Artificial Inteligence Data Learning Technology 
Leapfrogging
FEBRUARY 28, 2024
Embracing the Evolution: AI Meets Design Thinking The intersection of artificial intelligence and design thinking is poised to redefine the landscape of innovation and strategy. From healthcare to finance, AI’s ability to process vast amounts of data at unprecedented speeds has revolutionized traditional practices.
Harvard Business Review
JANUARY 23, 2024
Machine learning might be the world’s most important general-purpose technology, but it’s notoriously difficult to launch. Outside of Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing?
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Harvard Business Review
JUNE 12, 2023
Artificial intelligence is based around prediction. But large language models represent a key advance: OpenAI has found a way to teach its AI human judgment by using a simple form of human feedback, through chat. But decision making requires both prediction and judgment.
Daniel Burrus
OCTOBER 16, 2019
Technological change is the only constant in today’s business world, disrupting everything from large organizations to small start-ups. Recently, technology company Sage conducted surveys pertaining to AI and individuals’ understanding of it. Machine Learning ? Deep Learning. What Exactly Is AI ?
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IdeaScale
AUGUST 16, 2017
When people talk about machine learning they are a referring to a type of artificial intelligence that has the ability to learn without explicit programming. And machine learning could be applied towards many of those already (for example: using data to optimize for consumer buying behavior).
Leapfrogging
MARCH 19, 2024
From its inception to the current state, the processes governing the development of new products and services have continuously evolved to incorporate new methodologies and technologies. Traditional Phases and Gates Processes Traditionally, the phases and gates model has been a cornerstone in structuring innovation management.
Speaker: Dr. Greg Loughnane and Chris Alexiuk
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
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