From Data to Decisions: A Guide to the Core AI Technologies
Imagine a world where technology can automate repetitive tasks, freeing humans from heavy labor. Thanks to the advent of Artificial Intelligence (AI), this vision has become a reality. Since its inception in 1955, AI has proven to be a remarkable technological breakthrough and it has experienced rapid growth over the years, fueling its exponential expansion. Today, the power of AI is reshaping industries and redefining the way we work and live. Statistically speaking, the global conversational AI market is expected to grow from 4.8 billion in 2020 to USD 13.9 billion by 2025 at a compound annual growth rate (CAGR) of 21.9%.
AI has many sub-technologies and applications ranging from biometrics and computer vision to intelligent devices and self-driving cars. These AI technologies, coupled with abundant data, computing power, and cloud processing innovations, have catalyzed a sharp growth in AI adoption. Now, companies have access to an unprecedented amount of data, including dark data they didn’t know they had. These treasure troves have proved to be a boon for the growth of AI.
Though AI has long been considered a source of business innovation, it can add value only when done correctly. For this, we need to understand the core technologies working behind AI processes. However, AI is not one thing. It is a constellation of several technologies that enable machines to perceive, understand, act and learn with human-like intelligence. The AI landscape includes technologies like machine learning, natural language processing, and computer vision, which we will elaborately discuss in this article.
- What is artificial intelligence?
- Key components of AI applications
- Key AI technologies used in AI development
- The layered approach of artificial intelligence technologies
What is artificial intelligence?
Artificial intelligence is broadly defined as a set of technologies that can perform tasks similar to human cognitive functions. As defined by John McCarthy, “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It relates to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods.” AI allows computers to perform advanced functions such as understanding, translating, seeing and interpreting spoken and written languages, analyzing data, making recommendations and more. It unlocks the value of individuals and businesses by automating processes and providing insight into large data sets. Many AI applications, including robots, can navigate warehouses by themselves. Cybersecurity systems continuously analyze and improve themselves. Virtual assistants understand and respond to what people say.
In AI, it is a practice to develop theories, techniques, technologies, and application systems to simulate the expansion of human intelligence. Artificial intelligence research aims to allow machines to perform complex tasks that intelligent humans cannot. AI can perform not only repetitive tasks that can be automated but also the ones that require human intelligence.
Key components of AI applications
AI applications typically involve data, algorithms, and human feedback. Ensuring that each component is properly structured and validated to develop and implement AI applications is crucial. Here we will discuss how these components influence the AI development and implementation of AI applications.
Data growth has been evident in almost all industries in the last decade due to the increasing use of mobile technology and digitization. Data has also become a key part of the services industry’s business model. Service firms across industries can now collect data from internal and external sources, a key reason for AI exploration. Any AI application’s success or training will depend on its data quality. AI applications are designed to analyze data and identify patterns to make predictions or decisions based on the discovered patterns. Applications continuously learn from errors made by these applications and improve their outputs as a result. This is usually done through human review and new information. AI applications generally yield the best results when the underlying data sets are large, valid, current and substantial.
There are different stages of data collection and refinement, as discussed below:
AI is dependent on the data that it gathers. AI works the same way as our brains which absorb huge amounts of information from the environment around us. This data can be sourced from many places in the AI technology stack. For example, the Internet of Things is a continuous rollout that allows millions of devices, from large-scale machinery to mobile phones, to be connected, allowing them to communicate with each other. An AI stack’s data collection layer comprises the software that interfaces to these devices and a web-based service that supplies third-party data. These services range from marketing databases that contain contact information to news, weather, and social media application programming interfaces (APIs) that provide third-party data. Data collection can be done from human speech on which natural language processing can work to convert the speech into data regardless of background noise or commands being issued to a machine.
