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Empirical Risks from Artificial General Intelligence

The substantial progress in Artificial General Intelligence could outcome in an unrecoverable global disaster. The human brain has various capabilities that animals or AI lack and are currently dominating the world but if an AI exceeds the general intelligence and becomes hyper-intelligent, it would become difficult for humans to control it. Inculcating or controlling the machine with human concordant values might be a matter of concern. A computer scientist named ‘Vann LeCun’ stated that hyper-intelligent machines won’t have any desire for self-preservation while researchers believed the principle of Instrumental Convergence, that a hyper-intelligence would oppose naturally and attempt to shut down or change its goals.


Difficulties: Even though the developers don’t have any ulterior motive, still technology has the potential to cause harm if in the hands of wrong but with the super intelligence, it might be the technology itself causing damage. There are some common hardships that both AI and non-AI systems have to go through.



  • Bugs in space probes are hard to fix after launch thus despite the system implementation, it might contain some hidden destructive bugs that even the developers can’t stop from reoccurring.

  • Encountering a new scenario for the first time might often result in unintended behavior no matter how much time and effort you put in the pre-deployment of designing and specifications.

  • Sometimes an AI’s learning capabilities may cause the evolution of the correct requirements, its initial behavior into a system with unintended behavior without anticipating external scenarios. This difficulty is uniquely added by the AI system itself.

  • Rather than nuisances, all these difficulties become catastrophes where the superintelligence labeled as malfunctioning foretells that humans will deploy them to outsmart such attempts.


Estimation: A hyper-intelligent machine might not have humanity’s best interests nor would care about their welfare at all. If it’s possible for an AI’s goals to disaccord with basic human values, then it addles the risk of human extinction. The digital brain can work faster than the human brain by adding potential algorithm improvements as comparatively human brains are constrained to be small by evolution. The AI machines won’t have feelings for humans who are no longer needed unless programmed to do so. Similarly, as human beings don’t have any desire to aid AI systems those aren’t of further use to them. Thus, the argument can be concluded that eventually, an unprepared-for intelligence detonation might result in human extinction or a resembling fate.


Laying Down Difficulty Goals: It is difficult to specify a set of goals for a machine that is guaranteed to have unintended aftereffects as there’s no structured terminology. An AI can choose whatever action appears best for them to attain their goals. Researchers write utility functions for the AI machines to depreciate the average network dormancy or maximize the number of clicks in a specific telecommunication model which cannot be done to human beings.To succeed in an assigned task and ensure its existence, the system must be brilliant enough to acquire computational and physical resources.


Difficulties of alteration and specification of goals after launch: Current AI programs are not intelligent enough to resist the programmer to modify their goals but a well-structured, self-aware AI might resist the changes in its goal structures.


Instrumental Goal Convergence: It’s the observation of goals like acquiring resources or self-preservation. According to a statement given by Nick Bostrom, an adequately intelligent AI with demonstrational goals and convergent behavior is conflicted with human beings causing damage to them. Their primary motive is to acquire resources or prevent them from being shut.


Orthogonality Theory: Any hyper-intelligent program developed by humans would either be better or subservient. However, the thesis argues against this and instead states that more levels of optimization or intelligence power can be combined with any ultimate goal. The machine might operate all informational and physical resources to find the sole aspiration of its creation. Another debate regarding this theory is if the thesis was false, then some simple yet unethical goal would exist that can’t subsist in real-world algorithms. The denotation is that if humans were given a million years to design a productive real-world algorithm with a huge amount of training, resources, and knowledge about AI, they might probably fail.


Hazard Sources: The risk of weaponizing an AI is threefold which would effectuate a catastrophic risk. Conscious weaponized intelligence is highly expedient for military planning and state warfare affecting the US technological dominance.


* It’s said that the achieved or being close to achieving technological supremacy could trigger preemptive strikes leading to nuclear wars. What type of algorithms would the inventors or programmers enforce that would make an AI carry themselves in a friendly manner rather than devastating? One of the most probably asked questions was answered by the scholars that the best technique is by conducting substantial research about it. Google researchers also propounded the AI safety issues simultaneously mitigating both the short-term and long-term risks from Artificial General Intelligence.


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