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What children and AI systems have in common

Similar to child learning, reinforcement learning is based on the AI systems interaction with its environment. It performs actions that seek to maximize the reward and avoid punishments. Driven by curiosity, children are active learners that simultaneously explore their surrounding environment and predict their actions outcomes, allowing them to build mental models to think causally. If, for example, they decide to push the red car, spill the flower vase, or crawl the other direction, they will adjust their behavior based on the outcomes of their actions.

Children experience different environments in which they find themselves navigating and interacting with various contexts and objects dispositions, often in unusual manners. Just as child brain development could inspire the development of AI systems, the RL agents learning mechanisms are parallel to the brains learning mechanisms driven by the release of dopamine a neurotransmitter key to the central nervous system which trains the prefrontal cortex in response to experiences and thus shapes stimulus-response associations as well as outcome predictions.

Biology is one of the most promising beneficiaries of artificial intelligence. From investigating mind-boggling combinations of genetic mutations that contribute to obesity to examining the byzantine pathways that lead some cells to go haywire and produce cancer, biology produces an inordinate amount of complex, convoluted data. But the information contained within these datasets often offers valuable insights that could be used to improve our health.

In the field of synthetic biology, where engineers seek to rewire living organisms and program them with new functions, many scientists are harnessing AI to design more effective experiments, analyze their data, and use it to create groundbreaking therapeutics. I recently highlighted five companies that are integrating machine learning with synthetic biology to pave the way for better science and better engineering.

Artificial general intelligence (AGI) describes a system that is capable of mimicking human-like abilities such as planning, reasoning, or emotions. Billions of dollars have been invested in this exciting and potentially lucrative area, leading some to make claims like data is the new oil.

Among the many companies working on general artificial intelligence are Googles DeepMind, the Swiss AI lab IDSIA, Nnaisense, Vicarious, Maluuba, the OpenCog Foundation, Adaptive AI, LIDA, and Numenta. Organizations such as the Machine Intelligence Research Institute and OpenAI also state AGI as their main goal. One of the goals of the international Human Brain Project is to simulate the human brain.

Despite a growing body of talent, tools, and high-quality data needed to achieve AGI, we still have a long way to go to achieve this.

Today, AI techniques such as Machine Learning (ML) are ubiquitous in our society, reaching from healthcare and manufacturing to transportation and warfare but are qualified as narrow AI. They indeed process and learn powerfully large amounts of data to identify insightful and informative patterns for a single task, such as predicting airline ticket prices, distinguishing dogs from cats in images, and generating your movie recommendations on Netflix.

In biology, AI is also changing your health care. It is generating more and better drug candidates (Insitro), sequencing your genome (Veritas Genetics), and detecting your cancer earlier and earlier (Freenom).

As humans, we are able to quickly acquire knowledge in one context and generalize it to another environment across novel multiple situations and tasks, which would allow us to develop more efficient self-driving car systems as they need to perform many tasks on the road concurrently. In AI research, this concept is known as transfer learning. It assists an AI system in learning from just a few examples instead of the millions that traditional computing systems usually need to build a system that learns from first principles, abstracts the acquired knowledge, and generalizes it to new tasks and contexts.

To produce more advanced AI, we need to better understand the brains inner workings that allow us to portray the world around us. There is a synergistic mission between understanding biological intelligence and creating an artificial one, seeking inspiration from our brain might help us bridge that gap.

Acknowledgment: Thank you to Louis N. Andre for additional research and reporting in this article. Im the founder of SynBioBeta, and some of the companies that I write about including some named in this article are sponsors of the SynBioBeta conference (click here for a full list of sponsors).

Originally posted here:

Can Synthetic Biology Inspire The Next Wave of AI? - SynBioBeta