Machine Learning & Artificial Intelligence
What is machine learning?
Machine learning (ML) is a kind of artificial intelligence (AI) that allows software applications to evolve more accurately at predicting results without being explicitly programmed to do so. Machine learning algorithms use historical details as input to predict new outcome values.
Suggestion engines are a common usage point for machine learning. Another famous advantage we have is fraud detection, spam filtering, malware threat detection, business process automation (BPA), and predictive maintenance.
For machine learning example, an algorithm would be prepared with pictures of dogs and other things, all marked by humans, and the machine would know ways to identify pictures of dogs on its own. Supervised machine learning is the most common type utilized today.
Why is machine learning important?
Machine learning is significant because it gives the company a view of trends in consumer behavior and business operational patterns, as well as supports the growth of new products. Many ruling enterprises, such as Facebook, Google, and Uber, create machine learning as a central part of their operations. Machine learning has become an effective competitive differentiator for many organizations.
Various types of machine learning?
Classical machine learning is usually classified by how an algorithm learns to become more accurate in its predictions. There are four primary techniques: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The type of algorithm data scientists prefers to use relies on what type of data they like to predict.
- 1. Supervised learning : In this type of machine learning, data scientists supply algorithms with labeled training data and specify the variables they like the algorithm to consider for correlations. Both the input and the outcome of the algorithm are selected.
- 2. Unsupervised learning : Machine learning applies algorithms that train on unlabeled data. The algorithm scans via data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or suggestions they outcomes are predetermined.
- 3. Semi-supervised learning : This approach to machine learning applies a mix of the two preceding types. Data scientists may deliver an algorithm primarily marked training data, but the model is free to examine the data on its own and create its own version of the data set.
- 4. Reinforcement learning : Data scientists generally use reinforcement learning to teach a machine to achieve a multi-step method for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to achieve a task. But for the most part, the algorithm determines on its own what steps to take along the way.
Applications of Machine learning
Machine learning is a buzzword for today's technology, and it is growing fast day by day. We are using machine learning in our day-to-day life even without knowing it such as Google Maps, Google Assistant, Alexa, etc. Below are some most trending real- world applications of Machine Learning:
Image recognition is one of the numerous applications of machine learning. It is operated to identify objects, persons, places, digital images, etc. The popular benefit of image recognition and face detection is, Automatic friend tagging suggestion:
Facebook delivers us a feature of auto friend tagging suggestions. Whenever we post a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning's face detection and recognition algorithm.
It is established on the Facebook project named "Deep Face," which is accountable for face recognition and person identification in the image.
Automatic Language Translation
Nowadays, if we visit a new place and we are not aware of the language then it is not a concern at all, as for this also machine learning helps us by translating the text into our known languages. Google's GNMT (Google Neural Machine Translation) gives this feature, which is Neural Machine Learning that translates the text into our familiar language, and it is known as automatic translation.
The technology behind the automatic translation is a sequence to the sequence learning algorithm, which is operated with image recognition and translates the text from one language to another language.
Stock Market trading
Machine learning is widely utilized in stock market trading. In the stock market, there is always a risk of ups and downs in the shares, so to overcome this machine learning's long short-term memory neural network is used for the prediction of stock market trends.
While using Google, we get a chance of "Search by voice," which comes under speech recognition, and it's a famous application of machine learning.
Speech recognition is a method of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." At present, machine learning algorithms are widely operated by various applications of speech recognition. Google Assistant, Siri, Cortana, and Alexa are using speech recognition technology to track voice instructions.
Artificial Intelligence is a broad and complicated concept that has been around for decades. Artificial Intelligence is used to define a concept or a system that simulates the cognitive functions of the human brain. It can be used to define a situation where machines can act or behave in a way that mimics human behavior. Artificial Intelligence is often used to define a system that can understand from experience, can use knowledge to execute tasks, reason, and create decisions.
There are multiple different kinds of Artificial Intelligence. For instance, there are expert systems, neural networks, and fuzzy logic.
Benefits of Artificial Intelligence
Artificial Intelligence machines use machine learning algorithms to mimic the cognitive abilities of human beings and solve easy or difficult problems.
