One sign that we’re still very early in the evolution of AI is how much heavy lifting is still left to the user to figure out. As Community Leadership Core founder Jono Bacon laments, even the very act of “need[ing] to choose between [large language] models” to run a query is “complex and confusing for…
Though generative AI is relatively new compared with other artificial intelligence technologies, it is already being used to support tasks ranging from vetting job applicants to diagnosing and recommending treatments for illnesses. IDC predicts that by 2028, 80% of chief information officers will rely on generative AI tools to accelerate analysis, boost decision-making, improve customer…
It’s well documented that software development efforts that incorporate generative AI include mistakes that are radically different than what any human programmer would ever make. And yet, most enterprise plans for remediating AI coding mistakes rely on simply inserting experienced human programmers in the loop. Cue train wreck. (No need to click if you are…
As organizations of all sizes and sectors race to develop, deploy or buy AI and LLM-based products and services, what are the things they should be thinking about from a regulatory perspective? And if you’re a software developer, what do you need to know? The regulatory approaches of the EU and US have, between them,…
The decades-long pursuit to capture, organize and apply the collective knowledge within an enterprise has failed time and again because available software tools were incapable of understanding the noisy unstructured data that comprises the vast majority of the enterprise knowledge base. Until now. Large language models (LLMs) that power generative AI tools excel at processing…
Imagine a world where measuring developer productivity is as straightforward as checking your fitness stats on a smartwatch. With AI programming assistants like GitHub Copilot, this seems within reach. GitHub Copilot claims to turbocharge developer productivity with context-aware code completions and snippet generation. By leveraging AI to suggest entire lines or modules of code, GitHub…
Software developers are already benefiting from generative AI, enjoying the ability of AI-powered programming assistants to streamline time-consuming tasks, learn new languages and frameworks, and boost productivity. Now, the data analytics arena is also starting to experience the efficiencies of AI seen by developers. The implementation of large language models (LLM) on data analytics platforms…
In the era of generative AI, large language models (LLMs) are revolutionizing the way information is processed and questions are answered across various industries. However, these models come with their own set of challenges, such as generating content that may not be accurate (hallucination), relying on stale knowledge, and employing opaquely intricate reasoning paths that…
Large language models (LLMs) by themselves are less than meets the eye; the moniker “stochastic parrots” isn’t wrong. Connect LLMs to specific data for retrieval-augmented generation (RAG) and you get a more reliable system, much less likely to go off the rails and “hallucinate,” which is a relatively nice way of describing LLMs that lie…
Google and online educator Udacity have teamed up to offer a free online course to teach developers and designers how to use the Google AI Studio developer tool and Google’s Gemini API AI model. Participants will be taught how to build generative AI capabilities into applications, websites, products, operations, and services. Entitled “Gemini API by…
