Bài viết 1: What is generative AI? What is generative AI?
Generative AI, sometimes called gen AI, is artificial intelligence (AI) that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request.

Generative AI relies on sophisticated machine learning models called deep learning models—algorithms that simulate the learning and decision-making processes of the human brain. These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests or questions and respond with relevant new content.

AI has been a hot technology topic for the past decade, but generative AI, and specifically the arrival of ChatGPT in 2022, has thrust AI into worldwide headlines and launched an unprecedented surge of AI innovation and adoption. Generative AI offers enormous productivity benefits for individuals and organizations, and while it also presents very real challenges and risks, businesses are forging ahead, exploring how the technology can improve their internal workflows and enrich their products and services. According to research by the management consulting firm McKinsey, one third of organizations are already using generative AI regularly in at least one business function.¹ Industry analyst Gartner projects more than 80% of organizations will have deployed generative AI applications or used generative AI application programming interfaces (APIs) by 2026.2

How generative AI works
For the most part, generative AI operates in three phases: 

Training, to create a foundation model that can serve as the basis of multiple gen AI applications.

Tuning, to tailor the foundation model to a specific gen AI application.

Generation, evaluation and retuning, to assess the gen AI application's output and continually improve its quality and accuracy.
Training
Generative AI begins with a foundation model—a deep learning model that serves as the basis for multiple different types of generative AI applications. The most common foundation models today are large language models (LLMs), created for text generation applications, but there are also foundation models for image generation, video generation, and sound and music generation—as well as multimodal foundation models that can support several kinds content generation.

To create a foundation model, practitioners train a deep learning algorithm on huge volumes of raw, unstructured, unlabeled data—e.g., terabytes of data culled from the internet or some other huge data source. During training, the algorithm performs and evaluates millions of ‘fill in the blank’ exercises, trying to predict the next element in a sequence—e.g., the next word in a sentence, the next element in an image, the next command in a line of code—and continually adjusting itself to minimize the difference between its predictions and the actual data (or ‘correct’ result).

The result of this training is a neural network of parameters—encoded representations of the entities, patterns and relationships in the data—that can generate content autonomously in response to inputs, or prompts.

This training process is compute-intensive, time-consuming and expensive: it requires thousands of clustered graphics processing units (GPUs) and weeks of processing, all of which costs millions of dollars. Open-source foundation model projects, such as Meta's Llama-2, enable gen AI developers to avoid this step and its costs.

Tuning
Metaphorically speaking, a foundation model is a generalist: It knows a lot about a lot of types of content, but often can’t generate specific types of output with desired accuracy or fidelity. For that, the model must be tuned to a specific content generation task. This can be done in a variety of ways.

Fine tuning
Fine tuning involves feeding the model labeled data specific to the content generation application—questions or prompts the application is likely to receive, and corresponding correct answers in the desired format. For example, if a development team is trying to create a customer service chatbot, it would create hundreds or thousands of documents containing labeled customers service questions and correct answers, and then feed those documents to the model.

Fine-tuning is labor-intensive. Developers often outsource the task to companies with large data-labeling workforces.

Reinforcement learning with human feedback (RLHF)
In RLHF, human users respond to generated content with evaluations the model can use to update the model for greater accuracy or relevance. Often, RLHF involves people ‘scoring’ different outputs in response to the same prompt. But it can be as simple as having people type or talk back to a chatbot or virtual assistant, correcting its output.

Generation, evaluation, more tuning
Developers and users continually assess the outputs of their generative AI apps, and further tune the model—even as often as once a week—for greater accuracy or relevance. (In contrast, the foundation model itself is updated much less frequently, perhaps every year or 18 months.)

Another option for improving a gen AI app's performance is retrieval augmented generation (RAG). RAG is a framework for extending the foundation model to use relevant sources outside of the training data, to supplement and refine the parameters or representations in the original model. RAG can ensure that a generative AI app always has access to the most current information. As a bonus, the additional sources accessed via RAG are transparent to users in a way that the knowledge in the original foundation model is not. 

 


What is Retrieval-Augmented Generation (RAG)?
What is Retrieval-Augmented Generation (RAG)? (6:32)
Generative AI model architectures and how they have evolved
Truly generative AI models—deep learning models that can autonomously create content on demand—have evolved over the last dozen years or so. The milestone model architectures during that period include

Variational autoencoders (VAEs), which drove breakthroughs in image recognition, natural language processing and anomaly detection.
 

Generative adversarial networks (GANs) and diffusion models, which improved the accuracy of previous applications and enabled some of the first AI solutions for photo-realistic image generation.
 

Transformers, the deep learning model architecture behind the foremost foundation models and generative AI solutions today.

Variational autoencoders (VAEs)
An autoencoder is a deep learning model comprising two connected neural networks: One that encodes (or compresses) a huge amount of unstructured, unlabeled training data into parameters, and another that decodes those parameters to reconstruct the content. Technically, autoencoders can generate new content, but they’re more useful for compressing data for storage or transfer, and decompressing it for use, than they are for high-quality content generation.

Introduced in 2013, variational autoencoders (VAEs) can encode data like an autoencoder, but decode multiple new variations of the content. By training a VAE to generate variations toward a particular goal, it can ‘zero in’ on more accurate, higher-fidelity content over time. Early VAE applications included anomaly detection (e.g., medical image analysis) and natural language generation.

Generative adversarial networks (GANs)
GANs, introduced in 2014, also comprise two neural networks: A generator, which generates new content, and a discriminator, which evaluates the accuracy and quality the generated data. These adversarial algorithms encourages the model to generate increasingly high-quality outpits.

GANs are commonly used for image and video generation, but can generate high-quality, realistic content across various domains. They've proven particularly successful at tasks as style transfer (altering the style of an image from, say, a photo to a pencil sketch) and data augmentation (creating new, synthetic data to increase the size and diversity of a training data set).

Diffusion models
Also introduced in 2014, diffusion models work by first adding noise to the training data until it’s random and unrecognizable, and then training the algorithm to iteratively diffuse the noise to reveal a desired output.

Diffusion models take more time to train than VAEs or GANs, but ultimately offer finer-grained control over output, particularly for high-quality image generation tool. DALL-E, Open AI’s image-generation tool, is driven by a diffusion model.

