Machine learning has been one of the top tech new topics in recent months and is now being widely applied to businesses. Briefly, machine learning (ML) is an application of AI (artificial intelligence) that allows systems to learn and improve without being directly programmed. Focussing on the development of computer programs that can access data in order to learn autonomously, machine learning is being used by Google on its AI Platform which is bringing all its services, from data preparation to the training, tuning, deploying, collaborating and sharing of machine learning models.
Today ML has the ability to compute vast quantities of data and to collect metrics while developing more intelligent algorithms that will be able to perform complex tasks. Take Periscope Data which is invested in taking machine learning and AI to evolve into a deeper evolution of data analysis and access where humans and machines in what is a quickly evolving business culture today. Where real-time intelligence for complex decision-making is crucial for businesses today, that forecasting the performance of the markets in future years will be best accomplished with ML over human force.
There are challenges with the integration of AI within businesses which are often resistant to change. For instance, there needs to be a prioritization of IT applications over IT architecture where companies ought to stop separating digital from AI and instead think of their desegregation. Employee engagement with AI has recently been shown to increase performance and retention in the same way that the Internet Of Things (IoT) has also demonstrated similar advantages. Additionally, AI can function to promote a healthier work culture as TechRepublic recently reported that by analyzing email conversations and biometric data, companies can more easily promote a sense of belonging among employees, identify red flags, and create an engaging work environment.
In fact, ML has been used across various disciplines from healthcare to education and it is showing no sign of slowing down. What is clear from the advantages of using AI within the business is that a majority of companies are actively working on a roadmap for handling data (68 percent), yet only 11 percent of these companies have completed this task. The models which are the most successful today are those which allow certain tasks to be taken over by AI whereby machine learning can acquire more information from and predict consumer behavior. Current ML models allow for rapid iteration of data and they deliver quick, reliable data sets which impact directly on the culture of work for businesses involved in any sort of real-time analytics, data integration and management, sales/revenue forecasting, and personal security and data processing.
As machine learning has provoked worries in many quarters that our jobs will be replaced by AI, the reality is that machine learning is already merely allowing humans to get on with the more interesting facets of their jobs as AI slogs away at the more mundane aspects of operations such as data mining. It's time for us to embrace machine learning for what it offers us instead of worrying what it might take away. In the end, we can look to ML as a time-saving device that allows humans to explore their more creative ambitions while ML is in the background crunching numbers and generally taking on the more mundane tasks.