Artificial Intelligence

Artificial Intelligence

Artificial intelligence (AI) approaches have recently made major impacts in the healthcare field, igniting a heated discussion over whether AI physicians would eventually replace human doctors. Human doctors are unlikely to be replaced by machines anytime soon, but AI may assist physicians make better clinical decisions or even replace human judgment in certain areas of healthcare (e.g., radiology). The increased

availability of healthcare data and the rapid development of big data analysis tools have made recent productive applications of AI in healthcare possible. When driven by appropriate clinical queries, powerful AI systems may find clinically valuable information hidden in enormous volumes of data, which can help clinical decision making. The internet of things (IoT) is a network of many interconnected things

that may communicate with one another across a computer network. We may get information from this global network by connecting sensors to it. Thanks to the computer network, we can obtain this information from anywhere on the planet. The internet of things (IoT) enables physical objects to connect to the internet and create systems using various technologies such as near-field communication

(NFC) and wireless sensor networks (WSN). Advances in remote-sensing, sensor and robotic technology, machine learning, and artificial intelligence (AI) – smart algorithms that learn from patterns in complex data or big data are rapidly transforming agriculture. This presents huge opportunities for sustainable viticulture, but also many challenges. This

provides a state-of-the-art review of the benefits and challenges of AI and big data, highlighting work in this domain being conducted around the world. A way forward, that incorporates the expert knowledge of wine-growers (i.e., human-in-the-loop) to augment the decision-making guidance of big data and automated algorithms, is outlined. Future work needs to explore the coupling of expert systems to AI

models and algorithms to increase both the usefulness of AI, its benefits, and its ease of implementation across the Viti viniculture value-chain. While studying AI in Health Sector we consider,

 

1.      WSN Artificial Intelligence Deep Learning IOT and Health Sector

2.   Artificial Intelligence Big Data Climate Change Decision Support Expert knowledge Viti viniculture Risks

3.       AI Review objective and methodology

4.       AI learning algorithms and model types

 

AI-based systems can simply consist of software (e.g., voice assistants, image analysis programs, search engines, voice and facial recognition systems), but AI can also be embedded in devices hardware (e.g., advanced robots, self-driving cars, drones, or Internet of Things applications).

AI is used daily, for example, to translate from one language to another, generate subtitles in videos, or block unsolicited email (spam). Far from being science fiction, AI is already part of our lives, in the use of a personal assistant to organize our workday, in the movement in a self-driving vehicle or in the songs or restaurants suggested by our phones.

AI is about developing systems capable of solving problems and performing tasks by simulating intellectual processes. The AI ​​can be taught to solve a problem, but it can also study the problem and learn how to solve it itself without human intervention. Different systems can achieve different levels of autonomy and can act independently. In this sense, its operation and its results are unpredictable, since these systems function as “black boxes”

A certain issue cannot be regulated without establishing a solid definition of what is regulated. Therefore, it is essential to establish a generally accepted definition of AI that is common and flexible and does not hinder innovation, considering that AI is becoming more and more sophisticated.

The principles enunciated by UNCITRAL when establishing in their Model Laws on Electronic Commerce or on Electronic Signature the procedures and basic principles, as well as fundamental, to facilitate the use of modern techniques can serve as a starting point of communication, in order to record and communicate information in various types of circumstances, such as: nondiscrimination, neutrality with respect to technical means, and functional equivalence. These principles are widely recognized as fundamental elements of electronic commerce [4]. At the same time, they are reflected in the enunciation of the requirements that electronic communications must meet.

Technologies based on artificial intelligence influence aspects such as health, safety, productivity, or leisure, and in the medium term, they will have a great impact on energy, transport, education, and domestic activities. Regarding education, it is essential to find new models and methodologies that integrate ethical concerns in relation to the impact of artificial intelligence on humanity, especially in everything related to security, freedom, privacy, integrity, and dignity; self-determination and nondiscrimination, and the protection of personal data.

The complexity of AI entails the need to create an ethical and efficient framework, for which the principle of transparency must be based on, which consists of the fact that it must always be possible to justify any decision that has been adopted with the help of artificial intelligence and that can have a significant impact on the life of one or more people. On the other hand, it should always be possible to reduce the calculations of the AI system to a form understandable to humans.

 

Artificial intelligence is used in Blockchain, IIoT, Big Data Analytics, Cloud Manufacturing techniques, robotics etc. Broadway. Depending on the permissions required to be part of a blockchain, three categories can be distinguished:

Public: Where anyone can download the necessary programs on their computer and set up a node and participate in the consensus process, anyone who is a party can send transactions through the Internet, which will be included in the blockchain.

Federated or consortium: In this class, they do not allow anyone to configure a node on their PC and participate in the transaction validation process since access permission is needed, which is usually granted to members of a certain group, for example: the group of financial entities.

Private: In these blockchains, the authorizations to carry out transactions are conceived by private organizations that will determine the conditions under which they will allow the reading of the transactions carried out. These types come up with distinct features like Decentralization, Origin, Security, Register distributed, Consensus, Speed etc.

It is also used with IoT. We have to study the background of IIot before implementing it with AI. That is IoT architecture consists of devices that have sensors and edge computing which has embedded devices, fog computing such as gateway and servers, cloudlets such as base stations, and the last component being cloud computing, which can be any cloud platform. In general,

There are three main layers that are devices, network, and cloud computing. There are maximum five layer: Devices, edge Computing, fog computing, cloudlets, cloud computing. IoT faces some security challenges. As we know there are two types of AI: there are two types of AI: weak AI where machine can act intelligently and strong AI where machine can really think.

First category is knowledge-based in which the main component is the existence of inference engine, and it is known as expert system (ES). The second category is machine learning (ML) where different algorithms are used to allow the machine to learn from the dataset The core element is knowledge engineering in order to build either the dataset for ML or the fact database for ES. The data preparation phase needs to make use of other technology such as data mining and Big data techniques. The ML sub-categories are supervised learning, reinforcement learning, and un-supervised learning. The ES types of systems are rule based, Fuzzy-logic, and frame-based.