Читать книгу Machine Learning For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 59

People already work with machines on a regular basis — they may just not realize it. For example, when you talk to your smartphone and it recognizes what you say, you’re working with a machine to achieve a desired goal. Most people recognize that the voice interaction provided with a smartphone improves with time — the more you use it, the better it gets at recognizing your voice. As the learner algorithm becomes better tuned, it becomes more efficient at recognizing your voice and obtaining the desired result. This trend will continue.

However, machine learning is used in all sorts of ways that might not occur to you. When you point a camera at a subject and the camera can put a box around the face (to help target the picture), you’re seeing the result of machine learning. The camera is helping you perform the job of taking a picture with far greater efficiency. In addition, the camera automatically removes at least some of the effects of shaking and bad lighting. Cameras have become quite good at assisting humans to perform tasks with aplomb.

The use of declarative languages, such as SQL (Structured Query Language), will become more pronounced as well because machine learning will make advancements possible. In some respects, a declarative language simply lets you describe what you want and not how to obtain it. However, SQL still requires a computer scientist, data scientist, database administrator, or some other professional to use. Future languages won’t have this limitation. Eventually, someone who is trained to perform a particular task well will simply tell the robot assistant what to do and the robot assistant will discover the means to do it. Humans will use creativity to discover what to do; the details (the how) will become the domain of machines.