AI ( Artificial Intelligence ) condition after 10 years
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I think AI after 10 years would be certainly much advance!
The future of AI involves advanced cognitive systems capable of doing what machine learning systems can't. They will intelligently and fluently interact with human experts, providing them with articulate explanations and answers, even at the edge of the network or in robotic devices
The Future: Toward Truly Intelligent Artificial Intelligences
This is a very important AI problem as we still do not know how to integrate all of these components of intelligence. We need cognitive architectures (Forbus, 2012) that integrate these components adequately. Integrated systems are a fundamental first step in someday achieving general AI.
Among future activities, we believe that the most important research areas will be hybrid systems that combine the advantages of systems capable of reasoning on the basis of knowledge and memory use (Graves et al., 2016) with those of AI based on the analysis of massive amounts of data, that is, deep learning (Bengio, 2009). Today, deep-learning systems are significantly limited by what is known as “catastrophic forgetting.” This means that if they have been trained to carry out one task (playing Go, for example) and are then trained to do something different (distinguishing between images of dogs and cats, for example) they completely forget what they learned for the previous task (in this case, playing Go). This limitation is powerful proof that those systems do not learn anything, at least in the human sense of learning. Another important limitation of these systems is that they are “black boxes” with no capacity to explain. It would, therefore, be interesting to research how to endow deep-learning systems with an explicative capacity by adding modules that allow them to explain how they reached the proposed results and conclusion, as the capacity to explain is an essential characteristic of any intelligent system. It is also necessary to develop new learning algorithms that do not require enormous amounts of data to be trained, as well as much more energy-efficient hardware to implement them, as energy consumption could end up being one of the main barriers to AI development. Comparatively, the brain is various orders of magnitude more efficient than the hardware currently necessary to implement the most sophisticated AI algorithms. One possible path to explore is memristor-based neuromorphic computing (Saxena et al., 2018).
Other more classic AI techniques that will continue to be extensively researched are multiagent systems, action planning, experience-based reasoning, artificial vision, multimodal person-machine communication, humanoid robotics, and particularly, new trends in development robotics, which may provide the key to endowing machines with common sense, especially the capacity to learn the relations between their actions and the effects these produce on their surroundings. We will also see significant progress in biomimetic approaches to reproducing animal behavior in machines. This is not simply a matter of reproducing an animal’s behavior, it also involves understanding how the brain that produces that behavior actually works. This involves building and programming electronic circuits that reproduce the cerebral activity responsible for this behavior. Some biologists are interested in efforts to create the most complex possible artificial brain because they consider it a means of better understanding that organ. In that context, engineers are seeking biological information that makes designs more efficient. Molecular biology and recent advances in optogenetics will make it possible to identify which genes and neurons play key roles in different cognitive activities.
As to applications: some of the most important will continue to be those related to the Web, video-games, personal assistants, and autonomous robots (especially autonomous vehicles, social robots, robots for planetary exploration, and so on). Environmental and energy-saving applications will also be important, as well as those designed for economics and sociology. Finally, AI applications for the arts (visual arts, music, dance, narrative) will lead to important changes in the nature of the creative process. Today, computers are no longer simply aids to creation; they have begun to be creative agents themselves. This has led to a new and very promising AI field known as computational creativity which is producing very interesting results (Colton et al., 2009, 2015; López de Mántaras, 2016) in chess, music, the visual arts, and narrative, among other creative activities.
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