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Tom Golway Books

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“To me empathy is the single most important trait of a leader. A leader needs to make others feel good about themselves, about their company and about the importance of their contributions to the good of the company. The reality is that no one is immune to bad days or even a longer stretch of challenges at work and/or in our personal lives. A great leader will recognize this and respond appropriately to ensure that the people working for them feel a sense of strength and hope. Tom Golway 2009”

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“Winston Churchill once said "We shape our buildings, and afterwards our buildings shape us". In a broader context, we have shaped our technology to achieve greater productivity, responsiveness and hyperconnectivity in the global community. The unintended consequence is that our technology is now playing a greater role in defining who we are by shaping our culture creating an "on-demand" society that is disconnected from our local communities.”

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“It is becoming more difficult to find differentiated information on the Internet. Likewise it is becoming more difficult to contribute meaningful input because of the tendency of people only hearing recognizable voices. As this plays out, we will see more fracturing of society with people only hearing siloed information without an intelligent contrary view to provide balance.”

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Book:Planning and Managing Atm Networks
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“The issue I have with most of what is written about the Metaverse is that the conventional wisdom narrows the focus on the Metaverse to the next increment to social media or a the next step beyond a Zoom call. Where's the vision? Think beyond. When Boeing leverages a digital twin for airplane design/modeling, is that a form of the Metaverse? How about Da Vinci which is a robotic surgical system? Have you ever been on Flight of Passage at Disneyworld? Conventional wisdom might say no, those are not Metaverse. I say, forget conventional wisdom and forget the social media use case for Metaverse. The use cases are there to extract value from the current state of Metaverse technology, but they are not within the scope of the current conversations.”

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“The issue I have with the current discussions about the Metaverse is that the conventional wisdom narrows the focus on the Metaverse to the next increment to social media or the next step beyond a Zoom call. Where's the vision? Think beyond. When Boeing leverages a digital twin for airplane design/modelling, is that a form of the Metaverse? How about Da Vinci which is a robotic surgical system? Have you ever been on Flight of Passage at Disneyworld? Shouldn’t AI training (like for autonomous cars) be more like the Metaverse? Conventional wisdom might say no, those are not Metaverse. I say, forget conventional wisdom and forget the social media/office use case for Metaverse. The use cases are there to extract value from the current state of Metaverse technology, but they are not within the scope of the current conversations.”

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“As people find the balance with online and in-person socialization, the social net will enhance their lives and bring greater understanding to the world. People will maintain an ‘inner circle’ of relationships that will primarily be in person, while staying connected with people around the world through various forms of the social net. This will allow the average person to gain a greater understanding of the world from the eyes of other average people from different countries and cultures. Governments will need to adjust to this new world society.” —Tom Golway 2010”

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“Unleashing Reliable Insights from Generative AI by Disentangling Language Fluency and Knowledge Acquisition Generative AI carries immense potential but also comes with significant risks. One of these risks of Generative AI lies in its limited ability to identify misinformation and inaccuracies within the contextual framework. This deficiency can lead to mistakenly associating correlation with causation, reliance on incomplete or inaccurate data, and a lack of awareness regarding sensitive dependencies between information sets. With society’s increasing fascination with and dependence on Generative AI, there is a concern that the unintended consequence that it will have an unhealthy influence on shaping societal views on politics, culture, and science. Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to knowledge acquisition, humans typically rely on transparent, trusted, and structured sources. In contrast, large language models (LLMs) such as ChatGPT draw from an array of opaque, unattested sources of raw, unfiltered, and unstructured content for language and communication training. LLMs treat this information as the absolute source of truth used in their responses. While this approach has demonstrated effectiveness in generating natural language, it also introduces inconsistencies and deficiencies in response integrity. While Generative AI can provide information it does not inherently yield knowledge. To unlock the true value of generative AI, it is crucial to disaggregate the process of language fluency training from the acquisition of knowledge used in responses. This disaggregation enables LLMs to not only generate coherent and fluent language but also deliver accurate and reliable information. However, in a culture that obsesses over information from self-proclaimed influencers and prioritizes virality over transparency and accuracy, distinguishing reliable information from misinformation and knowledge from ignorance has become increasingly challenging. This presents a significant obstacle for AI algorithms striving to provide accurate and trustworthy responses. Generative AI shows great promise, but addressing the issue of ensuring information integrity is crucial for ensuring accurate and reliable responses. By disaggregating language fluency training from knowledge acquisition, large language models can offer valuable insights. However, overcoming the prevailing challenges of identifying reliable information and distinguishing knowledge from ignorance remains a critical endeavour for advancing AI algorithms. It is essential to acknowledge that resolving this is an immediate challenge that needs open dialogue that includes a broad set of disciplines, not just technologists Technology alone cannot provide a complete solution.”

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