What is Generative AI, ChatGPT, and DALL-E?

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Generative AI refers to AI technology that can create a variety of content such as text, images, audio, and synthetic data. Much of the new buzz surrounding Generative AI has to do with these newly opened floodgates-what users will create once it takes virtually no effort and almost no time at all to create high-value text, graphics, or videos.

Worth noting, that generative AI is not new; it was first introduced in the 1960s with chatbots. But it wasn’t until 2014 when the introduction of generative adversarial networks, a subset of machine learning algorithms-that this form of AI got really good at creating hyper-realistic images, videos, and audio of actual people.

On one hand, this newfound ability has brought with it the possibility of better movie dubbing and the creation of enriching educational content, while on the other hand, it has raised concerns about deepfakes-digitally manipulated images or videos, and harmful cybersecurity attacks against businesses, including those devious requests that sound just like an employee’s boss.

Two recent breakthroughs, to be discussed below, have helped push generative AI into the limelight: transformers and the game-changing language models they have enabled.

Transformers represent a form of machine learning that has allowed researchers to train larger models without having to pre-label all the data. The breakthrough made it possible to train new models on much larger tracts of text, producing responses with increased depth.

In addition, transformers introduced a paradigm called attention that allowed models to follow the connections between words from page to chapter and book, not just within sentences. Transformers even showed their flexibility by leveraging their attention mechanisms in the analysis of code, proteins, chemicals, and DNA.

With recent rapid progress, large language models, which are typically fitted with billions or even trillions of parameters, signal the beginning of a new era in which generative AI models can write engaging text, generate photorealistic images, and even create entertaining sitcoms on the fly.

Furthermore, advances in multimodal AI enable teams to produce content across multiple media formats such as text, graphics, video, and more. Such technology underpins the so-called Dall-E, a type of tool that generates an image from a textual description or provides the caption for an image.

Despite these breakthroughs, the use of generative AI applications in coherent text creation or photorealistic stylized graphics is still in its infancy. Initial implementations have grappled with issues related to inaccuracy, bias, and susceptibilities to generating hallucinations or peculiar responses.

However, the pace at which things are improving indicates that the intrinsic abilities of generative AI could significantly change how companies do business in enterprise technology.

Moving forward, the technology has the potential to assist in coding, designing new pharmaceuticals, product development, business process redesign, and the transformation of supply chains.

How does Generative AI work?

Generative AI starts with a prompt, which could come in the form of text, image, video, design, musical notes, or anything the AI platform can understand.

From there, different AI algorithms generate content in the form of creative responses to that given prompt by writing essays and solving problems convincingly to create fabrications based on imagery or audio recordings of an individual.

Initially, in the development phase of generative AI, sending data required an API or another roundabout process. The developers needed to familiarize themselves with specialized tools and code applications with the help of languages such as Python.

But pioneers in generative AI are now fine-tuning user experience, allowing users to specify needs in natural language. If some initial output is created, users can refine the results further through feedback on style, tone, and more, regarding what they want that generated content to convey.

Generative AI models

Generative AI models use a wide range of AI algorithms to represent and process content in the most meaningful way.

For instance, in text generation, raw characters, such as letters, punctuation, and words, are first processed using various techniques in natural language processing to form sentences, parts of speech, entities, and actions. These are then represented as vectors using several encoding techniques.

Similarly, images are translated into many different visual entities also represented as vectors. Note that these methods can end up encoding biases, racism, deception, and puffery in training data.

Once the developers find a way to represent the world, specific neural networks generate new content based on queries or prompts.

Techniques such as Generative Adversarial Networks and Variational Autoencoders are neural networks that feature both a decoder and an encoder and find applications in generating realistic human faces, synthetic data for training AIs, or even reproductions of specific individuals.

More recently, transformers like BERT by Google, GPT by OpenAI, and AlphaFold by Google DeepMind have pushed this a step further, creating neural networks that not only encode but could actually generate completely new languages, images, and even proteins.

What are ChatGPT, Dall-E, and Bard?

ChatGPT, Dall-E, and Bard stand out as widely recognized interfaces in the realm of generative AI.

ChatGPT

The AI-driven chatbot that took the world by storm in November 2022, was created on the back of OpenAI’s GPT-3.5 release.

OpenAI provided a way to interact and fine-tune text responses via a chat interface complete with interactive feedback, something earlier versions of GPT were only available through an API. GPT-4 was announced on March 14, 2023, moving the technology even further.

