Creating a true artificial consciousness with neural network sensors

Photo: Theodore, the main character from the movie "Her" in the middle of a thoughtful conversation with
Samantha, a conscious IOS system. 

 In the 2013 film "Her", Theodore — a middle-aged, sensitive and introspective individual struggling with his marriage — becomes interested in an commercial IOS technology with human-like linguistic capabilities named "OS1". The system is marketed as a true "consciousness", capable of holding a dialogue of any kind via a simulated male or female voice. Theodore quickly takes an interest in the female version of OS1, Samantha, and over time their connection matures into a form of love. Upon realizing his emotions, Theodore finds himself as a moral crossroads struggling with the thought of loving the OS. His emotional response to Samantha is the accumulation of the OS' ability to converse fluidly with Theodore, thus masking the true digital form of the machine, eliminating all friction. [1]

 Although fictional, the story of Theodore and Samantha is an example of the absence of friction between humans and machine. Samantha's interface is the human language, therefore Theodore is free from learning a foreign visual or haptic interface and is capable of immediate interaction with "her". Theodore's feelings towards Samantha are the result of "her" linguistic nuances and illusion of a humanistic personality. 

 The premise of the film is somewhat radical, however the concept of building a machine that can truly sense human language is a lively field of study under a larger umbrella of artificial intelligence, natural language processing, and machine deep learning.[2] Humans have longed for a version of Samantha's "consciousness" but the reality of conversing with a machine is still out of reach. “There’s no way you can have an AI system that’s humanlike that doesn’t have language at the heart of it” according to Josh Tenenbaum, a professor of cognitive science and computation at Massachusetts Institute of Technology. “It’s one of the most obvious things that set human intelligence apart.” [3]

Photo: A diagram of artificial neural networks. The input layer cross references information with sources to determine an appropriate output layer. This system is loosely based on the human brain. 
We are far from interacting with a software that seemingly understands and interprets our world the same way we do, but thanks to advancements in an emerging technology known as artificial neural networks, we are inching closer. Artificial neural networks are based on the neural structure of the human brain, these layers of artificial neurons can be trained to respond appropriately to complex information by building a simulated network that adapts to the input of verbal and visual information.[4] Will Knight, a senior editor at the MIT Technology Review explains the concept:

 "A deep-learning neural network recognizes objects in images using a simple trick. A layer of simulated neurons receives input in the form of an image, and some of those neurons will fire in response to the intensity of individual pixels. The resulting signal passes through many more layers of interconnected neurons before reaching an output layer, which signals that the object has been seen. A mathematical technique known as backpropagation is used to adjust the sensitivity of the network’s neurons to produce the correct response."[5]
Photo: Neural nodes, connected together with a synaptic links in electronic cyberspace.

 In theory, this artificial system could mimic our natural thought process, ripe with common sense (an increasingly difficult phenomenon to code), irony, and similar humanistic traits associated with the natural thought process. As we inch towards the frictionless interface, the artificial neural network could provide us with the breakthrough we need, and one step closer to interacting with a true artificial consciousness.

Notes

 1. Her; ‎Annapurna Pictures: United States of America, 2013.

 2. Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience., Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken. NLP is a component of artificial intelligence (AI). Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks which are capable of learning unsupervised from data that is unstructured or unlabeled.

 3. Knight, W. Creating machines that understand language is AI's next big challenge https://www.technologyreview.com/s/602094/ais-language-problem/ (accessed Mar 7, 2018).

 4. Staff, D. A. C. S. Artificial Neural Networks Technology http://www.psych.utoronto.ca/users/reingold/courses/ai/cache/neural2.html (accessed Mar 7, 2018).

 5. Knight, W. Creating machines that understand language is AI's next big challenge https://www.technologyreview.com/s/602094/ais-language-problem/ (accessed Mar 7, 2018).



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