The so -called “hacking of prizes ında in scientific circles stems from the fact that AI can find unexpected ways to achieve the target that does not correspond to developers’ expectations. For example, if a robot is instructed to put a cube on a certain place on the table, it can move the table itself, not a cube. This is not “crazy ,, but a mistake in the formulation of the task. To avoid such situations, scientists carefully configure the wage system that directs AI’s actions. In addition, there is a safe RL aspect where algorithms are developed not only to maximize the result but also observing solid safety frames. These technologies are used in autonomous vehicles, industrial automation and recommendation systems.
Another difficulty in the work of artificial intelligence is the balance between the examination of new opportunities and the use of already proven solutions. Imagine that you have chosen a restaurant: go to a familiar place with guaranteed delicious foods or get a chance and try new? AI faces a similar dilemma: to use a well -known strategy or to try with new ones that may be both better and worse. Scientists use different approaches to solve this problem. For example, the ε-Survival method allows AI to choose random actions for “trying something new”. Another approach is algorithms that increase the reward at the same time and make AI actions more diverse.
There are also methods that motivate AI to explore unknown areas and encourage new actions.
The transfer of artificial intelligence from simulations to the real world is another serious problem. Simulation is ideal in games such as chess or TH, but the real world is complex and unpredictable. For example, a robot trained in a simulator movement on a flat surface may not be able to cope in a real irregular way. To overcome this problem, scientists use the randomization method. Instead of fixed parameters such as friction or gravity, they determine random variations in simulation. This helps AI to adapt to real conditions. Another way is to learn real data. At first, it works in artificial intelligence simulation and then set on the basis of real tests that are cheaper than training in the real world than scratch.
These approaches help Make AI more reliable and safer. Scientists continue to work to ensure that AI systems are not only effective, but also to remain under the most difficult and unpredictable conditions.
Source: Ferra
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