Artificial Intelligence (AI) has exploded in recent years, driving notable computer science innovations and sparking predictions about what’s next for this industry. In 2016, AI mastered the complex 3,000-year-old board game Go, and beat the world’s best player, a human. In 2017, Google developed a chatbot that pondered the meaning of life, and concluded, “to live forever.” Now this innovative, yet divisive industry is expected to drive worldwide revenue from $8.0 billion to more than $47 billion by 2020.1Advances in AI have changed the way we think about technology and make us consider its impact on our personal and professional lives. As we jump into 2018, lets shed some light on the most recent AI innovations, and what we think this means for businesses and consumers.
Machine learning (ML) is a sub-discipline of computer science and a branch of artificial intelligence. The goal of this AI branch is to develop techniques that allow computers to learn without being explicitly programmed, and then use this knowledge and perform valuable automated tasks. While machine learning systems such as IBM’s Watson and Google’s AutoML platforms are gaining traction, they have yet to be applied to the mass-market. However, the knowledge and ability to build machine learning algorithms have recently grown. In 2018, Data scientists, ML researchers, and developers have access to ample training, tools, and API-driven ML services, giving them the ability to add intelligence to any application. Because of these developments, we are expecting machine learning to make great leaps in 2018. In fact, according to a survey by SoftServe Inc, machine learning is set to go mainstream this year. Some simple and realistic mainstream applications include using machine learning algorithms to assist with product fulfillment, automated shipping centers, on-site customer product recommendations, and website chatbots. For consumers and businesses, machine learning will create seamless, measurable, and customized user experiences.
User experience teams have historically relied on conventional metrics and tools such as usability tests, usage data, and heat maps to create user-friendly systems. However, UX is not solely about data, it’s also about intelligence. With the advent of AI, businesses are embracing a more quantitative approach to UX. AI has made it possible to infuse intelligence into different data sources, creating a more strategic UX decision-making process. AI-powered UI makes optimizing user experience easier due to its ability to analyze large amounts of data quickly, as well as AI’s ability to learn and adjust its behavior in real-time. A powerful UX should not just understand user interests and actions but predict them. User journey mapping with AI showcases the paths that a user is expected to take during their digital interactions with the brand. Merging AI with UX will ensure the creation of a simple, engaging, and profitable user interface.
NLG uses machine learning to develop actionable ideas and insights from a set of data. The technology articulates raw data into human-sounding sentences, statements, or paragraphs without intervention from a handler. If you have ever used a voice-activated system like Cortana or Siri, you know what we’re referring to. Integration of NLG into business processes can fully automate decision-making in organizations. It allows companies to gain a competitive advantage by automating more routine analysis and communication tasks, which not only produces insights-driven recommendations, but also increases productivity by allowing employees to focus on higher level activities. Forrester has named NLG as one of the most important AI technologies for businesses to consider for supporting human decision-making.
Possible applications of the technology include:
Sales: Retail sales organizations can benefit from NLG with its ability to extract key information from detailed reports, such as weekly team, competitor, or top sales reports.
Finance & Accounts: NLG can transfigure an organization’s spend data into meaningful and insightful reports, which in turn enables the company optimize processes and save money.
Customer Service: NLG can provide enhanced digital customer experiences across channels through the use of smart devices, chatbots, or customer support automation systems.
NLG can optimize data analytics and decision-making irrespective of the industry vertical. Failing to implement this technology will cause businesses to lose ground to competitors who adopt NLG early on.
Despite major developments, the AI industry is still at an early stage. However, with a number of tech giants and SMBs already delving into the powerful ecosystem of AI, we will surely witness AI make a dramatic rise with even better use cases in 2018 and beyond.
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