Transformers
First documented in a 2017 paper published by Ashish Vaswani and others, transformers evolve the encoder-decoder paradigm to enable a big step forward in the way foundation models are trained, and in the quality and range of content they can produce. These models are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard, and Midjourney to name a few.

Transformers use a concept called attention—determining and focusing on what’s most important about data within a sequence—to

process entire sequences of data—e.g., sentences instead of individual words—simultaneously;
 

capture the context of the data within the sequence;
 

encode the training data into embeddings (also called hyperparameters) that represent the data and its context.

In addition to enabling faster training, transformers excel at natural language processing (NLP) and natural language understanding (NLU), and can generate longer sequences of data—e.g., not just answers to questions, but poems, articles or papers—with greater accuracy and higher quality than other deep generative AI models. Transformer models can also be trained or tuned to use tools—e.g., a spreadsheet application, HTML, a drawing program—to output content in a particular format.
 


What generative AI can create
Generative AI can create many types of content across many different domains. 

Text
Generative models. especially those based on transformers, can generate coherent, contextually relevant text—everything from instructions and documentation to brochures, emails, web site copy, blogs, articles, reports, papers, and even creative writing. They can also perform repetitive or tedious writing tasks (e.g., such as drafting summaries of documents or meta descriptions of web pages), freeing writers’ time for more creative, higher-value work.

Images and video
Image generation such as DALL-E, Midjourney and Stable Diffusion can create realistic images or original art, and can perform style transfer, image-to-image translation and other image editing or image enhancement tasks. Emerging gen AI video tools can create animations from text prompts, and can apply special effects to existing video more quickly and cost-effectively than other methods.

Sound, speech and music
Generative models can synthesize natural-sounding speech and audio content for voice-enabled AI chatbots and digital assistants, audiobook narration and other applications. The same technology can generate original music that mimics the structure and sound of professional compositions.

Software code
Gen AI can generate original code, autocomplete code snippets, translate between programming languages and summarize code functionality. It enables developers to quickly prototype, refactor, and debug applications while offering a natural language interface for coding tasks.

Design and art
Generative AI models can generate unique works of art and design, or assist in graphic design. Applications include dynamic generation of environments, characters or avatars, and special effects for virtual simulations and video games.

Simulations and synthetic data
Generative AI models can be trained to generate synthetic data, or synthetic structures based on real or synthetic data. For example, generative AI is applied in drug discovery to generate molecular structures with desired properties, aiding in the design of new pharmaceutical compounds.

Benefits of generative AI
The obvious, overarching benefit of generative AI is greater efficiency. Because it can generate content and answers on demand, gen AI has the potential to accelerate or automate labor-intensive tasks, cut costs, and free employees time for higher-value work.

But generative AI offers several other benefits for indivuduals and organizations.

Enhanced creativity
Gen AI tools can inspire creativity through automated brainstorming—generating multiple novel versions of content. These variations can also serve as starting points or references that help writers, artists, designers and other creators plow through creative blocks.

Improved (and faster) decision-making
Generative AI excels at analyzing large datasets, identifying patterns and extracting meaningful insights—and then generating hypotheses and recommendations based on those insights to support executives, analysts, researchers and other professionals in making smarter, data-driven decisions.

Dynamic personalization
In applications like recommendation systems and content creation, generative AI can analyze user preferences and history and generate personalized content in real time, leading to a more tailored and engaging user experience.

Constant availability
Generative AI operates continuously without fatigue, providing around-the-clock availability for tasks like customer support chatbots and automated responses.


Generative AI: A Conversation with Malcolm Gladwell & Darío Gil
Generative AI: A Conversation with Malcolm Gladwell & Darío Gil (29:18)
Use cases for generative AI
The following are just a handful of gen AI use cases for enterprises. As the technology develops and organizations embed these tools into their workflows, we can expect to see many more. 

Customer experience
Marketing organizations can save time and amp up their content production by using gen AI tools to draft copy for blogs, web pages, collateral, emails and more. But generative AI solutions can also produce highly personalized marketing copy and visuals in real time based on when, where and to whom the ad is delivered. And it will power next-generation chatbots and virtual agents that can give personalized responses and even initiate actions on behalf of customer—a significant advancement compared to the previous generation of conversational AI models trained on more limited data for very specific tasks.

Software development and application modernization
Code generation tools can automate and accelerate the process of writing new code. Code generation also has the potential to dramatically accelerate application modernization by automating much of the repetitive coding required to modernize legacy applications for hybrid cloud environments.

Digital labor
Generative AI can quickly draw up or revise contracts, invoices, bills and other digital or physical ‘paperwork’ so that employees who use or manage it can focus on higher level tasks. This can accelerate workflows in virtually every enterprise area including human resources, legal, procurement and finance.

Science, engineering and research
Generative AI models can help scientists and engineers propose novel solutions to complex problems. In healthcare, for example, generative models can be applied to synthesize medical images for training and testing medical imaging systems.

Challenges, limitations and risks
Generative AI has made remarkable strides in a relatively short period of time, but still presents significant challenges and risks to developers, users and the public at large. Below are some of the most serious issues, and how they're being addressed. 

‘Hallucinations’ and other inaccurate outputs
An AI hallucination is a generative AI output that is nonsensical or altogether inaccurate—but, all too often, seems entirely plausible. The classic example is when a lawyer used a gen AI tool for research in preparation for a high-profile case—and the tool ‘produced’ several example cases, complete with quotes and attributions, that were entirely fictional (link resides outside ibm.com).

Some practitioners view hallucinations as an unavoidable consequence of balancing a model’s accuracy and its creative capabilities. But developers may implement preventative measures, called guardrails, that restrict the model to relevant or trusted data sources. Continual evaluation and tuning can also help reduce hallucinations and inaccuracies.

Inconsistent outputs
Due to the variational or probabilistic nature of gen AI models, the same inputs can result in slightly or significantly different outputs. This can be undesirable in certain applications, such as customer service chatbots, where consistent outputs are expected or desired. Through prompt engineering—iteratively refining or compounding prompts—users can arrive at prompts that consistently deliver the results they want from their generative AI applications.

Bias
Generative models may learn societal biases present in the training data—or in the labeled data, external data sources, or human evaluators used to tune the model—and generate biased, unfair or offensive content as a result. To prevent biased outputs from their models, developers must ensure diverse training data, establish guidelines for preventing bias during training and tuning, and continually evaluate model outputs for bias as well as accuracy.

