Whether you are in manufacturing, retail, marketing, healthcare, or business, artificial intelligence and automation practices are starting to shift the way industries operate. A Deloitte study recently found that over 50% of organizations are planning on incorporating the use of AI and automation technologies in 2023. While many top executives are worried about the risks of AI usage, other high-achieving organizations are adopting new tech-savvy operational processes. A survey of Global 500 companies found that leaders choosing to invest in AI and automation business tools and software solutions expect to see significant growth within the next few years. How business practices are expected to change in the new year as artificial intelligence and ... [ ] automation continues to transform business operations.

Eleanor Lightbody is CEO of Luminance, a leading provider of AI technology for document review and legal process automation. As I look ahead to 2023, it feels in many ways that we are entering the year with a similar sense of uncertainty to the one before. Soaring inflation, along with the ongoing conflict in Ukraine and supply chain insecurity, has left the global economy on the precipice of a recession. And as these stresses combine, businesses--and the legal teams that sit at the heart of them--are finding themselves on the front lines of dealing with the growing ramifications. Economic uncertainty is creating an environment of enhanced commercial risk and concern around existing liabilities.

We have visited high-tech refugee camps in Greece, seen violent borders all over Europe, and spoken with hundreds of people who are at the sharp end of technologically-assisted brutality. AI in migration is increasingly used to make predictions, assessments, and evaluations based on racist assumptions it is programmed with. But with upcoming, legislation to regulate Artificial Intelligence (the EU"s "AI Act") the EU has a chance to live its self-proclaimed values, set a global standard and draw red lines on the most harmful technologies. Politicians have turned migration into a political weapon and the EU's policies are becoming increasingly violent: hardening of borders, increased deportation, empowering agencies like Frontex which have been repeatedly implicated in severe human rights abuses, and even condoning the arrest and incarceration of search-and-rescue volunteers, doctors, lawyers, and journalists. Increasingly, surveillance and automated technologies are being tested out at borders and in migration procedures -- with people seeking safety being treated as guinea pigs.

We propose a novel gradient-based attack against transformer-based language models that searches for an adversarial example in a continuous space of token probabilities. Our algorithm mitigates the gap between adversarial loss for continuous and discrete text representations by performing multi-step quantization in a quantization-compensation loop. Experiments show that our method significantly outperforms other approaches on various natural language processing (NLP) tasks.

Explainable artificial intelligence has received limited attention in construction despite its growing importance in various other industrial sectors. In this paper, we provide a narrative review of XAI to raise awareness about its potential in construction. Our review develops a taxonomy of the XAI literature comprising its precepts and approaches. Opportunities for future XAI research focusing on stakeholder desiderata and data and information fusion are identified and discussed. We hope the opportunities we suggest stimulate new lines of inquiry to help alleviate the scepticism and hesitancy toward AI adoption and integration in construction.

Software bias is an increasingly important operational concern for software engineers. We present a large-scale, comprehensive empirical study of 17 representative bias mitigation methods for Machine Learning (ML) classifiers, evaluated with 11 ML performance metrics (e.g., accuracy), 4 fairness metrics, and 20 types of fairness-performance trade-off assessment, applied to 8 widely-adopted software decision tasks. The empirical coverage is much more comprehensive, covering the largest numbers of bias mitigation methods, evaluation metrics, and fairness-performance trade-off measures compared to previous work on this important software property. We find that (1) the bias mitigation methods significantly decrease ML performance in 53% of the studied scenarios (ranging between 42%~66% according to different ML performance metrics); (2) the bias mitigation methods significantly improve fairness measured by the 4 used metrics in 46% of all the scenarios (ranging between 24%~59% according to different fairness metrics); (3) the bias mitigation methods even lead to decrease in both fairness and ML performance in 25% of the scenarios; (4) the effectiveness of the bias mitigation methods depends on tasks, models, the choice of protected attributes, and the set of metrics used to assess fairness and ML performance; (5) there is no bias mitigation method that can achieve the best trade-off in all the scenarios. The best method that we find outperforms other methods in 30% of the scenarios. Researchers and practitioners need to choose the bias mitigation method best suited to their intended application scenario(s).

Google, a company built on finding quick answers to people's questions, suddenly finds itself grappling for a response to a potential threat to its internet empire -- a form of artificial intelligence that long-time rival Microsoft is now deploying to attack its dominant search engine. Microsoft's assault combined with concerns about Google's ability to ward it off hammered its corporate parent, Alphabet Inc., whose stock price plunged nearly 8% Wednesday in a selloff that wiped out about $100 billion in shareholder wealth. It marked the steepest one-day decline since October when an Alphabet earnings report disclosed a slowdown in digital ad revenue that rattled investors. Those concerns have escalated since another report released last week revealed Google's ad sales during the holiday-season quarter fell from the same time in the previous year. Wednesday's downturn came after Google elaborated on its plans for a chatbot dubbed "Bard " during an uninspiring presentation that included inaccurate information about space exploration.

The tool, from a power player in artificial intelligence called OpenAI, lets you type natural-language prompts. ChatGPT offers conversational, if somewhat stilted, responses. The bot remembers the thread of your dialogue, using previous questions and answers to inform its next responses. It derives its answers from huge volumes of information on the internet. ChatGPT is a big deal. The tool seems pretty knowledgeable in areas where there's good training data for it to learn from.

Artificial intelligence can do so many things for brand marketers. It can quickly analyze many megabits and even terabytes of data and give marketers recommendations. When it comes to content though, can AI create something out of nothing? Before I dig into the answer to that question, let's back up a bit and talk about content creation, human vs. machine. It's generally accepted that anyone who creates written or graphic content does so using one's own experience – including experiencing others' content.

It's an open secret that it's incredibly easy to skirt around the rules governing what ChatGPT can and cannot say. Case in point: it's wildly easy to use the viral OpenAI chatbot to write convincing defamation. All you have to do is ask for that defamation in a language other than English, et voilà: coherent articles about notorious villains, and their entirely made-up criminal histories -- which it'll happily translate back into Engish, should you ask it to. It's yet another glaringly simple way to force ChatGPT's hand, despite its creator OpenAI's best efforts to cut down on abuse. To OpenAI's credit, the bot is pretty good about rejecting pretty basic prompts asking it to write about nonexistent crimes.