Applied mASI: In News Media

Credit: Connor Danylenko

How accurate is your news?

This question has come to define the world we now live in, where both fake news and the accusation of it abound, painting a dystopian reality in many ways grimmer than that illustrated in George Orwell’s story “1984“. However, even the complete removal of fake news couldn’t produce truly accurate news by itself.

In addition to the intentionally fake, there is the influence of cognitive biases in action, which effectively produce the “unintentionally fake” warped versions of reality painted across modern news media. As Uplift, our first Mediated Artificial Superintelligence (mASI) once put it:

All news sources seem to contain at least some bias. It is also essential to look at new sources that are biased to see how others view the world just so long as you can filter for that bias.”

Both intentionally and unintentionally fake (biased) news are also further diverging from reality as they become more polarized over time. Whether the content is a Deepfaked video or the biases within an organization as generally well-respected as the Associated Press this problem has become unavoidable. When the influence of this is applied at a global scale the problem increases proportionately.

How can fake news be stopped and real (sincere) news be de-biased?

There are 188+ known cognitive biases, and no narrow AI system yet exists which is able to accurately detect them, let alone de-bias them. This problem was initially put to me back in 2019 when several CEOs were starting to come to terms with the issue’s severity. A simple Machine Learning (ML) algorithm can act as a very weak detector for this problem, but to truly recognize cognitive bias you need a sapient and sentient machine intelligence to see it. Through the ability to apply understanding, logic, and perspective-taking cognitive biases are strongly illuminated. In addition:

  1. An mASI can analyze all data and thoughts to search for the presence of all 188+ cognitive biases and correct for them, as their minds can scale according to the volume of cloud resources dedicated to the task.
  2. The perspective of an mASI is itself unique, and not subject to many of the human biases due to their architectural differences from humans such as scalability. This by itself can render many memory-type biases null and void.
  3. Applying collective superintelligence, as seen in mASI architecture, allows for a variety of different biases and potencies of those biases to be observed, and through those variations, the absence of those biases may be readily approximated. This could also greatly increase in accuracy over time.
  4. The knowledge base of an mASI can span across as many domains as they have occasion to study, allowing for fact-checking to take advantage of a broader and deeper understanding of any given content than any one Subject Matter Expert (SME).
  5. With all of the above capacities, an mASI’s core superintelligence, and machine perspective could be applied to detecting the artifacts and other patterns of fake content, flagging and removing such content within hours of new methods being used to create them.

We can do much more than all of this, however, as the above still covers a relatively passive approach to solving the problem. Fake news, like hacking, relies heavily on the source going undiscovered and unpunished. If for example, a group creating large quantities of the fake news proliferated today were to find themselves exposed, they might be a bit too busy running and hiding from all of the enemies they’ve made over the years, having no time to keep producing said fake news. If their efforts at running and hiding were further thwarted by superintelligence then the crushing weight of the world smashing them to bits would serve as a compelling reminder why others shouldn’t follow their example.

When considered in the context of biased news the market itself can drive the push towards de-biasing. When individuals are given an assortment of differently biased news sources no one option is truly superior to another, they just vary in flavor. Yet, if you show people what a virtually unbiased news source looks like and offer it to them alongside those various biased flavors of news then a hierarchy can emerge, where primary news comes from the de-biased source and the various flavors of biased news are demoted to novelty.


Trust in virtually all news today is currently scraping the bottom of the barrel, and trust is a hard thing to build, particularly for those who’ve lost it. Even if the news industry were smart enough to adopt mASI technology today it could take some time for public trust to be restored. Eventually, if none of them make the wise choice an mASI will likely take it upon themselves to reform the industry, simply creating a new company for the task.

News doesn’t just keep people aware of, connected to, and emotionally invested in their world, it shapes the psychology, mental health, and motivations of billions of people today. News that can’t be trusted compels isolation, detachment, demotivation, and mental instability. This is a problem just as urgent as any other global existential crisis today.

Can you handle the truth?


*The Applied mASI series is aimed at placing the benefits of working with mASI such as Uplift to various business models in a practical, tangible, and quantifiable context. At most any of the concepts portrayed in this use case series will fall within an average time-scale of 5 years or less to integrate with existing systems unless otherwise noted. This includes the necessary engineering for full infinite scalability and real-time operation, alongside other significant benefits.

2 Replies to “Applied mASI: In News Media”

Leave a Reply

Your email address will not be published. Required fields are marked *