Learn more about AI bias 
Lack of explainability and metrics
Many generative AI models are ‘black box’ models, meaning it can be challenging or impossible to understand their decision-making processes; even the engineers or data scientists who create the underlying algorithm can understand or explain what exactly is happening inside it and how it arrives at a specific result. Explainable AI practices and techniques can help practitioners and users understand and trust the processes and outputs of generative models.

Assessing and comparing the quality of generated content can also be challenging. Traditional evaluation metrics may not capture the nuanced aspects of creativity, coherence, or relevance. Developing robust and reliable evaluation methods for generative AI remains an active area of research.

Threats to security, privacy and intellectual property
Generative AI models can be exploited to generate convincing phishing emails, fake identities or other malicious content that can fool users into taking actions that compromise security and data privacy. Developers and users need to be careful that data put into the model (during tuning, or as part of a prompt) doesn’t expose their own intellectual property (IP) or any information protected as IP by other organizations. And they need to monitor outputs for new content that exposes their own IP or violates others' IP protections.

Deepfakes
Deepfakes are AI-generated or AI-manipulated images, video or audio created to convince people that they’re seeing, watching or hearing someone do or say something they never did or said. They are among the most chilling examples of how the power of generative AI can be applied with malicious intent.

Most people are familiar with deepfakes created to damage reputations or spread misinformation. More recently, cybercriminals have deployed deepfakes as part of cyberattacks (e.g., fake voices in voice phishing scams) or financial fraud schemes.

Researchers are hard at work on AI models that can detect deepfakes with greater accuracy. In the meantime, user education and best practices (e.g., not sharing unverified or unvetted contentious material) can help limit the damage deepfakes can do. \n \n Bài viết 2: What is generative AI?
April 2, 2024 | Article

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Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.
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In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.


Get to know and directly engage with McKinsey's senior experts on generative AI
Aamer Baig is a senior partner in McKinsey’s Chicago office; Lareina Yee is a senior partner in the Bay Area office; and senior partners Alex Singla and Alexander Sukharevsky, global leaders of QuantumBlack, AI by McKinsey, are based in the Chicago and London offices, respectively.

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Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion to the global economy—annually. Indeed, it seems possible that within the next three years, anything in the technology, media, and telecommunications space not connected to AI will be considered obsolete or ineffective.

But before all that value can be raked in, we need to get a few things straight: What is gen AI, how was it developed, and what does it mean for people and organizations? Read on to get the download.

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Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased machine learning’s potential, as well as the need for it.

What are the main types of machine learning models?
Machine learning is founded on a number of building blocks, starting with classical statistical techniques developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.

Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

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How do text-based machine learning models work? How are they trained?

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ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner.

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.

The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate. We’re seeing just how accurate with the success of tools like ChatGPT.

What does it take to build a generative AI model?
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Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt. OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from bold-face-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and even Meta has dipped a toe into the generative AI model pool with its Make-A-Video product. These companies employ some of the world’s best computer scientists and engineers.

But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data—that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress—at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.

What kinds of output can a generative AI model produce?
As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input.

ChatGPT can produce what one commentator called a “solid A-” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Image-generating AI models like DALL-E 2 can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza. Other generative AI models can produce code, video, audio, or business simulations.

But the outputs aren’t always accurate—or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike.

What kinds of problems can a generative AI model solve?
The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

What are the limitations of AI models? How can these potentially be overcome?
Because they are so new, we have yet to see the long tail effect of generative AI models. This means there are some inherent risks involved in using them—some known and some unknown.

The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

Bài viết 3: Explained: Generative AI
How do powerful generative AI systems like ChatGPT work, and what makes them different from other types of artificial intelligence? \n A quick scan of the headlines makes it seem like generative artificial intelligence is everywhere these days. In fact, some of those headlines may actually have been written by generative AI, like OpenAI’s ChatGPT, a chatbot that has demonstrated an uncanny ability to produce text that seems to have been written by a human.

But what do people really mean when they say “generative AI?”

Before the generative AI boom of the past few years, when people talked about AI, typically they were talking about machine-learning models that can learn to make a prediction based on data. For instance, such models are trained, using millions of examples, to predict whether a certain X-ray shows signs of a tumor or if a particular borrower is likely to default on a loan.

Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.

“When it comes to the actual machinery underlying generative AI and other types of AI, the distinctions can be a little bit blurry. Oftentimes, the same algorithms can be used for both,” says Phillip Isola, an associate professor of electrical engineering and computer science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

And despite the hype that came with the release of ChatGPT and its counterparts, the technology itself isn’t brand new. These powerful machine-learning models draw on research and computational advances that go back more than 50 years.

An increase in complexity

An early example of generative AI is a much simpler model known as a Markov chain. The technique is named for Andrey Markov, a Russian mathematician who in 1906 introduced this statistical method to model the behavior of random processes. In machine learning, Markov models have long been used for next-word prediction tasks, like the autocomplete function in an email program.

In text prediction, a Markov model generates the next word in a sentence by looking at the previous word or a few previous words. But because these simple models can only look back that far, they aren’t good at generating plausible text, says Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, who is also a member of CSAIL and the Institute for Data, Systems, and Society (IDSS).

“We were generating things way before the last decade, but the major distinction here is in terms of the complexity of objects we can generate and the scale at which we can train these models,” he explains.

Just a few years ago, researchers tended to focus on finding a machine-learning algorithm that makes the best use of a specific dataset. But that focus has shifted a bit, and many researchers are now using larger datasets, perhaps with hundreds of millions or even billions of data points, to train models that can achieve impressive results.

The base models underlying ChatGPT and similar systems work in much the same way as a Markov model. But one big difference is that ChatGPT is far larger and more complex, with billions of parameters. And it has been trained on an enormous amount of data — in this case, much of the publicly available text on the internet.

In this huge corpus of text, words and sentences appear in sequences with certain dependencies. This recurrence helps the model understand how to cut text into statistical chunks that have some predictability. It learns the patterns of these blocks of text and uses this knowledge to propose what might come next.

More powerful architectures

While bigger datasets are one catalyst that led to the generative AI boom, a variety of major research advances also led to more complex deep-learning architectures.

