Skip to main content

Can AI be dangerous

CAN AI BE DANGEROUS?


Most researchers agree that a superintelligent AI is unlikely to exhibit human emotions like love or hate, and that there is no reason to expect AI to become intentionally benevolent or malevolent. Instead, when considering how AI might become a risk, experts think two scenarios most likely:
  1. The AI is programmed to do something devastating: Autonomous weapons are artificial intelligence systems that are programmed to kill. In the hands of the wrong person, these weapons could easily cause mass casualties. Moreover, an AI arms race could inadvertently lead to an AI war that also results in mass casualties. To avoid being thwarted by the enemy, these weapons would be designed to be extremely difficult to simply “turn off,” so humans could plausibly lose control of such a situation. This risk is one that’s present even with narrow AI, but grows as levels of AI intelligence and autonomy increase.
  2. The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal: This can happen whenever we fail to fully align the AI’s goals with ours, which is strikingly difficult. If you ask an obedient intelligent car to take you to the airport as fast as possible, it might get you there chased by helicopters and covered in vomit, doing not what you wanted but literally what you asked for. If a superintelligent system is tasked with a ambitious geoengineering project, it might wreak havoc with our ecosystem as a side effect, and view human attempts to stop it as a threat to be met.
As these examples illustrate, the concern about advanced AI isn’t malevolence but competence. A super-intelligent AI will be extremely good at accomplishing its goals, and if those goals aren’t aligned with ours, we have a problem. You’re probably not an evil ant-hater who steps on ants out of malice, but if you’re in charge of a hydroelectric green energy project and there’s an anthill in the region to be flooded, too bad for the ants. A key goal of AI safety research is to never place humanity in the position of those ants.

Comments

  1. Prayan Learning24 April 2020 at 10:04

    Great blog. Semantic segmentationis a pixel-level labeling which identifies all the pixels in an image and segments it into its component objects for a more meaningful representation.

    ReplyDelete

Post a Comment

Popular posts from this blog

What is Artificial Intelligence,its type and applications

AI is a part of Computer Science.AI is anything that makes machine more intelligent. AI is a technology which can not replace human intelligence but rather it can extend human capabilities.Now a days AI is most wanted and powerful technology.Every industry wants to have its AI system because it can process very large amount of data in less time. How does AI learn ? The intelligence which machine having is only provided by us.So basically we provide to machine  ability to examine and create machine learning models based on inputs and desired output and we do this based on supervised learning,unsupervised learning and reinforcement learning. Types of AI based on Strength Weak or Narrow AI- Narrow AI is that applied on specific domain.It can perform specific task but can not learn new things its own.It can only make decision based on programmed algorithms and trained data.For example AI powered based searches,virtual assistant,self driving cars,language translato...
4 comments
Read more

What is deep learning and how it is improving the machine learning

While Machine Learning is a subset of Artificial Intelligence , Deep Learning is a specialized subset of Machine Learning . Deep Learning layers algorithms to create a Neural Network, an artificial replication of the structure and functionality of the brain, enabling AI systems to continuously learn on the job and improve the quality and accuracy of results. This is what enables these systems to learn from unstructured data such as photos, videos, and audio files. Deep Learning, for example, enables natural language understanding capabilities of AI systems, and allows them to work out the context and intent of what is being conveyed. Deep learning algorithms do not directly map input to output. Instead, they rely on several layers of processing units. Each layer passes its output to the next layer, which processes it and passes it to the next. The many layers is why it’s called deep learning. When creating deep learning algorithms,developers and engineers configure the number...
1 comment
Read more