Artificial Superintelligence or ASI, sometimes referred to as digital superintelligence is the advent of a hypothetical agent that possesses intelligence far surpassing that of the smartest and most gifted human minds. AI is a rapidly growing field of technology with the potential to make huge improvements in human wellbeing. However, the development of machines with intelligence vastly superior to humans will pose special, perhaps even unique risks.
Most surveyed AI researchers expect machines to eventually be able to rival humans in intelligence, though there is little consensus on when or how this will happen.
One only needs to accept three basic assumptions to recognize the inevitability of superintelligent AI:
– Intelligence is a product of information processing in physical systems.
– We will continue to improve our intelligent machines.
– We do not stand on the peak of intelligence or anywhere near it.
Philosopher Nick Bostrom expressed concern about what values a superintelligence should be designed to have.
Any type of AI superintelligence could proceed rapidly to its programmed goals, with little or no distribution of power to others. It may not take its designers into account at all. The logic of its goals may not be reconcilable with human ideals. The AI’s power might lie in making humans its servants rather than vice versa. If it were to succeed in this, it would “rule without competition under a dictatorship of one”. Elon Musk has also warned that the global race toward AI could result in a third world war. To avoid the ‘worst mistake in history’, it is necessary to understand the nature of an AI race, as well as escape the development that could lead to unfriendly Artificial Superintelligence.
To ensure the friendly nature of artificial superintelligence, world leaders should work to ensure that this ASI is beneficial to the entire human race.
Here is a beginner’s introduction to artificial intelligence, machine learning, and deep learning. Learn quick stats on the growth of AI, how to use and distinguish supervised learning from unsupervised learning, what is labeled data, when to use a clustering algorithm, and more.
This episode helps you compare deep learning vs. machine learning. You’ll learn how the two concepts compare and how they fit into the broader category of artificial intelligence. During this demo we will also describes how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting.
In the past few videos in this series, we have delved quite deep into the field of machine learning, discussing both supervised and unsupervised learning. The focus of this video then is to consolidate many of the topics we’ve discussed in the past videos and answer the question posed at the start of this machine learning series, the difference between artificial intelligence and machine learning!
Let machines learn, think and do things for us: that was the original idea behind artificial intelligence. The concept isn’t new, but modern hardware and algorithms have brought its potential to a new level. During the COVID-19 pandemic, we have already witnessed digital minds fighting alongside humans. Meanwhile, artificial intelligence is also seen as one of the main engines for the next industrial revolution. So, what will the future look like with powerful AI? What can it be ultimately capable of? And what are the concerns that surround this promising technology? In the future, where will be the boundary between human and machine?