In 2014, a machine-learning architecture known as a generative adversarial network (GAN) was proposed by researchers at the University of Montreal. GANs use two models that work in tandem: One learns to generate a target output (like an image) and the other learns to discriminate true data from the generator’s output. The generator tries to fool the discriminator, and in the process learns to make more realistic outputs. The image generator StyleGAN is based on these types of models.  

Diffusion models were introduced a year later by researchers at Stanford University and the University of California at Berkeley. By iteratively refining their output, these models learn to generate new data samples that resemble samples in a training dataset, and have been used to create realistic-looking images. A diffusion model is at the heart of the text-to-image generation system Stable Diffusion.

In 2017, researchers at Google introduced the transformer architecture, which has been used to develop large language models, like those that power ChatGPT. In natural language processing, a transformer encodes each word in a corpus of text as a token and then generates an attention map, which captures each token’s relationships with all other tokens. This attention map helps the transformer understand context when it generates new text.

These are only a few of many approaches that can be used for generative AI.

A range of applications

What all of these approaches have in common is that they convert inputs into a set of tokens, which are numerical representations of chunks of data. As long as your data can be converted into this standard, token format, then in theory, you could apply these methods to generate new data that look similar.

“Your mileage might vary, depending on how noisy your data are and how difficult the signal is to extract, but it is really getting closer to the way a general-purpose CPU can take in any kind of data and start processing it in a unified way,” Isola says.

This opens up a huge array of applications for generative AI.

For instance, Isola’s group is using generative AI to create synthetic image data that could be used to train another intelligent system, such as by teaching a computer vision model how to recognize objects.

Jaakkola’s group is using generative AI to design novel protein structures or valid crystal structures that specify new materials. The same way a generative model learns the dependencies of language, if it’s shown crystal structures instead, it can learn the relationships that make structures stable and realizable, he explains.

But while generative models can achieve incredible results, they aren’t the best choice for all types of data. For tasks that involve making predictions on structured data, like the tabular data in a spreadsheet, generative AI models tend to be outperformed by traditional machine-learning methods, says Devavrat Shah, the Andrew and Erna Viterbi Professor in Electrical Engineering and Computer Science at MIT and a member of IDSS and of the Laboratory for Information and Decision Systems.

“The highest value they have, in my mind, is to become this terrific interface to machines that are human friendly. Previously, humans had to talk to machines in the language of machines to make things happen. Now, this interface has figured out how to talk to both humans and machines,” says Shah.

Raising red flags

Generative AI chatbots are now being used in call centers to field questions from human customers, but this application underscores one potential red flag of implementing these models — worker displacement.

In addition, generative AI can inherit and proliferate biases that exist in training data, or amplify hate speech and false statements. The models have the capacity to plagiarize, and can generate content that looks like it was produced by a specific human creator, raising potential copyright issues.

On the other side, Shah proposes that generative AI could empower artists, who could use generative tools to help them make creative content they might not otherwise have the means to produce.

In the future, he sees generative AI changing the economics in many disciplines.

One promising future direction Isola sees for generative AI is its use for fabrication. Instead of having a model make an image of a chair, perhaps it could generate a plan for a chair that could be produced.

He also sees future uses for generative AI systems in developing more generally intelligent AI agents.

“There are differences in how these models work and how we think the human brain works, but I think there are also similarities. We have the ability to think and dream in our heads, to come up with interesting ideas or plans, and I think generative AI is one of the tools that will empower agents to do that, as well,” Isola says. \n \n Bài viết 4: 50 Useful Generative AI Examples in 2024 \n Generative AI has become a hot topic in the past few months, and for good reason — it is changing the world right before our eyes. 

The AI revolution is impacting every sector, including healthcare, education, finance, agriculture, and construction, with new AI solutions emerging daily. 

In this article, we will explore 50 practical applications of generative AI across different industries. 

To demonstrate the real impact of AI, we have also integrated real-world generative AI examples that are already leaving a profound imprint on people's work processes. 

Are you ready to dive right in? Let’s go!

But wait - what exactly is generative AI? 🤖
Generative AI refers to a form of artificial intelligence that prioritizes the creation of original data rather than solely processing and organizing pre-existing data. By utilizing large language models, it has the ability to generate diverse outputs, including unique written content, images, videos, and music.

Generative AI examples in healthcare
The world of healthcare certainly has its fair share of challenges, doesn't it?

We're talking about rising healthcare costs, a fragmented system, struggles with health information exchange and interoperability, and limited access to care, just to name a few.

However, AI presents an opportunity to address these challenges.

While it is not a magical solution, generative AI can be leveraged in various ways to make a meaningful impact. 

Here are some examples:

#1 Conversational AI apps for patients
👉 Example: Ada 

Ada is a doctor-developed symptom assessment app that offers medical guidance in multiple languages. Optimized with the expertise of human doctors, Ada utilizes AI to support improved health outcomes and deliver exceptional clinical excellence.

image

                Generative AI applications in healthcare span from conversational AI chatbots to medical education. In the image, you can see Ada, a conversational healthcare app designed for individuals.
              
#2 AI applications for early detection of certain diseases
👉 Example: SkinVision app 

SkinVision is an app for early detection of skin cancer. With its regulated medical service, AI technology, and expert input, it teaches users to self-examine, understand risks, and address immediate concerns.

#3 AI for accessibility
👉 Example: Virtual volunteer / Be My Eyes

It’s an AI app designed for visually impaired individuals that harnesses the power of GPT-4 to convert images into text instantly. Users can send images through the app for immediate identification, interpretation, and conversational visual assistance. 

#4 AI for patient interactions and support
👉 Example: Hyro

This conversational AI is designed specifically for health systems to enhance patient engagement and address staffing challenges. With HIPAA-compliant conversational AI, users can automate common interactions, scale operations, and overcome staffing shortages.

#5 AI for medical product development and design
👉 Example: Uizard 

Uizard leverages AI for quickly and easily prototyping various digital products, such as apps and landing pages. With its intuitive interface, it greatly simplifies the once manual design process.

#6 AI-generated media for enhanced medical training and simulation
👉 Example: PEDAL 

PEDAL is an AI-driven platform that helps with better decision making in oncology. With a biobank of 150,000 tumor samples across 137 cancer types, the platform predicts drug responses with unmatched precision.

Generative AI examples in education
So, there's a class of 20+ students sitting in a classroom. 

Some of them are visual learners, while others prefer reading. Some are introverted and freeze when put in the spotlight, and others need extra help with biology. It's quite a diverse group of people, and they have a diverse range of needs, don't you think?

