Virtue reliabilists argue that an agent can only gain knowledge if she responsibly employs a reliable belief-forming process. This in turn demands that she is either aware that her process is reliable or is sensitive to her process’s reliability in some other way. According to a recent argument in the philosophy of mind, sometimes a cognitive mechanism (i.e. precision estimation) can ensure that a belief-forming process is only employed when it’s reliable. If this is correct, epistemic responsibility can sometimes be (...) explained entirely on the subpersonal level. In this paper, I argue that the mechanism of precision estimation–the alleged new variety of epistemic responsibility–is a more ubiquitous phenomenon than epistemic responsibility. I show that precision estimation operates at levels that are not always concerned with the epistemic domain. Lastly, I broaden this argument to explain how all subpersonal epistemologies are likely to fall prey to the problem of demarcating cognitive agency and the problem of attributing beliefs. (shrink)

We might worry that our seamless reliance on AI systems makes us prone to adopting the strange errors that these systems commit. One proposed solution is to design AI systems so that they are not phenomenally transparent to their users. This stops cognitive extension and the automatic uptake of errors. Although we acknowledge that some aspects of AI extension are concerning, we can address these concerns without discouraging transparent employment altogether. First, we believe that the potential danger should be put (...) into perspective – many unreliable technologies are unlikely to be used transparently precisely because they are unreliable. Second, an agent who transparently employs a resource may also reflect (opaquely) on its reliability. Finally, agents can rely on a process transparently and be yanked out of their transparent use when it turns unreliable. When an agent is responsive to the reliability of their process in this way, they have epistemically integrated it, and the beliefs they form with it are formed responsibly. This prevents the agent from automatically incorporating problematic beliefs. Responsible (and transparent) use of AI resources – and consequently responsible AI extension – is hence possible. We end the paper with several design and policy recommendations that encourage epistemic integration of AI-involving belief-forming processes. (shrink)

Virtue reliabilism provides an account of epistemic integration that explains how a reliable-belief forming process can become a knowledge-conducive ability of one’s cognitive character. The univocal view suggests that this epistemic integration can also explain how an external process can extend one’s cognition into the environment. Andy Clark finds a problem with the univocal view. He claims that cognitive extension is a wholly subpersonal affair, whereas the epistemic integration that virtue reliabilism puts forward requires personal-level agential involvement. To adjust the (...) univocal view, Clark recommends a subpersonal epistemic integration account that also paves way for a wholly subpersonal epistemology. Accordingly, an epistemic agent can take responsibility for her reliable belief-forming process by way of entirely subpersonal mechanisms. The aim of this thesis is to argue against a subpersonal epistemology and the need for it. First, I bring into question the conditions that motivate extended cognition: the so-called ‘glue and trust’ requirements and the functional parity principle. Neither of these conditions demands that extension should be understood in entirely subpersonal terms. On the contrary, the glue and trust conditions suggest that agents should personally and actively engage with their external vehicles to extend cognition. Further, I consider an important disparity between the two kinds of integration. Integration that prompts extension can happen immediately, but the integration that makes a reliable belief-forming process knowledge-conducive is almost always a slow process. In light of this and other similar inconsistencies between the two integration accounts, I conclude that a type of univocal view is difficult to defend. And, since that type of univocal view is the main reason for a subpersonal epistemology, the need for it does not arise. Next, I locate Clark’s main motivation for his subpersonal epistemology in his account of the predictive brain. While the predictive brain provides useful insight into how an external process might extend one’s cognition, I show it runs into problems when it tries to account for epistemic integration. Then, I explore how the concept of epistemic defeaters informs epistemic integration. Agents have to meet a specific no-defeater condition to be sensitive to the reliability of their belief-forming processes and to employ them responsibly. In an entirely subpersonal epistemology, defeasibility theory has no means to explain how an agent is rendered sensitive to the reliability of her process. Subpersonal mechanisms can become sensitive to the new process, but that does not allow the agent to employ said process responsibly. Finally, I use an AI case, based on Amazon Alexa, to argue that all accounts of epistemic integration ought to explicitly describe how one’s subpersonal mechanisms link to one’s whole person, even if the link is weak and indirect. Without this connection between one’s subpersonal mechanisms and one’s person, it can become difficult to ascribe beliefs to the agent. This becomes apparent when Alexa leads to ever-increasing layers of cognitive, mental, and agential extension. (shrink)

