What is Machine Learning?

Machine Learning

Everything in the world is powered by a machine. Through the advancement in technology engineers today, can develop machines that you learn on their own. This is called machine learning, a field that seeks to create predictive models and algorithms. Giving computers the ability to carry out tasks without being explicitly programmed. This is somewhat similar to when you draw a line of best fit with an initial set of data and how simple linear regression attempts to improve its predictive line of best fit with added data.

Some applications of machine learning that we all use on a day-to-day basis are Google search engines, recommendations from Amazon, Netflix and YouTube, and suggested friends on Facebook. Engineers are now implementing machine learning to a new and rapidly growing field called artificial intelligence, which consists of systems that enable computers to perform intelligent human tasks without being explicitly programmed.

Unsupervised vs Supervised Machine Learning

Artificial intelligence with unsupervised machine learning means the machine needs to identify patterns on its own. Whereas, supervised machine learning means that the program is trained on a certain amount of input data, like the linear regression model. Examples of day to day common uses of artificial intelligence include Siri, computer game playing, fraud detection on credit cards, online customer support using chatbots, and security surveillance.

Machine Learning examples - a headband that control and experience lucid dreams.

However, there are more extreme applications of AI, such as the iBand+, a headband that allows you to experience lucid dreams and control them, through the use of electroencephalogram technology. Which is a test that is used to detect abnormalities of electrical activity in the brain. This raises ethical concerns because these tests are performed by doctors to diagnose and monitor disorders. But, now companies are able to create products that can access your brainwaves and EEG data.

Machine Learning examples - road accident detector with ML and AI powered products.

Since computer hardware and software are rapidly advancing, many AI products are past the capabilities of human experts. There is great potential in use of technology such as fewer errors in medical practice and fewer road accidents. AI products have features such as faster speed integration of cameras and precise speech recognition that allow them to perform some tasks better than humans.

Machine Learning vs AI

Machine Learning and Artificial Intelligence Technologies

The underlying technologies of ML and AI are very complex but essentially machine learning technologies are composed of three parts: the model, the parameters, and the learner system. The model is the system that makes the predictions and identifications. The parameters are the signals and factors that the model uses to make its decisions. The learner is a system that adjusts the parameters by looking at the differences and predictions and the actual outcomes.

Artificial intelligence is much more complex and each program depends greatly on the purpose of the product. But, they all have three components: a data structure, inputs and outputs, and an associative learning system. There are two types of data structures, needed one for long-term storage and the other for short-term storage. Inputs and outputs are the core sources of data. Some examples of inputs are sensors and downloaded data. An associative learning system is the most crucial component of an AI system. this presents us with a learning system of the machine and test boundaries, gives us the ability to perceive and learn new information and allows for cooperation and social intelligence upon human interaction.

Artificial Intelligence examples - Robot.

Although machine learning and artificial intelligence are extremely useful technologies, they also post several ethical dilemmas. An analysis concluded that by 2034 forty-seven percent of all jobs in the United States can become automated which means that essentially robots will take over human employment. This however may be appealing to companies since robots don't require salaries. other questions that debate are if the machine fails, who is to blame, the programmers or the end users of the product.

Since machines do not have advanced social intelligence how will they make complex and moral decisions. The AI revolution is just like the Industrial Revolution, except now robots are taking over human grains instead of physical abilities. a terrifying thought that is causing many individuals to raise questions about why humans even exist.

In my opinion, I believe that AI should be implemented primarily to benefit lives and facilitate everyday tasks with limited supervision. To ensure the concept of superhuman intelligence does not become a reality.

Machine Learning Algorithms

There are many machine learning algorithms out there, so which machine learning algorithm to use. There is no perfect algorithm that works best for every problem. It depends on how big is the dataset and other factors. For a newbies or anybody that want to learn about basic machine learning techniques, these are most popular machine learning algorithms use by data scientist to cultivate big data.

  1. Linear Regression
  2. Logistic Regression
  3. Linear Discriminant Analysis
  4. Classification and Regression Trees
  5. Naive Bayes
  6. K-Nearest Neighbors
  7. Learning Vector Quantization
  8. Support Vector Machines
  9. Bagging and Random Forest
  10. Boosting and AdaBoost