When you travel to a new country, have you ever had to translate a word quickly? Or what about automatic replies to E-Mail? Have you noticed that there are recommendations for possible answers?

In the above examples, machine learning is used. It is everywhere, from computer-controlled translators to E-Mail recommendations and movie suggestions on Netflix to autonomous cars. All this is possible through machine learning.

You will be surprised where we can find these applications anywhere in the real world. Here are some examples of what machine learning managed to achieve recently. You will see how these systems make our lives easier.

Internet

Artificial intelligence allows companies to automate processes, thus saving costs and working more efficiently. To do this, they don’t have to turn the entire corporate structure upside down. In most cases, even minor adjustments are enough. Even small increases in productivity or cost savings can benefit to their business.

Most AI applications are developed by tech corporations like Google, Apple, Microsoft, PayPal & Co. Companies integrate these solutions into their products and we might even not notice it at first. A popular example is virtual assistants like Siri. Their core model is based on the use and further development of the latest technologies, so they have the necessary resources and know-how. Wish to know more about AI and it’s implementation, check this ML blog.

Marketing

Marketing teams are very excited to implement machine learning. For example, the CRM (Customer Relationship Management) solution Salesforce provides tools for the Marketing Cloud to intelligently and dynamically create customer classifications and then provide targeted customer journeys for the customer.

Salesforce also supports sales in CRM Sales Cloud with several intelligent features to optimize sales efforts:

• Lead Scoring assigns a point system to leads based on experience and prioritizes them to determine which lead is most likely to convert.
• A similar principle applied to opportunities. Machine learning programs analyze the conditions under which they have been particularly successful in the past and were sure to close.
• Intelligent text recognition and automatic integration of communication tools take the tracking out of your hands and automatically capture any communication with the customer.
• Relevant information about an account that appears in communications or press releases are shown to you.

You can also support sales with intelligent conversation support such as qurious.io, a tool that uses speech recognition to automatically create guides from recorded conversations. It significantly improves customer conversations.

Tesla’s autonomous cars

The most well-known event in machine learning is the introduction of Tesla self-driving cars. They use machine learning for autonomous cars to make intelligent decisions. The collected data is based on the movements of the vehicles on the road by so-called “imitation learning.” That is, the reactions of drivers are collected to get an overall picture of how one should behave on the road. The sensors record the movements of the drivers with the steering wheel, the accelerator pedal, the brakes, and the turn signal.

Tesla’s machine learning model uses neural networks to learn what went wrong from the erroneous movements of some drivers. From the erroneous actions, images are saved to prevent the same errors from happening again in the future.

A possible advantage of autonomous cars is that parking becomes less tedious. It is especially true when the vehicles have to park in a confined space, which is a problem for some people. In addition, these cars used to avoid human error. People often drive while talking on the phone, which can be dangerous. With self-driving vehicles, there would be fewer distractions, which would lead to fewer road accidents.

However, unexpected interference from the sensors may occur in autonomous vehicles, which poses a risk to the safety of passengers. It is because sometimes machines can not distinguish what is right and what is wrong. Therefore, it can be difficult for machines to make intelligent decisions.

Tesla still has some testing and development time ahead of it, but as you can see, machine learning is popping up in many areas, and it is only the beginning.