Will generative AI be a tool of misinformation & risk to civilization & humanity?

It's still too early to tell.

There are 2 considerations:

1. Speed - The rate that these tools can now produce, not just accurate, but convincing content means there is increased potential for the market to be saturated with information (good or bad)

2. Quality - Google outlined recently, that it doesn't penalise AI generated content, but it does penalise poor quality content. This means that a human using AI to augment their skills and produce higher quality content is going to be better than AI content that is copied and pasted.

So yes, there is increased risk of the spread of misinformation. 

BUT - There are some very smart people working hard to ensure that poor quality / dangerous content is stamped out as much as possible.

The second part of your question on the 'Risk to civilisation and humanity'...

This depends on your what camp you sit in:

A) Intelligent AI will be bad and take over the world
B) Intelligent AI will be good and be a force for good

Irrespective of this, it all hinges on 2 factors:

1. The speed that AI reaches the point of general / super-intelligence
2. How the AI is programmed when it gets to this stage

Some argue that we will see this within the next decade.

How it will be programmed. Well. I remain optimistic, as being pessimistic serves little purpose (for me anyway)

What does everyone else think?

Generative AI has the potential to be misused for misinformation, identity theft, fraud, and propaganda, posing risks to society

It can be used to create realistic but entirely fabricated content, leading to misinformation and fake news.

GenAI can also be harnessed to create highly convincing fake news, videos, and audio recordings, contributing to disinformation campaigns.

Industry leaders have warned that AI technology could be used at scale to spread misinformation and propaganda, posing risks to society

Efforts to prevent or mitigate the impact of mis/disinformation generated by AI include establishing ethical guidelines, using independent fact-checking organisations, and implementing safeguards

While generative AI holds tremendous promise, it also poses unique threats, especially in the realm of disinformation, and its potential risks should be addressed proactively

Absolutely not. Like everything, it must be moderated, registered, and monitored over time. The tool itself is not the problem, but rather the way it is used. As always, responsibility falls on the person, not on their creation.

This is my point of view.

As any technology without a proper governance.

Kai-Fu Lee, in his book called AI Superpowers believes that AI will lead to natural gravitation towards monopolies for the early and lead horses in the race and can even have far reaching consequences to broader society and political landscape. I assert that the observed evolution in adoption of LLM models by enterprises may help avoid Kai-Fu’s predicted polarized outcome. If done right, LLMs holds the biggest opportunity for us to democratize the future of open data. The biggest opportunity for this to happen sits in the growing evolution of LLMs towards customised proprietary data based solutions for enterprises. To get this right, knowledge graphs will be a great enabler.

A knowledge graph is an information-rich structure that provides a view of entities and how they interrelate. Expressing these relationships as a graph can uncover facts that were previously obscured and lead to valuable insights. You can even generate embeddings from this graph (encompassing both its data and its structure) that can be used in machine learning pipelines or as an integration point to LLMs. This helps solves major challenges with LLM’s. The models are by design “black box” deep learning models and as thus explainability and transparency lacks with LLMs. Knowledge graphs add the ability to be transparent, explicit, and deterministic to the models, making this a huge plus for areas of application that demand this. Equally, training LLMs existing knowledge graph, solutions such as chatbots are enabled to respond to product and service questions without hallucinations. This allows for adoption of LLMs with greater context. The knowledge graphs also help manage the risk of bias that may arise from the data that the foundational models are trained. This protects adopters of these technologies from perpetuating or/and amplifying these biases in their own environments.