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Home Future Tech AI

Can we choose to remember the good and bad ?

by Sandra Theres Dony
May 2, 2021
in AI, Future Tech
Reading Time: 3 mins read
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DecNef
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DecNef

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Memories are what make a human. They are the basic shades that create the intricate canvas called human life, complete with its subtleties and nuances. While good memories make for a beautiful highlight, the bad ones, particularly those involving trauma and pain, can shade a person’s life and identity. Therefore, wouldn’t it be nice if we got to choose what to remember. Weren’t there days when we had wished for a delete button that could erase the traumatic memories that caused a great deal of pain and grief? Seems like machine learning and artificial intelligence might just bring the line of reality closer to the idea of erasing memories. Though the technology is futuristic at the moment, it might be closer than we anticipate.

The technological “Obliviate” spell

The technology will be of substantial help in the effective treatment of PTSD patients, and at present, it is in the proof-of-concept stage. The treatment goes by the name decoded neurofeedback(DecNef).

If and when it touches the line of success, the technology will facilitate the modification of painful memories by using machine learning to collect brain signals. The entire process will be carried out with ease, without even the subject being aware of it.

Although the technology entails a long list of ethical concerns, if executed in the right way, it can prove to be a blessing, providing immense relief to a great number of people suffering from PTSD.

How does it work?

The brain signals form a core part of our cognitive functions and DecNef is based on these brain signals. Computational neuroscientists use these signals as steering wheel which will navigate this technology in the right path. DecNef

According to Aurelio Cortese, a computational neuroscientist, the technology uses neuroimaging data that scans the human brain and keeps track of any change or difference in the oxygen levels in the cerebral blood. A local computer then carries out the job of processing this data, enabling the selection of relevant data. This big magnet is a constituent of the fMRI machine.

According to Cortese,

“Machine learning is used to learn the neural representation of the target mental representation in the first place.  This machine learning decoder is then used in the neurofeedback procedure, to detect the activation patterns and compute the likelihood that it corresponds to a target pattern.”

The Benefits

If the technology touches the finish line of victory, the benefits can be immense. It can be used for attention training, enhancing memory function, or alleviating physical pain. However, its major focal point of the technology right now is the treatment for PTSD and providing relief for individuals suffering from disorders of the mind. In contrast to the traditional method of treatment which involves exposing the patient to the traumatic memory, which adds to the distress, the innovative technology can get the job done without the additional trauma of being re-exposed to a painful memory or experience.

The Ethical Concern

Although the technology is gift-wrapped in an abundance of promises and potential, it also entails a long chain of ethical concerns. Despite the good and bad nature, memories form a crucial part of the human psyche, to the extent of defining one’s personality. And modifying these memories might be equivalent to redefining and completely changing a person’s identity, particularly when it is used for the wrong reasons.

In the words of Philipp Kellmeyer,

“Targeted elimination or inception of memories for purposes other than medical treatment obviously entails huge ethical problems, including the possibility of interfering with a person’s identity or instrumentalizing individuals by using false memory inception to influence their behavior.”

Ultimately, it all comes to how the technology is used. If the right shades are added, it forms a beautiful canvas, and if not, the consequences will follow, staining and straining the canvas.

 

Tags: AIFuture TechMachine Learning
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Sandra Theres Dony

Content writer at Techstory, dealing with topics, Artificial Intelligence, Virtual Reality and Augmented Reality.

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