You need to store the data once you have collected it or created streams that allow it to flow into your AI-enabled system in real-time. AI data can be structured or unstructured and could be big data that requires a lot of storage and must be accessible quickly. This is often where cloud technology plays a major role. Some organizations use Spark and Hadoop to give them the ability and resources to create their own distributed data centers that handle a large amount of information. Sometimes, however, third-party cloud infrastructure such as Amazon Web Services and Microsoft Azure is a better solution. Third-party cloud platforms allow organizations to scale storage as needed and save money. These platforms offer a variety of integration options with analytics services.
Data processing and analytics
Data processing is one of the key areas for artificial intelligence. Machine learning, deep learning, image recognition, etc., all take part in AI processing. The algorithms of these technologies can be accessed via a third-party API, deployed on a private or public cloud, within a private or public data center, data lake, or at the point of data collection. These algorithms are powerful, flexible and self-learning capable, making the current wave of AI different from the previous. The deployment of graphics processing units (GPUs) is responsible for increasing raw power. They are a great choice for data crunchers because of their mathematical prowess. In the near future, a new generation of processor units designed specifically for AI-related tasks will provide an additional quantum leap in AI performance.
Reporting and data output
Your AI strategy should aim to improve the efficiency and effectiveness of machines (e.g., predictive maintenance or decreasing power or resource consumption). This technology will communicate the insights from your AI processing to the systems that can benefit. Other insights might be useful for humans. For example, sales assistants can use handheld terminals to access insights and make recommendations for customers. Sometimes the output can be in the form of charts, graphs and dashboards. This technology can also be used as a virtual personal assistant, such as Microsoft’s Cortana and Apple’s Siri. These products use natural language generation to convert digital information into human-friendly language. This, along with visuals, is the easiest form of data output that can be understood and acted upon.
An algorithm is a machine’s organized set of steps to solve a problem or generate output from input data. Complex mathematical code is used in ML algorithms, allowing machines to learn from new input data and create new or modified outputs based on those learnings. A machine is not programmed to do a task but to learn to perform the task. Open-source AI algorithms have helped to fuel innovation in AI and make the technology more accessible to industries.
All stages of an AI application’s lifecycle require human involvement, including preparing data and algorithms, testing them, retaining models, and verifying their results. Human reviews are crucial to ensure that the data is appropriate for the application and the output is accurate, relevant, and useful as algorithms sort through the data. Technology and business stakeholders often work together to analyze AI-based outputs and provide feedback to the AI systems to improve the model. Lack of review can generate inadequate, inaccurate, or unsuitable results from AI systems, leading to inefficiencies, forgone opportunities, or new risks if action is taken based on faulty results.
Key AI technologies used for AI development
Machine Learning (ML)
Machine learning is a subfield within artificial intelligence intending to mimic intelligent human behavior to perform complex tasks like human problem-solving. Data is the foundation of machine learning which includes photos, numbers, and text. Data is collected and stored to provide the training data for the machine learning model. The more data you have, the better the program is. Once the data is ready, programmers choose a machine-learning model to feed the data into it, and the model will train itself to predict patterns or make predictions. The programmer can tweak the model over time, changing its parameters and helping it to produce more accurate results. Some data is kept aside from the training data for evaluation, allowing the model to evaluate its accuracy when presented with new data. The model can be used with other data sets in the future.
There are three types of machine learning:
Supervised machine learning models are trained using labeled data sets, allowing them to learn and become more precise over time. An algorithm could be trained with images of dogs and other objects, allowing it to recognize dogs by itself. Image classification and spam detection are some examples of supervised machine learning. This is the most popular type of machine learning model today.
Unsupervised machine learning searches for patterns in unlabeled data and can find patterns and trends that users aren’t explicitly seeking. An example of this is an unsupervised machine-learning program that could examine online sales data to identify different clients who make purchases. Clustering and anomaly detection are other examples of unsupervised machine learning applications.
