AI can even outperform humans in many tasks such as winning games like Go and poker as well as engaging in creative endeavours in writing novels and music. Despite this, it is still a long way from building artificial human intelligence. Current AIs are only designed to excel in their intended functions and cannot generate knowledge to new tasks and situations. For AI to achieve artificial human intelligence requires us to study and understand the human brain. Neuroscience is the study of the anatomy and physiology of the human brain. It provides us interesting insights into how the brain works to develop better AI systems. Conversely, better AI systems can help drive neuroscience forward and further unlock the secrets of the brain. Neuroscience and AI are closely related. The synergy of the two will benefit each other. Besides the benefits of neuroscience for AI research, neuroscience also has important implications for machine learning. This chapter discusses the implications of neuroscience for general artificial intelligence and the benefits of AI for neuroscience research.
TopIntroduction
AI is a buzz word everywhere. It has penetrated all aspects of our life. Today we have the smart speaker, sweeping robot, payment system based on face recognition technology, intelligent voice assistant, etc. It can even outperform humans in many tasks such as winning games like chess, Go and poker, as well as engaging in creative endeavours such as writing novels and music. There is also an emerging trend of AI-powered automation in industries like driverless cars.
Artificial Intelligence techniques have traditionally been divided into two categories; Symbolic A.I. and Connectionist A.I. Symbolic A.I. is sometimes referred to as GOFAI (Good Old Fashioned A.I.). It is the classical approach of encoding a model of the problem and expecting the system to process the input data according to this model to provide a solution. The systems in Symbolic A.I often involve deductive reasoning, logical inference, and some search algorithm that finds a solution within the constraints of the specified model. Expert systems are examples of such category.
The connectionist branch of artificial intelligence aims to model intelligence by simulating the neural networks in our brains. Connectionist A.I. is originally from typical network topology that most of the algorithms in this class employ. The most popular technique in this category is the Artificial Neural Network (ANN). This consists of multiple layers of nodes, called neurons, that process some input signals, combine them together with some weight coefficients, and feed them to the next layer. This chapter examines only one aspect of AI: connectionism.
AI has grown from data models for problem-solving to artificial neural networks — a computational model based on the structure and functions of human biological neural networks. It is a key technology in Industry 4.0 because of all the advantages it brings to companies. The advance in AI will bring many benefits: cheaper and better goods and services, medical advances, and new scientific discoveries,
Industry 4.0 and AI
Industry 4.0 is defined as “the current trend of automation and data exchange in manufacturing technologies, including cyber-physical systems, the Internet of things, cloud computing and cognitive computing and creating the smart factory”. It is also known as intelligent networking of machines and processes for industry with the help of information and communication technology. From an industry 4.0 point of view, AI technologies can be seen as enablers for systems to perceive their environment, process the data they acquire and solve complex problems, as well as to learn from experience to improve their capability to solve specific tasks. (Peres et al 2020).
Artificial intelligence has revolutionised the management and business models of organisations. Its main applications in the 4.0 industry are:
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Quality, which continuously improves production quality.
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Generative design through AI and automation algorithms.
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OEE optimization through predictive repair and maintenance.
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Robotics through robotic and collaborative machines that support the operators to free them from methodical and/or extremely precise tasks.
Manufacturers are using Industry 4.0 solutions across many industrial applications. Bell is using AI technologies to prepare an intelligent unmanned aircraft to identify landing zones and land autonomously. 3M uses AI in its smart factory, customizing the capabilities of the industrial internet of things (IOT) to reduce downtime to save money and effort. GE Aviation’s Digital Group used AI capabilities to bring multiple sources of data together to build a flexible digital model of any aircraft and analyse its health, efficiency, and complete history. Microsoft uses autonomous smart building technology to work toward the goal of becoming carbon negative by 2030.