Artificial Intelligence (AI) spreads quickly as new technologies and services take over modern society. The need to regulate AI design, development, and use is strictly necessary to avoid unethical and potentially dangerous consequences to humans. The European Union (EU) has released a new legal framework, the AI Act, to regulate AI by undertaking a risk-based approach to safeguard humans during interaction. At the same time, researchers offer a new perspective on AI systems, commonly known as Human-Centred AI (HCAI), highlighting the need for a human-centred approach to their design. In this context, Symbiotic AI (a subtype of HCAI) promises to enhance human capabilities through a deeper and continuous collaboration between human intelligence and AI. This article presents the results of a Systematic Literature Review (SLR) that aims to identify principles that characterise the design and development of Symbiotic AI systems while considering humans as the core of the process. Through content analysis, four principles emerged from the review that must be applied to create Human-Centred AI systems that can establish a symbiotic relationship with humans. In addition, current trends and challenges were defined to indicate open questions that may guide future research for the development of SAI systems that comply with the AI Act.

We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology. SAIS parallels the three key stages (i.e., mutualism, commensalism and parasitism) of population updating from the Symbiotic Organisms Search (SOS) algorithm. This parallel approach effectively addresses the challenges of large population size and enhances population diversity in AIS, which traditional AIS and SOS struggle to resolve efficiently. We conducted a series of experiments, which demonstrated that our SAIS achieved comparable performance to the state-of-the-art approach SOS and outperformed other popular AIS approaches and evolutionary algorithms across 26 benchmark problems. Furthermore, we investigated the problem of parameter selection and found that SAIS performs better in handling larger population sizes while requiring fewer generations. Finally, we believe SAIS, as a novel bio-inspired and immune-inspired algorithm, paves the way for innovation in bio-inspired computing with the symbiotic paradigm.

Harvey Castro talks about how AI could be used in cold cases and the symbiotic relationship between AI and a detective. New Jersey police are deploying new technology to try to break an unsolved case in what some experts believe could be the greatest advancement in cold-case investigations since forensic genetic genealogy caught the infamous Golden State Killer in 2018. A police department in the 70-square-mile town of Middle Township, along with the Cape May County Prosecutor's Office, will use artificial intelligence to try to solve the case of Mark Himebaugh, an 11-year-old child who seemingly vanished on Nov. 25, 1991. In the 30-plus years since Himebaugh went missing, law enforcement's strongest leads are a composite sketch of a person of interest and a theory that a convicted child sex predator, who's currently in prison, is involved. But neither are strong enough to bring charges or even advance the case.

Recently, end-to-end trained models for multiple-choice commonsense question answering (QA) have delivered promising results. However, such question-answering systems cannot be directly applied in real-world scenarios where answer candidates are not provided. Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices. On the OpenCSR challenge set, many questions require implicit multi-hop reasoning and have a large decision space, reflecting the difficult nature of this task. Existing work on OpenCSR sorely focuses on improving the retrieval process, which extracts relevant factual sentences from a textual knowledge base, leaving the important and non-trivial reasoning task outside the scope. In this work, we extend the scope to include a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. The subgraph can be seen as a concise and compact graphical explanation of the prediction. Experiments on two OpenCSR datasets show that the proposed model achieves great performance on benchmark OpenCSR datasets.

We are on the brink of a massive technological revolution as we slowly move from the water and steam-powered first industrial revolution to the artificial intelligence-powered fourth industrial revolution. The theories backing data science and machine learning have existed for hundreds of years. There used to be times when proto-computers would take almost forever to compute a billion calculations. No one dared think of artificial intelligence or related technology. All thanks to machine learning and data science, we can now calculate data at a capacity of 5 billion calculations per second.

If you follow me, you've seen I'm fascinated with GPT-3 both as a tool for productivity and as a tool for information retrieval through natural questions. You've also seen that GPT-3 often provides correct answers to a question, but sometimes it does not and it can even be misleading or confusing because its answer appears confident despite being wrong. In some cases, but not always, when it cannot find a reasonable completion (i.e. it "doesn't know" the answer) it tells you so, or it just doesn't provide any answer. I showed you that factual accuracy can be improved by fine-tuning the model, or more easily, by few-shot learning. But it isn't easy to decide what information to use in these procedures, let alone how to apply it.

Artificial intelligence rests on the premise of minimising human interference to the extent possible. How close are machined to human-like thinking? The day is not far when this question would sound meaningless. Artificial intelligence and neuroscience are augmenting each other like Siamese twins drawing life-blood from each other. Artificial Neural Networks (ANNs), the virtual twins of neurons are capable of simulating human thought processes and it’s only a matter of time before they attain thinking capabilities. In short, artificial intelligence and Neuroscience share a mutually benefici...

How close are machined to human-like thinking? The day is not far when this question would sound meaningless. Artificial intelligence and neuroscience are augmenting each other like Siamese twins drawing life-blood from each other. Artificial Neural Networks (ANNs), the virtual twins of neurons are capable of simulating human thought processes and it's only a matter of time before they attain thinking capabilities. Though the medical field has made tremendous advances in diagnoses and treatment of mental diseases, neurology has remained a difficult area for medical researchers.

Although it may sound blunt, the truth is that several organizations have no idea about how to implement AI correctly. Unlike what many business executives may believe, AI is not just about the automation of business processes. Enterprise AI implementation must be made with long-term data-driven strategies in mind. To ingrain the technology within the fabric of your business, you'll need to clearly explore how your business and AI can align perfectly to maximize its potential for widening your ROI, revenue generation, growth and diversification. For that purpose, understanding the symbiotic relationship between AI and your enterprise is vital.

" Cognitive science and AI have a symbiotic relationship between them" Living organisms always contributed to the inspirations for so many modern technologies. Of course the first example that come to mind will be cameras, which can be seen as an artificial eye having lenses and light sensitive surfaces. These are well studied in the subject called bio mimicry. Now it's reached the time for more research on mimicking human intelligence, which gave birth to the broad field of artificial intelligence. Discussing the significance of AI lead us to the huge applications like self-driving cars, intelligent e-commerce websites etc. and accomplishments include playing games like chess and go, in which, you should be surprised to know that the games like Go are extremely complex and its possible configurations are beyond the number of particles in this universe.