ChatGPT integrates the conversation history with a user into its responses, mirroring the dynamics of an actual conversation. After the success of the new GPT interface, Microsoft showed its commitment with serious investment into OpenAI, integrating a version of GPT into its search engine Bing.

Dall-E

It has been trained on a large set of images and their corresponding text descriptions. It is a good example of a multimodal AI application connecting diverse media types, including vision, text, and audio.

This application particularly relates the meaning of words to visual elements and was built using the OpenAI GPT implementation in 2021.

In 2022, Dall-E 2 was released, which was an improved model allowing users to create imagery in different styles based on user input.

Bard

First pioneered by Google as one of the early adopters of transformer AI techniques to handle language, proteins, and various content types, it open-sourced some of these models for the research community.

However, Google did not release a public interface for these models until Microsoft’s integration of GPT into Bing prompted a hurried entry into the market.

Less than a month later, a public-facing Google Bard was unleashed, but it used a simplified variant of its LaMDA family of larger language models.

Google’s quick push to launch Bard, initiated when Microsoft got on stage, saw its stock decrease significantly. That came amid reports the program had blundered while referring to the Webb telescope as being responsible for discovering a planet in an “alien” solar system- it was not.

Meanwhile, Microsoft and ChatGPT implementations also received criticism when first released for their inaccuracies and unpredictable behavior. Then, Google released a new version of Bard based on its most advanced Large Language Model to date, PaLM 2. This upgrade helps Bard give more efficient, visually appealing responses to a user’s query.

What are the use cases for generative AI?

Generative AI finds its applications across a wide variety of scenarios, which will allow practically any type of content to be created. Recent breakthroughs, like versatile tools such as GPT, which can then be fine-tuned for more specific applications, make the technology increasingly accessible to end-users.

Some of the more popular applications of generative AI include:

Chatbots: Improve customer service and technical support.
Deepfakes: replicate individuals or specific personas.
Improving dubbing techniques for films and educational material in any language.
Creating email responses, dating profiles, job resumes, and term papers.
Creating photorealistic works of art in specific styles.
Enhancing product video demonstrations.
Suggesting new pharmaceutical drug candidates to experiment with.
Designing physical products or buildings.
Optimizing the architecture design of a new computer chip.
Creative music generation in a certain style or mood.

What advantages does generative AI offer?

Generative AI applies to a wide range of business domains, by making use of already available content and automatically generating new content. Developers are practically studying how generative AI can improve established workflows and are further considering a rewrite of workflows to take full advantage of the technology.

Possible benefits that generative AI could provide for businesses include:

Automating the manual process of creating content in writing.
Streamlining the effort required for responding to emails.
Enhancing responses to specific technical inquiries.
Generating lifelike representations of individuals.
Condensing intricate information into a cohesive narrative.
Simplifying the content creation process in specific styles.

What constraints does generative AI have?

The initial deployments of generative AI vividly highlight numerous constraints inherent in the technology. Several challenges stem from the specific methodologies used to address particular use cases. For instance, a condensed summary of a complex subject may be more digestible than an explanation integrating multiple supporting sources for key points. However, the accessibility of the summary comes at the cost of users being unable to verify the information’s origins.

Outlined below are some considerations regarding limitations when implementing or using a generative AI application:

Lack of consistent identification of content sources.
Difficulty in assessing the bias present in original sources.
Realistic-sounding content complicates the detection of inaccuracies.
Challenges in understanding how to fine-tune for new scenarios.
Results may overlook underlying biases, prejudices, and expressions of hatred.

What are the issues associated with generative AI?

The rise of generative AI has engendered a range of concerns that include issues of output quality, potential for misuse, and capability to disrupt existing business models.

The following are some specific problematic dimensions introduced by the current state of generative AI:

Inaccurate and Misleading Information: This is where the generative AI can give results that are inaccurate and misleading.
Challenges in Trusting Unverified Information: Without knowledge of the source and provenance, it becomes more difficult to trust information created by generative AI.
Promotion of New Forms of Plagiarism: It may enable new forms of plagiarism with complete disregard for the rights of originators of such content and art.
Disruption of Established Business Models: Existing business models around search engine optimization and advertising are likely to get disrupted.
Facilitation of Fake News Generation: Generative AI makes it easier to generate fake news.
Ease of Denying Authentic Photographic Evidence: The technology is making it easier to refute the authenticity of real photographic evidence based on the rationale that such could have been created by AI.
Potential for Impersonation in Social Engineering Cyber Attacks: Generative AI can be used to impersonate people, thereby increasing the success of social engineering cyber attacks.

