by Carey Wedler, The Anti Media:

Researchers at Carnegie Mellon University recently made a scientific breakthrough using machine algorithms to accurately guess what people are thinking. In other words, as the university referred to it, they have “harness[ed] ‘mind reading’ technology to decode complex thoughts.”

The researchers report that they can “now use brain activation patterns to identify complex thoughts, such as, ‘The witness shouted during the trial.’”

Though the technology does not identify the actual words, lead researcher Marcel Just, D.O. Hebb University Professor of Psychology in the Dietrich College of Humanities and Social Sciences, has made enough progress that “machine learning algorithms with brain imaging technology” can effectively ‘read minds.’

As the researchers’ press release explains:

“The new study demonstrates that the brain’s coding of 240 complex events, sentences like the shouting during the trial scenario uses an alphabet of 42 meaning components, or neurally plausible semantic features, consisting of features, like person, setting, size, social interaction and physical action.”

According to the study, published in Human Brain Mapping:

“Regression models were trained to determine the mapping between 42 neurally plausible semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activation patterns of various cortical regions that process different types of information. Given a semantic characterization of the content of a sentence that is new to the model, the model can reliably predict the resulting neural signature, or, given an observed neural signature of a new sentence, the model can predict its semantic content.”

Digital Trends explained for the less scientifically inclined:

“Using the smart algorithm, the team could discern what was being thought about at any given time — and even the order of a particular sentence. After training the algorithm on 239 of the 240 sentences and their corresponding brain scans, the researchers were able to predict the final sentence based only on the brain data.”

They were able to make predictions with 87% accuracy and, in a reverse exercise, accurately predicted brain activity based on sentence information.

The research has concerning implications for the future of the criminal justice system. In 2013, Marcel Just shared his earlier findings — which, at that time, were less advanced — with Alan Alda of PBS. He said he saw “brain research playing a role in courtrooms because the judicial process often includes not only what an accused person did, but also what he or she was thinking when they did it.”

This is cause for concern as the technology progresses, but even more unsettling is who is funding the research and, evidently, has a stake in it: the Office of the Director of National Intelligence (ODNI) via the Intelligence Advanced Research Projects Activity (IARPA), an organization within ODNI.

According to the Office of the Director of National Intelligence’s own website, the government organization’s roots can be traced back to the early years of the CIA:

“The idea of a Director of National Intelligence dates to 1955 when a blue-ribbon study commissioned by Congress recommended that the Director of Central Intelligence employ a deputy to run the CIA so that the director could focus on coordinating the overall intelligence effort.”

They claim this idea persisted, and following the 2001 terrorist attacks, George W. Bush codified the agency. In 2005, the ODNI was officially launched:

“In February 2005, the President nominated John D. Negroponte, ambassador to Iraq, as the first director of national intelligence and U.S. Air Force Lt. Gen. Michael V. Hayden as the first principal deputy DNI, promoting him to General. On April 21, 2005, in the Oval Office, Amb. Negroponte and Gen. Hayden were sworn in, and the ODNI began operations at 7 a.m. on April 22, 2005.”

John D. Negroponte is a British-born “diplomat” with questionable ties to authoritarian regimes. As Esquire reported after he endorsed Hillary Clinton in the 2016 election:

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