What if AI could do the same? Bilt, a company that offers local shopping and restaurant deals to renters, recently deployed several million agents with the hopes of doing just that. Bilt uses technology from a startup called Letta that allows agents to learn from previous conversations and share memories with one another. Using a process called "sleeptime compute," the agents decide what information to store in its long-term memory vault and what might be needed for faster recall. "We can make a single update to a [memory] block and have the behavior of hundreds of thousands of agents change," says Andrew Fitz, an AI engineer at Bilt. "This is useful in any scenario where you want fine-grained control over agents' context," he adds, referring to the text prompt fed to the model at inference time.

In an era dominated by data, the management and utilization of domain-specific language have emerged as critical challenges in various application domains, particularly those with industry-specific requirements. Our work is driven by the need to effectively manage and process large volumes of short text documents inherent in specific application domains. By leveraging domain-specific knowledge and expertise, our approach aims to shape factual data within these domains, thereby facilitating enhanced utilization and understanding by end-users. Central to our methodology is the integration of domain-specific language models with graph-oriented databases, facilitating seamless processing, analysis, and utilization of textual data within targeted domains. Our work underscores the transformative potential of the partnership of domain-specific language models and graph-oriented databases. This cooperation aims to assist researchers and engineers in metric usage, mitigation of latency issues, boosting explainability, enhancing debug and improving overall model performance. Moving forward, we envision our work as a guide AI engineers, providing valuable insights for the implementation of domain-specific language models in conjunction with graph-oriented databases, and additionally provide valuable experience in full-life cycle maintenance of this kind of products.

Elon Musk says Tesla is raising compensation for its AI engineers, saying OpenAI is "aggressively recruiting" them. 'CRAZIEST TALENT WAR': Tesla CEO Elon Musk said the electric vehicle giant is giving its artificial intelligence engineers a raise as the automaker tries to fend off poaching efforts by ChatGPT creator OpenAI. COSTLY GAME: More and more sports bettors appear to be turning to artificial intelligence to help counter the notoriously unpredictable tournament, which is often referred to as March Madness. LEISURE TIME: Billionaire investor and New York Mets owner Steve Cohen said in a Wednesday appearance on CNBC's "Squawk Box," that he believes that the majority of workers will eventually have a four-day work week and three-day weekend, which will expand opportunities for individuals to engage in leisurely pursuits. FIGHT AGAINST AI: Comedian George Carlin's estate has agreed to a settlement with the media company it sued earlier this year over the use of artificial intelligence.

Hangar 13 is looking for a AI Engineer who enjoys a collaborative and creative work environment and is looking to work directly with content creators to give them the power to create amazing experiences. Design, develop, implement, and debug AI systems in Unreal Engine for new Mafia title. Hangar 13 is an internal 2K development studio based in four locations: Brno & Prague (Czech Republic), Novato (California), and Brighton (UK), working collaboratively as one team. Each of our locations provides the opportunity to Bravely Create on big-budget, AAA console titles in a diverse and inclusive environment. At Hangar 13, we want people driven by curiosity -- Visionaries who aren't afraid to explore new territory.

For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its predecessors have since long time been used to define and measure quality characteristics. Recently, quality models for AI systems, based on ISO25000, have been introduced. This paper applies one such quality model to a real-life case study: a deep learning platform for monitoring wildflowers. The paper presents three realistic scenarios sketching what it means to respectively use, extend and incrementally improve the deep learning platform for wildflower identification and counting. Next, it is shown how the quality model can be used as a structured dictionary to define quality requirements for data, model and software. Future work remains to extend the quality model with metrics, tools and best practices to aid AI engineering practitioners in implementing trustworthy AI systems.

There is a significant need for individuals with expertise in artificial intelligence. Voice recognition, image processing, management of business processes, and even medical diagnosis all have almost unlimited potential to be improved and simplified by artificial intelligence. If you are technically inclined and have some experience with a programming language, you may want to learn more about the lucrative field of artificial intelligence (AI) and how to become a certified artificial intelligence engineer in 2023. Are you curious about what it takes to become an artificial intelligence engineer? Or maybe you're currently studying to become one?

Report says pattern seen in films such as Ex Machina risks contributing to lack of women in tech. A relentless stream of movies, from Iron Man to Ex Machina, has helped entrench systemic gender inequality in the artificial intelligence industry by portraying AI researchers almost exclusively as men, a study has found. The overwhelming predominance of men as leading AI researchers in movies has shaped public perceptions of the industry, the authors say, and risks contributing to a dramatic lack of women in the tech workforce. Beyond the impact on gender balance, the study raises concerns about the knock-on effects of products that favour male users because they are developed by what the former Microsoft employee Margaret Mitchell called "a sea of dudes". "Given that male engineers have repeatedly been shown to engineer products that are most suitable for and adapted to male users, employing more women is essential for addressing the encoding of bias and pejorative stereotypes into AI technologies," the report's authors write.

I-Chiao Lin is a computer vision specialist who builds AI applications for virtual reality headsets. Previously, she developed computer vision systems for baby monitors that detect a child's sleep quality. She spoke to us about how she prepares for job interviews, the lessons she has learned developing consumer products, and how a Pixar movie inspired her to pursue a career in AI. Tell me about your current job. What are your main responsibilities and what is your day-to-day work like?

Requirements Engineering (RE) is the discipline for identifying, analyzing, as well as ensuring the implementation and delivery of user, technical, and societal requirements. Recently reported issues concerning the acceptance of Artificial Intelligence (AI) solutions after deployment, e.g. in the medical, automotive, or scientific domains, stress the importance of RE for designing and delivering Responsible AI systems. In this paper, we argue that RE should not only be carefully conducted but also tailored for Responsible AI. We outline related challenges for research and practice.

A relentless stream of movies, from Iron Man to Ex Machina, has helped entrench systemic gender inequality in the artificial intelligence industry by portraying AI researchers almost exclusively as men, a study has found. The overwhelming predominance of men as leading AI researchers in movies has shaped public perceptions of the industry, the authors say, and risks contributing to a dramatic lack of women in the tech workforce. Beyond the impact on gender balance, the study raises concerns about the knock-on effects of products that favour male users because they are developed by what the former Microsoft employee Margaret Mitchell called "a sea of dudes". "Given that male engineers have repeatedly been shown to engineer products that are most suitable for and adapted to male users, employing more women is essential for addressing the encoding of bias and pejorative stereotypes into AI technologies," the report's authors write. Researchers at the University of Cambridge reviewed more than 1,400 films released between 1920 and 2020 and whittled them down to the 142 most influential movies featuring artificial intelligence.