Is artificial intelligence a super cool technology?

While people’s opinions may vary depending on their experiential insight about AI, one thing stands uncontested – AI is already impacting human lives. And it is expanding its reach to every aspect of industrial application. From automating repetitive tasks to mitigating manual load and enabling multi-tasking to relieve the workload on resources and remaining operational without downtime, the capabilities of AI technologies are limitless.

Since AI was first coined around 1940-1960 in the wake of cybernetics, it has evolved and expanded rapidly, becoming a revolutionary technological breakthrough post the advent of the Internet. 

Statistical Overview Of Artificial Intelligence: Growth, Investment, Revenues

Based on the report by Statista, the global software market of AI is expected to grow by nearly $126 billion by 2025. When we talk about the AI market, it actually encompasses a broad range of applications, such as –

●      Machine learning (arguably the most potent part of AI)

●      Robotic process automation

●      Natural language processing

Some of the most popular names in the tech industry, by recognizing the limitless potential of AI, have made considerable investments in AI acquisitions and AI-related research and development. 

Reportedly, AI startups attracted an investment of nearly 36 billion U.S. dollars in the first six months of 2021, reaching $38 billion subsequently. One of the examples of AI power is greatly exemplified in the report, stating the technology can increase labor productivity in developed countries by nearly 40% by 2035.

For example, the expected productivity growth due to ai will be around 37% in Sweden, whereas countries like Japan (34%) and the U.S. (35%) are likely to benefit from AI impacts considerably. 

According to Grand View Research, the revenue value of the global market size of ai was at USD 136.55 billion in 2022 and is expected to grow at a CAGR of 37.3% from 2023 to 2030.

The report also highlights that the research and innovation directed by tech giants is contributing to the growth in the adoption of advanced technologies in industry verticals, such as –

●      Automotive

●      Retail

●      Finance

●      Manufacturing 

Artificial intelligence has widespread application areas, ranging from computer vision and biometrics to self-driving automobiles and intelligent devices. Therefore, the rapidity in the growth of AI adoption is the cause of these AI technologies (biometrics, computer vision, etc.). Moreover, large data volumes, computing power, and cloud processing advancements have also played their roles in the growth of AI adoption.

It goes without saying that companies today have access to mountainous data including the treasure troves (dark data) that have catalyzed the sharp growth of AI. However, businesses can only take full advantage of AI values if they understand how to maximize the technology to innovate themselves.

One of the best ways businesses can use to get started in the context of harnessing the power of AI is to understand the core technologies behind it. In other words, if you can get to the bottom of what drives artificial intelligence, you can utilize the technology as a source of business innovation.

Defining Artificial Intelligence, and Key Parts Of AI Applications

With numerous definitions of artificial intelligence floating over the internet these days, finding the one that accurately and concretely defines AI is an experience of finding a needle in a haystack. Anyway, according to Wikipedia, artificial intelligence is machine-demonstrated intelligence in contrast to the one demonstrated by human or animal cognition. 

Noted computer scientist, John McCarthy said AI is the science and engineering of making intelligent computer programs. Adding further, he said that AI does not involve itself in the methods that are biologically observable. In his view, intelligence in AI is the computational part of the ability to attain goals in the world.

In contrast to popular notions that AI simulates human intelligence, John McCarthy didn’t endorse that notion, saying most of the works of AI involve studying the problems that the world presents to intelligence.

Anyway, in modern days, artificial intelligence is believed to perform actions by mimicking human cognitive functions. Technology empowers computers to perform tasks, such as analyzing, understanding, perceiving, translating, and synthesizing. It enables computer systems to interpret languages and data to make recommendations and perform other actions based on the study.

Most AI applications and robots are capable of navigating warehouses without human intervention, thereby showcasing their promising competence in supply chain companies. ChatGPT is already revolutionizing tech companies by enabling them to measure the efficacy of their customer care methods to understand their customers better, improve user experience, and increases brand loyalty and higher revenue.

Key Essentials That Play Pivotal Role In Building AI Applications 

Want to build an AI application? Considering how AI is creating a tectonic shift in revolutionizing tech companies worldwide, every business today thinks of harnessing the limitless power of AI to gain competitive advantages. In the context of how AI technologies are transcending the business growth of companies, AI application development is more than just a need for organizations these days.

In this section, we are going to discuss how data, algorithms, and human feedback constitute key essentials behind building AI applications. 

Enormous Amounts Of (Quality) Data 

The rapid growth of mobile technology and digitization has catalyzed a sharp surge in data growth, substantiating why almost all industries in the last decades have been maximizing data as a key essential for their business model. Data collected from internal and external sources have been empowering service firms across industries to explore the limitless potentiality of AI. However, it’s the quality of data that’s the real deal here.

