Robots improve productivity when they are applied to tasks that they perform more efficiently and to a higher and more consistent level of quality than humans without causing total hours to decline. In a study focused specifically on robotics for the Centre for Economic Performance at the London School of Economics by Georg Graetz and Guy Michaels (2015) concluded that robots increased annual growth of GDP and labor productivity between 1993 and 2007 by about 0.37 and 0.36 percentage points respectively across 17 countries studied, representing 10% of total GDP growth in the countries studied over the time period and comparing with the 0.35 percentage point estimated total contribution of steam technology to British annual labour productivity growth between 1850 and 1910. Also, a more recent study carried out by the Centre for Economics and Business Research (2017) found that investment in robots contributed 10% of growth in GDP per capita in OECD (The Organization for Economic Co-operation and Development) countries from 1993 to 2016 and the same study found that a one-unit increase in robotics density is associated with a 0.04% increase in labour productivity. Looking ahead, the McKinsey Global Institute (2017) predicts that up to half of the total productivity growth needed to ensure a 2.8% growth in GDP over the next 50 years that will be driven by automation.
As an example, keeping the productivity of the workforce constant, if the machines they use increase in productivity, the Total Factory Productivity (TFP) still rises. Robots are unquestionably making the “machine” aspect of production facilities more efficient. Even if the human component of factories remains constant, increased efficiencies from robotics inevitably leads to more productivity growth.
Besides that, Robots can work in any environmental condition whether in the vacuum of space, deep depths of the ocean, and even in extreme heat or pressure where human safety is a huge concern.
Artificial Intelligence of today using algorithms aids in predictive policing and it is being viewed by many as the future of law enforcement as police departments in California, particularly Santa Cruz and London are among the countries who are already using predictive policing and it has proven rather successful in reducing the crime rate. According to Charlie Beck (2017) who the chief of the Los Angeles Police Department says predictive policing will make the law enforcement agencies proactive, rather than reactive which means instead of focusing on what happened, police will be able to focus of what is about to happen and work on preventing it. UCLA Anthropology professor Brantingham (2011) and his colleagues began testing in Los Angeles Police Department’s Southwest division, and two of Kent’s districts predicted crime types were burglary and auto theft while Kent tested the aforementioned crimes in addition to violent crimes such as robbery or assault. Besides that, Professor Brantingham (2011) revealed that the areas assigned by the algorithm, and patrolled by the officers, had 4.3 fewer crimes than the Police had expected (a reduction of 7.4%), while the analyst without the predictive model predicted 2 crimes a week and the results in crime reduction led not only the adoption of the algorithm by both the LAPD and Kent police, but the creation of PredPol, The Predicitve Policing Company, which is available to the Police Departments nationwide and abroad. For example, Police and law enforcement around the world will find it beneficial as researches calculated using the predictive algorithm, the LAPD would save about $9 million dollars a year and will greatly reduce the crime rate for years to come.