In 2017, soon after Google researchers invented a new kind of neural network called a transformer, a young OpenAI engineer named Alec Radford began experimenting with it. What made the transformer architecture different from that of existing A.I. systems was that it could ingest and make connections among larger volumes of text, and Radford decided to train his model on a database of seven thousand unpublished English-language books--romance, adventure, speculative tales, the full range of human fantasy and invention. Then, instead of asking the network to translate text, as Google's researchers had done, he prompted it to predict the most probable next word in a sentence. The machine responded: one word, then another, and another--each new term inferred from the patterns buried in those seven thousand books. Radford hadn't given it rules of grammar or a copy of Strunk and White.

Phishing has been a prevalent cyber threat that manipulates users into revealing sensitive private information through deceptive tactics, designed to masquerade as trustworthy entities. Over the years, proactively detection of phishing URLs (or websites) has been established as an widely-accepted defense approach. In literature, we often find supervised Machine Learning (ML) models with highly competitive performance for detecting phishing websites based on the extracted features from both phishing and benign (i.e., legitimate) websites. However, it is still unclear if these features or indicators are dependent on a particular dataset or they are generalized for overall phishing detection. In this paper, we delve deeper into this issue by analyzing two publicly available phishing URL datasets, where each dataset has its own set of unique and overlapping features related to URL string and website contents. We want to investigate if overlapping features are similar in nature across datasets and how does the model perform when trained on one dataset and tested on the other. We conduct practical experiments and leverage explainable AI (XAI) methods such as SHAP plots to provide insights into different features' contributions in case of phishing detection to answer our primary question, "Can features for phishing URL detection be trusted across diverse dataset?". Our case study experiment results show that features for phishing URL detection can often be dataset-dependent and thus may not be trusted across different datasets even though they share same set of feature behaviors.

In November 2022, OpenAI released ChatGPT, a major step forward in creative artificial intelligence. ChatGPT is OpenAI's interface to a "large language model," a new breed of AI based on a neural network trained on billions of words of text. ChatGPT generates natural language responses to queries (prompts) on those texts. In bringing working versions of this technology to the public, ChatGPT has unleashed a huge wave of experimentation and commentary. It has inspired moods of awe, amazement, fear, and perplexity.

How might The Terminator have played out if Skynet had decided it probably wasn't responsible enough to hold the keys to the entire US nuclear arsenal? As it turns out, scientists may just have saved us from such a future AI-led apocalypse, by creating neural networks that know when they're untrustworthy. These deep learning neural networks are designed to mimic the human brain by weighing up a multitude of factors in balance with each other, spotting patterns in masses of data that humans don't have the capacity to analyse. While Skynet might still be some way off, AI is already making decisions in fields that affect human lives like autonomous driving and medical diagnosis, and that means it's vital that they're as accurate as possible. To help towards this goal, this newly created neural network system can generate its confidence level as well as its predictions.

How might The Terminator have played out if Skynet had decided it probably wasn't responsible enough to hold the keys to the entire US nuclear arsenal? As it turns out, scientists may just have saved us from such a future AI-led apocalypse, by creating neural networks that know when they're untrustworthy. These deep learning neural networks are designed to mimic the human brain by weighing up a multitude of factors in balance with each other, spotting patterns in masses of data that humans don't have the capacity to analyse. While Skynet might still be some way off, AI is already making decisions in fields that affect human lives like autonomous driving and medical diagnosis, and that means it's vital that they're as accurate as possible. To help towards this goal, this newly created neural network system can generate its confidence level as well as its predictions.

MIT researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output. The advance could enhance safety and efficiency in AI-assisted decision making. A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes. Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making.

AI is receiving a push from the race to find a vaccine, diagnostics and effective treatments for the COVID-19 virus, and the push has also heightened awareness of the need to implement AI that is transparent and free of bias--AI that can be trusted. The World Economic Forum is one organization that has responded. With ethics in mind, the organization's AI and Machine Learning team recently announced its Procurement in a Box toolkit with concrete advice for purchasing, risk assessments, proposal drafting and evaluation. To produce the toolkit, the Forum worked over the past year with many organizations, including the United Kingdom's Office for AI in the Department for Digital, Culture, Media & Sport, with Deloitte, Salesforce and Splunk, as well as 15 other countries and more than 150 members of government, academia, civil society and the private sector. The development process incorporated workshops and interviews with government procurement officials and private sector procurement professionals, according to a recent account in Modern Diplomacy.

Are you ready to seize the opportunities that will arise as we move into this automated era? To enhancing your business potential, automation of business & operational processes is one kay factor to look upon. With minimal initial investment, it provides quick organizational benefits. This happens without creating any type of disruption in the underlying systems. There are multiple of traditional solutions that does this approach.