Generative AI

Everything you need to know about generative AI

In this guide, we explore what generative AI is, its benefits, and how Slack is tapping into AI to further increase user productivity.

By the team at SlackFebruary 5th, 2024

Generative AI is a subset of artificial intelligence that can create new content based on a user’s prompts or inputs. By learning from existing data, it can generate realistic, human-like output, including music, poetry, business plans, emails, lines of code, marketing videos, digital art and even synthetic data for supplemental information.

Generative AI is everywhere right now, but it dates back decades. In the 1960s, computer scientist Joseph Weizenbaum created a chatbot named ELIZA that communicated with humans by generating programmed generic responses.

From ELIZA to today’s chatbots, we at Slack understand the immense power AI can unlock, and it’s exciting to see how it can supercharge team productivity. That’s why we’re strategically incorporating AI across our product.

How does generative AI work?

The lifeblood of any generative AI model is training data—lots of it.

To create original content from existing data, generative AI uses neural networks, which are machine-learning models that mimic how the brain identifies patterns, relationships and structures within data sets. The models comprise densely interconnected nodes called neurons that process input data into meaningful output.

Types of generative AI

There are several model types, but these are some of the most commonly used:

  • Generative adversarial networks (GANs): GANs are made up of two neural networks: a generator and a discriminator. The generator produces new data, which the discriminator evaluates and then flags as either real or fake. The process continues until the generator creates data that’s hard to tell from real data.
  • Variational autoencoders (VAEs): Much like GANs, VAEs consist of two neural networks: the encoder and decoder. The encoder maps input data to a latent space, which is an abstract, probabilistic representation of compressed data. The decoder then takes samples from the latent space and reconstructs the original data. In this way, VAEs can generate new data that is both diverse and realistic.
  • Transformers: Transformers are attention-based architectures that use self-attention—a mechanism that focuses on important data and disregards what’s irrelevant—to identify contextual relationships and dependencies between words in sequence. Large language models (LLMs) like GPT-3 and BERT are based on transformers.

Benefits of generative AI

Generative AI use cases run the gamut, but this type of AI continues to demand attention for its many applications and benefits.

  • Content generation: Create social media and other types of content. Respond to technical queries. Simplify complex information.
  • Task automation: Automate repetitive tasks such as data entry. Provide instant responses to common customer support questions. Automate data analysis and reporting.
  • Code generation: Write code snippets or even complete sections of code to speed up the coding process.
  • Art creation: Create realistic representations of people, objects or scenes. Generate unique art creations and designs.
  • Knowledge retention and expansion: Create and maintain documentation of systems and processes. Generate learning materials, guides and tutorials. Summarize lengthy documents.
  • Anomaly detection: Identify pattern deviations within data sets, a crucial capability in fraud detection, cybersecurity and quality control.
  • Data augmentation: Create synthetic data for research and training.

Generative AI vs. other AI technologies

Again, generative AI is just one subset of artificial intelligence. Understanding the distinctions between these technologies can provide valuable insights into their unique capabilities and applications.

Traditional AI

Traditional AI, also known as narrow or weak AI, executes tasks using predetermined rules and algorithms. It’s designed to perform a single activity or a limited set of tasks exceptionally well.
Examples of traditional AI applications include Google Search, image and facial recognition systems and recommendation engines like those on Netflix and Amazon.

Conversational AI

This type of AI enables machines to communicate with people using natural language. Through natural language understanding (NLU), conversational AI apps interpret what people are saying through voice or text and respond in ways that simulate conversation. Think customer support chatbots, conversational assistants like Siri and Alexa, and language translation services.

Adaptive AI

Sometimes called learning AI, adaptive AI systems can adjust to changing environments. A mix of different AI technologies power them, including machine learning, evolutionary algorithms and reinforcement learning. By continuously learning from new data and feedback, they can change their behavior and improve performance over time.
Examples: Intelligent healthcare systems that let clinicians customize treatment plans; tools for predicting machine maintenance in industrial settings; and credit card fraud detection.

Popular generative AI tools

These are some of the most popular generative AIs, including the types of content they create:

  • Text: ChatGPT, Bard, Jasper and
  • Images: Dall-E, Midjourney and Stable Diffusion
  • Music: Amper, AIVA and MuseNet
  • Code: GitHub Copilot, Amazon CodeWhisperer and MutableAI
  • Video: Descript, Synthesia and InVideo

Coming soon: Slack AI, generative AI built natively in Slack

Every message, video clip and canvas shared within Slack is valuable knowledge your organization can leverage. Slack’s native search function does an excellent job helping users find the information they need to get things done, but we’re not stopping there. Watch for Slack AI, generative AI built natively in Slack, bringing you three powerful features to boost productivity many times over.

  • Channel recaps: Slack AI lets you surface crucial themes and highlights in any channel so people can quickly absorb what matters most. Great for new hires or colleagues joining mid-project.
  • Thread summaries: If you need stakeholder input but they’ve been out of the loop so far, Slack AI has a one-click overview of lengthy discussions. It flags the most relevant info so people can make decisions or investigate more thoroughly.
  • Intelligent search results: Traditional search can prove tricky if you don’t know how to phrase what you’re looking for. Slack AI makes it easy. Just ask questions like you’re talking to a friend and get clear, concise answers based on relevant Slack messages.

AI-powered customer insights with Salesforce

Slack Sales Elevate is an add-on integration for Salesforce Sales Cloud. It helps sales teams collaborate better, close deals faster and keep up with deal progression and team performance. Here’s how:

  • View and update Salesforce opportunities in Slack: Any updates made in Slack automatically sync with Salesforce.
  • Create custom notifications: Stay on top of administrative tasks, important activities and deadlines.
  • Set up metrics: Gain valuable insights into individual and team performance. Up to three metrics can appear in “Your Insights” for a quick overview of progress toward sales targets.
  • Access Salesforce from anywhere: Update opportunities, manage notifications, and view key metrics anywhere, on any device.

AI workflow automation with Workflow Builder

Workflow Builder is a no-code automation tool that helps teams explore creative new ways to automate work in Slack.

  • Workflow connectors simplify task automation across Slack and tools like Asana, Jira, Google Sheets and DocuSign. Just choose how your workflow starts, add steps and publish!
  • You can extend Salesforce Flow to Workflow Builder and securely customize Slack workflows with admin-approved Salesforce automation.
  • Repurpose user-created workflows by duplicating and customizing them in Workflow Builder.

Security implications of trusted AI

In one KPMG survey, 86% of consumers cited data privacy as a growing concern. Many Slack customers echo the same sentiment. We hear you loud and clear.

Slack never mixes proprietary information between customers. With our Enterprise Key Management (EKM) system, you have complete control over your data. You can manage access to it using your own encryption keys, which you’re free to revoke whenever you please. Our third-party certifications and attestations also signify our ongoing commitment to compliance with data security standards like HIPAA, FedRAMP, FINRA and SOC 2.

Generative AI and the future of work

Generative AI is changing how we work, taking productivity to great new heights. From data analytics and intelligent automation to collaborative platforms, not only can it streamline operations; it can also inspire new levels of efficiency and creativity. To see for yourself how generative AI can unleash the power of your data in Slack, sign up for the upcoming Slack AI pilot today.

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