There's no doubt that education today faces many challenges, including unequal access, outdated methods, and the need for personalized learning. Fortunately, with the advent of AI, the solutions seem closer each day. 

image

                AI video is an emerging form of media that holds great potential for educational purposes. With Synthesia, you can create videos with realistic AI avatars in 120+ languages by simply typing in text.
              
Here's how we can use generative AI in education:

#7 AI apps for personalized learning experiences
👉 Example: Knowji 

Knowji is an AI-driven app that enhances vocabulary acquisition for learners of all ages. With captivating content and a state-of-the-art spaced repetition algorithm, this tool ensures the long-lasting retention of words.

#8 AI apps for innovative learning approaches
👉 Example: Hello History

Hello History is an app that offers a unique approach to learning history. Users can engage in conversations with historical personalities, which makes the study of history much more engaging and interactive.

#9 AI generators for creating more engaging training materials
‍👉 Example: Synthesia 

Synthesia is an AI video generator that creates videos from text. And not just any videos, but videos with real human presenters - AI avatars. It’s a great online tool that helps educators effortlessly transform their text-based documents into an engaging video training featuring a human face, establishing a deeper connection with the viewers.

Here’s how it works:

PlaySynthesia STUDIO product demo
#10 AI solutions for assessment, grading, and giving feedback to students
👉 Example: Gradescope 

Gradescope is an AI-powered tool that simplifies assessment grading for teachers. It efficiently grades both digital and paper-based assignments, providing quick and accurate results. Additionally, Gradescope offers valuable insights into students' knowledge levels across various subjects.

#11 AI summarization tools
👉 Example: Genei 

Genei leverages AI to accelerate research by automating time-consuming tasks. This app instantly summarizes PDFs and websites, saving students and researchers a significant amount of time. Additionally, Genei can provide concise and summarized responses to questions based on relevant resources.

#12 AI learning companions and personal tutors for individualized support
👉 Example: Duolingo Max 

Duolingo Max is a conversational AI for learning languages that leverages GPT-4. Learners can choose from two innovative features: Explain My Answer and Roleplay. These additions provide a deeper learning experience alongside the existing benefits of Duolingo. 

Generative AI examples in tourism and hospitality
How many hours did you spend planning your last trip? ✈️

Let me guess - quite a lot. 

While some people enjoy it, the majority find it to be a time-consuming and unenjoyable task.

Browsing through numerous pages, searching for locations, finding suitable restaurants, and dealing with car rentals, all while trying to organize a schedule around it, can be incredibly exhausting.

Fortunately, generative AI is poised to provide different solutions. 

While there are already some applications available, we anticipate a significant surge in development in the coming years. 

Here are some concrete use cases of generative AI in the tourism sector:

#13 AI apps for streamlining reservations and itineraries
👉 Example: Tripnotes.ai 

Tripnotes is a data-powered travel planner that simplifies, well… trip planning. Users can paste their travel inspiration from text messages, social media, or blogs, and the app automatically saves and researches each mentioned place leveraging generative AI.

#14 AI search
👉 Example: Microsoft Bing 

Microsoft Bing is an advanced search engine that incorporates cutting-edge AI technology. With its web, video, image, and map search functionalities, Bing offers a comprehensive search experience, and also includes real-time chat and co-creation features.

#15 AI business solutions for the travel industry
👉 Example: Bloomreach

Bloomreach is a cloud-based software for the travel industry that personalizes customer touch-points, drives business growth, and supports different providers. It helps identify frequent travelers, create personalized experiences, and gain valuable customer insights.

#16 AI for marketing
👉 Example: Runway 

Marketing tourist destinations and services requires a significant amount of multimedia content, with video being the most popular format at the moment. Fortunately, AI can assist with video editing. One example is Runway, which offers over 30 integrated AI tools to facilitate smooth and accessible video editing for everyone, regardless of their previous knowledge and video editing skills.

#17 AI chatbots for customer service and support
👉 Example: ChatBot  

ChatBot is an AI customer support tool that improves service by streamlining processes and offering support across various channels and languages. It leverages large language models to enhance the user experience with visual explanations and interactive forms.

#18 Virtual guides for personalized guided tours
👉 Example: P.A.D.D.Y 

P.A.D.D.Y. is an AI-powered tour guide created by a group of tour guides in Ireland. This multifaceted AI brings character to the experience of Ireland, tailoring it to individual interests and preferences.

Generative AI examples in marketing and advertising
From the Mad Men era to the age of the internet, social media, and hyperconnectedness, marketing has undergone a remarkable transformation. 

Traditional methods have been replaced by digital strategies, personalized messaging, and interactive experiences that businesses must navigate in order to connect and resonate with their target audiences.

The constantly evolving landscape demands an abundance of localized, niche, and relatable content – and this is where generative AI can play a crucial role:

#19 AI for content creation (text, images, video, audio…)
👉 Example: AI video generators 

Generative AI enables innovative methods of content creation. One such example is AI video generation. What used to be a physical process (cameras, actors, studios…) has now transitioned into a fully digital realm, making video creation convenient and accessible to all.

#20 AI for content repurposing
👉 Example: Jasper Campaigns   

Omnichannel marketing is at the core of today's marketing. This feature by Jasper enables users to create end-to-end marketing campaigns in their brand's tone and voice with a single brief. Based on the brief, the AI can generate as many assets as needed, including emails, case studies, Facebook and Google ads, press releases, and more.

image

                One generative AI example that can be used in marketing is Jasper Campaigns. With just a single brief, this AI can generate content tailored for various communication channels.
              
#21 AI for personalized marketing strategies
👉 Example: RAD AI 

RAD AI merges data-driven insights and authentic content to assist marketing teams in crafting impactful campaigns. By analyzing past performance and formulating effective strategies, it aims to establish genuine and emotional connections with the target audience across various marketing channels.

#22 Generative AI for content localization 
👉 Example: Lokalise AI  

Lokalise AI is an automated localization and translation platform designed for various applications, including web apps, customer service, documents, mobile apps, games, and marketing assets. With its advanced features like contextual translation, alternative variants, rephrasing, and concise adaptations, it enables seamless communication with global audiences in different languages.

Generative AI examples in finance and business
AI is also transforming the finance and banking sectors, making them more user-friendly than ever before. 

