generative ai

Generative AI: What it is and How it Works

The buzz surrounding generative ai and what is generative ai is enormous and it keeps expanding. It is one of the most significant and quickly developing technologies that will revolutionize productivity.

Here are a few of the most important predictions for generative AI:

  • By 2025, generative artificial intelligence will produce 10% of all data (it currently produces less than 1%), with 20% of all test data being used for consumer-facing use cases.
  • 50% of medication discovery and development projects will use generative AI by 2025.
  • By 2027, 30% of manufacturers will employ generative AI to increase the efficiency of their product development.

Not giving the subject the proper attention from our side would be a major oversight. So, let’s dive in.

What exactly is Generative AI?

A computer can use existing information, such as text, audio and video files, photos, and even code, to generate new potential content using unsupervised and semi-supervised machine-learning techniques. The main goal is to create 100% original artifacts that closely resemble the originals.

Two generative AI models are now most often utilized.

  • Technologies called Generative Adversarial Networks, or GANs, can produce visual and multimedia artifacts from input data that includes both pictures and text.
  • Transformer-based models: tools include Generative Pre-Trained (GPT) language models, which can take data from the Internet to generate text, such as press releases and articles for websites.

Because this technology can learn to replicate any data distribution, generative AI in general, and GANs, in particular, has enormous potential. That implies that AI can be instructed to create universes that closely resemble our own in any field.

For example, generative AI may be used to precisely transform satellite photos to map views in the logistics and transportation industries, which heavily rely on location services, allowing for the investigation of as-yet-unexplored locations.

With the aid of sketches-to-photo translation utilizing GANs, X-rays or CT scans can be transformed into photo-realistic images for use in healthcare. Due to improved image quality, serious diseases like cancer can be detected early on in this method.

By learning from the available data to anticipate the behavior of a target group in commercials and marketing efforts, generative AI can also assist with client segmentation in marketing. To strengthen upselling and cross-selling techniques, it can also artificially manufacture outbound marketing messages.

Although it could appear that way, generative AI doesn’t perform all of this by magic: To enable it to produce artifacts from content found in the real world, it must be modeled.

Discriminative modeling, Generative modeling

Let’s examine the differences between different generative ai models (discriminative and generative modeling) in order to comprehend the generative AI concept.

Existing data points are classified using discriminative modeling. It usually applies to activities requiring supervised machine learning.

The goal of generative modeling is to comprehend the dataset’s structure and provide analogous examples. It primarily pertains to machine learning activities that are unsupervised or semi-supervised.

The fields of discriminative and generative modeling are expanding as neural networks become more pervasive in our daily lives. Let’s get into more detail about each now.

Discriminative modeling

Predictions are typically made using machine learning algorithms. Given a set of attributes, discriminative algorithms attempt to categorize input data and predict a label or class to which a certain data example belongs.

For generative ai examples, let’s say that our training data includes a variety of pictures of tomatoes and cucumbers. They may also be known as samples. Each sample has output class labels and input characteristics (X) (Y). Additionally, we have a neural network that can analyze an image and determine if it is a tomato or a cucumber by focusing on the characteristics that set them apart.

In a way, this model essentially “recalls” what the object looks like based on what it has already seen after it has been taught and used to distinguish between tomatoes and cucumbers.

So, even if you show the model an image from a completely unrelated class, like an animal, it can probably recognize that it’s a tomato. In this instance, the expected output (y) from the training dataset is contrasted with the predicted output (). We can determine how and what needs to be modified in an ML pipeline to get more accurate outputs for specific classes based on the comparison.

In summary, the discriminative model compresses information on the distinctions between tomatoes and cucumbers without attempting to define either vegetable.

Generative modeling

Instead of predicting a label given to various features, generative algorithms attempt to predict features given a specific label. While generative models are concerned with how you get x, discriminative algorithms are concerned with the relationships between x and y.

Generative modeling enables us to quantify the likelihood that two events, x and y, will occur simultaneously. Instead of learning the border, it learns the distribution of distinct classes and traits.

Returning to our earlier example, generative models provide insight into the nature of the “tomato itself” or “cucumber itself.” Additionally, if the model is aware of the various varieties of tomatoes and cucumbers, then it is also aware of the variations between them.

An information-free holistic process modeling is the goal of a generative algorithm. Why do we even need discriminative algorithms, you might be asking. The truth is that a more targeted discriminative algorithm frequently provides a better solution to the issue than a broader generative one.

However, there is a large class of issues where generative modeling enables you to achieve outstanding outcomes. For instance, cutting-edge technology like transformer-based algorithms and GANs.

Text-to-Speech Generation

GANs have also been utilized by researchers to create synthetic speech from text input. Natural-sounding human speech is synthesized using cutting-edge deep learning technology. These models operate directly on input sequences of characters or phonemes and provide raw speech audio outputs. You can see these technologies be used in sync with AI video makers like Elai: they serve as the technology that helps to create voiceovers for human-like avatars that the platform provides. The best thing is, speech can be really anything you write. 

In Conclusion

Do you have to worry that AI technology will, and already is, flood the tech world? We assure you – you do not. There are numerous generative ai use cases that can be useful to you and your business. 

Play with them, and use them to create something new – with any AI technology you like. For example, why not start with creating a video with a digital avatar from just text? 

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