As machine learning continues to lead the way in the tech industry, the British Royal Society's report underlines the importance of directing investment to ensure everyone benefits. Here's how chatbots with robust natural language processing can solve this.
It is already one of our most important technological subfields and will only increase in relevance in the coming years. But only 9% of people polled by the Royal Society as part of their report into the value of machine learning had even heard of the term.
Despite the general lack of awareness surrounding the terminology, people are already utilizing machine learning on a daily basis. Meanwhile, we have not even begun to realize its full potential. In fact, the British academy calls for a greater investment into its research in order to ensure it benefits everyone.
There is an opportunity to reap huge rewards from machine learning for both the public and businesses if they play their cards right. To maximize their hand investors could concentrate on chatbots and search solutions with robust natural language processing (NLP).
An increased appetite from the public
Machine learning can provide a better standard of living for everyone in the form of improved medical diagnoses, making expensive services more readily available or by taking on routine tasks. These examples of “high social value, low social risk” services received highly positive feedback from the public.
Fortunately, chatbots and search solutions can already fulfill these criteria when built on a foundation of intelligent NLP.
Machine learning: thriving where humans struggle
Machine learning with NLP is already providing decision-making support for doctors. Given that emergency services are already employing chatbots to provide basic diagnoses, a bot which utilizes both machine learning and NLP is the next step.
Doctors at the University Hospital of Marburg are diagnosing rare diseases using a combination of the two technologies. Machines were fed vast amounts of data from patient histories and case reports in order to be able to come up with the correct diagnosis for rare diseases in seconds. Not only is the process accurate, but the speed means the hospital could reduce their waiting list of 6000 people.
Machine learning thrives where humans struggle: in their unbiased approach to analyzing data. Humans are more likely to be attached to their own theories and will be inclined to maintain a dogmatic approach to a diagnosis. Meanwhile, machine learning is able to review information it is fed in order to make rational connections, forming sensible hypotheses based on a large and unemotional data set.
BenevolentAI provides a compelling example, using NLP to formulate new ideas to improve human health from the databases and scientific papers it reads. Given that living organisms are essentially complex systems similar to a computer, it is conceivable that a combination of machine learning and NLP will be the perfect tool to promote human health.
In fact, chatbots have already started to support emergency services by offering diagnoses for some of the more routine ailments. The next big step is creating bots powered by machine learning and NLP in order to immediately tackle the more complex diseases. Not only will it relieve the pressures on hospital staff by identifying illnesses, but it will also be a massive PR success in the eyes of a public keen to see an improvement in services. This could create a global interactive database of medical conditions allowing healthcare providers in remote locations access to extensive medical knowledge.
Making services more available
The legal sector is already making services more available thanks to chatbots powered by a combination of machine learning and NLP.
Law firms are able to examine simple legal queries and provide advice using machine learning through a chatbot-style interface. Aqimus ID is an example of one such method, claiming it is able to complete anti-money laundering and compliance checks in a matter of seconds compared to the nearly 45 minutes it takes an unassisted human.
The introduction of chatbots such as Do Not Pay, which offers free legal advice to people for everything ranging from parking fines to asylum support, shows society’s appetite for a solution. Adding machine learning to any chatbots that incorporate NLP will increase their accuracy and, therefore, the public’s trust in the technology.
Taking on the routine jobs
Machine learning and natural language processing are already responding to customer requests and performing administrative tasks.
While NLP seeks to understand the context of the requests, machine learning is able to improve its response as it builds up more data on customer interactions. In theory, chatbots which employ both NLP and machine learning will be able to answer customer questions and complete routine tasks at a high accuracy rate as soon as it goes live – machine learning will then be able to build on that to create unprecedented matching levels.
High social value, low social risk
The response within the Royal Society’s report highlights what people want to see as machine learning takes on a more prevalent role in our lives. In essence, these are solutions which are objective and accurate while delivering enhanced services.
To ensure people are confident in machine learning’s capabilities it is crucial to offer evidence of how the technology is more accurate than a human. The recent experiences in the fields of law and medicine show the answer is to direct investment towards fields where machine learning can make services more accessible by reducing costs and speeding up the discovery or matching process.
The machine learning bandwagon is gaining speed and smart investors will jump on it now while there is still space.
Inbenta utilizes its patented natural language processing and +11 years of research & development to create interactive chatbots with an industry leading +90% self-service rate.
Companies around the world including Ticketmaster UK utilize the InbentaBot to maintain a personal service for their customers while reducing support tickets.
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