Artificial Intelligence and its Impact on Society

By Parker Weil

What is ‘AI’

“The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”

Neural Networks

• A single algorithm
• Created to model a human brain
• Shifted towards solving specific tasks
• Requires structured data
• Found in:
• Simple game AI
• Character and voice recognition
• Interpreting raw photos and videos (Medical imaging, Facial recognition, Robotics)

​

“Our first goal for these neural networks, or models, is to achieve human-level accuracy. Until you get to that level, you always know you can do better.”

-Ivan Gomez, Data Scientist and Consultant At Zencos

​

Machine Learning

• Neural networks are a subfield of Machine learning
• A set of algorithms that parse data and learn patterns
• Used for analyzing huge amounts of data of any type
• Supervised Learning
• Learning an algorithm from a training data set
• Needs human guidance
• Using new input data to find output variables
• Unsupervised learning
• Finding the underlying structure of data to learn more about it
• Only input data, no corresponding output variables
• Found in:
• Targeted marketing
• Healthcare
• Fraud protection
• Astrophysics

Deep Learning

• Breakthrough subset of Machine learning
• Doesn’t require human intervention
• A deep learning model is able to learn through its own method of computing
• Creates and structures its own algorithms
• Found in:
• Human-like AI
• Widely used in most current AI’s

Understanding AI

• 18% said they ‘know a lot’ about AI, 33% know nothing about it
• More than twice as many Chinese consumers said they knew a lot about it compared to consumers in other places
• Facial recognition on CCTV to catch jaywalkers
• As AI becomes more prominent in our society, we’ll learn more about it
• 45% think AI will have a positive impact on society, while only 7% think the impact will be negative�

Bias in AI

• Soap dispensers
• Significant underrepresentation of women and other minorities in STEM
• Only 20% of computer programmers are women
• 16% Black
• 12% Hispanic
• Programming AI requires multiple perspectives to avoid implicit bias

AI as a tool

“But this is the duality of this technology. Certainly, my conviction is that AI is not a weapon; AI is a tool. It is a powerful tool, and this powerful tool could be used for good or bad things. Our mission is to make sure that this is used for the good things, the most benefits are extracted from it, and most risks are understood and mitigated.”

-Irakli Beridze, Head Of The Centre For Artificial Intelligence And Robotics At UNICRI, United Nations

Regulation and Ethics

• Concern for the rate at which AI is being developed
• Who is going to regulate AI? How?
• Premature regulation harms innovation
• Policymakers working with firms, not against
• Many organizations exist to safely guide development
• Trade secrets vs public information
• Black Box medicine
• Acetaminophen
• Result oriented
• Firms are hiring researchers focusing in AI ethics
• Internal AI ethics boards

Takeaways

• Breakthroughs with deep learning greatly expanded the capabilities and possibilities of AI
• Diversity in STEM fields, specifically AI programming, is not just recommended but necessary
• Ethics will play a large role in the regulation and development of AI
• AI has the potential to be the most powerful tool that humanity has ever created, but it needs to be carefully monitored

Sources:

https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html

https://www.sas.com/en_us/insights/analytics/neural-networks.html

https://www.sas.com/en_us/insights/analytics/machine-learning.html

https://www.sas.com/en_us/insights/analytics/deep-learning.html

https://www.mckinsey.com/featured-insights/artificial-intelligence/the-role-of-education-in-ai-and-vice-versa

https://hbr.org/2016/10/what-do-people-not-techies-not-companies-think-about-artificial-intelligence

https://www.independent.co.uk/news/world/asia/china-police-facial-recognition-technology-ai-jaywalkers-fines-text-wechat-weibo-cctv-a8279531.html

https://futurism.com/artificial-intelligence-experts-fear

https://www.analyticsindiamag.com/how-to-create-your-first-artificial-neural-network-in-python/

https://existek.com/blog/ai-programming-and-ai-programming-languages/

https://www.educba.com/machine-learning-vs-neural-network/

https://www.symmetrymagazine.org/article/studying-the-stars-with-machine-learning

https://www.zendesk.com/blog/machine-learning-and-deep-learning/

https://becominghuman.ai/artificial-intilligence-machine-learning-deep-learning-df6dd0af500e

https://ngcproject.org/statistics

https://www.cleanitsupply.com/p-49075/gojo-tfx-touch-free-1200-ml-foam-hand-soap-dispenser-nickel-goj278912.aspx

https://swisscognitive.ch/2018/07/19/can-artificial-intelligence-solve-your-bias-problem/

https://www.nytimes.com/2016/02/26/upshot/dont-blame-recruiting-pipeline-for-lack-of-diversity-in-tech.html

https://www.flickr.com/photos/unicri/25685123594

https://www.americanactionforum.org/insight/primer-how-to-understand-and-approach-ai-regulation/

https://www.lawyer-monthly.com/2018/07/ai-vs-regulation-friends-or-foes/

​