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This article is meant to draw attention to the vast amount of opportunities resulting from artificial intelligence as an emerging technology in the field of entrepreneurship.
This is best illustrated by portraying a few cases in which AI helped great minds to build new solutions making our world better.
Xnor.ai founded by Washington University associate professor Ali Farhadi developed a solution for the inference phase of machine learning. The inference phase in machine learning uses computing and battery power. Xnor.ai developed an algorithm that reduces both, computing as well as battery usage (Forbes, 2018).
Robert Fratila, Co-Founder and CTO of Aifred Health uses AI in the field of mental health. He puts his solution as following:
“We take in self-reported data from the patient—and in the future, biomarker data—and run this information through our deep learning model, trained on thousands of patient records from clinical trials, and produce predicted remission rates for each potential treatment,” Fratila says. Doctors use that information to zero in on the best treatment. […]” (Forbes, 2018).
A great example of how technology can improve human lives, by providing new solutions to medical problems.
Another AI-driven solution is developed by Kieran Snyder, Co-Founder, and CEO of Textio. Using machine learning, Textio is able to advise (in real-time) on written content to be changed towards a certain response goal from readers (Forbes, 2018). This helps in various fields such as job posts to be formulated for a certain target group to respond (Forbes, 2018).
Even though these solutions yield great benefit for businesses and society, there are risks to AI. As large amounts of data are the source for machines to learn, there is a chance that the data input from which is learned is not correct (Brynjolfsson and McAffee, 2017). This screwed input data could in result screw the output. Especially in the medical field, this could lead to devastating consequences. Generally, using data to solve problems has its benefits, though machine and deep learning practices should be developed with caution on the risks.

Three examples of how machine and deep learning helped solve problems. The list of entrepreneurs striving to create solutions using technology such as AI is a lot longer. You can check https://www.forbes.com/sites/insights-intelai/2018/11/29/5-entrepreneurs-on-the-rise-in-ai/ for more examples. AI yields great benefits but has to be used with caution in regard to risks.
I hope you got inspired by some leading entrepreneurs in the field of AI.
Thanks for reading!

References
Forbes, 2019. 5 Entrepreneurs On The Rise In AI. Retrieved from check https://www.forbes.com/sites/insights-intelai/2018/11/29/5-entrepreneurs-on-the-rise-in-ai/

Brynjolfsson, E., & Mcafee, A. N. D. R. E. W. (2017). The business of artificial intelligence. Harvard Business Review.

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Researches from RMIT created a chip inspired by a methodology called optogenetics used in brain science which mimics the brain’s function of having memories (RMIT University, 2019).

Optogenetics is a method in brain science to modify and manipulate activities of living beings using high precision light (Max-Planck-Gesellschaft, 2019). “Light-responsive proteins are allowing scientists to turn neurons on or off selectively with unprecedented precision. Introducing these proteins into cultured cells or the brains of live animals allows investigation of the structure and function of neural networks.” (Max-Planck-Gesellschaft, 2019). The chip developed by the Functional Materials and Microsystems Research Group at RMIT uses light to create photocurrent. Having photocurrent, differently colored light is used to create positive or negative current, which equals the creation or erasion of a memory respectively, i.e. learning and forgetting (RMIT University, 2019). The process of creating photocurrent on a chip has essentially the same function as the light used to modify and manipulate the brain of living beings. Having a chip performing these behavioral neural activities of creating and erasing memories, brings research a step closer to the realization of a bionic brain and light-based computing (RMIT University, 2019).

This is especially relevant for the research and development of artificial intelligence, as scientists have not yet gained a full understanding of how the human brain learns, stores, and forgets information. Reaching a higher level of understanding of the human brain’s functions ultimately leads to more sophisticated artificial neural networks moving closer to general artificial intelligence.

In his book Life 3.0 (2017) Max Tegmark lines out a range of perceptions on when general artificial intelligence (GAI – referring to a state of machines at which they can understand and perform or learn any task humans can do) is being reached. The time estimate until GAI arrives varies among the groups of experts from ‘within this decade’ to ‘not within the next 300 years’ (Tegmark, 2017).

In my personal opinion, the next 10 years will be crucial in the development of artificial intelligence. Technological breakthroughs such as the optogenetics inspired chip from the RMIT University showcase the rapid and self-enforcing research in the field of brain science and AI. I find it hard to understand the technocratic perspective of GAI to take centuries until realization. My lack of understanding of this estimation is not last due to the explosion of (digital) technologies and the exponential growth in data generated.

What do you think about bionic brains on a chip, General Artificial Intelligence and how we as a society should handle it in case it arrives?

Comment below, thanks 🙂

 

References

RMIT University. (2019, July 16). Electronic chip mimics the brain to make memories in a flash: Engineers have mimicked the human brain with an electronic chip that uses light to create and modify memories. ScienceDaily. Retrieved September 28, 2019 from www.sciencedaily.com/releases/2019/07/190716103408.htm

Max-Planck-Gesellschaft. (2019, September 28). Optogenetics. Max-Planck-Gesellschaft. Retrieved September 28, 2019 from https://www.mpg.de/18011/Optogenetics

Max Tegmark. (2017, August 23). Life 3.0 Being Human in the Age of Artificial Intelligence. Doubleday.