Introduction to Gen A.I for Product Managers
"Generative AI is a game-changer. It is going to transform the way we create, consume, and interact with the world around us." - Satya Nadella
What is Generative A.I
Generative AI is a rapidly evolving field of machine learning that can create new content, translate languages, write different kinds of creative content, and answer your questions in an informative way. It has the potential to revolutionize the way we build and use products.
Generative AI refers to any Artificial Intelligence model that generates novel data, information, or documents.
For example, many businesses record their meetings - both in person and virtual. Here are a few ways Generative AI can extract value from these recordings:
It can automatically generate a list of action items to ensure that meetings have actionable value.
It can generate a summary of the meeting for people who couldn’t make it to the meeting, distilling the important information down to improve efficiency.
It can generate context-relevant answers to questions that come up during the meeting.
History of Generative AI
Generative AI has a relatively short history, dating back to the 1950s with the development of neural networks. However, it has seen rapid progress in recent years, particularly with the introduction of generative adversarial networks (GANs) in 2014 and the rise of large language models like ChatGPT in 2023.
Key Milestones
1950s: Alan Turing publishes his paper on machine thinking and creates the Turing test.
1957: Frank Rosenblatt invents the first neural networks that can be trained.
1980s and beyond: Neural networks become widely used in AI.
2014: Generative adversarial networks (GANs) are introduced.
2023: Large language models like ChatGPT are developed.
How generative AI works – in simple terms
Generative AI models typically work by using a process called adversarial training. In adversarial training, two models are pitted against each other: a generator model and a discriminator model. The generator model is responsible for generating new content, while the discriminator model is responsible for distinguishing between real data and generated data. The two models are trained together in a competitive loop, with the generator model trying to fool the discriminator model and the discriminator model trying to catch the generator model.
Generative AI is a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and more. The use cases and possibilities span all industries and individuals.
Think of generative AI as rolling a weighted dice. The training data determine the weights (or probabilities).
If the dice represents the next word in a sentence, a word often following the current word in the training data will have a higher weight. So, “sky” might follow “blue” more often than “banana”. When the AI “rolls the dice” to generate content, it’s more likely to choose statistically more probable sequences based on its training.
So, how can LLMs generate content that “seems” original?
Let’s take a fake listicle – the “best Eid al-Fitr gifts for content marketers” – and walk through how an LLM can generate this list by combining textual cues from documents about gifts, Eid, and content marketers.
Before processing, the text is broken down into smaller pieces called “tokens.” These tokens can be as short as one character or as long as one word.
Example: “Eid al-Fitr is a celebration” becomes [“Eid”, “al-Fitr”, “is”, “a”, “celebration”].
This allows the model to work with manageable chunks of text and understand the structure of sentences.
Each token is then converted into a vector (a list of numbers) using embeddings. These vectors capture the meaning and context of each word.
Positional encoding adds information to each word vector about its position in the sentence, ensuring the model doesn’t lose this order information.
Then we use an attention mechanism: this allows the model to focus on different parts of the input text when generating an output. If you remember BERT, this is what was so exciting to Googlers about BERT.
If our model has seen texts about “gifts” and knows that people give gifts during celebrations, and it has also seen texts about “Eid al-Fitr” being a significant celebration, it will pay “attention” to these connections.
Similarly, if it has seen texts about “content marketers” needing specific tools or resources, it can connect the idea of “gifts” to “content marketers“.
Now we can combine contexts: As the model processes the input text through multiple Transformer layers, it combines the contexts it has learned.
So, even if the original texts never mentioned “Eid al-Fitr gifts for content marketers,” the model can bring together the concepts of “Eid al-Fitr,” “gifts,” and “content marketers” to generate this content.
This is because it has learned the broader contexts around each of these terms.
After processing the input through the attention mechanism and the feed-forward networks in each Transformer layer, the model produces a probability distribution over its vocabulary for the next word in the sequence.
It might think that after words like “best” and “Eid al-Fitr,” the word “gifts” has a high probability of coming next. Similarly, it might associate “gifts” with potential recipients like “content marketers.”
How can Generative AI be used to build products?
Generative AI can be used to build products in a variety of ways. For example, it can be used to:
Generate new creative content, such as blog posts, articles, code, and images.
Translate languages.
Personalize user experiences.
Generate new product ideas and features.
Improve the efficiency of product development.
Examples of Generative AI model
There are a number of Generative AI products already available on the market. Here are a few examples:
The underlying principle behind generative AI varies depending on the specific model or algorithm used, but some common approaches include:
Variational Autoencoders (VAEs): VAEs are a type of generative model that learns to encode input data into a latent space and then decode it back into the original data. The "variational" part of the name refers to the probabilistic nature of the latent space, allowing the model to generate diverse outputs.
Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, that are trained simultaneously through adversarial training. The generator creates new data, and the discriminator evaluates how well the generated data matches real data. The competition between the two networks leads to the generator improving over time in creating realistic outputs.
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks: These types of neural networks are often used for generating sequences, such as text or music. RNNs and LSTMs have a memory that allows them to capture dependencies over time, making them suitable for tasks where the order of elements matters.
Transformer Models: Transformer models, particularly those with attention mechanisms, have been highly successful in various generative tasks. They can capture long-range dependencies and relationships in the data, making them effective for tasks like language translation and text generation.
Autoencoders: Autoencoders consist of an encoder and a decoder, and they are trained to reconstruct the input data. While they are primarily used for representation learning and data compression, variations like denoising autoencoders can be used for generative tasks.