Reinforcement machine learning trains machines through trial and error to take the most effective action by setting up a reward system. Reinforcement learning is used to train models to play games and to train autonomous vehicles to drive. It tells the machine when it has made the right decision, allowing it to learn which actions to take over time. Game playing and robotic control are some of the areas of reinforcement machine learning applications.
Machine learning powers the recommendation engines that power Youtube and Netflix. It also determines what information you see on your Facebook page and for product recommendations. To detect fraudulent credit card transactions, log-in attempts or spam emails, machines can analyze patterns such as where someone spends their money or what they shop. Similarly, for chatbots and automatic helplines, customers and clients interact with machines instead of humans. These bots use machine learning and natural language processing to learn from past conversations to provide the right responses. Similarly, many technologies behind self-driving cars are based on machine learning, particularly deep learning. Machine learning programs can also be trained to analyze medical images and other information and look for signs of illness. For example, a program that can predict the risk of developing cancer-based on a mammogram.
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Natural Language Processing (NLP)
Natural Language Processing is a branch of computer science concerned with computers understanding text and spoken words like humans can. NLP combines computational linguistics-rule-based modeling of human language with statistical, machine learning, and deep learning modes. These technologies allow computers to process text and voice data from human beings and to understand their full meaning. NLP is a set of computer programs that can quickly translate text from one language into another, respond to spoken commands, and quickly summarize large amounts of text in real time. You are likely to have interacted with NLP through voice-operated GPS systems and digital assistants. NLP is also a key component of enterprise solutions, which help to streamline business operations, improve employee productivity, and simplify mission-critical business processes.
NLP tasks help computers understand what they are ingesting by breaking down text and voice data. These are just a few of the tasks:
- Speech recognition, also known as speech-to-text, is used to reliably convert voice data into text. Any application that uses voice commands to answer or follow spoken questions depends on speech recognition. It is difficult because people speak fast, often slurring words together and with different accents and emphases.
- Part of speech tagging is a process that determines the part of speech for a word or piece of text based on its context and use. For example, part of speech is used to identify ‘make’ in the following sentences as a verb in ‘I can make a house’ and as a noun in ‘What make of car do you own?’
- Word sense disambiguation refers to the process of semantic analysis in which the word that makes sense in the context is determined. Word sense disambiguation, for example, helps to distinguish between the meanings of the verb “make” in “make the grade” and “make a place”.
- Named entity recognition, or NEM identifies words and phrases as useful entities. NEM recognizes “Kentucky” as a place or “Fred” as a man’s name.
- Co-reference resolution is the task of identifying if and when two words refer to the exact same entity. This commonly involves identifying the person or object to whom a particular pronoun refers (e.g., ‘she’ = ‘Mary’). However, it can also include identifying a metaphor or an idiom within the text.
- Sentiment analysis attempts to extract subjective qualities like attitudes, emotions, sarcasm, confusion, and suspicion from the text.
- Natural language generation is sometimes referred to as speech recognition or speech-to-text. It is the process of converting structured information into human-readable language.
The new addition to natural language processing is transformer models. Some examples of transformer models used in NLP include:
- BERT (Bidirectional Encoder Representations from Transformers), developed by Google, is a pre-trained model that can be fine-tuned for various natural language understanding tasks such as named entity recognition, sentiment analysis, and question answering.
- GPT-2 (Generative Pre-trained Transformer 2), developed by OpenAI, is a pre-trained model that can be fine-tuned for various natural language generation tasks such as language translation, text summarization, and text completion.
- T5 (Text-to-Text Transfer Transformer), developed by Google, is a pre-trained model that can be fine-tuned for various natural language understanding and generation tasks using a simple text-based task specification.
- RoBERTa (Robustly Optimized BERT), developed by Facebook AI, is an optimized version of the BERT model that uses dynamic masking, larger batch sizes and longer training time to achieve better performance on various NLP tasks.
- ALBERT (A Lite BERT), developed by Google, is a version of BERT designed to be smaller and faster while maintaining comparable performance on natural language understanding tasks.
