From the end of the 20th century and up until today, technology has advanced in ways we still may not fully understand. This shift in technology has reshaped the way in which companies work, with some fearing a robot revolution – where robots replace humans in every industry. In reality, robotics and machine learning are improving productivity, boosting decision-making and helping employees to abandon boring and time-consuming activities such as data collection from different sources which enables people to use their time to focus on strategic/creative tasks. According to the Data Dilemma Report, 12.5% of staff time is lost in data collection. That’s five hours out of a 40-hour work week. Machine learning has the potential to transform entire industries in ever-changing and exciting ways, by using algorithms to analyze huge amounts of information in a short period of time. The potential of machine learning lies in its ability to learn on its own and improve over time, to better analyze data and give improved feedback. Using machine learning techniques, combined with AI, opens technology up to a whole new world of possibilities. Let’s take a look at some of the industries which are being radically transformed by machine learning technology right now.
The financial world is an industry that struggles to embrace change. Financial workflow is still paperwork-based, requires many types of manual processes, and the need to sift through large amounts of information keeps it an inefficient system. Nevertheless, the investment banking industry has began to notice the potential of machine learning technology, with hedge funds adopting machine learning algorithms for predicting fund trends. Using machine learning, fund managers can now identify market changes earlier than before, as opposed to traditional investment models which take much longer. Trading algorithms based on sentiment and insights from social media, as well as other public data sources, can be utilized to monitor financial news and predict the behavior or possible trends of the stock market. Self-learning AI based algorithms are able to build trends after eliminating negative perceptions, fears, and concerns across the general market atmosphere. By ignoring negative perceptions, the algorithms are able to quickly identify trends in the market. Until recently, monitoring social media trends and news used to be a slow manual process, but as iknowfirst explains, algorithmic trading is designed to react extremely quickly to market changes, measuring trading opportunities in milliseconds.
Understanding consumer behavior is a vital aspect of marketing. It helps marketers understand how potential customers will react to a new product or service. It also helps companies identify new opportunities in the market. When analyzing consumer behavior, marketers can rely on different sources of information such as social media activity, mobile app usage or customer service polls. Regardless, the amount of information to be analyzed can be overwhelming and time consuming. Machine learning software has the ability to make predictions based on past consumer behavior and learn what’s the most effective strategy for each customer. As a result, by better understanding the consumer, companies can build stronger relationships with them. Chatbots are a good example of machine learning applied to building customer relationships. Chatbots are “conversational agents” and use text or voice commands to answer user questions or guide consumers with their purchases. Online chatbots save time and effort by automating customer support and personalizing the answers to specific questions from consumers, instead of displaying a generic, long list of information. Additionally, chatbots can be used to collect information about users for future questions or marketing campaigns analysis.
The healthcare system is another paperwork-based industry that made huge advancements when electronic medical records were first implemented. Machine learning algorithms can process large amounts of healthcare records without breaching confidentiality contracts. In addition, machine learning goes one step forward and uses the power of analytics to better inform clinicians of a patient’s situation so doctors can arrive at an accurate diagnosis faster, and a possible treatment, while understanding the possible outcomes and cost of each one. Machine learning can be trained to look at images, identify abnormalities, and point to areas that need attention, thus improving the accuracy of all these processes and medical predictions.
Robotic surgery allows surgeons to perform procedures that wouldn’t be possible by human hand. Some systems use computer vision, aided by machine learning, to identify distances or a specific body part. Self-supervised learning approaches enable robots to generate their own training examples in order to improve performance.
In 2011, IBM watson’s question-answering system wowed the tech industry when it defeated two of the best Jeopardy contestants. IBM Watson Supercomputer uses machine learning to process vast volumes of content within short time periods, but its main goal is to deliver deep insight regarding the information processed while developing its learning capability. Robots might be smarter than humans but there’s nothing to be afraid of, on the contrary, IBM watson was already applied in the healthcare industry to help oncologists find the most effective therapies and available clinical trials for each type of cancer and mutation. Similar examples of its effectiveness can be found within the marketing and finance field, among others. We are entering a world where people and machines can finally work in harmony. A special report from Bank of America, Merrill Lynch predicts the global market for AI and robots will be just under $153 billion by 2020, and some industries will experience an increase of up to a 30% in productivity through the use of those technologies alone. Companies embracing the potential of machine learning will provide their teams with a tool to do their jobs in a more effective way, allowing them to focus in areas where they can be more creative rather than focusing on repetitive manual tasks that belong to the past. Machine learning can be implemented in a wide range of industries, from autonomous vehicles to an algorithm that prices diamonds. Machine learning helps companies take a step forward into the future by making a real impact in the present.