Generative AI is changing the face of technology. Let’s take a quick look at the history of AI and how it has evolved over the years leading to the emergence of generative AI.
It’s a very exciting time for techies. My own IT career has undergone a massive transformation, from mainframe, client server, enterprise apps, internet to cloud and AI. In today’s digital economy, technology leaders are the bridge between business agenda and leadership strategy. These technology leaders are uniquely positioned to challenge the status quo by setting the direction of companies driven primarily by technological innovation. Over the past few months, all such leaders have been doing their best to realign their organizations’ technology strategies with generative AI.
Let’s take a look at what generative AI is, how it differs from traditional AI, why it’s gaining popularity today, and what open source technologies are available to take advantage of this new, cutting-edge innovation. But before diving into what generative AI is, it’s important to take a quick look at what AI really is and how it has evolved over the years.
History of AI
In 1950, Alan Turing questioned whether machines could think. That one thought has led to the development of things like self-driving cars and self-healing applications. AI has played a key role in the quest to develop a smart and productive world, especially over the past two decades. Traditional AI evolved into Generative AI when Generative Adversarial Network (GAN) algorithms were developed to create compelling, authentic images, videos and audios of real humans.
AI has benefited greatly from advances in machine learning (ML), natural language processing (NLP), deep learning (DL), and other data processing frameworks.
What is generative AI?
Generative AI is unsupervised or semi-supervised machine learning that uses data to learn representations of artifacts and uses those representations to create entirely new artifacts while maintaining similarity to the original data. Refers to algorithm-based AI systems. The idea is to generate an artifact that looks real. Generative AI represents a category of artificial intelligence that can generate new content rather than analyzing existing data. The models you train with are the references used to build and evolve your own understanding to develop your ability to create new content such as articles, blog posts, images, and sounds. Typical AI that has existed for decades focuses on analyzing existing data. While this is supervised machine learning, generative AI is unsupervised or semi-supervised machine learning that uses existing input data, image, audio, or video content to generate new content that is as realistic as possible. can.
A supervised machine learning algorithm’s job is to find the correct answer in new data, because the old data already contains the correct answer.
Unsupervised machine learning is a technique that reveals hidden patterns in data. Using this method, machine learning models independently look for patterns, structures, similarities, and differences in data. No human involvement is required.
Supervised machine learning is discriminative modeling and unsupervised machine learning works as generative modeling. A hybrid version of the two models is known as semi-supervised machine learning.
Semi-supervised learning (SSL) uses a significant amount of unlabeled data to train a predictive model and also uses a small amount of labeled data. The main advantage of this model is that within a short span of obtaining a sample data model, the pseudo-labeling self-training machine learning process can be started. There are many variations of this model, including joint training, which trains two separate classifiers for her based on her two views of the data. These models are developed based on needs and usage expectations.
| learning range | supervision | semi-supervised | not supervised |
| Input data | Labeled | Some labeled data with large amounts of unlabeled data | no label |
| model feed | Input and output variables | Input and output variables Use trained dataset |
input variables only |
| human involvement | most involved | some involvement | least involved |
| Functional characteristics | collection/preparation of qualitative data; Data labeling is tedious and time consuming. more accurate results |
Reduced data preparation time; Self-training with minimal supervision |
The slowest learning model. Complexity increases as the amount of data increases. Inaccurate results |
| when to use | Look for known data patterns/analysis | crawler and content totalling |
Look for unknown data patterns/analysis |
| popular algorithms | Supports vector machines. random forest. Naive Bayes; decision tree |
modified match; mix match; graph-based SSL algorithm |
Gaussian mixture model; principal component analysis; frequent pattern growth; K-means |
| Common use case | demand forecast; price forecast; sentiment analysis; image recognition |
voice recognition; Classification of content; document hierarchy; Website annotation |
Prepare data for supervised learning. anomaly detection; recommended system; customer segmentation |
Table 1: Characteristics of supervised, semi-supervised, and unsupervised ML models
As adoption and use cases evolve, each of these machine learning models is destined to advance and evolve dramatically in its core capabilities. Today, semi-supervised learning is applied everywhere from data aggregation to image or audio processing. The generalization of semi-supervised machine learning provided by performing data classification based on a small number of predefined variables is very attractive and highly popular among all machine learning models. Table 1 shows the differences between the three machine learning models and the types of algorithms commonly used for different use cases. It should provide an idea of where and how these models would be most useful.
Most artificial intelligence enterprise applications are written in popular and popular open source languages such as Python, Lisp, Java, C++, and Julia. Most businesses embarking on their digital transformation journey find themselves leveraging AI in everyday scenarios to improve operational efficiency and automation of everyday processes. Fortunately, the most popular open source AI frameworks allow developers to use the language and implementation model of their choice (supervised, semi-supervised, or unsupervised learning). Here are some popular AI frameworks in use today.
TensorFlow is probably the most popular AI framework. Developed by Google to support neural network training with an easy and extensible setup. It is also the most widely adopted deep learning framework.
PyTorch is a Python framework for building machine learning algorithms that can rapidly evolve from prototype to production.
Keras is designed with the developer in mind. This enables a plug-and-play framework for rapidly building, training, and evaluating machine learning models.
scikit-learn contains high-level abstractions from common machine learning algorithms and is suitable for prediction, classification, or statistical analysis of data.
Knowing what problem you need to solve and which libraries best support it will determine the toolsets and frameworks you will use to develop a machine learning module that will give you the expected results. helps.
Research into generative AI continues, and the technology is now being applied across a wide range of industries, including life sciences, healthcare, manufacturing, materials science, media and entertainment, automotive, aerospace, military, and energy.
Whatever direction AI takes in the future, its impact will be very long-lasting. Today, generative AI innovations are leading to organoid intelligence. Organoids are three-dimensional laboratory-grown tissues derived from stem cells. As we embrace various forms of AI into our daily lives, one thing is certain: innovation in this area will continue to create cutting-edge technology.