What are examples of generative AI tools?

Generative AI consists of diverse modalities, including text, imagery, music, code, and voices. Below are some popular AI content generators worth exploring in each category:

Text Generation Tools:

GPT
Jasper
AI-Writer
Lex

Image Generation Tools:

Dall-E 2
Midjourney
Stable Diffusion

Music Generation Tools:

Amper
Dadabots
MuseNet

Code Generation Tools:

CodeStarter
Codex
GitHub Copilot
Tabnine

Voice Synthesis Tools:

Descript
Listnr
Podcast.ai

AI Chip Design Tool Companies:

Synopsys
Cadence
Google
Nvidia

Applications of Generative AI across different industries

Emerging Generative AI technologies are often likened to general-purpose technologies such as steam power, electricity, and computing, given their potential to significantly impact various industries and use cases.

It’s important to note that, similar to earlier general-purpose technologies, the optimization of workflows to fully exploit the new approach may take years, as opposed to merely expediting segments of existing workflows.

Here are ways in which Generative AI applications could influence different industries:

Finance: Enhanced fraud detection systems can be developed by analyzing transactions within the context of an individual’s history.
Manufacturing: Manufacturers can use generative AI to amalgamate data from cameras, X-rays, and other metrics for more accurate and cost-effective identification of defective parts and their root causes.
Legal: Generative AI can aid legal firms in the creation and interpretation of contracts, evidence analysis, and formulation of arguments.
Medical Industry: Generative AI can expedite the identification of promising drug candidates, improving efficiency in the medical industry.
Film and Media: Generative AI offers film and media companies the ability to produce content more cost-effectively and translate it into other languages using the original actors’ voices.
Architecture: Architectural firms can leverage generative AI to swiftly design and adapt prototypes.
Gaming: Gaming companies can employ generative AI for the design of game content and levels.

Ethics and bias in generative AI

Despite the potential benefits of newer generative AI tools, many thorny ethical issues crop up. Examples include accuracy, trust, bias, hallucination, and plagiarism. The process of addressing all such ethical considerations is foreseen to be quite slow.

None of these challenges are new in AI per se, but recent instances serve to illustrate the complexity thereof. For example, Microsoft’s first effort with the chatbot Tay in 2016 had to be shut down when it began publishing inflammatory rhetoric on Twitter.

What sets this new breed of generative AI applications apart is the way they seem coherent-a fact that is also dangerously misleading.

The combination of human-like language and coherence does not mean human intelligence, which makes many question whether generative AI models can ever be trained to reason. Last summer, for example, a Google engineer was threatened with termination for going public with the assertion that the company’s generative AI app, Language Models for Dialog Applications (LaMDA), was sentient.

The convincing realism of generative AI content creates a new array of dangers, making it harder to detect AI-generated content and, importantly, making the detection of inaccuracies so much more difficult.

This could also create significant challenges when relying on generative AI results for things like coding or providing medical advice.

Most of the generative AI outputs are not transparent, and hence, many issues such as potential copyright infringement or problems with the sources informing the results may not be ascertained. When there is no insight into how AI arrived at a conclusion, reasoning about the accuracy of its output becomes challenging.

Generative AI vs. AI

Generative AI is a class of AI designed to generate new and innovative content, from chat responses to designs, synthetic data, and even deepfakes. It is of special value in creativity and for solving novel problems, able to create a variety of outputs independently.

As mentioned earlier, generative AI relies on neural network techniques including transformers, GANs, and VAEs. By contrast, other forms of AI make use of convolutional neural networks, recurrent neural networks, and reinforcement learning.

Most generative AI starts with a prompt, wherein a user or other data source provides an initial query or dataset on which to base content generation. This can be an iterative process to explore variations of the content. By contrast, traditional AI algorithms tend to process data based on some fixed set of rules and arrive at a given solution.

Both approaches, in their own right, have strengths and weaknesses depending on the nature of the problem at hand. Active generative AI is a niche in NLP, the creation of new content, and everything that goes with it, whereas traditional algorithms are more effective when dealing with rule-based processing to achieve a predetermined outcome.

