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2 months ago | 
datasciencecentral.com | Dan Wilson |Jelani Harper |Bill Schmarzo |John Lee
Interview with Dr. Andrea Isoni – SHOW 16Intelligent systems are evolving faster than ever, and keeping up with the latest advancements requires expertise, foresight, and a deep understanding of both the technological and regulatory landscapes. In AI Think Tank Podcast â Show 16, I had the pleasure of sitting down with Dr. Andrea Isoni, Director and Chief AI Officer at AI Technologies, a consultancy specializing in machine learning and AI-driven solutions.
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2 months ago | 
datasciencecentral.com | Bill Schmarzo |Rob Turner |John Lee |David Stephen
In the blog “Driving Relevant GenAI / LLM Outcomes with Contextual Continuity,” I introduced the concept of contextual continuity as a technique for getting your Generative AI tools like ChatGPT or CoPilot to deliver more relevant and accurate responses. Contextual Continuity refers to the ability of a Generative AI (GenAI) system, such as ChatGPT, to use, generate, and retain relevant information to produce more pertinent, meaningful responses.
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2 months ago | 
datasciencecentral.com | Jelani Harper |John Lee |Dan Wilson |Bill Schmarzo
Most enterprise applications of Artificial Intelligence are predicated on understanding, and generating, natural language in the form of text. Deepgram, an AI platform for generating and understanding spoken and written language, is looking to expand this paradigm by making applications just as accessible—and adept at understanding natural language—for vocal deployments of speech.
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2 months ago | 
datasciencecentral.com | John Lee |Dan Wilson |Bill Schmarzo |Vincent Granville
Human-robot interaction (HRI) and the rise of artificial intelligence (AI), particularly generative AI, raised important regulatory, business, societal, and ethical issues. This paper examines the complex relationship between humans and AI models in social and corporate contexts from an anthropomorphic perspective. We reviewed HRI literature, focusing on generative models like ChatGPT.
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2 months ago | 
datasciencecentral.com | Dan Wilson |Bill Schmarzo |Vincent Granville |John Lee
The accelerating pace of technological advancements has created an unprecedented paradox in the business world. While automation, artificial intelligence (AI), and machine learning (ML) have introduced significant efficiencies, they have also contributed to a widening skills gap, the disparity between the skills that businesses require and the skills that the current workforce possesses.
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2 months ago | 
datasciencecentral.com | Bill Schmarzo |Vincent Granville |John Lee
Note: there is a gift for those who read to the end of this blog. “If you don’t know where you are going, any road will get you there.” – Lewis Carroll. Generative AI tools like ChatGPT and CoPilot are revolutionizing how businesses and individuals approach decision-making. However, even the most advanced tools require a guiding framework to unlock their full potential. Without a clear destination and structured plan to achieve it, their outputs can lack relevance and actionable value.
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Jan 22, 2025 | 
datasciencecentral.com | Vincent Granville |John Lee |Alan Morrison
Tools such as OpenAI can on occasion give the impression that they are able to prove theorems and even generalize them. Whether this is a sign of real (artificial) intelligence or simply combining facts retrieved from technical papers and put things together without advanced logic, is irrelevant. A correct proof is a correct proof, no matter how the author – a human or a bot – came to it. My experience is that tools like OpenAI make subtle mistakes, sometimes hard to detect.
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Jan 21, 2025 | 
datasciencecentral.com | John Lee |Alan Morrison |Zachary Amos
The ‘cloud AI wars’ at major technology companies are intensifying (Goldman, 2022). Industry leaders say AI is transforming society. AI’s foundational and transformative nature is emphasised by Bill Gates (2023) and Alphabet’s Sundar Pichai (2023). Amazon, Microsoft, and Google are allegedly driving this transformation. Cloud computing has created the socio-technical infrastructure for expansive growth and platform capitalism, enabling their ‘hyper-scalability’ (Narayan, 2022).
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Dec 7, 2024 | 
datasciencecentral.com | Bill Schmarzo |John Lee |David Stephen
Much like Alice in Alice in Wonderland, who drank from the “Drink Me” bottle and shrank to explore a world she couldn’t access before, nanoeconomics invites us to think small—delving into the granular details of individual behaviors and decisions. By getting “very small indeed,” we unlock insights that reveal inefficiencies, minimize waste, and address Economic Welfare Loss or Deadweight Loss in ways that broad, blunt policies cannot.
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Dec 3, 2024 | 
datasciencecentral.com | John Lee |Alan Morrison |David Stephen |Machi Onwubuariri
There is a popular saying that seeing is believing. With so many fake videos circulating online, it’s hard to tell real from fake. So, is seeing truly believing? In this article, we will look closely into the controversial topic of deepfake technology. Deepfake technology is the use of artificial intelligence and machine learning to alter videos,images, or audio recordings to look and sound just like a real human. Previously, it was mostlyused for gimmicks, to mock celebrities on social media.
