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 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.
The future culture of work is already upon us as many companies have shifted toward the “community” model of working as the boring tasks are left to ML and decisions will be more and more data-driven and teamwork entirely coordinated by AI. In fact, Microsoft announced its research last Fall which shows that companies using AI are outperforming by 5% those which have no AI strategy. Another outcome of AI on business culture is that more decisions within businesses will be based on data causing the business model to have no strict format. Where probability will trump planning and strategy, businesses will have to become more flexible. But how will this boon to work culture translate to business today?
Studies have shown that many clients still do not trust AI which makes it difficult to convince those within any specific business culture that AI can work to their advantage. A recent study conducted by the research firm Savanta surveyed 5,000 consumers around the world about their views of AI, morality, ethical behavior, and empathy. The results demonstrate that over half of the respondents believe that AI is biased and less than one-third of the respondents felt comfortable with businesses using AI to interact with them. While consumer culture’s distrust of AI might not initially seem to inflect business culture, the reality is that machine learning cannot fully take off within business culture until consumers are also on board. Imagine, if you will, flying in a plane where only half the plane has life vests under their seat. To create a healthy culture where machine learning is fully integrated, everyone needs to be on board.
In the recent and important discussions involving basic income, we need to look to ML as a means to an end in a workforce which is quickly being reduced by automation and which can profit from the more human and creative side of labor. The future of business culture is not only in flux, but so is our current culture of work and everyday living.
originally posted on Forbes.com by Julian Vigo