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Generative AI vs. predictive AI vs. conversational AI

Distinguishing itself from generative AI, predictive AI leverages patterns within historical data to anticipate outcomes, classify events, and derive actionable insights. Organizations employ predictive AI to enhance decision-making processes and formulate strategies grounded in data.

Conversely, conversational AI is instrumental in enabling AI systems, such as virtual assistants, chatbots, and customer service apps, to interact and engage with humans in a manner that emulates natural conversation. Using techniques from natural language processing (NLP) and machine learning, conversational AI comprehends language and delivers responses in human-like text or speech.

Generative AI History

The earlier version of generative AI, the Eliza chatbot-which was invented by Joseph Weizenbaum in the 1960s, was a rule-based technique. These very early examples, however, tended to have limited vocabulary, little to no context, and overdependence on pattern reliance, causing them to easily break.

Similarly, any effort at customizing and extending the earlier version of chatbots had been non-trivial exercises. The field saw a resurgence with the improvements in neural networks and deep learning around 2010.

These breakthroughs helped the technology to autonomously learn to analyze existing text, identify elements in images, and transcribe audio.

Indeed, in 2014, Ian Goodfellow, at the time working on research, introduced to the academic audience the generative adversarial network-a deep learning generative novelty.

GANs give an unusual style for structuring competing neural networks as producers that create and examine permutations in any variety of contents, which allows one to spawn people, voices, and music, realistic voice tones that tantalize and intimidate everybody-arguably generative AI to make faked reality audio-video material like impersonated fake copies.

Further development of a number of different techniques and architectures in neural networks has continued to broaden generative AI. These range from techniques such as VAE, LSTMs, transformer-based techniques, diffusion models, and neural radiance fields.

Optimal Approaches for Leveraging Generative AI

Recommended ways to use generative AI will depend on different modalities, workflows, and goals. Yet in all uses, accuracy, transparency, and ease of user interaction should be stressed. To these ends, the following are conducive:

Label all generative AI content for users and consumers.
Check the validity of created content by cross-referencing with primary sources if possible.
Check for and mitigate bias in the potential outputs from generative AI.
Verify the quality of code and other content created by AI using ancillary tools to check the validity.
Gain knowledge about the inherent strengths and limits of each particular generative AI tool.
Learn typical failure modes in results and how to work with or around the failures effectively.

The Future of Generative AI

The deep capabilities and ease of use of ChatGPT have accelerated the broad-based adoption of generative AI. There’s little doubt that the rapid diffusion of generative AI applications has exposed some of the difficulties in deploying this technology safely and responsibly.

These early difficulties, however, have catalyzed research into developing better tools for detecting AI-generated text, images, and video. The popularity of generative AI tools like ChatGPT, Midjourney, Stable Diffusion, and Bard has also spawned a wide range of training courses at all levels of proficiency.

Many of these courses aim to help developers build AI applications, while others target business users looking to leverage this new technology throughout enterprise operations.

With time, industries and societies will develop better tools to trace the origin of information, thus helping in the development of more reliable AI.

Generative AI will continue to evolve, realizing breakthroughs in translation, drug discovery, anomaly detection, and the creation of various types of content such as text, videos, fashion design, and music.

While impressive independently, these tools in their respective areas are only the tip of the iceberg regarding the transformation of generative AI in the future: seamlessly embedding those capabilities into our current tools.

For instance, grammar checkers will see improvements, design tools will seamlessly integrate more valuable recommendations into our workflows, and training tools will automatically identify best practices in an organization to enhance more efficient employee training.

These represent just a fraction of the ways generative AI will reshape our activities in the short term.

The impact of generative AI is unknown for the future, but as we increasingly use this technology to automate and improve human tasks, we will increasingly have to address the nature and value of human expertise.

Generative AI Frequently Asked Questions

Here are some commonly asked questions about generative AI:

How might generative AI impact employment?

Generative AI has the potential to replace various jobs, including:

Writing product descriptions.
Crafting marketing copy.
Generating basic web content.
Initiating interactive sales outreach.
Responding to customer inquiries.
Creating graphics for webpages.

While some companies may seek to replace human roles where feasible, others will leverage generative AI to complement and enhance their existing workforce.

Who is credited with creating generative AI?

Generative AI was first created by Joseph Weizenbaum in the 1960s as part of the Eliza chatbot. Ian Goodfellow demonstrated generative adversarial networks for creating realistic-looking and -sounding people in 2014.