In many real-world use cases, NLP is the driving force behind machine intelligence. Some of its applications are spam detection, machine translation like Google translate, chatbots and virtual agents, social media sentimental analysis, and text summarization.
Computer Vision is an area of artificial intelligence that allows computers and systems to extract meaningful information from digital images, videos and other visual inputs. Based on this information, they can take action or make recommendations. In simple terms, computer vision is the ability to see, understand, and observe with AI. Computer vision trains machines to perform these functions, which requires less time and more data, algorithms, cameras and data than it does with retinas, optic nerves, and visual cortex. You can detect subtle defects or issues in thousands of products and processes per minute using computer vision.
This is possible using two technologies: a type of machine learning called deep learning and a convolutional neuro network (CNN). These layered neural networks allow a computer to learn from visual data. The computer can learn how to distinguish one image from another if there is enough data. The computer uses a CNN to “look at” the image data as it feeds through the model. CNN is used to help a machine learning/deep learning model understand images by breaking them into pixels. These pixels are then given labels that allow for training specific features (image annotation). The AI model uses labels to make predictions and convolutions about what it “sees.” It then checks the accuracy of its predictions iteratively until they meet expectations.
There are two types of algorithm families in computer vision, specifically for object detection. The single-stage algorithm aims for the fastest processing speed and highest computational efficiency. RetinaNet and SSD are the most popular algorithms. On the other hand, multi-stage algorithms are multi-step and provide the best accuracy, but they can be quite heavy and resource intensive. Recurrent Convolutional Networks (RCN) are the most popular multi-stage algorithms, including Fast RCNN and Mask-RCNN.
Here are some computer vision techniques:
Image classification is the simplest computer vision method, mainly categorizing an image into one or several different categories. Image classifier takes the image and gives information about the objects in it. It would not provide any additional information, such as the number of persons present, tree color, item positions and so on. There are two main types of image classification: binary and multi-class classification. As the name implies, binary image classification looks for one class in an image and returns results based on whether or not it has that object. We can train an AI system to detect skin cancer in humans and achieve amazing results by using images with skin cancer and images without skin cancer.
Another popular computer vision technique is object detection which can be used after image classification or uses image classification to detect objects in visual data. It’s used to identify objects within the boundaries boxes and determine the object class in an image. Object detection uses deep learning and machine learning technologies to produce useful results. Humans can recognize objects in visuals and videos within seconds. Object detection aims to reproduce human intelligence to locate and identify objects. There are many applications for object detection, such as object tracking, retrieval and image captioning. Many methods can be used for object detection, including R-CNN and YOLO v2.
Semantic segmentation does more than detect the classes in an image and includes image classification. It classifies every pixel in an image to identify what objects they have. It attempts to identify the role of each individual pixel within the image and classifies pixels in a specific category without distinguishing the object instances. It can also be said that it classes similar objects from all pixel levels as one class. Semantic segmentation will place an image with two dogs under the same label. It attempts to determine the role of each pixel within an image.
Instance segmentation can classify objects at the pixel level, similar to semantic segmentation but with a higher level. Instance segmentation can classify objects of similar types into different categories. If the visual comprises many cars, semantic segmentation can identify them all. However, instance segmentation can be used to label them according to color, shape, etc. Instance segmentation, a common computer vision task, is more difficult than other techniques because it requires visual data analysis with different backgrounds and overlapping objects. CNN and Convolutional Neural networks can be used to segment instances. They can find the objects at the pixel level instead of just bounding them.
Panoptic segmentation combines instance and semantic segmentation and is one of the most powerful computer vision techniques. Pantopic segmentation can classify images at the pixel level and identify individual instances of the class.
Keypoint detecting is a technique that identifies key points within an image to provide more information about a particular class of objects. It detects people and locates their key points, mainly focusing on two key areas: body keypoint detection and facial keypoint detection.
