Dots
60 LEADERS ON AI-COVER-FINAL2.png
60L-FINAL-COVER.jpg

'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.

ABOUT 60 LEADERS

'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks from AI and what should be done to protect individuals and humanity. View the leaders. 

DOWNLOAD (PDF 256 pages)

'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.

DOWNLOAD (PDF 286 pages)

How would you explain AI to a 5-year-old?

Alex Wang

Q.

Human intelligence can be defined as the general mental ability that allows us to perform crucial activities like reasoning, problem-solving, and learning. Unlike human intelligence, Artificial Intelligence is built to solve specific problems. With AI techniques, we can perform various cognitive tasks, such as facial recognition, predictions, or even more complex such as music composition, etc. Moreover, AI enables a machine to make its own decisions, for example, a self-driving car is making decisions in real-time, while driving without human intervention.

AI systems are trained on specific data to solve a particular problem. But, whenever there is a need to address another type of problem, we have to obtain new data, specific to the new problem to be solved and train a new AI model. This is known as Narrow AI, according to which we have to follow this approach and train a specific AI model for every single problem we want to solve.
In the future, we want to move to General AI – a technology that can deal with many different problems. When we reach this point, we will be feeding a single AI system with a variety of data. This single system will be able to learn how to deal with multiple different problems. To some extent, General AI will have similar capabilities to what we humans have. When we reach the point where technology exceeds the capabilities of human intelligence, we will be entering the state of Su-per AI – a class of systems able to build new forms of AI. Currently, we don't know when this will be feasible or even if we want this to happen in the future.

What we have today is Narrow AI. A characteristic technology is the Neural Networks - computer algorithms that imitate certain functions of the human brain. Neural networks are made up of interconnected layers of virtual neurons. The neurons are the core units responsible to pass on the information and perform calculations. The connections between the neurons have values associated with them, called weights. A bigger value means that the information that is carried by this connection is more important to the next layer. The values of neurons and the weights of the connections are learned from data through model training - the process during which the Neural Network learns from the input data via an optimization process based on backpropagation that attempts to find optimal values of the parameters that minimize the ‘loss function’. This is how Neural Networks learn from failure.

References:
- Human intelligence and brain networks, Roberto Colom 1, Sherif Karama, Rex E Jung, Richard J Haier
- Chalfen, Mike, ‘The Challenges Of Building AI Apps’. TechCrunch. Retrieved 27 November 2021
- The Cambridge handbook of artificial intelligence. Frankish, Keith., Ramsey, William M., 1960-. Cambridge

To some extent, General AI will have similar capabilities to what we humans have.

Alex Wang has extensive experience as a data science consultant. She works at UTS as a casual academic, and a member of AI4Diversity, which is a non-profit initiative that engages and educates diverse communities about AI.

LI-In-Bug.png

Alex Wang

"With AI, we can perform cognitive tasks, such as facial recognition, or even more complex such as music composition."

Data Science Consultant

MEET THE LEADERS #

ARTIFICIAL INTELLIGENCE

linkedin.jpg

Created by many, offered to all. Help us reach more people!

facebook.jpg
twitter.jpg