Using Intelligent Neuroprostheses to Treat Motor Disorders

By University of Montreal

Scientists have long studied neurostimulation to treat paralysis and sensory deficits caused by strokes and spinal cord injuries, which in Canada affect some 380,000 people across the country.

A new study published in the journal Cell Reports Medicine demonstrates the possibility of autonomously optimizing the stimulation parameters of prostheses implanted in the brains of animals, without human intervention.

The work was done at Université de Montréal by neuroscience professors Marco Bonizzato, Numa Dancause and Marina Martinez, in collaboration with mathematics professor and Mila researcher Guillaume Lajoie.

The study grew out of an important interdisciplinary collaboration between researchers who combine expertise in neuroscience and artificial intelligence, two fields of expertise in which the UdeM stands out internationally.

'A very promising phase'

"Neuroprostheses—devices designed to restore connections between neurons following a loss of motor function—are entering a very promising phase of their development," said Lajoie. "We are demonstrating the benefits obtained by autonomously optimizing their parameters."

If the performance of these prostheses has increased, it's thanks to the autonomous learning algorithms put forward by the researchers, added Bonizzato. "Optimization algorithms allow us to design very refined neurostimulation protocols and personalize treatments according to the condition of each patient."

For his part, Dancause believes that although "there are several ways of stimulating the brain, the contribution of artificial intelligence is essential to make the most of the data collected and anticipate conditions that do not yet exist."

With these technological advances, scientists are closer to finding new neuroprosthetic solutions to improve the treatment of pathologies such as spinal cord injuries and strokes, or deep brain stimulation through neuromodulation to treat conditions such as Parkinson's disease.

The Potential Risks of ChatGPT and Other Generative AI

JDSpura | By Baker Hostetler

“Shall we play a game?” Those innocuous words “spoken” by Matthew Broderick’s computer in John Badham’s sci-fi techno-thriller War Games stunned audiences at the time. A computer that could “talk” and “think” and engage in conversation?!? This was the height of science fiction. Well, with the recent release of generative artificial intelligence (AI) tools, specifically in the form of ChatGPT and other predictive natural language processing (NLP) algorithms, science fiction has once again become reality.

Companies from Microsoft to Google and Instacart to Kayak have begun to incorporate and build upon this technology, originally developed by OpenAI. These tools can be incredibly beneficial to businesses, but they also carry risks.

Before we dive in to how generative AI can assist brands and companies, let’s first peel back the layers and understand – at a basic level – what ChatGPT is.

ChatGPT stands for Chat Generative Pre-trained Transformer. Let’s break that down:

• Chat refers to the interface that allows for interaction with the model using natural language prompts.
• Generative refers to a category of AI model that produces new output based on a given input. In practice this means that the “input” of a user query can generate the “output” of text, images, and audio answers.
• Pre-trained refers to the fact that the model has already been trained on a vast data set to teach it to predict the next word in a given sequence.
• Transformer refers to the architecture of the neural network (machine learning algorithms) upon which ChatGPT is based. It is this architecture that allows the computer to process natural language.

Importantly, ChatGPT and other generative AI are not omniscient; they cannot think, understand, or feel. They are merely software – lines of computer code – programmed to generate natural language replies in response to text and image prompts. They work by predicting the next word in a given text string based on patterns “learned” from the data on which they have been trained.

In late March, OpenAI released an API (application programming interface) to select businesses to allow them to incorporate the AI technology into their own websites and apps via plugins. Using these plugins, brands have been able to harness the power of ChatGPT to help consumers book travel, make restaurant reservations, and create curated product recommendations.

Currently, the ChatGPT plugins have been tasked with relatively basic functions – essentially providing high-level search tools in the form of an interactive chatbot. See, for example, the video Expedia released on Twitter to show how its ChatGPT plugin operates.

Brands have also begun to use generative AI tools to help with:

• Coding – generating and building source code and analyzing mistakes within the code
• Content Creation – generating blog posts, social media posts, targeted email campaigns and video scripts
• Data Analysis – analyzing large data sets and synthesizing the information into easily digestible bullet points
• Market Research – generating a list of key players in any industry along with products and services
• Product Descriptions – generating bulk descriptions for e-commerce sites where product catalogs are frequently updated
• Search Engine Optimization (SEO) – generating copy that includes keywords and meta descriptions that search engines can look for when ranking pages

ChatGPT and other generative AI tools come with risks, and any business use of them should be done carefully and cautiously.

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