The development of Artificial Intelligence (AI) has been remarkable and, in recent years, has experienced several breakthroughs. Will 2017 continue this trend? Commercial enterprises are procuring and developing systems that use AI to aid in their corporate goals, enhancing their performance through the use of autonomy.
By exploiting advancements in big data analytics, processing power and clearer computer systems and networks, companies can use automation and AI to augment their current work processes and generate methods of long-term value creation.
The stage is set, however, for AI to rise higher than ever before as large players including Apple, Facebook, Google and Microsoft all open-source or share their latest research in AI, to advance collective advancement.
AI, Automation and Machine Learning
The benefits of AI and automation have been public for some time. A key example of the power of automation is that of the US credit, debit, and prepaid card industry using automated systems to monitor more than 1200 transactions per second with automated tools that identify fraudulent transactions within ten milliseconds on a customer base of more than 1.3 billion cards.
Computers and robotic machinery can not only perform a range of routine, physical work activities better and more cheaply than human workers, but they are also increasingly capable of outperforming humans and accomplishing activities that include “cognitive capabilities once considered too difficult to automate successfully, such as making tacit judgments, sensing emotion, or even driving.”
According to research by Mckinsey & Company, “almost every occupation has partial automation potential, as a proportion of its activities could be automated,” concluding that approximately $15trn in wages could be absorbed by robotic counterparts due to the fact that about half of all the activities people are paid to do in the world’s workforce “could potentially be automated by adapting currently demonstrated technologies.”
First, the most susceptible to automation are jobs that possess a physical element, located in highly structured and predictable environments, alongside data processing roles. Within the US, approximately 51% of activities in the economy are made up by roles that have a component which is easily automated, accounting for almost $2.7trn in wages, including roles in manufacturing, accommodation, retail and trade. Secondly, it is not merely low-skill work that could be automated; as processes are transformed by, and integrated with, the automation of individual activities, “people will perform activities that complement the work that machines do, and vice versa.”
To take the legal profession as an example, several international firms including Clifford Chance, Dentons, Travers Smith and Allen & Overy have adopted systems which speed up research, help with due diligence and absorb routine legal work, freeing up time to be spent on more bespoke tasks, such as negotiation and dispute resolution.
One such AI is ROSS Intelligence. The system is an advanced research tool that sifts through all connected databases and large volumes of unstructured, text-based data to collect and source key information. The system is designed to cut down on the many, unbillable hours of information retrieval and research that lawyers conduct.
The AI’s unique ability to understand plain language questions from users (questions one would normally ask a lawyer) removes the need for specificity and technical knowledge of Boolean searches or the requirement of keywords. What’s more, the less time lawyers are spending on their work, the cheaper it ultimately is for the client. Thus, since clients are the lifeblood of a law firm, it is in the best interests of firms to push for such a change, calling for tech-savvy lawyers.
Change Is Coming
Law is not the only profession that is seeing change. FinTech, for example, is changing the face of the financial world and its impact will be seen more in 2017. However, automation will not occur overnight nor will robots come to take your job anytime soon, as currently less than 5% of professions in America can be automated fully.
Additionally, the effects at a micro-level might be more apparent such as the effect on individual workers, but the impact is still slow at a macro-level. Furthermore, in a recent survey, Forrester Research, which surveyed 612 business and technology professionals, found that while 58% of the respondents said their organisations are researching AI, only 12% said they use AI systems at work. As a result, one can see the demonstrable gap between interest in AI in 2106, and its adoption rate, proving that, currently, it is still a long way from full integration.
As shown, while the adoption rate is still relatively low, AI has the potential to cannibalise low-level work across several industries. It is this opportunity. Therefore, that will enable keen companies to innovate and enjoy large financial benefits, particularly as machine learning develops.
A further development in the AI sphere is machine learning. According to Ovum, an analyst and consultancy firm specialising in global coverage of IT, machine learning itself as a technology is the “shiny new thing”, and it will be the “biggest disruptor for big data analytics in 2017.” Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed to do so. To learn, the system sifts through data and looks for patterns. Yet, rather than using the data for human consumption, machine learning instead uses that data to map patterns and adjust processes and actions accordingly. Within this, there are supervised and unsupervised algorithms. Supervised algorithms can apply what has been learned in the past to new data, while unsupervised algorithms can draw inferences from datasets.
In supervised algorithms, one knows the input and output variables, and thus one knows the answer. Thus, the algorithm can be thought of as a teacher, showing how one gets from the input to the output, as there is a correct answer. Unsupervised algorithms, on the other hand, are left to their own devices to map data, modelling underlying structures to learn more about the data itself rather than present an answer.
To take Facebook’s newsfeed feature as an example, the platform uses machine learning to personalise each member’s newsfeed. If a user spends more time on certain pages or browsing a friend’s posts, the feed will show more posts by that particular page or friend. If the user no longer spends as much time on the particular item, the feed will adjust accordingly.
While AI and machine learning appear incredibly beneficial, automating systems for companies are expensive to deploy. Thus, only the larger players in the industry can afford to utilise these systems.
West vs. East
While the top players in the US – Apple, Facebook, Google and Microsoft -are utilising open source to share their advancements and promote AI development, one must not forget about other top contributors. 2017 could be the year that China presents itself as a key player in the AI arms race.
Not only has China’s leading search company, Baidu, had an AI-focused lab for some time, enabling it to utilise voice recognition and natural language processing, other players are now catching up. Tencent, with its WeChat messaging app, created an AI lab last year and kick-started a technological talent recruiting drive. Further, Didi, the ride-sharing giant that bought Uber’s Chinese operations in 2016, is reportedly working on its own driverless cars. These developments are underlined by the Chinese government signalling its desire to see the country’s AI industry blossom, pledging to invest about $15bn by 2018.
Whether it is using automation to simply cut down on work process time or make a process more efficient, AI still has the potential to disrupt current business models, improve customer experiences and be exploited by enterprises for a competitive advantage and 2017 will see progress towards that reality. Quentin Gallivan, the CEO of Pentaho, predicts that “the early adopters of AI and machine learning in analytics will gain a huge first-mover advantage in the digitalization of business.” Alan O’Herlihy, CEO of Everseen, opines that “AI will inform, not just perform, across industries.”
On top of automating processes and leading to cleaner work systems, advancing AI also demands stronger business infrastructure, using better internal structures with better cyber security defences. AI will bring a range of improvements in the space of businesses using technology.
One can see AI as a connected but distinct part of a network of things that can be used to advance business goals. From developing customer services to using AI to accurately predict the best investment strategies, this disruptive technology will soon become a sustaining technology as more and more companies adopt it.
2017 will see improvements in AI and connected systems, not only for companies that have already adopted these technologies but for consumers as a whole.
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