Phenomenal transparency was once widely believed to be necessary for cognitive extension. Recently, this claim has come under attack, with a new consensus coalescing around the idea that transparency is neither necessary for internal nor extended cognitive processes. We take these recent critiques as an opportunity to refine the concept of transparency relevant for cognitive extension. In particular, we highlight that transparency concerns an agent’s employment of a resource – and that such employment is compatible with an agent consciously apprehending (...) (or attending to) a resource. This means it is possible for an object to be transparent and opaque to an agent, even at a single moment in time. Once we understand transparency in this way, the detractors’ claims lose their bite, and existing arguments for transparency’s necessity for cognitive extension return to apply with full force. (shrink)

Background: Broccoli is an edible green plant in the cabbage family (family Brassicaceae, genus Brassica) whose large flowering head, stalk and small associated leaves are eaten as a vegetable. A leaf of Broccoli might be affected of Several Diseases descriped in this paper . When symptoms is encountered, it requires some kind of medical care. If appropriate Survival of Broccoli Diseases is not taken quickly, it can lead to Broccoli to die . Objectives: The main goal of this expert system (...) is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers in diagnosing many of the broccoli diseases such as : Damping Off,Club root of crucifers or Finger and toe disease,Alternaria leaf spot,Black rot,Downy mildew, and White rust. (shrink)

Plant diseases are numerous in the world of agriculture. These diseases cause a lot of trouble to most farmers. Among these common diseases, we single out the diseases that affect the Passion fruit, which is affected by about seven diseases, with different symptoms for each disease. Today, technology is facilitating human life in all areas of life, and among these facilities are expert system, a computer program that uses artificial-intelligence methods to solve problems within a specialized domain that ordinarily requires (...) human expertise. The first expert system was developed in 1965 by Edward Feigen Baum and Joshua Lederberg of Stanford University in California, U.S. Dendral, as their expert system was later known, was designed to analyses chemical compounds. Expert systems now have commercial applications in fields as diverse as medical diagnosis, petroleum engineering, and financial investing and other areas, and with reference to expert systems and their importance to humans, an integrated expert system has been created in the agricultural field that diagnoses Passion diseases using CLIPS Expert System language. The system was used to design and implement the proposed expert system. The system facilitates the diagnosis of Passion -related diseases. There is no doubt that this expert system will help farmers and those involved in the agricultural field to diagnose Passion -related diseases. Objectives: is to help farmers diagnose Passion diseases in the correct way and how to treat these diseases. Method: The system contains a program project that diagnoses 7 diseases that affect Passion and the seven diseases are: Brown spot, Septoria spot, Root and crown rot, Fusarium wilt, Anthracnose, Woodiness virus, Scab. Results: The expert system was evaluated by farmers and praised for helping them with it. Conclusion: The expert system for diagnosing Passion diseases is effective and usable. (shrink)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

The cucumber is a creeping vine that roots in the ground and grows up trellises or other supporting frames, wrapping around supports with thin, spiraling tendrils. The plant may also root in a soilless medium, whereby it will sprawl along the ground in lieu of a supporting structure. The vine has large leaves that form a canopy over the fruits. Among these common diseases, we single out the diseases that affect the cucumber, which is affected by about 22 diseases, with (...) different symptoms for each disease. Today, technology is facilitating human life in all areas of life, and among these facilities are expert systems that have become an integral part of human life as they contain several systems and areas, for example: Artificial Intelligence (AI), which refers to systems or devices that simulate Human intelligence to perform tasks that can improve itself based on some human information, and other areas, and with reference to expert systems and their importance to humans, an integrated expert system has been created in the agricultural field that diagnoses cucumber diseases using CLIPS Expert System language Delphi language. The system was used to design and implement the proposed expert system. The system facilitates the diagnosis of cucumber-related diseases. There is no doubt that this expert system will help farmers and those involved in the agricultural field to diagnose cucumber-related diseases. Objectives: is to help farmers diagnose pear diseases in the correct way and how to treat these diseases. Method: The system contains a program that diagnoses 22 diseases that affect cucumber. Results: The expert system was evaluated by farmers and praised for helping them with it. Conclusion: The expert system for diagnosing cucumber diseases is effective and usable. (shrink)