Why does the quality of data matter in AI?

We know that an AI model is capable of predicting the outcome with near accuracy based on how extensively and rigorously it has gone through training in a huge dataset. Therefore, collected data sans quality will not prove effective for AI model explainability, resulting in inaccurate insight that would lead to unpredictable decisions in human context models. 

The AI apps are programmed to scrutinize data and identify patterns to be able to predict according to the patterns identified. They get better by learning from errors and improving their outputs based on human feedback and new data. Let me reiterate, the quality of data matters significantly, as AI apps usually generate the optimum results when the latent data sets are enormous, valid, and fresh.  

Data Collection:

The working mechanism of AI is similar to the cognitive function of human brains, which sources information from environments around us to draw conclusions. We need to understand that AI without quality data can’t function at all. AI’s predictive capabilities largely depend on the huge volume of quality and valid data it collects. The data is collected from various places in the AI tech stack whose data collection layer comprises software that interfaces to millions of connected devices, and web-based services ranging from marketing databases to weather, news, and social media APIs. 

Storing Collected Data: 

This is the next step to follow once you have collected AI data (structured or unstructured). Obviously, an ocean of data would require a lot of storage with easy accessibility. While considering third-party cloud infrastructure, like Amazon Web Services and Microsoft Azure could be an ideal option to choose, most organizations opt for Apache Hadoop to create distributed data centers to handle massive data. 

Data Processing: 

Data processing for AI lays the groundwork for translating raw data into usable information. Technologies that participate in data processing involve machine learning, image recognition, deep learning, etc. The powerful algorithms of these technologies can self-learn and are flexible. They can be accessed via third-party API, hosted on a public or private cloud within a public or private data center, at the point of data collection. 

Do you know the role of graphics processing units (GPUs) is valuable for dramatically increasing computational processes of deep learning? That said, GPUs are a key part of artificial intelligence infrastructure and are used in parallel for massive distributed computational methods.

To sum up, the days are just a short distance from today when AI performance will significantly be improved due to a new generation of processors designed specifically to process AI-related tasks. 

Algorithms:

Al algorithms can be defined as a set of instructions that tell the machine to perform activities, such as solving a problem or generating output from input data. In machine learning algos, complex mathematical code is involved, enabling it to learn from new input data. As a result, it can create a new or modified output based on the learning. Interestingly, the machine is not programmed to perform tasks automatically but to LEARN to perform the task. 

Human Involvement: 

Isn’t it self-explanatory? I mean, how can you think of building an ai application without human involvement? In fact, every stage of building an AI application mandates human interaction. For instance, human feedbacks help validate the relevance of data for the AI application. They ensure the accuracy and relevancy of the output based on how the algos sort through the data.

Obviously, if there is no human feedback available, the likelihood of generating unintelligent results for the AI system is bound to occur. Unwanted results can also happen if the faulty results become the base of an action. 

Core Parts Of AI Technologies 

1. Machine Learning  

This is the most vital, subfield of artificial intelligence technology. In fact, most of what we witness today in the field of AI is actually the handiwork of machine learning. The technology enables computer systems with the ability to learn from examples called neural networks.

Machine learning relies heavily on data, such as photos, numbers, texts, etc. The ML model uses training data which is quite an intensive task to make the machine able to predict patterns or make predictions. As a programmer, you can also make relevant changes to the machine learning development model, empowering it to generate more accurate results. 

 What Are The Types Of Machine Learning? 

 Based on the methods and way of learning, machine learning constitutes four distinct parts, such as –

●      Supervised ML

●      Unsupervised ML

●      Semi-supervised ML

●      Reinforcement ML

Supervised ML 

The supervised learning model is trained using a labeled dataset containing both input and output parameters. It enables the model to learn and become more accurate over time. The model requires a supervised learning technique with the help of a “labeled” dataset to be able to predict the outcomes.

Understand that in training the model, data splitting involves the ratio 80:20, meaning 80% is the training data while the rest is testing data. Interestingly, the learning exercise of the model requires only training data.

The supervised learning model is based on two distinct types; CLASSIFICATION, and REGRESSION. The first one has an output having defined labels while the latter has an output having no defined labels. In addition, supervised learning algos include random forest, decision trees, logistics regression, linear regression, etc.

Unsupervised ML 

An unsupervised machine learning model doesn’t have a labeled or classified dataset. The algo processes the information on its own, without guidance or supervision. What the machine does here is group unsorted data by deciphering similarities, patterns, and differences. The process is executed without prior data training. To say the least, the machine is on its own to identify the hidden structure in unlabeled data.

The model is classified into two distinct categories, clustering, and association. The first category means discovering the inherent groupings in the data, like grouping customers based on their purchasing conduct. The second category of unsupervised ML model means identifying rules that govern large segments of data.