For example, facial keypoint detection detects key features of the face like the nose, eyes and corners. Face detection, pose detection, and others are some of the main applications for keypoint detection. Pose estimation allows us to determine what pose someone uses in an image. This usually includes their head, eyes, and nose location and where their arms, shoulders, legs, hands, neck, chest, and knees are. It can be done one-to-one or for multiple people depending on the need.
Person segmentation is an image segmentation technique that distinguishes a person from the background. This can be done after the pose estimation. With this, we can identify the exact location and pose of the person within the image.
Depth perception is a computer vision technique that provides visual abilities to computers to determine the depth and distance of objects from their source. It can be used for many purposes, such as reconstructing objects in augmented reality, robotics, and self-driving cars. LiDAR (Light detection and ranging) is one of the most popular techniques for in-depth perceptual. Laser beams are used to measure the distance between objects by shining laser light on them and measuring their reflection with sensors.
As its name implies, image captioning is about adding a caption to an image that describes it, and it uses neural networks. When we feed an image as the input, it generates a caption that describes the image, which is not a task for computer vision but also an NLP task.
3D object reconstruction
3D object reconstruction, as the name implies, is a technique for extracting 3D objects from 2D images. It is a rapidly developing area of computer vision and can be done differently depending on the object. PiFuHD is one of the most popular papers on this technique which discusses 3D human digitization.
Companies are rapidly adopting computer vision technology across all industries to solve automation issues with computers that can see. Visual AI technology is rapidly improving, enabling new computer vision projects and ideas to be implemented. For example, PPE detection, object counting, automated product inspection and process automation are some of the applications of industrial computer vision techniques in manufacturing. Similarly, automated human fall detection is a widely used application of computer vision in healthcare. Computer vision is also applied in agriculture, security, smart cities, retail, insurance, logistics and pharmaceutical.
Deep learning is a machine learning technique that teaches computers how to naturally do things humans can. It teaches computers to process data like the human brain’s thought process. Deep learning models can recognize complex text, image, and sound patterns and produce precise insights and predictions. Using deep learning, we can automate tasks that normally require human intelligence. The components of deep learning are as follows:
- Input layer
A neural network has many nodes that input data into it. These nodes form the input layer of an artificial neural network.
- Hidden layer
The input layer processes the data and passes it on to the layers in the neural network. These hidden layers process information at different levels and adapt their behavior as they receive new information. Deep learning networks can analyze a problem from many angles using hundreds of hidden layers. “Deep” is often used to refer to the number of hidden layers of a neural network.
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If you were to be given an image of an unknown creature that you needed to classify, then you would compare it with animals that you are familiar with. You would examine the animal’s shape, ears and size, fur pattern, and the number of legs. You might try to spot the patterns with cows, deer or other animals with hooves. Deep neural networks’ hidden layers work in the exact same way. Deep learning algorithms use these hidden layers to categorize an animal image. Each layer processes a different aspect of the animal to help them classify it.
- Output layer
These nodes produce the data that make up the output layer. Two nodes are required for deep learning models that output “yes” or “no” answers. However, models that produce a wider range of answers have more nodes. Deep learning uses neural network architectures, so many deep learning models are called high-performance neural networks. Deep networks can contain up to 150 layers.
Deep learning models can be trained using large amounts of labeled data. Using neural network architectures, it learns features from the data without requiring feature extraction.
Some of the widely used deep learning algorithms are as follows:
- Long Short Term Memory Networks (LSTMs)
- Recurrent neural network (RNN)
- Convolution neural network (CNN)
- Restricted Boltzmann machine(RBM)
- General Adversarial Networks (GANs)
- Radial Basis Function Networks (RBFNs)
- Multilayer Perceptrons (MLPs)
- Deep Belief Networks (DBNs)
- Self-driving cars, voice-controlled assistance, automatic image caption generation and automatic machine translation are some deep learning applications.