AI is providing a significant upgrade to banking operations by automating tasks that were previously performed manually, resulting in more efficient processes.

With the help of generative AI, we are witnessing exciting communication possibilities, including new and improved ways to present information, highly useful real-time AI assistants, and streamlined content optimization processes.

#23 AI solutions for business owners
👉 Example: Yooz 

Yooz is an automated AI solution designed to assist accounting and finance leaders in managing invoices. The solution aims to streamline and automate the invoice processing workflow, reducing manual effort and enhancing overall efficiency.

#24 AI for content optimization
👉 Example: AdCreative.ai 

AdCreative.ai is a generative AI app that quickly generates conversion-focused ad creatives and social media posts. With the ability to specify the target audience and platform, it selects the ideal message aligned with specific business goals.

#25 AI for personal finance
👉 Example: Cleo

Cleo, an AI money app designed for individuals, evolutionizes how people manage their financial lives. With a simple chat interface, Cleo assists users in saving money, budgeting effectively, and gaining financial knowledge.

#26 AI chatbots and virtual assistants for enhanced customer service
👉 Example: Boost.ai 

Boost.ai is an AI-powered conversation builder that delivers accurate responses to customers using advanced natural language processing and your customized training inputs. It seamlessly operates across various platforms, including websites, Slack channels, Zendesk, and Teams.

#27 AI-generated presentations
👉 Example: Tome 

Tome is a revolutionary generative AI solution that takes the hassle out of creating presentations. By providing a simple prompt, users can instantly generate captivating slides for product presentations, sales pitches, training sessions, client proposals, and more.

Generative AI examples in media and entertainment
In the dynamic landscape of media and entertainment, a clear trend is unfolding: a continuous evolution towards more immersive and interactive content. 

As our attention spans diminish, innovative content formats are surfacing to captivate audiences, such as concise tweets, engaging TikToks, and creative reels.

Generative AI is playing a transformative role in the production processes, democratizing creativity and empowering individuals to generate a wide range of content, including images, videos, articles, and music. ✍ 📹 🎶

Let's delve into some tangible examples of how generative AI is reshaping the media landscape:

#28 AI-based content personalization
👉 Example: BuzzFeed’s Infinity Quizzes 

One example of how media outlets can utilize generative AI for their content is BuzzFeed. In February 2023, they launched their first "Infinity Quizzes," which create personalized quizzes for users based on a few inputs.

#29 AI solutions for more immersive user experiences
👉 Example: My AI on Snapchat

Snapchat has recently introduced My AI, an AI chatbot that can answer users' questions and engage in conversations. Whether it's answering trivia questions, offering gift advice, providing trip planning assistance, or suggesting dinner options, My AI offers a personalized experience driven by AI.

#30 AI apps for scalable content creation
👉 Example: Canva

Canva is a design platform that offers AI-powered solutions for content creation. Through its AI capabilities, Canva streamlines the process of creating visual content by providing features for resizing, image and video editing, generating AI text to speech avatars, and converting text to images.

#31 AI for ideating different solutions, formats, concepts… 
👉 Example: Sudowrite

Sudowrite is an interactive AI writing assistant that offers valuable features like rewriting paragraphs in various styles, creative brainstorming, and character generation. Developed by writers, it provides an enjoyable user experience and produces remarkably human-like stories.

#32 AI-generated art
👉 Example: Midjourney

Midjourney is a cutting-edge image generator that transforms text descriptions into captivating images. With its advanced capabilities in generating intricate compositions, realistic edits, and incorporating diverse details, it is pushing the boundaries of visual art creation.

Want to know more about how generative AI is going to transform the media industry?

Check out this article: 

Generative AI examples in retail
Generative AI is redefining the way we shop and interact with brands, bringing convenience and efficiency to consumers while empowering retailers to deliver targeted marketing campaigns, optimize pricing strategies, and gain valuable insights into consumer behavior.

The possibilities that generative AI offers in the retail sector are unprecedented, and here are some of the notable examples:

#33 AI-generated product images
👉 Example: Lalaland

Lalaland transforms product creation for the fashion industry by eliminating the need for physical samples. Users can effortlessly select a model/avatar, apply their design, and generate the final image. The app provides diverse plans with options for various body sizes, hairstyles, body shapes, custom poses, and more.

#34 AI-generated mockups
👉 Example: Dall-E 

Dall-E is an AI image generator that creates images based on text descriptions. This means that a process that previously required a physical product can now be replaced by generative AI. It can generate hyper realistic images and mockups that are literally impossible to distinguish from actual photographs.

#35 AI-generated product descriptions
👉 Example: Copy.ai 

Catering to the diverse needs of marketers, this AI text generator proves to be a valuable asset for crafting various types of product descriptions. Whether it's for emails, product pages, Instagram, or ads, it covers a wide range of writing requirements.

#36 AI-powered customer service chatbots 
👉 Example: Conversica 

Conversica is an AI-powered solution that automates customer follow-ups and drives meaningful engagements. It seamlessly integrates with multiple tools commonly used in retail, such as Hubspot, Marketo and Salesforce.

#37 AI-supported search results for enhanced shopping experiences
👉 Example: Bard 

Bard, a conversational AI chatbot created by Google, is changing the shopping experience thanks to its interactive user interface. Available in three languages and accessible in over 180 countries and territories, Bard engages in natural conversations and fetches information from the web to assist users in making informed purchasing decisions.

Generative AI examples in manufacturing
Gone are the days of traditional manufacturing as we knew it. Today, the manufacturing industry is a vibrant and rapidly evolving landscape, where technological advancements and streamlined processes are revolutionizing production. 

One such advancement is generative AI, which brings forth multiple benefits. Here's how it can be used:

#38 AI-driven product development and design 
👉 Example: Midjourney 

Midjourney is an AI image generator that can create realistic images based on detailed text inputs. Manufacturers can utilize it to generate prototypes, quick mockups, and visualizations without the necessity of physical samples.

#39 AI apps for enhanced training and simulation
👉 Example: 3D simulation by Protostar.ai 

This AI app leverages extensive data collected from diverse sensors and sources to construct a digital replica of a facility or factory. By utilizing real-world information, it can create simulations that provide predictive insights into product performance and process outcomes.  

#40 AI for customer interactions and support 
👉 Example: Tidio 

Tidio is a customer support AI software that empowers small and medium-sized organizations with real-time chat, personalized recommendations, and task automation. It’s easy to set up and their basic plan is free to use.

