Our views on AI strategies, best practices, and real-world applications.
Gen AI is set to revolutionize business by automating numerous steps in enterprise processes. But where should one begin? We suggest establishing a robust cycle of Ideation, Prioritization, Testing, and Analysis. Here are some typical errors and best practices for the Ideation stage.
I provided a large language model with last year's market outlook reports and asked it to generate aggressive growth portfolios. Next, I backtested the portfolios against historical market data. Here are the results.
Market segmentation is an essential part of any go-to-market strategy. Segmenting a large list of prospects can be very challenging. Fortunately, it is precisely the kind of work that large language models excel at, but it does take a few tricks.
With AI assistants popping up in every tool and service, it's easy to be an AI taker. However, using generally available tools does not build a lasting competitive advantage. One must learn to shape AI to make a difference.
If you were impressed by the first generation of large language models, wait till you interact with a multi-modal one, such as Gemini, GPT4V, or LlaVA. In addition to text, these models can process and understand images, videos, and speech, which presents new opportunities for workflow automation.
Today, December 6, 2023, we announce the availability of AnyQuest PyAQ, an open-source low-code platform for cognitive applications.
According to OpenAI, over two million developers and more than 92% of Fortune 500 organizations are experimenting with or deploying generative AI. To help them assess their progress, we created a simple maturity model.
There are many practical examples of cognitive applications: intelligent workflows, agents, and chatbots. In this article, we describe features shared by all cognitive applications. We also introduce the notion of a semantic broker, a platform that accelerates their development and deployment.
Generative AI solutions enrich user prompts with proprietary data, and grass-roots adoption of generative AI poses significant security, privacy, and compliance challenges. To manage these risks, companies must create Generative AI Centers of Excellence.
Information security and risk management are the top concerns for companies deploying generative AI solutions. Most companies focus on risks presented by AI models while paying insufficient attention to other solution elements. In this post, we paint the whole picture.
Training, deploying, and managing AI models used to be prohibitively expensive. Large foundation models changed everything. Suddenly, every business application can be AI-enabled with relatively little effort. But there is a catch.
We are witnessing a historic moment in enterprise IT – the emergence of semantic brokers, a new category of business-critical enterprise software responsible for AI enablement and risk management.
We present a list of the top 10 business use cases that were recently considered too difficult or impossible and can now be enabled with generative AI.
Generative AI is very different from other types of AI. It will transform every sphere of human activity. Fortunately, experiments are inexpensive and can be plentiful. Business and tech leaders would be wise to start now.
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