Is 2017 the year of Artificial Intelligence?
It’s included in most of the published Top 10 Lists of Disruptive Technologies for 2017, but will AI live up to the hype?
It’s included in most of the published Top 10 Lists of Disruptive Technologies for 2017 - Gartner has it at No. 1 on its list - yet Artificial Intelligence (AI) has such a long pedigree in the 20th Century that it’s remarkable that AI is still considered HOT in the 21st.
Way before Siri, Watson and Uber's self-driving cars, in the 1950s the nascent field of AI technology was hailed for its promise of an imminent dawn for of a new age of intelligent machines simulating the human brain.
Over subsequent decades the dream was never realised, but there are many factors behind a renewed belief in 2017 that we may be on the cusp of AI fulfilling its promise.
Forrester Research predicts a +300% increase in investment in artificial intelligence in 2017 compared with 2016.
IDC estimates that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.
"Software developers and end user organisations have already begun the process of embedding and deploying cognitive/artificial intelligence into almost every kind of enterprise application or process," said David Schubmehl, research director, Cognitive Systems and Content Analytics at IDC.
"Recent announcements by several large technology vendors and the booming venture capital market for AI startups illustrate the need for organisations to be planning and undertaking strategies that incorporate these wide-ranging technologies. Identifying, understanding, and acting on the use cases, technologies, and growth opportunities for cognitive/AI systems will be a differentiating factor for most enterprises and the digital disruption caused by these technologies will be significant."
For a business owner, where is this likely to make an impact? While landmarks such as Watson’s victory in a game of Jeopardy or Google’s AI beating the best Go player in the world, are critical to the evolution of AI technology, practical solutions that solve real business problems are required.
Of these, technologies such as voice recognition (VR) and natural language processing are leading the charge.
It is widely recognised that voice recognition has a long way to go to mimic human capabilities, although it can be effective with a trained user using a limited vocabulary. The complex challenge is illustrated by imagining telling a computer how to appropriately respond to the casual question, "What's up?"
The technically correct answer - "Up is the opposite of down," – is one unlikely to be given by a human.
We've all experienced poor speech recognition when calling businesses or struggled to interpret a text message converted from a voicemail.
Microsoft claims its new Cortana speech recognition software has reached parity with humans but still isn't perfect.
Text recognition technologies are already being used to extract relevant data from documents and speed up the filling in of fields. ABBYY Compreno technology for instance, is able to understand the meaning of text. It allows banks to receive a borrower's information and make sound credit decisions. It speeds up the technical support process and can respond to a client directly (or direct a client to a proper tech support engineer). It helps HR with routine work such as reviewing CVs, extracting required potential candidate information and matching data with job descriptions.
Although David Yang, a co-founder of ABBYY, author of a large number of scientific publications and who holds many patents, believes, “There is still a huge gap between what many people are doing at work, and what robots and AI can do.
“It is relatively easy to automate everyday tasks, but there are many non-standard challenges which machines cannot manage. For example, there are many routine tasks which seem easy for humans, but are impossible for a machine.
“This phenomenon is known as Moravec's paradox. To fold a towel, a person needs only a few seconds. In 2010, a robot performed this task and it took nearly 25 minutes. From this we may conclude that that cooks, gardeners, plumbers, and dentists won’t be replaced by AI in the near future. All of these jobs are associated with sensorimotor skills and many of them require brainstorming, recognition of huge volumes of images, and making insights.”
IDC believes the industries that will invest the most in cognitive/AI systems are banking and retail, followed by healthcare and discrete manufacturing.
At JPMorgan Chase & Co., the global financial services firm and one of the largest banking institutions in the United States, a learning machine is already processing financial deals that once kept legal teams busy for thousands of hours.
According to reports, “The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of work each year by lawyers and loan officers. The software reviews documents in seconds, is less error-prone and never asks for vacation.”
Natural language processing, or NLP, refers to a field of technology focused on the application of algorithms and mathematical models to analyse human language. Its use has grown sharply as companies grapple with data volumes that make it virtually impossible to perform data analysis using techniques that don't take advantage of analysis and automated summarisation.
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