The success of television series Westworld, groundbreaking in its portrayal of AI, is testament to how the technology now pervades the public consciousness. It is no longer just futurists who concern themselves with how it will impact society, businesses and individuals are all using it every day and making real plans for adoption on a wide scale.
Almost every major tech giant is developing new applications for AI and machine learning. Facebook, Google and Microsoft invested more than $8.5 billion on AI research, acquisitions, and talent in 2015 alone. Over 2016, we saw a number of advances in the field, and the pace of evolution is only going to increase next year as investment goes up. According to Forrester, there will be an increase of more than 300% in investment in 2017 compared with 2016, while Gartner has put AI at the top of its new ‘Top 10 Strategic Technology Trends for 2017’ report.
However, AI implementations next year will be focused on incremental improvements. Earlier this year, Google's Head of Machine Learning John Giannandrea told a Google I/O panel at the company's developer's conference that we are currently 'kind of in an AI spring.' It is heating up rapidly though, and summer is not far away. Businesses that use AI, big data and IoT technologies to uncover new business insights ‘will steal $1.2 trillion per annum from their less informed peers by 2020,’ according to Forrester, so organizations need to stay ahead of the game if they are to maintain competitive edge.
We’ve looked at some of the main areas that AI will have an impact next year.
Medical diagnostics and treatment
Healthcare is one of the most pressing applications for AI, with hospitals collecting increasingly large amounts of data through wearables and other devices. CBI insights has identified 22 companies developing new programs for imaging and diagnostics, and the next year should see some significant advances.
In July, Google-owned DeepMind announced that it had partnered with Moorfield Eye Hospital in the UK. Machine learning algorithms will be applied to one million anonymous OCT (Optical Coherence Tomography) scans, the goal being a system that teaches itself how to recognize conditions that pose a threat to someone’s eyesight from just one digital eye scan. This could prevent a host of eye-related diseases, from age-related macular degeneration to sight loss that occurs as a result of diabetes.
Moorfields Professor Sir Peng Tee Khaw said of the partnership: ‘Our research with DeepMind has the potential to revolutionize the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration. With sight loss predicted to double by the year 2050 it is vital we explore the use of cutting-edge technology to prevent eye disease.’
AI can even be applied in emergency rooms. Beth Israel Deaconess Medical Center in Boston, for one, has applied machine learning algorithms to workflow processes to enable medical staff to better capture patients' ‘chief complaints’ on arrival. Steven Horng, an emergency physician and computer programmer at Beth Israel Deaconess Medical Center in Boston, noted that: ‘Being able to capture chest pain as a discreet entity can be very valuable downstream for clinical care and in launching things like order sets and clinical pathways.’
Tech giants Microsoft and IBM are also deploying their vast resources to create AI that can analyze tumors and help design new medication regimes, and with such prestigious backing behind it, it is likely that next year we will see the first springs of a machine-led healthcare system, one that’s more efficient, cost-effective, and accurate.
Mark Zuckerberg this summer announced that he has built an AI system. In his spare time. Which, aside from being another reason to be jealous of Mark Zuckerberg, is evidence that we may be closer to living like the Jetsons than we think.
There is even a robot that can fold your laundry set to go on sale next year. The ‘Laundroid’, developed over 10 years by Japanese firm Seven Dreamers with $49 million investment from Panasonic uses AI to fold a shirt in about four minutes, saving around 375 days of folding over your lifetime. While some critics have called the technology ‘ridiculous, frivolous, and a waste of engineering talent,’ it could help usher in a brave new world where clothes are just thrown into a laundry basket and then washed, dried, folded, and put back into the cupboard with no human involvement whatsoever.
In a 2016 TechEmergence survey of AI executives and startup founders, 37% said virtual agents and chatbots were the AI applications most likely to take off in the next five years. Apple’s Siri, Microsoft’s Cortana, Google’s OK Google, and Amazon’s Echo services are already far advanced relative to where they were a couple of years ago, and developments made in speech analytics and natural-language processing means that they are getting better all the time. Even business app giant Oracle is creating chatbots for its apps. Market research firm, TMA Associates, estimate that the chatbot and digital assistant market will reach $600 billion market by 2020 as a result of such conversational user interfaces.
This has huge implications for the customer support industry. According to IBM, 65% of millennials prefer interacting with bots to talking to live agents, and as we get more accustomed to it, this number will only go up. Automated customer service would mean an end to the waiting in line and likely happier customers. Equally, it should mean less call center workers spending their days getting screamed at for something that wasn’t their fault.
Google recently introduced two new AI tools that allow companies to analyze language and convert speech into text. They are already being used by UK-based grocery delivery service Ocado to rank and respond to customer queries based on how irate the complainant sounds, which seems to be the future, and next year should see more wide scale adoption. Equally, once people cotton on to the fact that their AI customer service is bumping them up the queue based on how irate they sound, it is likely that they are going to be getting some truly horrific messages from people with minor complaints looking to get dealt with first.
A 2015 report from McKinsey & Company revealed that a dozen European banks had moved from traditional statistical analysis modeling to machine learning. They cited increased new product sales of 10% and churn and capital expenditure falling by 20% as their reasons.
The financial services industry has long been at the forefront of technology, with anything that can enable greater speed in trading, financial analysis, and risk assessment likely to bring huge profits. AI and machine learning can provide this speed, and enables more in-depth risk assessment to help analysts and underwriters find information that may have been hidden, deliberately or otherwise, to ensure investments are the right ones. The amount of information available to analysts already greatly outstrips their ability to comprehend it, and AI is really the only option.
Zest Finance, for example, helps lenders in different credit segments by assessing their clients by taking every bit of customer data they can legally get their hands on and applying machine learning algorithms to analzye it. Their model has been shown to beat the best-in-class industry score by 40%.
At the moment, AI in banking is still at a fairly nascent stage, a McKinsey survey found that just 3.5% have actively deployed AI. This number is going to rise rapidly over the next year, however, and banks look to find any way they can to hold on to their position.
According to UN projections, the global population will reach 8.5 billion by 2030 and 9.7 billion by 2050. As a society, we cannot feed the 7.3 billion people we currently have, with roughly 795 million people in the world lacking sufficient food to lead a healthy active life.
There is vast scope for agricultural productivity to improve. Traditional farming practices are still shockingly outdated in many parts of the world. AI is one tool that can help best achieve this, and a number of startups are already making progress. Switzerland-based agricultural tech firm Gamaya, for example, this year announced $3.2 million in funding for its AI project - drones equipped with hyperspectral cameras that capture changes in water and fertilizer use, crop yields, and pests, data from which is analyzed using AI algorithms to highlight potential issues to farmers. The technology can also be used for finding patterns that can predict outcomes of farmers’ decisions, giving them a better idea of where to invest and apply appropriate resources.
Another team of researchers at Penn State and the Swiss Federal Institute of Technology (EPFL) have fed a network of computers with over 53,000 photos of both healthy and unhealthy plants in an attempt to recognize specific plant diseases. Such technology will provide the basis for field-based crop-disease identification using smartphones. The system has been able to identify both crops and diseases – from photos – with an accuracy rate of up to 99.35%.
Applications such as agriculture and healthcare are proof of AI’s ability to serve humanity and help us. There are justified concerns about what will happen to jobs, but we can be hopeful that AI will help 2017 be a better year than 2016.