Generative AI includes unsupervised or semi-supervised machine learning algorithms that allow computers to use existing text, audio, and video files and even code to create new content. The goal is to create original items that look exactly like the real thing. Generative AI is a method that allows computers to abstract the underlying patterns of input data to enable them to generate new output content.
Instead of predicting which features will be given a particular label, generative algorithms attempt to predict what features will be given to that label. While discriminative algorithms focus on the relationships between x, and y, generative models are concerned with how to get x. Mathematically, we can use generative modeling to calculate the likelihood of x or y occurring together. It does not learn the boundary but the distribution of individual features and classes. Generative models learn features and their relations to understand an object. These algorithms can recreate images of objects even if they are not part of the training set. A generative algorithm is a method that models a process holistically without discarding any data. GANs and other transformer model algorithms are examples of such innovative technologies.
Let’s discuss two of the most popular generative AI models.
- Generative Adversarial networks or GANs are technologies that create multimedia artifacts using both textual and imagery input data. It is a machine learning algorithm that pits two neural networks – a generator and a discriminator against one other. This is why it is called adversarial. The contest between two neural networks forms a zero-sum game where one agent wins, and another loses. GANs are composed of two models:
Generator – A neural network that creates fake input or fake samples from a random vector (a list containing mathematical variables whose values are unknown).
Discriminator – This neural network can identify fake samples from a generator and real samples from the domain. The binary discriminator returns probabilities of a number between 1 and 0. The more likely the output is fake, the closer it is to 0. The reverse is true: numbers closer to 1 indicate a greater likelihood that the prediction will be accurate.
Both the generator and discriminator can be used as CNNs, particularly when working with images.
- Transformer-based models – Technologies such as Generative Pretrained (GPT) language models use internet information to create textual content, from press releases to whitepapers to website articles. GPT3 and LaMDA are two of the most popular examples of transformer-based models.
A transformer converts one sequence into another. In this model, semi-supervised learning is trained using large, unlabeled data sets and then fine-tuned with supervised training to improve performance. The encoder processes the input sequence, which extracts all the features from a sequence and converts them into vectors. (e.g., vectors that represent the semantics and position of a word in the sentence). It then passes them on to the decoder. The decoder is responsible for the output sequence. Each decoder takes the encoder layer outputs and derives context to create the output sequence. Transformers use sequence-to-sequence learning, meaning that the transformer uses a sequence of tokens to predict the next word in an output sequence. Iterating encoder layers do this.
Some generative model applications include image generation, image-to-text generation, text-to-image translation, text-to-speech, audio generation, video generation, image and video resolution enhancement, and synthetic data generation.
An expert system is an interactive, reliable, computer-based AI decision-making system that uses facts and heuristics to solve complex decision-making issues. The highest-level human intelligence and expertise solve the most difficult problems in a particular domain.
An expert system’s Knowledgebase is what gives it strength. This is a collection of facts and heuristics organized about the system’s domain. An expert system is constructed in a process called Knowledge Engineering, during which knowledge about the domain is gathered from human experts and other sources. Expert systems are marked by the accumulation of knowledge in knowledge banks, which can be used to draw conclusions from the inference engine. An expert system’s knowledge base contains both factual knowledge and heuristic information. Knowledge representation is how knowledge is organized in the knowledge base. These knowledge bases represent notions such as actions that can be taken according to circumstances, causality and time.
The inference engine combines the facts of a particular case with the knowledge in the knowledge base to produce a recommendation. The inference engine controls the order to apply production rules in a rule-based expert system. The case facts are recorded in the working memories, which act as a blackboard and accumulate the relevant knowledge. Inference engines repeatedly apply the rules to the working memories, adding new information until the goal state is reached or confirmed.
An expert system works mainly on two mechanisms as described below:
Forward chaining is a data-driven strategy. The inferencing process leads to a conclusion from the facts of the case. This strategy is based on the facts in the case and the business cases that can satisfy them. Inference engines attempt to match the conditions (IF), part of each rule, in a knowledge base with facts currently in the working memory. When multiple rules match, a conflict resolution procedure will be invoked. For example, the rule with the lowest number that adds new information is fired. The firing rule’s conclusion is added to the working memories. Forward-chaining systems can solve open-ended design and planning problems, such as establishing the configuration for a complex product.
