When I asked Carnegie Mellon University Professor Sean Ammirati in a recent interview what technologies his graduate students were most excited about, he answered with no hesitation, “generative AI.”
Bear in mind that Carnegie Mellon University (CMU) is not your average university when it comes to technology and, in particular, artificial intelligence (AI). CMU has been a research leader in AI since Herbert Simon and Allen Newell invented the field in the 1950s. For example, during our conversation, Sean recalled being a CMU graduate student in the early 2000s and seeing autonomous vehicles, which use AI, driving around campus. It’s a good place to see the future of AI.
In my experience as a CEO, as well as in my work as an analyst at Acceleration Economy, I’ve been an advocate for AI in business. To suggest that AI technology is a game-changer is an understatement. Yet, many, if not most, C-level executives I talk to are still trying to figure AI out. In this analysis, we’ll look at why they need to understand AI and generative AI in particular and how they should think about using it in their businesses.
The Artificial Intelligence Market
There’s no doubt that AI use is on the rise. A 2020 Statista report revealed that the global AI software market was expected to grow approximately 54% year-on-year and reach a forecast size of $22.6 billion by 2025. A 2018 Servion Global Solutions report predicted, within the next five years, AI would come to power 95% of customer interactions.
There are already thousands of AI applications actively used in business today, across a wide range of industries. Including:
- Human Resources
- Health Care
- Social Media
. . . and more being developed every day.
Two Impactful AI Tools
I’ve had experience with a range of AI tools and will note two that had a remarkable impact on our successful turnaround and growth strategy at thomasnet.com:
- Gong: Gong uses artificial intelligence to analyze customer-facing communication across email, voice, and web conferencing to identify the keys to successful sales. Rather than leaving us wondering what key themes resonated with prospects and customers, Gong gave us insights on closed sales that infused our sales and marketing strategies going forward.
- FullStory: FullStory collects and structures digital experience data and uses AI to glean insights from behaviors. We used FullStory to rapidly refine the user experience of the thomasnet.com platform.
As noted in the list above, these are two among thousands of commercially available AI tools in business today. For a great resource to keep pace and learn about the top AI vendors, check out the Hyperautomation Channel on Acceleration Economy.
The Potential of Generative AI
Generative AI refers to artificial intelligence systems that can generate new, original content based on a given set of inputs. These systems have the potential to revolutionize many industries by automating the creation of content, such as text, images, and audio.
While generative AI has been associated with deep fakes and data journalism, the technology is now being applied to help automate the repetitive processes used in digital image correction and digital audio correction. Generative AI is also being used experimentally in manufacturing as a tool for rapid prototyping and in business to improve data augmentation for Robotic Process Automation (RPA).
To accelerate your learning curve, here are a few Generative AI tools you can start using today:
- ChatGPT: ChatGPT automatically generates grammatically-flawless text based on written prompts in a fashion that’s much more advanced, in-depth, and creative than chatbots. Microsoft made a $1 bllion investment in ChatGPT parent OpenAI and has announced plans to integrate Generative AI into Bing and its Office applications.
- Luma AI: AI-based API using NeRF (neural radiance field) tech that lets you scan any object with your iPhone and create a 3D model of it for digital use.
- Runway Machine Learning: This company offers a suite of Generative AI tools for in-painting, video editing, and text-to-image generation.
- dall-e: By now you may be familiar with this new AI system, which creates realistic images and art from a description in natural language.
As you may have gleaned from the above tools, Generative AI provides several potential benefits:
- Efficiency: Generative AI systems can produce large amounts of content quickly and accurately, freeing up human resources to focus on other tasks.
- Cost savings: Generative AI can help reduce the cost of creating content by automating the process and reducing the need for human labor.
- Personalization: Generative AI systems can produce customized content for individual users, increasing the effectiveness of marketing efforts and improving the customer experience.
- Innovation: Generative AI can generate new ideas and concepts that might not have been possible with human-generated content alone, leading to new products and services.
Generative AI’s Risks and Drawbacks
Let me demonstrate both the potential pros and cons of Generative AI, however. The query “Why Should CEO’s Understand Generative AI and How to Use it?” on ChatGPT resulted in these two paragraphs:
“CEOs should understand generative AI because it is a rapidly growing field with the potential to significantly impact a wide range of industries. Generative AI techniques, such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), have already been used to generate synthetic images, audio, and text, and they have the potential to revolutionize fields such as healthcare, finance, and manufacturing.
For example, generative AI could be used to create personalized healthcare plans, design new financial products, or optimize manufacturing processes. As a CEO, it is important to be aware of these possibilities and to consider how generative AI could be leveraged to improve your business. Additionally, understanding generative AI can help CEOs make informed decisions about strategic partnerships and investments in the field”.
While the above is written well with no grammatical errors and starts off relatively clearly, the second paragraph is all too general and begs more questions than it answers. For example, how does generating “synthetic images, audio, and text” help “create personalized healthcare plans, design new financial products, or optimize manufacturing processes”?
I have no doubt that generative AI can be used in these endeavors, but the answer above just needs to be fleshed out with specific examples and context. It could also likely be improved by ChatGPT itself with even more specific queries. In other words, to be useful, ChatGPT works best with human input, which is in line with the “human plus AI” approach for most AI tools.
Generative AI also holds the potential for misuse, from the widespread distribution of misinformation to authorship and plagiarism issues. Universities are already seeing examples of student papers that appear to have been written using ChatGPT. CEOs need to understand the ethical use of generative AI in business; for insight into the practical and responsible use of AI in business, I’d highly recommend the book “Man + Machine: Reimagining Work in the Age of AI” by Paul Daugherty.
The power of artificial intelligence is already being leveraged in most every industry, and we are still in the early stages of understanding its potential. There are stories in the general media touting fears of an AI-controlled future. While this makes for great science fiction, the reality is that these fears are overblown. All of the mainstream AI tools and applications are focused on augmenting human work and capabilities through automation. As with any new technology, it’s important that we understand how AI works and learn how to use it responsibly, not only to avoid some of the darker repercussions we are seeing on, say, social media and the information ecosystem today, but also because the tools just work better when used in collaboration with humans.
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