Subsequent research into Large Language Models (LLMs) by OpenAI and Google has fueled recent enthusiasm, leading to the development of tools like ChatGPT, Google Bard, and Dall-E.

How is a generative AI model trained?

Training a generative AI model is oriented to a particular application. The advances in the field of LLMs serve as a very good starting point for the adaptation of applications to various scenarios.

Indeed, the currently popular models, such as GPT by OpenAI, have been successfully tested for generating text, writing code, and even images from a textual description of the same.

Training involves setting the model parameters for a wide range of applications and then fine-tuning on specific training data.

For example, a call center would train a chatbot with many diverse customer questions and service representative responses. On the other hand, an image-generating application might use labels that describe content and style in order to train the model for new image generation.

How do you build a generative AI model?

A generative AI model begins by effectively encoding a representation of the content it will generate. For example, with a text-based generative AI model, it would start by creating vectors of the similarity of words that are used in similar sentences or have similar meanings.

Recent development of LLMs allowed extending this encoding process into patterns that could be found in a range of domains, from images to sounds, proteins, DNA, drugs, and even 3D designs.

This is what is called a generative model of AI, which would, in an effective manner, represent the desired content type and generate valuable variations iteratively.

How does generative AI impact creative work?

Generative AI could completely change the way creatives work, as it allows artists and designers to explore a series of ideas. An artist can start with a simple design and play with its variations.

Similarly, an industrial designer can explore variations in products, and an architect variations in layout, seeing these as a starting point for further development.

Moreover, generative AI may democratize aspects of creative work: business users, for example, will be able to investigate product marketing imagery based on text descriptions and iterate on the results via simple commands or suggestions.

What lies ahead in the future for Generative AI?

The fact that ChatGPT can create human-sounding text has triggered broad-based interest in what generative AI can do, but also in the many challenges and problems it raises.

In the near term, work will focus on using generative AI to improve user experiences and workflows. In this phase, establishing trust in the output created by generative AI will be important.

Many companies will embark on the customization of generative AI using their own data to make their branding and communications better. The programming teams will use generative AI in making company-specific best practices prevail and thus create more readable, maintainable code.

In response, vendors will build more generative AI capabilities into their products to make workflows around creating content smoother and drive innovation in productivity improvement.

Generative AI will have significant roles to play in many areas of data processing, transformation, labeling, and vetting in augmented analytics workflows.

Semantic web applications can use generative AI to automatically map internal taxonomies that describe job skills to those on skills training and recruitment sites. Business teams can also use generative AI models to transform and label third-party data, building out capabilities for sophisticated risk assessments and opportunity analysis.

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Going forward, generative AI models are expected to widen their scope and include 3D modeling, product design, development of drugs, digital twins, supply chains, and business processes.

In that way, it will enable the generation of new product concepts, testing of various organizational models, and exploration of multiple business ideas.

Some Generative Models Developed for NLP:

GPT stands for Generative Pre-trained Transformer, developed by OpenAI. It includes models such as GPT-3, GPT-2, and predecessors. These models have been pre-trained on huge volumes of text data and are able to generate coherent, contextually relevant text on the basis of a given prompt.

BERT means Bidirectional Encoder Representations from Transformers, a deep learning model developed by Google to catch the contextual relation between words in a sentence.

Pre-trained on large corpora, the system becomes very useful in sentiment analyses, named entity recognition, question answering, among other tasks.

T5: The Text-To-Text Transfer Transformer by Google also uses a unified framework in which every NLP task is treated as a text-to-text problem. It has achieved strong performance on a variety of NLP benchmarks.

XLNet: This model combines ideas from autoregressive models, like GPT, and autoencoder models, like BERT. It overcomes some limitations of both approaches by considering all permutations of words when predicting the next word in a sequence.

RoBERTa (Robustly optimized BERT approach): This is an optimized variant of BERT developed by Facebook AI. It removes the next sentence prediction objective and makes certain changes to the training dynamics in order to yield better results on a number of downstream tasks.

CTRL: This is another model from Salesforce, designed to allow users to control the style and content of generated text by conditioning the model on specific instructions.

ERNIE: Enhanced Representation through kNowledge Integration; Baidu-developed model including knowledge graphs in pre-training, enhancing the model’s entity and relation understanding.

LaMDA: Google’s Language Model for Dialogue Applications: An effort by Google to study and develop conversational AI models. LaMDA seeks to improve the naturalness of conversation and understanding in conversations for any conversation-based applications.

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