Large language models (LLMs) have significantly advanced natural language processing, excelling in areas like text generation, summarization, and question-answering. Despite their capabilities, these models face challenges when fine-tuned on small, domain-specific datasets, often struggling to generalize and deliver accurate results with unfamiliar inputs. To tackle this issue, we introduce RIRO, a novel two-layer architecture designed to improve performance in data-scarce environments. The first layer leverages advanced prompt engineering to reformulate inputs, ensuring better alignment with training data, while the second layer focuses on refining outputs to minimize inconsistencies. Through fine-tuning models like Phi-2, Falcon 7B, and Falcon 1B, with Phi-2 outperforming the others. Additionally, we introduce a benchmark using evaluation metrics such as cosine similarity, Levenshtein distance, BLEU score, ROUGE-1, ROUGE-2, and ROUGE-L. While these advancements improve performance, challenges like computational demands and overfitting persist, limiting the potential of LLMs in data-scarce, high-stakes environments such as healthcare, legal documentation, and software testing.

Artificial intelligence, or AI, is an ever-evolving field of study aimed at replicating or augmenting human intelligence through the use of technology. The field encompasses diverse applications, ranging from natural language processing and image recognition to robotics and autonomous vehicles. AI is poised to reshape every industry, from healthcare and finance to transportation and communications. AI is built upon five main pillars: machine learning, natural language processing, robotics, expert systems, and vision. Machine learning is the process by which an AI system learns to improve its decision-making by analyzing and adapting its model based on data.

Society-changing technology invariably leads to the invention of never-before-seen jobs. Social media managers didn't exist before Facebook, and AI database managers didn't before the first AI either. Talent acquisition teams and prospective candidates now have more complex yet exhilarating duties. Seek up-and-coming talent that embraces and perfects these skills in an emerging market when education hasn't solidified curricula. For a time, students may not have all the qualifications listed in the job announcement.

The art of creating beautiful 3D spaces has always been a challenge, but thanks to the wonders of machine learning technology, we now have a powerful new tool at our disposal. I'm talking about a revolutionary deep neural network that can accurately generate a 3D file from a single line of text. Now, I know what you're thinking. "But Dariusz, we've seen similar tools before, what makes this one so special?" Well, my friends, let me tell you. The new AI tool goes beyond what any other method has achieved before.

In 2022 AI in Manufacturing is valued at USD 2.3 billion and is projected to reach 16.7 billion by 2027 according to a recent report. The result of adopting AI in any shape or form – from automation and predictive analytics, to natural language processing (NLP) and computer vision, can be seen in early adopters such as IBM, Intel, GE, Siemens, and their success and business growth. In this article, we'll take a look at just some of the ways manufacturing companies can benefit from implementing AI in their processes. Furthermore, we'll share the diverse applications of AI that will help you save costs and improve processes regardless of the product specifics. As Harald von Heynitz, Head of Industrial Manufacturing, KPMG has said "Taking advantage of advances in robotics, 3D printing, and AI is critical to driving greater efficiency, lowering costs, and improving safety for many sectors and particularly niche suppliers". The benefits AI brings to Manufacturing are twofold.

It sounds odd, but some folks are much better at Googling than others. They find the link first, whether it's tickets for a show, a niche answer to a trivia question or a pair of shoes you've lusted for since you saw them on TV. They've been raised by search engines, but they aren't intimidated by the vastness of an open-ended search. And in the era of search engine optimization, there's less chance involved once you type in a high volume keyword: there's a company out there tailoring its content to your chosen search, and a million others chasing its tail. When you search "boots for men," for example, you'll probably run into Gear Patrol's boots buying guide.

It is no secret that AI has revolutionized the retail industry. Since its inception, it has transformed almost every sector of this industry. AI is undergoing improvements every day, increasing the chances of growth for every business utilizing AI. A couple of decades ago, AI was a dream that has now materialized. In a matter of years, any retail business that doesn't climb up the AI bandwagon will most likely become just a part of history.

Malicious campaigns involving the use of deepfake technologies are a lot closer than many might assume. Furthermore, mitigation and detection of them are hard. A new study of the use and abuse of deepfakes by cybercriminals shows that all the needed elements for widespread use of the technology are in place and readily available in underground markets and open forums. The study by Trend Micro shows that many deepfake-enabled phishing, business email compromise (BEC), and promotional scams are already happening and are quickly reshaping the threat landscape. "From hypothetical and proof-of-concept threats, [deepfake-enabled attacks] have moved to the stage where non-mature criminals are capable of using such technologies," says Vladimir Kropotov, security researcher with Trend Micro and the main author of a report on the topic that the security vendor released this week.

Andreas Braun is the Director Artificial Intelligence & Data Science at PwC Luxembourg. He is passionate about technologies that have the human factor at the forefront. Biometrics, in particular in its usage of Machine Learning and AI methods is his area of expertise. We caught up with him to find out how his role has developed, and why AI is at the forefront of the most innovative fintech solutions. I am the AI lead at PwC Luxembourg, which gives me the great opportunity to have an impact on all the different sectors that are touched by AI – which pretty much means all of them.

Utilizing technology is not a new practice in the agriculture and food and beverage sectors. We've already seen intelligent robots that can weed and pick apples, biometrics that can offer real-time animal information, and sophisticated equipment that can pump milk from cows with minimal human intervention. However, as technology improves, more clever technologies have appeared to help farmers increase their yield and speed up crop production. AI in the farming industry encourages farmers to increase their output while minimizing negative environmental consequences. The agriculture industry completely and publicly incorporated AI in its operations to change the overall result. Technology is transforming the way we grow our food, resulting in a 20% reduction in agricultural field emissions.