Machine learning is an application pertaining to artificial intelligence that provides computers with the ability to learn, or improve, without being clearly programmed. It basically lays emphasis on the success of computer programs that can instantly acquire data, using it automatically. This process of computer learning commences with observing data, instructions or direct experiences with one aim, to make better decisions in the near future. The main aim is to let computers learn without human assistance adjusting their actions.
“Machine Learning” is a term that is used in our everyday lives in place of artificial intelligence. We encounter the term almost daily with voice assistants like Siri and Alexa, facial recognition of Facebook ,and Microsoft, Netflix and Amazon recommendations. This is also used in the technology found in self driven cars, so they do not crash into approaching objects.
Although it is not as advanced as the human brain, machine learning has been successful in attaining impressive tasks like beating humans at games like Chess, Texas Hold’em, Jeopardy and Go. For years machine learning or AI was not taken seriously, calling it unrealistic or hyped-up, but over the last few years it is enjoying a strengthening reputation due to many technological developments, and the presence of large data available for machine learning.
What Exactly is Machine Learning?
Machine learning is basically a typical human programmed computing application. It is different from traditional software, that is good when it came to following instructions, but bad at improvising. The machine learning system has the ability to code itself, and develop instructions by generalizing through examples. Image recognition is a good example of machine learning. If the machine learning system is shown several photos of dogs - labeled as ‘dogs’- and photos of trees, cats, flowers or other objects - labeled ‘not dogs’ - a correctly programmed system will be able to recognize canines without human intervention or assistance.
Another good example of machine learning in action is the spam filter of your email program. When the program is exposed to billions of spam email samples as well as spam samples, it will be able to recognize the basic characteristics of such messages. This system is not perfect, but is usually quite accurate.
Methods of Machine Learning
Supervised Machine Learning
Supervised machine learning algorithms is when past learning is applied to new data to make predictions of future events. When a machine learning algorithm is exposed to large data, its output examined and its settings constantly tweaked till the expected results are produced, it is called supervised learning. The common classification tasks of supervised learning are image recognition and spam detection, while the prediction or regression problems pertain to predicting stock prices. This system is able to successfully give targets for new outputs after giving sufficient training.
Unsupervised Machine Learning Algorithms
Unsupervised learning is another form of machine learning, whereby the system studies large amounts of data to understand what ‘normal’ data is so that any hidden patterns or anomalies can be detected. This system is used when the information is not labeled or classified. It basically studies how systems show a hidden structure from an unlabeled piece of data.
Unsupervised learning comes in handy when you can’t train the system to find something when you yourself are not sure what you are looking for. This kind of learning is used by banks to detect fraudulent transactions, by marketers to recognize customers with similar attributes and by security software to detect any hostile or abnormal activity on a network. It can identify certain patterns in large amounts of data much faster than a human brain can. Two examples of unsupervised learning are clustering and association rule learning. Association rule is often used for recommendation engines, while clustering is the main rule behind customer segmentation.
Semi-supervised Machine Learning
Since under the semi-supervised system a large amount of unlabeled data and a small amount of labeled data is used, it lies somewhere between supervised and unsupervised learning. This method helps to improve accuracy in learning. It also assists in obtaining labeled data that requires relevant and skilled resources, while obtaining unlabeled data does not need additional resources.
Reinforcement Machine Learning
Reinforcement machine learning is a method that interacts through actions, discovering errors or rewards in the process. Trial and error is the main feature of reinforcement learning. Under this method, machine and software agents are allowed to automatically determine the behavior within a particular context, so the performance is maximized. A simple reward feedback, also known as reinforcement signal, is used by the agent to determine which action is the best.
Weaknesses of Machine Learning
As machine learning makes its own connections, its working cannot be clearly stated. It works well depending on the data it is exposed to. If the exposed data is insufficient, the results produced will not be called wrong, but will be discriminatory. There were some issues regarding this in 2009 with HP, where the facial recognition technology on the HP Media Smart laptop webcam was not able to distinguish the faces of Afro-Americans. A similar case was detected in 2015, when the faulty algorithms of Google’s Photo app mistook two black Americans as gorillas. Another ill-fated example was in 2016 when Microsoft’s Taybot, that could emulate human conversation, was turned into a hate-speech chatbot by Twitter trolls.
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The Wordbook of Machine learning
Machine learning can be called the tip of Artificial Intelligence. There are several other terms related to machine learning like deep learning, neutral networks and cognitive computing.
Deep Learning: This is a machine learning technique based on steroids, but using a deep or multilayered neural network. It arrives at conclusions based on incomplete or imperfect information. ‘DeepStack’, a deep learning technique, constantly recomputed its strategy that led to the defeat of 11 professional poker players last year.
Neutral Network: A computer expert designed the neuron structure in our brain, just like artificial neurons in a microcircuit that connect to other neurons in the system. Neural neurons are organized in layers, with data being passed from one layer to the next, going on like this till they reach the output layer. It is in this last final layer that neural network gives its guesses and results with confidence. Various types of neural networks are used for solving multiple problems. There are ‘deep neural network’ too, where several layers of networks are used. Neural nets are not the only tool, but they are one of the most important tools in machine learning.
Cognitive Computing: According to IBM, the difference between artificial intelligence and cognitive computing is that cognitive computing is designed to increase, rather than replace, human intelligence. Cognitive computing is used by financial managers to make better recommendations, by doctors to diagnose various illnesses and by lawyers to research case laws.
Machine learning thus enables the analysis of large quantities of data. It delivers fast and accurate results when it comes to recognizing profitable opportunities, or inevitable dangers. A little extra time and resources are required for learning properly. Combining machine learning with artificial intelligence and cognitive technologies can very effectively process massive volumes of information. Despite everything, it can be concluded that machine learning and other technologies associated with it can be detrimental in changing the future of the world.
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