Maritime Investor Goals, Part 2 examines in more depth, why humans are intelligent, and AI is not. The analysis aims to resolve possible misconceptions and moderate the expectations investors have of generative AI. And to highlight the power of machine learning, which is a human accomplishment, in organizing goal-based knowledge structures around maritime investor goals.
Maritime Investor Goals The essentials of creating training material for a machine that searches by relevance based on modelled knowledge structures The purpose of this
Maritime Investor Goals, Part 2 examines in more depth, why humans are intelligent, and AI is not. The analysis aims to resolve possible misconceptions and moderate the expectations investors have of generative AI. And to highlight the power of machine learning, which is a human accomplishment, in organizing goal-based knowledge structures around maritime investor goals.
What The Experts Say About making software work better for real shipping processes What do the Experts Say about making software work for the Maritime
AI benefits from an extraordinary amount of promotion. With many arguments going on between advocates and opponents. And there’s another group, the common sense types like marine investors, who ask “What is the angle?” Who is driving the promotion and why?”
Regulators of cyber-security systems can appeal to common sense to steer expert and casual users through complexity of system threats and vulnerabilities…
Partners and Projects! Have a peak at some of our latest projects with some of our our partners: @VesselMan @DNV @ ECSO
One of the ways that can make computing more effective is situational awareness. I.e., covering ground to emulate the amount of varied information that humans can process.
Dimitris Lyras Common Sense Computing AI as a Problem Solver Paving A Way For The Kind Of AI We Need A practical step for AI
“Merging Software Development with AI: A way of looking at the future of AI”
An article by Dimitris Lyras, Ulysses Systems founder, weaves the case in favour of activity models -working together with ER models- to provide computers with knowledge to assist in problem-solving.