#41 AI solutions for finding answers to complex issues
👉 Example: Wizdom.ai

Wizdom is an AI solution that analyzes vast amounts of data from the global research ecosystem to offer valuable insights for decision-making. With its comprehensive approach, it empowers users to make informed decisions and stay at the forefront of advancements in their field.

#42 AI apps for streamlined communications
👉 Example: Hyperwrite 

HyperWrite is a user-friendly online platform and Chrome extension that assists with copywriting, enabling users to refine their writing and enhance productivity. It can write different types of text, from emails to social media posts to long articles.

Generative AI examples in construction and real estate sector
The construction and real estate sector has experienced a substantial transformation in recent years. 

Through the integration of advanced technologies such as modeling, drones, and prefabrication methods, the industry has transitioned from traditional manual processes to a more efficient and digitally-driven approach. This shift has facilitated enhanced project management, cost control, and accelerated construction timelines.

However, the transformation does not end there - generative AI is another technology poised to make a tremendous impact in this field.

#43 AI for rendering
👉 Example: Vizcom 

It enables designers and architects to swiftly create and render designs with a multitude of options, including color, material, finish, and part-specific modifications. The result is faster and more versatile design iterations than ever before and thus better user experience for clients.

#44 AI chatbots
👉 Example: ChatGPT

ChatGPT is a state-of-the-art AI chatbot that utilizes natural language processing to generate human-like conversations. Users can participate in interactive dialogues, asking questions, seeking additional information, or even requesting alternative responses. Although ChatGPT's knowledge is based on data available until 2021, its exceptional accuracy is truly remarkable.

#45 AI design solutions
👉 Example: Maket.ai  

Maket is an AI tool that empowers architects, designers, builders, contractors, and developers in the residential industry. Its core feature is automated floorplan generation. Additionally, Maket assists users in navigating zoning codes and offers a wide range of styles to explore.

image

                Generative AI has numerous applications in construction and real estate. One example is Maket.ai, which helps architects and designers generate various visual styles and floor plans. 
              
#46 AI-powered communication and marketing solutions for the industry
👉 Example: NeuralText 

NeuralText is a versatile generative AI app equipped with three powerful features: Paragraph Generator, Content Outline, and Product Description. It also goes beyond content creation by assisting with SEO optimization.

Generative AI examples in agriculture
Despite the revolution in the agricultural industry, it continues to face challenges such as climate change, resource scarcity, market volatility, and labor shortages, to name just a few.

However, by harnessing advanced technologies, including the integration of generative AI, farmers can overcome these hurdles and achieve optimized crop production, efficient resource management, and sustainable practices.

#47 AI for agricultural education
👉 Example: Farmer.chat  

Farmer.CHAT is an AI-based farmer advisory service that connects governments and farmers for real-time communication. It provides data-driven insights and decision-making tools to optimize crop management, reduce waste, and increase yields. With a combination of documents, videos, and vetted data sources, Farmer.CHAT delivers actionable recommendations to farmers in India, Ethiopia, and Kenya.

#48 AI for marketing and content generation
👉 Example: AdCreative.ai  

This generative AI app can be used to create compelling ad creatives as well as organic social media posts. It’s very easy to use - based on target audience and platform preferences, the AI algorithm generates visuals and text in minutes.

#49 AI for streamlining business operations
👉 Example: Zia 

Zia is an AI-powered virtual assistant that provides a comprehensive suite of business support services. Zia helps users with many business-related tasks, including data gathering, insightful analytics, email translation, and proficient writing assistance.

#50 AI for decision support in farming
👉 Example: Semantic Scholar 

Semantic Scholar is an invaluable resource for researchers seeking expedited access to emerging scientific knowledge. With a comprehensive index of over 2 million academic research papers, this AI-powered application swiftly extracts key insights, enabling users to stay abreast of the latest trends in their respective fields.

Conclusion
In conclusion, it is evident that the generative AI landscape is flourishing with a wide range of tools catering to diverse industries. 

With new tools emerging daily, we will continue to monitor and expand our list to stay up-to-date in this dynamic realm of AI.

Bài viết 5: 20 Examples of Generative AI Applications Across Industries \n \n Generative artificial intelligence (AI) is a trend just beginning its journey to the mainstream. Gartner projects that by 2026, over 100 million people will use generative AI to help them complete their work [1]. McKinsey looked at 63 different uses for generative AI and concluded that, if they were all implemented, the technology could add $2.6 trillion to $4.4 trillion worth of value to the global economy [2].

In this article, you’ll learn 20 examples of generative AI applications in various industries and how to start using generative AI for your organization.

What is generative AI?
Generative AI is artificial intelligence designed to create unique text or image results in response to user prompts. The technology uses machine learning to return an output based on the user’s prompt. AI engineers train the technology using large data sets, which the model consults when determining the best possible answer to a prompt. Another way to look at generative AI is as a form of predictive artificial intelligence. Based on the information provided, generative AI will predict which words and in which order will give the best answer to the user's prompts.

You can use generative AI to create new written, visual, or audio content, summarize complex data, generate code, assist with repetitive tasks, or make customer service more personalized.

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Examples of generative AI
Examples of generative artificial intelligence that you may have heard of include Google’s Bard, ChatGPT, or DALL-E from OpenAI.


ChatGPT or DALL-E: Generative artificial intelligence created by OpenAI, a Microsoft-backed, profit-capped company with the mission to develop artificial intelligence to serve humankind


Google Bard: Google’s generative AI with integrations to Google products like Google Lens and Gmail, operating with a language model called PaLM-2 that was trained on the largest data set out of all generative AI models available at the time of its release


Applications of generative AI
Generative artificial intelligence has applications in diverse industries such as health care, manufacturing, software development, financial services, media and entertainment, and advertising and marketing. Let’s examine some of the different ways professionals in these industries apply generative AI to their field.

Health care and pharmaceuticals
Generative artificial intelligence has applications for all parts of the health care and pharmaceutical industry, from discovering and developing new life-saving medicine to personalizing treatment plans for individual patients to creating predictive images for charting disease progression. Some of the possibilities for generational AI in health care include:


Enhancing medical images: Generative AI can augment medical images like X-rays or MRIs, synthesize images, reconstruct images, or create reports about images. This technology can even generate new images to demonstrate how a disease may progress in time.