Reinforcement ML 

This machine learning model is the science of decision-making by setting up a reward system. Data in this model is collected from ML systems that perform using a trial-and-error method. Interestingly, data is not part of the input in this model, as we see in supervised or unsupervised machine learning models.

There are two types of reinforcement learning, positive, and negative. In the first reinforcement learning, a particular behavior triggers an event, rendering positive effects on the behavior. In Negative reinforcement learning, behavior is strengthened due to avoiding a negative condition. 

2. Natural Language Processing (NLP) 

One of the most powerful subfields of AI, natural language processing analyzes human language to draw conclusions and insights. It facilitates the creation and reading of textual and visual data and more. Technically speaking, ML (machine learning) helps computers understand and interpret, human language. It showcases various applications, from automating translation to identifying insurance fraud and empowering chatbots. 

3. Computer Vision 

It may sound like one of the catchy terms in sci-fi movies. Technically speaking, computer vision enables computers to identify and process digital images, videos, and other visual inputs. For this, it replicates parts of the cognitive function of human brains. So, computer vision very much behaves, like extracting information, in the same way, that humans do.

Though there is no straightforward answer to how computer vision works, we can say it is all about pattern recognition. A computer goes through training using countless amounts of (if possible) labeled data. Technologies such as deep learning and convolutional neural network (CNN) partake in the process of computer vision.

For instance, CNN helps machine learning models to identify images broken down into pixels either labeled or given tags. Based on how it concludes the data it has been fed, it makes predictions, which technically means “seeing” by CNN. 

Top Techniques Of Computer Vision

●      Image classification, to classify the image into one or different categories.

●      Object detection, to identify the objects in visual data.

●      Semantic segmentation, to pixelate an image to classify what object it contains.

●      Instance segmentation, to classify similar types of objects into varying categories.

●      Panoptic Segmentation, to classify image objects at pixel levels and also identify discrete instance of the class.

●      Keypoint Detection, to detect main points in an image to output more details regarding a class of objects.

●      Person Segmentation, to isolate the person from the background in an image.

●      Depth perception to enable machines with the visual ability to gauge the 3D depth of an object from the source.

●      Image captioning to give a descriptive note to the image.

●      3D object reconstruction to extract 3D objects from a 2D image.

4. Deep Learning 

No doubt, deep learning is one of the most powerful subfields of artificial intelligence. A neural network with three or more layers (such as input layer, learnable layers, activation layers, etc.) deep learning teaches computers to process data by simulating the cognitive functionality of human brains.

Technically speaking, deep learning can automate tasks that usually depend on human intelligence. To be able to extract higher-level features from the raw input, deep learning makes use of multiple layers. 

5. Generative AI models 

Generative AI involves the use of unsupervised or semi-supervised ML algorithms. It enables computers to make use of available text, audio, video files, and even code to generate fresh content as an exact replica of the original.

Generative models, in this context, understand an object by learning its features and their relations. The power of generative algorithms is that they can generate images of objects despite they are not part of the training dataset.

Generative Adversarial networks (GANs) and transformer models are two distinct types of generative ai models.

GANs use textual and imagery input data to generate multimedia artifacts. In transformer-based models, technologies like GPT (generative pre-trained) language models utilize internet data, like press releases, whitepapers, and website articles/blogs to create textual content. 

The Four Layers That Constitute An AI Ecosystem

●      Data layer: It defines its credence in the functioning of AI technologies, like machine learning, natural language processing, image recognition, etc.

●      Algorithm layer: It involves ML frameworks (e.g., TensorFlow, XGBoost, PyTorch, etc.), providing key functionality to implement and train AI models. Developers can also make use of pre-built functions and classes to train models without problems.

●      Model layer: It enables the actual decision-making capability of the AI system.

●      Application layer: It portrays the efficacy of AI systems in solving specific tasks or problems.

Conclusion 

 Artificial intelligence is undoubtedly a futuristic technology creating a tectonic shift in the way businesses do their tasks. From automating repetitive tasks to reducing manual load and enabling multi-tasking, the capabilities of AI are limitless.

Not only that, the technology is immensely viable in the health industry. It is helping medical professionals in intelligent decision-making based on the prognosis data of the patients.

Being operational without downtime, AI technologies are shaping the human and tech industry beyond words to measure. Since it first came around 1940-1960, AI has been on a growth trajectory, unabated. It became a technological breakthrough after the advent of the Internet.

Machine learning and related technologies have contributed largely to the development of economic and social benefits, revolutionizing many sectors.

From improving decision-making for businesses to enhancing customer experience with AI, the contributions of AI technologies have been substantial. No doubt, companies utilizing these technologies will stay relevant in their niche with distinct competitive advantages.