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How do machines learn?
Although a machine learning model can apply a combination of different techniques, learning methods can generally be classified into three general types:
Supervised learning The learning algorithm receives tagged data and the desired outcome. For example, images of dogs labeled "dog" will help the algorithm identify the rules for classifying images of dogs.
Unsupervised learning: The data provided to the learning algorithm is not labeled and the algorithm is asked to identify patterns in the input data. For example, the recommendation system of an e-commerce website where the learning algorithm discovers similar items that are often bought together.
Reinforced learning: the algorithm interacts with a dynamic environment that provides feedback in terms of rewards and punishments. For example, autonomous cars are rewarded for staying on the road.
The basic process of machine learning is to provide training data to a learning algorithm. Then the learning algorithm generates a new set of rules, based on inferences from the data. In essence, it is about generating a new algorithm, formally called the machine learning model.
By using different training data, the same learning algorithm could be used to generate different models.
For example, the same type of learning algorithm could be used to teach the computer how to translate languages or predict the stock market.
Data related problems.
The development of a reliable ML system not only depends on excellent coding, but the quality and quantity of the training data play a fundamental role.
# 1. Lack of adequate data
Representative data sets are required to reasonably capture the relationship that may exist between input and output characteristics. If there is not enough data, there are options such as collecting more data or using external data sources. Another solution is to use data augmentation methods to artificially increase the sample size, but these methods often decrease the overall quality.
# 2. Complex extraction, transformation and loading procedures
One more requirement is that the data must be easy to work with, it must be well organized and stored in the proper format in one place (database, warehouse, cloud). Since this is not the case sometimes, some preparatory activities are needed (eg ETL processes).
# 3. Unstructured data processing
The next profitable factor is whether the data is structured or not. It is easier (consequently cheaper) to work with well-structured data. In some cases, the data is subject to cleaning, sorting, and conversion to semi-structured data. In addition, it allows you to work with missing, extreme and unexpected values, deal with outliers, obvious errors, etc. In practice, most companies handle unstructured (eg, free-form text notes) or semi-structured (eg, XML, email) data. There is a whole class of ML algorithms built to make use of this kind of data, and these projects generally cost more.
In our experience, collecting, cleaning, and preparing data can double the cost of development.
The advantages and benefits of AI
Making decisions and taking action based solely on historical precedent, simple analysis, and intuition no longer gets the job done, nor does pursuing shortsighted goals or commercialized technologies. And yet too many companies remain stuck in the status quo.
Increasingly, it is those who use analytics effectively who are successful; that is, those that extract information such as patterns, trends, and insights from the data to make decisions, take action, and produce results.
This includes both traditional analytics and advanced analytics, which are complementary. Furthermore, AI enables humans to use analytics in ways that they might not otherwise be able to do on their own.
Data is a critical asset if, and only if, you know how to use it.
Most companies should start thinking of themselves as data and analytics companies, regardless of what their top offerings are. As long as the data is involved, this is a critical step in getting ahead of the competition and, at the same time, gaining greater capacity to create huge benefits for both individuals and businesses.
Working together with AI
Artificial intelligence is not here to replace us. It increases our skills and makes us better at what we do. Because AI algorithms learn differently than humans, they see things differently. They can see relationships and patterns that escape us. This human association with AI offers many opportunities.
You can: Bring analytics to industries and domains where it's not currently used.
Improve the performance of existing analytical technologies such as computer vision and time series analysis.
Break down economic barriers, including language and translation barriers.
Increase existing skills and make us better at what we do.
Give us a better vision, a better understanding, a better memory and much more.
What are the challenges of using artificial intelligence?
Artificial intelligence is going to change all industries, but we have to understand its limits. The main limitation of AI is that it learns from data. There is no other way to incorporate knowledge. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis must be added separately.
Today's, AI systems are trained to perform a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an artificial intelligence system that detects healthcare fraud cannot accurately detect tax fraud or warranty claim fraud.
In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans. Likewise, self-learning systems are not autonomous systems. The imagined AI technologies you see in movies and TV are still science fiction.
But computers that can probe complex data to learn and refine specific tasks are becoming quite common.
How artificial intelligence works
AI works by combining large amounts of data with fast iterative processing and smart algorithms, allowing software to automatically learn from patterns or characteristics in the data. AI is a broad field of study that includes many theories, methods, and technologies, as well as the following major subfields:
Machine learning automates the building of analytical models. It uses methods from neural networks, statistics, operations research, and physics to find hidden information in data without being explicitly programmed about where to look or what to conclude.
A neural network is a type of machine learning that is made up of interconnected units (such as neurons) that process information by responding to external inputs, transmitting information between each unit. The process requires multiple passes through the data to find connections and derive meaning from the undefined data.
Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in vast amounts of data. Common applications include image and speech recognition.
Cognitive computing is a subfield of AI that strives for natural, human-like interaction with machines. Using artificial intelligence and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech, and then speak coherently in response.
Computer vision relies on pattern recognition and deep learning to recognize what's in an image or video. When machines can process, analyze and understand images, they can capture images or video in real time and interpret their environment.
Natural language processing (NLP) is the ability of computers to analyze, understand, and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.