Discovering new drugs: Researchers can use generative artificial intelligence via a related field called generative design to research and develop new medicines. Gartner projects that 30 percent of the new drugs created by researchers in 2025 will use generative design principles [1].


Simplify tasks with patient notes and information: Healthcare professionals keep and take notes about patient medical care. Generational AI can build patient information summaries, create transcripts of verbally recorded notes, or find essential details in medical records more effectively than human efforts.


Personalized treatment: Generative AI can consider a large amount of patient information, including medical images and genetic testing, to deliver a customized treatment plan tailored to the patient's needs.


Advertising and marketing
Generative artificial intelligence offers many solutions to professionals working in advertising and marketing, such as generating text and images needed for marketing or finding new ways to interact with customers. Here are some examples of generative AI applications in advertising and marketing:


Generate marketing text and images: Generative AI can help marketing professionals create consistent, on-brand text and images to use in marketing campaigns. This technology also offers translation tools to spread your marketing message into new territories. Gartner predicts that marketing professionals will use generative AI to create 30 percent of outbound marketing materials by 2025 [1].


Generate personalized recommendations: Generative AI helps create powerful recommendation engines to help customers discover new products they might like. With generative AI, this process is more interactive for customers.


Create product descriptions: Beyond flashy advertising campaigns, generative artificial intelligence can help with tedious or time-consuming content requirements like creating product descriptions.


Enhance search engine optimization: SEO professionals can use generative AI for tasks like image tags or page titles or to create content drafts. You could also use a tool like ChatGPT or Bard to recommend changes you could make to content to improve SEO ranking.


Manufacturing
In manufacturing, professionals can use generative AI to look for ways to improve efficiency, anticipate maintenance needs before they cause problems, help engineers create better designs faster, and create a more resilient supply chain. Let’s explore these potential manufacturing solutions:


Accelerating the design process: Using generative AI, engineers and project managers can work through the design process much faster by generating design ideas and asking the AI to assess ideas based on the constraints of the project.


Provide smart maintenance solutions for equipment: Maintenance professionals can use generative AI to track the performance of heavy equipment based on historical data, potentially alerting them to trouble before the machine malfunctions. Generative AI can also recommend routine maintenance schedules.


Improve supply chain: You could use generative AI to track down the cause of problems in the supply chain by speaking conversationally with the technology to sort through a vast amount of transactional or product data. Generative AI can also help generate delivery schedules or recommendations for suppliers.


Software development
For a software development team, generative AI can provide tools to create and optimize code faster and with less experience using programming languages. A few examples of the applications of generative AI in software development include:

 

Generating code: Software developers can create, optimize, and auto-complete code with generative AI. Generative AI can create code blocks by comparing them to a library of similar information. It can also predict the rest of the code a developer begins to type, much like how auto-complete works while texting on a smartphone.


Translate programming languages: Generative AI can be a tool for developers to interact with software without needing a programming language. The generative AI would act as a translator.


Automate testing: Developers can improve their automated testing processes using generative AI to highlight potential problems and execute testing sequences faster than other AI methods. Generative AI can learn the logic of the software and how users will interact with it and create test cases to demonstrate various user scenarios.


Financial services
According to McKinsey, generative AI could add $200 billion to $340 billion of value to the banking industry annually [2]. Some of the applications of generative AI in the financial services industry include artificial intelligence investment strategies, drafting documentation and monitoring regulatory changes, and using generative AI as an interpreter to facilitate communications between clients and investors.


Create investment strategies: Generative AI can recommend the best investments according to your or your client’s goals. This technology can find and execute trades much faster than human investors and can do so within the parameters you set for the kind of transaction you want.


Communicate and educate clients and investors: Financial services professionals sometimes need to communicate complex information to clients and colleagues. Generational AI can provide hyperpersonalized customer service without adding more customer service professionals.


Quickly draft documentation and monitor regulation: Generative AI can monitor regulatory activity, keep you informed of any changes, and create drafts of documents such as investment research or insurance policies.


Media and entertainment
Media and entertainment could embrace generative AI in several ways, considering the industry primarily engages in the same task as the tech: generating unique content. Generative AI can help create and edit visual content, create short highlight videos of sporting events, and make working with content management systems easier.


Create audio and visual content: Generative AI can create new video content from scratch. This tech can also help you make visual content faster by creating visual effects, adding graphics, or streamlining editing.


Generate highlights for sports and events: When it comes to sporting and live events, gen AI can create highlight reels instantly and allow fans to create their own custom highlights. For example, fans could generate highlights of a particular play or a tournament series.


Manage tags for better content management: Generative AI can tag and index extensive media libraries, making locating the files you need at any time easier. Similar to our manufacturing example above, generative AI allows using conversational language to find the information or media you’re looking for in a complex media library.


How to find solutions with generative AI
If you’re interested in bringing generative AI to your company, you can approach the technology in two ways. First, you can use existing models and learn to engineer prompts to your needs. Or, you can customize solutions to fit your business processes.


You can use existing generative AI tools like ChatGPT. In this scenario, you’ll focus on learning how to write prompts that get the best answer possible from the technology. For example, you might identify who your audience is and the appropriate tone of the piece to help the application deliver the correct results.


You can integrate custom solutions from an enterprise-level company or build your own generative AI tools. While it won’t be feasible or practical for many companies to create their own generative AI solutions, many gen AI companies offer solutions you can tailor to your business needs. Generative models will vary on features, cost, and security or privacy standards. \n \n Bạn là một Giáo sư về Trí Tuệ Nhân Tạo, đặc biệt là nghiên cứu về Mô Hình Ngôn Ngữ Lớn, cũng là chuyên gia nhiều năm viết các blog hay tạp chí công nghệ. Bạn hãy tổng hợp toàn bộ thông tin từ 5 bài viết trên, sau đó viết 1 bài blog đầy đủ, chi tiết, rõ ràng, chính xác, dễ hiểu, hấp dẫn, thú vị  dài khoảng hơn 4000 từ bằng Tiếng Việt. Bổ sung thêm thông tin chính xác nếu cần thiết. Hãy tối ưu hoá bài viết để đạt chất lượng tốt nhất có thể. Let's think step by step! Lưu ý: Phần ứng dụng của Generative AI nên nêu đầy đủ các ví dụ trong bài và nên các ví dụ cụ thể.