Machine learning is an exciting, proven concept that allows computers to figure things out for themselves. Instead of every action being explicitly coded, the computer applies pre-configured rules and data sets to perform complex calculations. This technology leverages the cloud in order maximize speed and cost-effectiveness.
Machine Learning In the Medical field
Machine Learning in the medical field has improved patient’s health with minimum costs. Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient. Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles.
More effective human labor.
The exciting and somewhat scary byproduct of increased reliance on AI is that we will need less man hours to compile reports and guide decision-making. This might sound like we’ll lose jobs and increase unemployment in 2018 due to robots taking human jobs. This isn’t the case. Data-entry personnel will find ways to use their experience to provide value to companies that no longer require as many hours of manual data mining.
Manual data entry
Inaccuracy and duplication of data are major business problems for an organization wanting to automate its processes. Machines learning (ML) algorithms and predictive modeling algorithms can significantly improve the situation. ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks. Also, knowledge workers can now spend more time on higher-value problem-solving tasks. An AI-based firm has developed a natural language processing technology which scans texts and determines the relationship between concepts to write reports.
Spam detection is the earliest problem solved by ML. Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML. Thanks to ‘neural networks’ in its spam filters, Google now boasts of 0.1 percent of spam rate. Brain-like “neural networks” in its spam filters can learn to recognize junk mail and phishing messages by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.
Most organizations have already streamlined their human resources department. Every organization’s goal is to operate efficiently and profitably. So, we aren’t talking about a loss of a huge number of jobs. We’re talking about continuing a trend toward tech integration and more effective use of human talent.