Scientists have uncovered hidden patterns in the Bible that challenge ancient beliefs about its origins. Using artificial intelligence, they discovered'fingerprints' in text throughout the Old Testament, suggesting multiple people wrote the stories. The traditional Jewish and Christian understanding is that Moses wrote the first five books of the Old Testament, including stories about creation, Noah's flood and the Ark of the Covenant. The new study found three distinct writing styles with distinct vocabulary, tone and focus areas, suggesting multiple authors and sources contributed to the books over time. Researchers used AI analyzed for 50 chapters across five books, uncovering inconsistencies in language and content, repeated stories, shifts in tone and internal contradictions.

Large language models, e.g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population. However, such chatbot models were developed as tools to support natural language communication between humans. Problematically, it is very much a ``statistical correlation machine" (correlation instead of causality) and there are indeed ethical concerns associated with the use of AI language models such as ChatGPT, such as Bias, Privacy, and Abuse. This paper highlights specific ethical concerns on ChatGPT and articulates key challenges when ChatGPT is used in various applications. Practical commandments for different stakeholders of ChatGPT are also proposed that can serve as checklist guidelines for those applying ChatGPT in their applications. These commandment examples are expected to motivate the ethical use of ChatGPT.

The field of artificial intelligence has seen explosive growth and exponential success. The last phase of development showcased deep learnings ability to solve a variety of difficult problems across a multitude of domains. Many of these networks met and exceeded human benchmarks by becoming experts in the domains in which they are trained. Though the successes of artificial intelligence have begun to overshadow its failures, there is still much that separates current artificial intelligence tools from becoming the exceptional general learners that humans are. In this paper, we identify the ten commandments upon which human intelligence is systematically and hierarchically built. We believe these commandments work collectively to serve as the essential ingredients that lead to the emergence of higher-order cognition and intelligence. This paper discusses a computational framework that could house these ten commandments, and suggests new architectural modifications that could lead to the development of smarter, more explainable, and generalizable artificial systems inspired by a neuromorphic approach.

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Otherwise, we're well on our way to a dystopian nightmare not unlike Fritz Lang's 1927 film Metropolis, in which workers ceaselessly toil in grotesque conditions to keep the city going. But if we take the attitude that the candidate is vital to the selection process, the best thing we can do is give them a clear, realistic view of a potential job to determine compatibility. A story in our book concerns a young woman who applied to both retail banking and call center jobs, withdrawing her interest in the latter after taking an assessment. The ultimate result is a better hiring decision because candidates who know they are a poor fit often self-select out, saving both parties time and needless hassle. AI usage can devolve into a black box trap whereby even the engineers responsible for its development cannot explain its performance.

Thou shalt not exploit workers in the 4th Industrial Revolution. The Great Resignation has opened people's eyes as to what is possible when workers push back--when they say enough is enough. Since its start, more than 50 million Americans have voluntarily left their jobs. Many cited dehumanizing conditions and lack of respect from their employer as reasons. Want your new hires to feel like more than cogs in the machine?

Many governments are increasingly approaching artificial intelligence with an almost religious zeal. By 2018 at least 22 countries around the world, and also the EU, had launched grand national strategies for making AI part of their business development, while many more had announced ethical frameworks for how it should be allowed to develop. The latest is Ireland, which has just announced its national artificial intelligence strategy, "AI – Here for Good". It aims to become "an international leader in using AI to benefit our economy and society, through a people-centred, ethical approach to its development, adoption and use". This is to be obtained via eight policy commandments, including increasing trust in and understanding of AI by using an "AI ambassador" – a veritable AI high priest – to spread the message around the country.

Many governments are increasingly approaching artificial intelligence with an almost religious zeal. By 2018 at least 22 countries around the world, and also the EU, had launched grand national strategies for making AI part of their business development, while many more had announced ethical frameworks for how it should be allowed to develop. The latest is Ireland, which has just announced its national AI strategy, "AI – Here for Good". It aims to become "an international leader in using AI to benefit our economy and society, through a people-centred, ethical approach to its development, adoption and use". This is to be obtained via eight policy commandments, including increasing trust in and understanding of AI by using an "AI ambassador" - a veritable AI high priest – to spread the message around the country. Another aspect is to promote AI adoption by Irish businesses and the government within a special moral and ethical framework.

A common anecdote in the programming world is painting the bike shed. It speaks of a programmer, or team of programmers, worrying about what colour a bike shed should be rather than asking important questions such as whether or not the shed can actually store bikes. Of course, the bike shed can be subbed out with a computer program which serves some purpose. In the machine learning world, you will hear an endless debate between R or Python, TensorFlow or PyTorch, books or courses, math or code first (both, remember the trinity), Spark or Hadoop, Amazon Web Services or Google Cloud Platform, VSCode or Jupyter, Nvidia or… actually there's no real alternative here. The real question you should be answering is: what allows me to build my ideas in the fastest, most reliable manner?

Artificial Moral Agents (AMA's) is a field in computer science with the purpose of creating autonomous machines that can make moral decisions akin to how humans do. Researchers have proposed theoretical means of creating such machines, while philosophers have made arguments as to how these machines ought to behave, or whether they should even exist. Of the currently theorised AMA's, all research and design has been done with either none or at most one specified normative ethical theory as basis. This is problematic because it narrows down the AMA's functional ability and versatility which in turn causes moral outcomes that a limited number of people agree with (thereby undermining an AMA's ability to be moral in a human sense). As solution we design a three-layer model for general normative ethical theories that can be used to serialise the ethical views of people and businesses for an AMA to use during reasoning. Four specific ethical norms (Kantianism, divine command theory, utilitarianism, and egoism) were modelled and evaluated as proof of concept for normative modelling. Furthermore, all models were serialised to XML/XSD as proof of support for computerisation.