Tag Archives: Generative AI

Top 9 Considerations for Enterprise AI

Leave a reply

Artificial intelligence (AI) is top of mind for executives, business leaders, investors, and most workplace employees everywhere. The impacts are expected to be large, deep, and wide across the enterprise, to have both short-term and long-term effects, to have significant potential to be a force both for good and for bad, and to be a continuing concern for all conscientious workers. In confronting these winds of change, enterprise leaders are faced with many new questions, decisions, and requirements – including the big question: are these winds of change helping us to move our organization forward (tailwinds) or are they sources of friction in our organization (headwinds)?

The current AI atmosphere in enterprises reminds us of the internet’s first big entrance into enterprises nearly three decades ago. I’m not referring to the early days of email and Usenet newsgroups, but the tidal wave of Web and e-Commerce applications that burst onto the business scene in the mid-to-late 1990’s. While those technologies brought much value to the enterprise, they also brought an avalanche of IT security concerns into the C-suite, leading to more authoritative roles for the CIO and the CISO. The fraction of enterprise budgets assigned to these IT functions (especially cybersecurity) suddenly and dramatically increased. That had and continues to have a very big and long-lasting impact.

The Web/e-Commerce tidal wave also brought a lot of hype and FOMO, which ultimately led to the Internet bubble burst (the dot-com crash) in the early 2000’s. AI, particularly the new wave of generative AI applications, has the potential to repeat this story, potentially unleashing a wave of similar patterns in the enterprise. Are we heading for another round of hype / high hopes / exhilaration / FOMO / crash and burn with AI? I hope not.

I would like to believe that a sound, rational, well justified, and strategic introduction of the new AI technologies (including ChatGPT and other generative AI applications) into enterprises can offer a better balance on the fast slopes of technological change (i.e., protecting enterprise leaders from getting out too far over their skis). In our earlier article, we discussed “AI Readiness is Not an Option.” In this article here, we offer some considerations for enterprise AI to add to those strategic conversations. Specifically, we look at considerations from the perspective of the fuel for enterprise AI applications: the algorithms, the data, and the enterprise AI infrastructure. Here is my list:

[continue reading the full article here]

https://blog.purestorage.com/solutions/ai-breakthrough-awards-best-ai-solution-airi-s/

AI Readiness is Not an Option

Leave a reply

This year, artificial intelligence (AI) has become a major conversation centerpiece at home, in the park, at the gym, at work, everywhere. This is not entirely due to or related to ChatGPT and LLMs (large language models), though those have been the main drivers. The AI conversations, especially in technical circles, have focused intensively on generative AI, the creation of written content, images, videos, marketing copy, software code, speeches, and countless other things. For a short introduction to generative AI, see my article “Generative AI – Chapter 1, Page 1”.

While there has been huge public interest in generative AI (specifically, ChatGPT) by individuals, there has been a transformative impact on organizations everywhere, both in strategy conversations and tactical deployments. Businesses and others are seeking to leverage generative AI to increase productivity (efficiencies and effectiveness) in nearly all aspects of their enterprise.

To support essential enterprise AI strategy conversations, here are 12 key points for organizations to consider within the context of “AI readiness is not an option, but an imperative”:

[continue reading the full article here]

Built for AI – https://purefla.sh/41oS2Dp

Generative AI – Chapter 1, Page 1

Leave a reply

Anyone who has been watching the AI space this year, even peripherally, will have noticed the flaming hot story of the year—ChatGPT and related chatbot applications. These AI applications are essentially deep machine learning models that are trained on hundreds of gigabytes of text and that can provide detailed, grammatically correct, and “mostly accurate” text responses to user inputs (questions, requests, or queries, which are called prompts). Specifically, these are LLMs—large language models. It is imperative, not an option, for organizations (and for most individuals) to be aware of what is going on here—not only because it is all over the news, but because it could affect your future self.

When I said “mostly accurate,” I meant that sometimes the ChatGPT responses go way off target—people refer to these as “hallucinations,” which is basically a reflection of the statistical basis of the models (see below)—the application will generate some plausible-sounding, grammatically correct statements that are complete falsehoods, such as “Leonardo da Vinci painted the Mona Lisa in 1815” (which is a real example of an observed ChatGPT hallucination).

I tested ChatGPT with my own account, and I was impressed with the results. I prompted it with various requests, including: Write a short story on a specific topic, provide a layperson’s explanations of some complex deep machine learning concepts, create a lesson plan to learn a tough subject, create an outline for a blog on a particular topic (no, not this one), and provide some financial advice on particular investments (no, it did not provide specific advice, but it did offer warnings like NFA “Not Financial Advice” and DYOR “Do Your Own Research”). You can find my results on my Medium blog site.

LLMs are so responsive and grammatically correct (even over many paragraphs of text) that some people worry that it is sentient. Guess what? It isn’t. It is merely a very large statistical model that provides the most likely sequence of words in response to a prompt. It is effectively a galaxy-sized statistically rich version of text autocomplete on your smartphone’s text messaging app, which already delivers some highly probable guesses for the missing words in a text message like this one: “Due to a client deadline, I will be working late at the ____ this ____, so I will be home late for ____.” LLMs can respond to much more complex (but well-posed) prompts, such as lesson plans for education, content for a business presentation, code for a software task, workflow steps for an IT project, and much more.

In order to help people to create well-posed prompts, the new discipline of prompt engineering has arisen. It’s not hard to find many online guides to prompt engineering, including guides for very specific industries, business tasks, workplace applications, and context-dependent scenarios. You don’t need prompt engineering to find those guides—a simple web search should do the trick. And guess what? When web search engines were first created, it took a while for us to learn how to submit well-posed keyword searches. That scenario is being played out again with ChatGPT and prompt engineering, but now our queries are aimed at a much more language-based, AI-powered, statistically rich application. If you understand Bayes’ Theorem and Bayesian statistics, then you will understand me when I say that we are talking here about an enormously more enriched set of priors, likelihoods, and evidence to feed the LLMs—so, it should not be surprising that the posteriors are shockingly good for large text outputs (most of the time).

LLMs are a subset of the deep learning field of natural language processing (NLP), which includes natural language understanding (NLU) and natural language generation (NLG). Think of chatbots and you get the idea, just expanded to a much, much larger domain of AI-based conversation.

Computer vision (CV) is another subset of deep learning, specifically aimed at object/pattern detection, recognition, and classification in images (including still images and video sequences). ChatGPT and LLMs are examples of generative AI using NLP for text generation. Stable Diffusion, Midjourney, and Dall-E are examples of generative AI using CV for image generation. Oh, by the way, I asked the generative AI at Stable Diffusion to create some images to go with my short story (which you can find on my Medium blog).

Beyond the individual examples of generative AI (and its components, ChatGPT, Stable Diffusion, etc.) that we can all experiment with, the applications in the enterprise can be tremendously impactful and transformative for organizations and the future of work. Those next chapters in the story are being written right now.

Continue reading about Enterprise AI in these posts:

  1. AI Readiness is Not an Option
  2. Top 9 Considerations for Enterprise AI
Source: https://marketoonist.com/2023/01/ai-tidal-wave.html