The training process for generative AI involves exposing the model to a large dataset and optimizing its parameters to minimize the difference between generated outputs and real data. The model's ability to generate realistic and diverse content depends on the complexity of its architecture, the quality and quantity of the training data, and the optimization techniques used during training.
The Pros and Cons of Generative AI
Like any major technological development, generative AI opens up a world of potential, which has already been discussed above in detail, but there are also drawbacks to consider.
Overall Advantages of Generative AI include:
Increasing Productivity: Generative AI can automate or speed up tasks, leading to enhanced productivity.
Removing Skill or Time Barriers: It lowers barriers for content generation and creative applications, making such processes more accessible.
Enabling Analysis of Complex Data: Generative AI facilitates the analysis and exploration of intricate datasets.
Creating Synthetic Data: It can be used to generate synthetic data for training and improving other AI systems.
Disadvantages of Generative AI include:
Hallucination: Some AI models exhibit hallucination, generating nonsense or errors that defy real-world logic.
Reliance on Data Labeling: Despite advancements in unsupervised training, many models still rely on human workers for data labeling, introducing issues of quality and veracity.
Content Moderation Challenges: AI models may struggle with recognizing and filtering inappropriate content, necessitating human intervention.
Ethical Concerns: Generative AI can replicate biases present in training data, leading to ethical issues such as discrimination and unfairness.
Legal and Regulatory Issues:
Copyright Issues: Verification of copyright compliance becomes challenging due to the vast and diverse datasets used in training.
Privacy Concerns: Generative AI raises issues related to the collection, storage, use, and security of both personal and business-related data.
Autonomy and Responsibility: Determining liability in cases of accidents involving autonomous systems, like self-driving cars, remains unclear.
Political Implications: Concerns arise around false information dissemination, media manipulation, and interference with democratic processes.
Energy Consumption: The significant energy consumption of AI models raises ecological concerns and impacts the environment as their usage expands.
How Product Managers can use Generative AI
Product Managers can use Generative AI to improve their products in a variety of ways. For example, they can use it to:
Generate new creative content for their marketing and sales materials.
Translate their products into multiple languages to reach a wider audience.
Personalize the user experience for each individual user.
Generate new product ideas and features based on user feedback and market trends.
Improve the efficiency of product development by using Generative AI to automate tasks such as code generation and testing.
Conclusion
Generative AI is a powerful tool that has the potential to revolutionize the way we build and use products. Product Managers who can understand and harness the power of Generative AI will be well-positioned to succeed in the future.
Next steps
In the next blog post in this series, we will take a deeper dive into the different types of Generative AI models and how they can be used to build products. We will also discuss some of the challenges and opportunities of using Generative AI in product development.
All Readings: Introduction to Generative AI
Welcome to a curated collection of readings that delve into the fascinating world of Generative AI. Whether you're a seasoned professional or just starting to explore this innovative field, these resources offer valuable insights and perspectives.
Generative AI Insights:
Ask a Techspert: What is generative AI? - Google's techsperts provide a comprehensive introduction.
Build new generative AI powered search & conversational experiences with Gen AppBuilder - Discover how to create generative apps effortlessly.
What is generative AI? - McKinsey's explainer breaks down generative AI concepts.
Google Research, 2022 & beyond: Generative models - A glimpse into Google's future in generative models.
Building the most open and innovative AI ecosystem - Explore the principles behind an open generative AI partner ecosystem.
Generative AI is here. Who Should Control It? - A thought-provoking piece on the ethical considerations of generative AI.
Stanford U & Google’s Generative Agents Produce Believable Proxies of Human Behaviors - Stanford and Google collaborate on realistic generative agents.
Generative AI: Perspectives from Stanford HAI - Gain insights from Stanford's HAI on Generative AI.
Generative AI at Work - A scholarly perspective on the application of generative AI.
The future of generative AI is niche, not generalized - Technology Review's take on the specialized future of generative AI.
Large Language Models Exploration:
NLP's ImageNet moment has arrived - A comprehensive look at NLP's breakthrough.
Google Cloud supercharges NLP with large language models - Uncover how Google Cloud is enhancing NLP with large language models.
LaMDA: our breakthrough conversation technology - Google's blog on the breakthrough in conversation technology.
Language Models are Few-Shot Learners - A technical paper on few-shot learning in language models.
PaLM-E: An embodied multimodal language model - Google's blog on embodied multimodal language models.
Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance - Insights into scaling language models for breakthrough performance.
PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications - Google Developers blog on starting with generative AI.
The Power of Scale for Parameter-Efficient Prompt Tuning - A deep dive into the efficiency of parameter tuning in large language models.
Google Research, 2022 & beyond: Language models - Google's vision for language models in the years to come.
Accelerating text generation with Confident Adaptive Language Modeling (CALM) - A blog post on accelerating text generation using CALM.
Additional Resources:
Attention is All You Need - A foundational paper on attention mechanisms.
Transformer: A Novel Neural Network Architecture for Language Understanding - Google's blog introducing the Transformer architecture.
Transformer on Wikipedia - Detailed information about the Transformer model.
What is Temperature in NLP? - An insightful article explaining the concept of temperature in NLP.
Bard now helps you code - Google's blog on how Bard aids in coding.
Model Garden - Google Cloud's Model Garden for machine learning models.
Auto-generated Summaries in Google Docs - Learn about auto-generated summaries in Google Docs.