Backward chaining is the process where the inference engine attempts to match the hypothesis with the conclusion (THEN). If such a rule can be found, the premise of the rule becomes the new subgoal. This strategy is good for an expert system with few possible goal states. In this process, the system will try to provide another goal state if the premises do not support a hypothesized goal. So, all possible conclusions are reviewed until a goal state can be supported in the premises.
Backward chaining works best for applications where the possible conclusions are small and clearly defined. These systems are often used for diagnosing or classifying patients. Each possible conclusion can then be verified against the data to confirm validity.
Loan analysis, stock market trading, virus detection, warehouse optimization, and airline scheduling are some of the applications of expert systems.
The layered approach of artificial intelligence technologies
We have so far described the key AI technologies; now, let’s have an overview of where they are placed in the layered architecture of the AI ecosystem.
- We can describe an AI ecosystem as having the following four layers.
- Data layer
- ML frameworks and packages or Algorithm layer
- Model layer
- Application layer
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Layer 1: Data layer
Artificial intelligence comprises many technologies, such as machine learning, natural language processing, image recognition, etc., discussed in the previous section. Data plays the most critical role in the functioning of all these technologies, which is also the foundation layer of AI technologies. This layer plays a key role in data preparation.
Sub-layer – The hardware platform
In the layered approach of core AI technologies, the hardware platform and low-level primitives can be considered a sub-layer of the data layer, as they provide the necessary infrastructure for training and running AI models. They allow developers to optimize AI models’ performance and use the most appropriate hardware for a specific task.
When it comes to large data crunching, powerful machines are key aspects. Iterative algorithms require continuous learning and simulations, which requires an elastic and reliable IT infrastructure. Additionally, the current state-of-the-art techniques, such as deep learning algorithms, require large computational resources.
GPU is an important addition to this requirement. Backend operations such as matrix calculations and parallel calculations of relatively easy equations are extremely useful for calculating ML algorithms. The GPUs can even be used to train neural networks-simulations for the human brain that form the basis of modern ML.The hardware design of these GPUs were originally intended for graphics computations and not AI. However, things are changing fast.
These machines, particularly the IaaS model, have given the computing and memory resources needed to crunch large amounts of data, greatly reducing the time required to train ML algorithms. What used to take several weeks to run in a traditional data center now takes only a few hours in the cloud.
Low-level software libraries, such as Intel Math Kernel Library and Nvidia CuDNN, that directly work with GPUs are another side of the equation. These have dramatically increased GPU ML processing speeds. The speed advantage is also available when the same code is integrated into a CPU, as in Intel MKL. CuDNN and MKL libraries can be created and integrated into frameworks to increase hardware utilization and information extraction without the need for software engineers.
Layer 2: The ML framework and packages or algorithm layer
The availability of large amounts of data, as well as the accessibility to robust infrastructures like AWS, is changing the landscape of ML. Machine learning engineers who work with data scientists to understand a particular field’s business and theoretical aspects create ML frameworks. Some of the popular ML frameworks are as follows:
- TensorFlow – An open-source software library for machine learning developed by Google Brain Team.
- PyTorch – An open-source machine learning library developed by Facebook’s AI Research lab.
- Scikit-Learn – A simple and efficient library for machine learning in Python.
- Keras – A high-level neural networks API written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
- Caffe – A deep learning framework developed by Berkeley AI Research and community contributors.
- Microsoft Cognitive Toolkit (CNTK) – A deep learning toolkit developed by Microsoft.
- LightGBM – A gradient-boosting framework that uses tree-based learning algorithms.
- XGBoost – A gradient-boosting library designed for speed and performance.
