Unpacking Gen AI: Demystifying the Current Artificial Intelligence Landscape

With the recent emergence of generative AI and its powerful capabilities for organizations, many leaders have started to wonder what this technology might mean for them, and how it works. In this blog series, our experts will be exploring some of the biggest questions and talking points around generative AI, beginning with this blog on the scope of the current AI landscape.

The state of AI

With 72% of professionals reporting they plan to adopt generative AI technologies to boost productivity across their organization,¹ it’s clear there’s immense buzz around the emerging popularity and viability of generative AI. But for some, this new technology has created additional confusion: What are all these AI technologies, and what can you reasonably expect from them in a business context? AI has a variety of applications across industries and can be leveraged for benefits to productivity, operational efficiency, and improving employee or customer experiences. Let’s unpack all the types of AI and how your business can benefit.

Decoding key terms

Artificial intelligence is part of a larger group of technologies that organizations can leverage for immense benefits. Here is a breakdown:

• AI: This technology uses computation to do tasks that we typically expect humans would need to complete.
• Machine Learning (ML): This takes in data to learn and improve predictions over time.
• Deep learning: A subset of ML, this technology also learns from data but is able to make informed, intelligent decisions on its own.
• Generative AI: While this subcategory of AI also learns from data, it is able to create brand-new content in the form of text, images, etc.

Applications for types of AI

What are models?

Part of the AI conversation is the concept of models, which have different specializations to enable solutions like generative AI. Models are the algorithms that allow these solutions to learn and then predict, make decisions or generate content. ML technologies specifically are designed to learn from previous outputs/results and optimize over time. This is why generative AI in particular is revolutionary: It’s not only able to generate content, but theoretically, it will only get better at doing so with time. Here are some examples of models:

• Foundation models: These are large ML models that are trained on broad sets of data and can be fine-tuned for a wide range of uses. An example of a foundation model is BERT.
• Large Language Models (LLMs): Trained on vast amounts of data, these models interpret and generate humanlike text output, such as ChatGPT.
  • Multimodal language models: These models work similarly to LLMs but are able to generate things other than text, such as images or videos.
  • Dialogue LLM: These models are trained to engage in open-ended conversation with humans, which is typically leveraged for chatbots.
  • QA LLM: These are trained to answer questions in a comprehensive and informative manner.

Putting it all together

With so many ways to tap into AI solutions, there is a wide range of possible applications for every organization. Tapping into these technologies doesn’t just improve internal efficiency or customer experiences, it can also be critical to establishing differentiation from competitors. With solutions ranging from machine learning to generative AI, organizations can choose more mature technologies and/or branch out as early adopters for even higher potential ROI.

Explore what’s possible. Our teammates are experts in the full scope of AI and can get your organization on a strategic path to innovation. Contact us.

Source:

¹ The Harris Poll. (2023). Generative AI Research Method & Topline Results. Slide 10. Insight Enterprises.