Machine learning has changed the world in a few short years. Machine Learning (ML) is one of many types of Artificial Intelligence (AI), and it refers to predictive models that use large data sets to learn information completely automatically rather than having humans program rules into them. The premise behind Machine Learning is that by automatically adjusting the model through training data, the learning algorithm will be able to predict future observations correctly. Machine Learning steadily climbs the ladder of the most valuable IT skills and the related jobs are changing to accommodate many of these new applications, but so are other industries.
Here are 10 ways Machine Learning will change everything:
1. Transportation
The already impressive list of companies using machine learning for transportation is expanding rapidly with big names like Tesla, Uber, and Google joining the fray. The developments made in this area include:
– Tesla, for example, uses Machine Learning to predict likely locations of charging stations based on behavioral data.
– Uber is also leveraging machine learning with its “Smart routes” program that uses historical traffic patterns to recommend which driver should take which route at any given time in order to decrease wait times during high traffic hours.
– Google’s self-driving car uses machine learning to make split second decisions during driving – such as whether to hit the brake or swerve out of the way of another vehicle.
2. Retail
With new companies like adidas and Under Armour using machine learning, it’s easy to see how it has advanced retail already. But it’s not just big brands making use of machine learning in retail – even the little guys are joining in and seeing huge benefits. The developments made in this area include:
– An automatic price optimization tool can help you beat your rivals by pricing your products competitively without having to spend hours analyzing data every day.
– Fashion retailers like Gilt and Zulily are using Machine Learning to recommend products based on customers’ previous shopping behaviors.
3. Healthcare
It’s no surprise that machine learning is finding its way into the healthcare sector since it can do everything from improve patient outcomes to save lives. The developments made in this area include:
– Clinical decision support systems use healthcare data to predict likely diagnoses or treatment options for new patients, saving doctors time and increasing their accuracy.
– A predictive model called the Time Machine uses historical research data to predict which new drugs will successfully treat patients suffering from diseases like cancer based on their genetic profile.
– Researchers are using machine learning in biomedicine to find new clues about the causes of disease to increase diagnostics and treatment options.
4. Advertising
Machine learning is no stranger to the world of advertising, and its presence has only increased with the popularity of ad blockers among internet users. The developments made in this area include:
– A demand forecasting tool leverages machine learning to predict future ad revenue based on historical data and make accurate daily predictions about whether you should compensate for under-performing ads.
– Companies like P&G and The New York Times use machine learning to specifically target younger ad viewers, finding that those who spend more than five minutes on a site are less likely to click on advertisements.
5. Security
Even with increased awareness and stricter regulations, security breaches are increasing due to the growing sophistication of hackers. The developments made in this area include:
– A password recommendation tool analyzes sensitive data to make informed recommendations about which credentials should be used for each login within an organization.
– Risk and Compliance Analytics use machine learning to sift through large quantities of often unstructured regulatory document data to identify violations and other risk factors.
– Business Security Decision Management Systems use machine learning to prevent fraud and other security risks, such as money laundering.
6. Marketing
A new startup called SharpestMinds uses machine learning to understand the language of sales to help sales teams do their jobs more effectively. The developments made in this area include:
– A natural language processing tool analyzes written emails and suggests key points that should be included in a response to an inquiry.
– A customer service chatbot uses machine learning algorithms to improve customer experience and drive up engagement rates for your business.
– An automated email assistant can sort through incoming messages and pick out those that need a response, as well as identify those that don’t require any action at all.
– Recommender Systems use machine learning to suggest new products that may be of interest to customers based on their previous purchases, browsing history or demographics.
7. Your Business
One of the biggest impacts that machine learning will have been on small businesses who can now use it to compete with much larger corporations. The developments made in this area include:
– A customer service chatbot uses machine learning algorithms to improve customer experience and drive-up engagement rates for your business.
– An automated email assistant can sort through incoming messages and pick out those that need a response, as well as identify those that don’t require any action at all.
– Recommender Systems use machine learning to suggest new products that may be of interest to customers based on their previous purchases, browsing history or demographics.
– A predictive model called the Time Machine uses historical research data to predict which new drugs will successfully treat patients suffering from diseases like cancer based on their genetic profile.
8. Education
One example is how machine learning can be used to improve language instruction by focusing more on active, rather than passive, use of a language, incorporating different resources and adapting to the needs of different students. The developments made in this area include:
– A tool that creates personalized lesson plans uses machine learning algorithms to understand each student’s strengths and weaknesses through a series of assessments in order to suggest customized activities for them based on their responses.
– A tool that identifies cheating in online classes analyzes multiple data points to determine whether or not students are violating the rules of an assignment, plagiarizing content or receiving help from another user.
– An intelligent tutoring system uses machine learning to track student progress and respond appropriately depending on how well they are doing.
– Some students also find it easier to learn a language using chatbots that can help them with grammatical rules, vocabulary words and sentence structure.
9. Healthcare
In healthcare, one popular application of machine learning is in medical imaging, which can help doctors make more accurate diagnoses. The developments made in this area include:
– An app that creates 3D models based on 2D images analyzes a series of data points to produce a high-quality model.
– Another app uses machine learning to detect patterns in chest X-rays and identify signs of pneumonia, tuberculosis, and other lung conditions.
– A tool that identifies skin cancer analyzes multiple data points such as shape, coloration, and texture to assess them for early warning signs of the disease.
– A tool that monitors hand hygiene in hospitals analyzes video footage from cameras placed in front of patient rooms to determine how often doctors and nurses wash their hands.
10. Public Safety & Security
In public safety, a tool that monitors the activity on a network identifies unusual behavior that might indicate criminal activity or a potential security threat. The developments made in this area include:
– A tool that predicts border incursions uses machine learning to analyze large numbers of data points, including geospatial data and a variety of economic variables, to identify potential risks.
– A tool that detects anomalies in images uses machine learning to assess multiple data points such as lighting, shadows and coloration, among others, to flag anything out of the ordinary.
– A tool that scans video footage from a police body camera analyzes multiple data points including lighting conditions, pixel color and size to determine if an interaction or incident is related to a crime.
In conclusion, machine learning is a versatile tool that can be used for many different applications, including technology that offers personalized customer experiences, delivers more effective education resources and identifies security threats.
Here is an overview of Jobs related to Machine Learning.