- Spark MLlib – A machine learning library for the Apache Spark platform.
- Random Forest – A popular ensemble method for classification and regression, implemented in many libraries such as scikit-learn, R, and Weka.
In the layered approach of AI core technologies, ML frameworks and packages can be considered part of the algorithm layer, as they provide the necessary functionality to implement and train AI models and allow developers to use pre-built functions and classes to construct and train models easily.
Layer 3: Model layer
The model layer of AI technology implements AI models, trained and fine-tuned using data and algorithms from the algorithm layer. This layer enables the actual decision-making capability of the AI system. Multiple components build this layer, as described below:
It is the most crucial component of the model layer and refers to the model’s architecture. This layer of AI technology determines the capacity and expressiveness of the model. It includes the number of layers, the number of neurons per layer, and the type of activation functions used.
Model structures can be classified into several categories, such as Feedforward Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoder, and Generative Adversarial Networks (GANs). The selection of a particular model depends on the available data, problem domain, and available resources.
It refers to the values learned during the training process, such as the weights and biases of the neural network. These parameters make predictions and decisions based on the input data. In neural networks, the model parameters are the weights and biases of the neurons in each layer, where weights determine the strength of the connections between neurons, and the biases determine the activation threshold of each neuron.
It is a metric used to evaluate the model’s performance during training. It measures the difference between the predicted output and the true output and is used to guide the optimization process of the model. The goal of the training process is to minimize the loss of function.
It’s worth mentioning that in some cases, it’s possible to use different loss functions depending on the stage of the training, for example, using BCE in the early stages of the training and then to switch to the CEL in the later stages; this technique is called curriculum learning.
The Optimizer in AI technology is an algorithm that adjusts model parameters to minimize the loss function. It is a crucial component of the model layer, responsible for updating model parameters during the training process.
There are several types of optimizers, each with its own strengths and weaknesses like Gradient Descent (GD), Adaptive Gradient Algorithm (AdaGrad), Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMSprop), Radial Base Function (RBF) and Limited-memory BFGS (L-BFGS). The choice of optimizer depends on the problem domain, the data available, and the resources available. For example, if it is sparse data and has many features, the AdaGrad optimizer is the better choice. Similarly, for the non-stationary data, Adam or RMSprop is more appropriate.
It’s worth mentioning that we can select the optimizer based on the type of model, the size of data and the computational resources available, and in some cases, the combination of multiple optimizers with different parameters can be used to improve the performance.
Regularization in AI technology is a technique that prevents overfitting, a common problem in machine learning. Overfitting occurs when a model is too complex and adapts too much to the training data, resulting in poor performance on new and unseen data. Regularization methods help to constrain the model and improve its generalization ability.
Several types of regularization methods, each with its own strengths and weaknesses, like L1 Regularization (Lasso), L2 Regularization (Ridge), Elastic Net, Dropout, and Early Stopping.
It’s worth mentioning that this layer can have different types of models, such as supervised, unsupervised, and reinforcement models, each with different requirements. The design of the models in this layer should consider the problem domain and the data available.
Layer 4: Application layer
This layer represents how AI systems are used to solve specific problems or perform certain tasks. This covers a broad range of applications, such as decision-making, natural language processing, and computer vision techniques. This layer solves real-world problems and provides tangible benefits for individuals and companies.
Robotics, gaming, bioinformatics, and education are a few other applications of AI.
Artificial intelligence and data analysis have the potential to bring significant advancements to various sectors. Already, there are large deployments in finance and national security. These developments have significant economic and social benefits, and the key AI technologies have made them possible. These technologies have brought about unprecedented advancements in various industries using machine learning, natural language processing, computer vision and deep learning. They have also created new opportunities for businesses and individuals to automate processes, improve decision-making, and enhance customer experience. As AI evolves and matures, individuals and organizations must stay informed and adapt to these new technologies to stay ahead of the curve. So embrace these key AI technologies and be a part of the future!
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