endless. As the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to a mind-boggling level. This data will hold extremely valuable insight into what's working well or what's not – pointing out conflicts that arise and providing high-value insight into new business risks and opportunities as correlations and associations are made.
While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Lumiata has developed predictive analytics tools that can discover accurate insights and make predictions related to symptoms, diagnoses, procedures, and medications for individual patients or patient groups. The Care Trio team has developed a three-pronged approach that helps doctors devise and understand the best care protocols for cancer patients.
Big data, AI and IoT wil have the biggest technological impact on healthcare, according to a Silicon Valley Bank survey. That is the outcome of a new Silicon Valley Bank survey among 122 health IT company founders, executives, and investors. Though big data will continue to be a primary driver of innovation in the healthcare industry, it may run into adoption challenges and regulatory hurdles in the near future. Forty-six percent of participants believe that big data will have the greatest impact on healthcare over the next year, followed by 35 percent who believe artificial intelligence (AI) will be a major game-changer. Almost 15 percent say the same about IoT (Internet-of-Things).
The Machine Learning as a Service (MLaaS) market size is estimated to grow from $613.4 million USD in 2016 to $3.7 billion USD by 2021, according to the company Research and Markets. The adoption of cloud-based technologies, strong need to understand customer behavior and advancements in technologies are increasing the adoption of MLaaS across end users. MLaaS – with the help of pattern recognition, advanced analytical methodologies and APIs – is able to make better decisions. The professional services segment is the largest contributor, whereas the managed services segment is expected to grow at the highest CAGR. Professional services include consulting and integration, support and maintenance, and network security services and analytics, which are increasing the growth and awareness of MLaaS as the biggest use case of NFV technology.
As healthcare moves to a model of any-time, any-place, continuous and personalized care, it is important to identify the key technologies that will enable this transition and work toward their implementation into different care settings. Frost & Sullivan's Visionary Healthcare research has identified several technologies that are most likely to impact healthcare paradigms by 2025. It is interesting to note that technological advances in the fields of computing, machine learning, nanotechnology and electronics are all playing a role in helping reshape the industry. The figure below provides an overview of the top technologies that will change this industry dramatically, and an analysis of the time frame for their commercialization and maturation. We are now beginning to see larger data sets in healthcare research and delivery to analyze and make sense of entire genome sequences; impact of environmental, behavioral and hereditary factors on health; population health data; patient generated health data; etc.