Increase work efficiency
Artificial Intelligence -powered machines are excellent at accomplishing a certain repetitive task with amazing efficiency. The simple reason is that they release human errors from their tasks to gain accurate results every moment they do that specific task.
Moreover, such machines can work 24X7, unlike humans. Thus, they eliminate the necessity to deploy two groups of employee’s working day and night shifts to work on essential tasks. For instance, Artificial Intelligence -powered chat assistants can answer consumer problems provide support to visitors every minute of the day, and increase the sales of a company.
Work with high accuracy
Scientists are working to teach artificial intelligence-powered machines to solve complicated equations and execute crucial tasks on their own so that the outcomes acquired have higher accuracy as compared to their human counterparts.
Their increased accuracy has created these machines indispensable to perform in the medical field particularly, owing to the criticality of the tasks. Robots are obtaining better at diagnosing serious conditions in the human body and acting delicate surgeries to minimize the risk of human lives.
Decrease the cost of training and operation
Artificial Intelligence uses machine learning algorithms like Deep Learning and neural networks to understand new things like humans do. This way they stop the requirement to document new code every time we require them to know new things.
Significant research and growth are going on in the world to develop Artificial Intelligence machines that optimize their machine learning abilities so that they learn much quicker about new techniques. This way the cost of training robots would evolve much lesser than that of humans. Moreover, devices already decrease the cost of functions with their high efficiency and accuracy of doing work. For instance, machines don’t take breaks and can perform the same mundane task every time without any downtime or difference in results.
The best part about Artificial Intelligence -powered machines being deployed for work is that they let us gather humongous amounts of data linked to their work. Such data can be processed to gather deep insights into the methods with quantitative research so that we can optimize them even further.
Machine learning abilities of Artificial Intelligence machines are growing further and further to do even the analysis by themselves.
Risks of Artificial Intelligence
Although hailed as a boon for humanity by tech pundits, artificial intelligence is worried by a lot of scientists and ordinary citizens alike. This anxiety has caused it to the silver screen several times in the form of movies depicting dystopian futures made by Artificial Intelligence machines that carried over the planet. The numerous notables of these is the Matrix and the Terminator.
Artificial Intelligence is Unsustainable
Intelligent machines have characteristically high computing powers contributed by an array of several processers. These computer chips have rare planet materials like Selenium as a major component. Besides, the batteries of such machines run on Lithium, also a rare component in the earth’s crust. The improved mining of these materials is irreversibly damaging our environment at a rapid pace. Moreover, they consume large amounts of power to work, which is placing severe pressure on our power plants and again harming the environment.
There is no doubt that machines do regular and repeatable duties much better than humans. Many companies would prefer machines instead of humans to boost their profitability, thus decreasing the jobs that are available for the human workforce. A threat to Humanity
Elon Musk is supposed to be one of the smartest people working on Artificial Intelligence in present times. He has also said publicly that Artificial Intelligence is the most significant threat to human civilization in the future. This indicates that the dystopian future that sci-fi movies show is not impossible. Also, Stephen Hawking has always shown his conflict with the advancement in the field of Artificial Intelligence.
The major risk associated with Artificial Intelligence is that machines would attain sentience and turn against humans in case they go rogue.
How will artificial intelligence change the future?
Artificial Intelligence is possible to replace regular jobs and repetitive duties like picking and packaging goods, dividing and segregating materials, responding to repetitive consumer questions, etc. Even today some of these functions are still accomplished by humans and Artificial Intelligence will take over these duties in the future
Difference between Artificial Intelligence and Machine Learning
Machine learning may have appreciated the huge success of late, but it is just one way for completing artificial intelligence.
At the birth of the field of Artificial Intelligence in the 1950s, Artificial Intelligence was described as any machine capable of executing a task that would generally need human intelligence.
Artificial Intelligence systems will typically show at least some of the following traits: planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, manipulation, and, to a lesser extent, social intelligence and creativity.
Alongside machine learning, there are several other approaches used to build Artificial Intelligence systems, including evolutionary computation, where algorithms undergo random mutations and combinations between generations in an attempt to "evolve" optimal solutions, and expert systems, where computers are programmed with rules that let them mimic the behavior of a human expert in a specific domain, for instance, an autopilot system flying a plane.