Saturday, May 20

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.

Monday, February 13

Solar Geoengineering – The Risks of Hacking our Climate

Solar engineering, also known as solar geoengineering, is the deliberate manipulation of the Earth's climate system to counteract the effects of greenhouse gas emissions. The goal of solar engineering is to reduce global warming and mitigate the impacts of climate change, such as sea level rise and extreme weather events. However, there are also risks associated with solar engineering that must be considered before any large-scale deployment.

As humans, we think of ourselves as a single organism even though we are systems with trillions of microbes. Well, we live in a self-contained living organism with far greater complexity that we call Earth.

In the branch of mathematics known as Chaos Theory as applied to natural science, we study the sensitive dependencies between structural units that co-exist in an organism. These units exist together as dynamical systems whose apparently random states of disorder and irregularities are governed by underlying patterns and deterministic laws that are highly sensitive to conditions at any point across a time domain.

Essentially, it’s the idea that small changes in system can have significant and unpredictable consequences.

But in living organisms, change is inevitable and often amplified over time. A living organism will self-optimize based on these changes to achieve a balance required to survive and evolve. There is an underlying predictability in this optimization, however the massive scale of inter-relationships and sensitive dependencies between units at the quantum and macro scale makes it impossible for us to comprehend given our current knowledge of science. Even with the current state of technologies such as AI and supercomputers it is beyond our ability to predict.

We have only begun to understand Earth’s atmosphere and its sensitivity to external forces. What we do know is that the atmosphere is highly dynamic and complex. Any solar geoengineering experiment could not yield useful results unless it is done at sufficient scale, both geo and time. This is primarily due to the dynamic nature of the atmosphere. The significant variations in a small-scale experiment would effectively make measurements across a time domain inconclusive.

On the other hand, introducing new components into the atmosphere at scale changes its dynamics and introduces new sensitive dependencies which we do not have the knowledge to model or predict.

The Earth's climate is a complex system, and it is difficult to predict how it will respond to changes. Some areas where solar engineering could have a negative impact would be the ozone layer, weather patterns and Earth’s fragile micro-ecosystems.

Solar geoengineering involves reflecting some of the sun's incoming energy back into space by injecting reflective particles into the upper atmosphere. Some of these particles could react with ozone molecules in the stratosphere leading to the depletion of the ozone layer. This could increase increased exposure to harmful ultraviolet radiation, increasing the risk of skin cancer, cataracts, among other health problems.

Injecting particles into the upper atmosphere will be a highly inexact process where the variable distribution of particles could alter how solar radiation is distributed across the planet. This could disrupt regional weather patterns. For example, a reduction in solar radiation over the Artic could impact the jet stream, changing patterns over Europe and North American. We could see more extreme weather events such as extreme temperatures, bringing droughts in some areas, flooding in others.

It could also disrupt the delicate balance of Earth’s micro-ecosystems having unintended consequences on biodiversity. A reduction of solar radiation could impact the amount and timing of rainfall in some regions. This would negatively impact the growth and reproduction of plant species. Temperature and precipitation changes could also affect migratory patterns and behaviours of animals, which could lead to declines in biodiversity. The injection of reflective particles into the atmosphere could affect the growth and survival of phytoplankton in the oceans, which form the base of many marine food webs.

Solar geoengineering is not intended to be the solution for climate change, it is only an effort to temporarily mitigate the impact until we have a long-term solution. Once a long-term solution is pervasive, we will experience the "clean up" effect. By reversing solar geoengineering too quickly, we risk shocking our ecosystem since for many species and ecosystems adaptation would be impossible.

The risks with solar geoengineering – disrupting weather patterns, declines in biodiversity, shocks to our fragile ecosystems. Does this sound familiar? Isn’t this what we are trying to avoid by finding a solution to climate change?

Undertaking solar geoengineering at this point in the development of human knowledge is inappropriate and irresponsible. The unintended consequences of these actions could have a greater negative impact than the problem we all agree that needs to be solved.

Though, If I were ever to write a Sci-Fi / Horror novel, the topic of solar geoengineering opens many possibilities.

Sunday, February 14

Beyond The Hype: Analysing Blockchain Innovation Ideas

Beyond The Hype: Analysing Blockchain Innovation Ideas.
Tom Golway has explained this situation very well. For established businesses, real blockchain innovation ideas should be transforming the existing business model to produce real value. For startups, blockchain innovation ideas should be efficiently solving real problems to get traction with the target audience. Anything short of this, and we can be sure that blockchain is either a marketing stunt or a waste of resources.

Monday, January 11

Pushing The Boundaries Of Classical, Quantum And Neural Computing

Pushing The Boundaries Of Classical, Quantum And Neural Computing.
Business leaders have reason to cheer on this cutting-edge research, even if they are not equipped to follow its developments in detail.

Friday, January 8

Top 10 job skills of tomorrow, & how long it takes to learn them

50% of all employees will need reskilling by 2025, as adoption of technology increases, according the the World Economic Forum's Future of Jobs Report. Critical thinking and problem-solving top the list of skills employers believe will grow in prominence in the next five years.

Wednesday, January 6

Decentralised Identity: What’s at Stake?

'Decentralised Identity: What’s at Stake?' - INATBA Identity Working Group Publishes Position Paper on Decentralised Identity - INATBA International Association for Trusted Blockchain Applications