In human society, trust is an essential component of social attitude that helps build and maintain long-term, healthy relationships which creates a strong foundation for cooperation, enabling individuals to work together effectively and achieve shared goals. As many human interactions occur through electronic means such as using mobile apps, the potential arises for AI systems to assist users in understanding the social state of their relationships. In this paper we investigate the ability of Large Language Models (LLMs) to reason about trust between two individuals in an environment which requires fostering trust relationships. We also assess whether LLMs are capable of inducing trust by role-playing one party in a trust based interaction and planning actions which can instil trust.

Generative AI models are the hot new thing in the Big Tech world, and everyone is joining the race. The buzz really only started with OpenAI's ChatGPT chatbot, a generative AI language model that is incredibly good at predicting which words should follow one another when you feed it with prompts. Google has long been working on a similar technology, dubbed LaMDA, and with ChatGPT taking the world by storm, the company saw itself forced to release some version of its AI model to the world. That's how we got Bard, Google's first publicly available chat-based generative language model, with access to many parts of the internet. But is Google really at the same level as ChatGPT already?

Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, you might consider getting a graduate degree in AI. U.S. News & World Report ranks the best AI graduate programs at computer science schools based on surveys sent to academic officials in fall 2021 and early 2022. Here are the top 10 programs that made the list as having the best AI graduate programs in the US.

The process of earning the PhD is very different from the process of earning a bachelor's or a master's degree. It is more like an internship or a job. The first two or so years of any PhD program will be largely coursework, but even at this stage you will be balancing spending time on your courses against spending time on research – either because you are rotating through different labs, because you are performing research for a qualifier, or because your advisor is attaching you to an existing research project to give you some experience and mentorship before you develop your own project. This means that getting a PhD is not actually a way to avoid "getting a job" or to "stay in school" – it is actually a job. This also means that just because you are good at or enjoy coursework does not mean you will necessarily enjoy or excel in a PhD program, and just because you struggled with coursework does not mean you will not flourish in a PhD program.

Further provisions relating to the positions as PhD Research Fellows can be found in the Regulations Concerning Terms and Conditions of Employment for the post of Post-Doctoral Research Fellow, Research Fellow, Research Assistant and Resident. The successful applicant will conduct research in the project Ubiquitous Connectivity via Autonomous Airborne Networks (AirBonnet). This project comprises a set of key developments intended to develop the technology of unmanned aerial vehicles (UAVs) such as zeppelins, balloons, and multicopters that autonomously navigate through the airspace to provide data connectivity in those locations where it is absent or unsatisfactory. While households in developed countries receive skyrocketing data rates through optical fibers and smartphones step into the 5G era, roughly one half of the world's population cannot connect to the internet. Even beyond developing economies, bringing data connectivity to areas where it cannot currently reach would drastically benefit applications such as the internet-of-things, smart agriculture/forestry, wildfire suppression, search-and-rescue missions, paramedical interventions, and emergency response handling to name a few.

Huyn Kim is the CEO and Co-Founder of Superb AI, a company that provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows. What initially attracted you to the field of AI, Data Science and Robotics? As an undergraduate majoring in Biomedical Engineering at Duke, I was passionate about genetics and how we can engineer our DNA to cure diseases or create genetically engineered organisms. I remember one wet-lab experiment distinctly that kept failing for like 6 months straight. The most frustrating part of it was that there was a lot of repetitive manual work, and in hindsight that was probably the root of some many potential errors.

The battery-powered sedan broadcasts a request to merge into the next lane, and other nearby vehicles automatically adjust as it glides over and exits the highway. Inside, the passenger finishes a quick email check, then clicks on a monitor to catch up with the day's news. For fans of automated vehicles (AVs), it's the holy grail of transportation: Cars would pilot themselves in an orderly fashion, making driving safer, cheaper, and faster. Easy transportation would be available to everyone without the environmental impacts or traffic congestion seen today. But those benefits could be offset or canceled out altogether, skeptics say, if the technology inadvertently encourages more driving among people who can't currently drive, and among commuters who might opt to travel all the way into cities rather than "park and ride."

It's 8:00 am on a Tuesday morning. You've awoken, scanned the headlines on your phone, responded to an online post, ordered a holiday sweater for your mom, locked up the house, and are driving to work, listening to some great new music on the radio. You've also used artificial intelligence (AI) more than a dozen times -- to be roused, to call up local weather report, to purchase a gift, to secure your house, to be alerted to an upcoming traffic jam, and even to identify an unfamiliar song. AI is already pervasive in our world, and it's making a huge difference in our everyday lives. But this is not the AI you've seen in sci-fi movies, with nervous scientists clacking on keyboards and attempting to halt machines from destroying the world. Sometimes it's obvious, like when you ask Siri to get you directions to the nearest gas station, or Facebook suggests a friend for you to tag in an image you posted online. Sometimes less so, like when you use your Amazon Echo to make an unusual purchase on your credit card (like that goofy holiday sweater) and don't get a fraud alert from your bank.

Zipfian Academy has graduated more than 50 alumni, placing graduates into data science roles at Facebook, Twitter, Airbnb, Tesla, Uber, Square, Coursera, and many more Silicon Valley companies. Participants in our program come from backgrounds in engineering, data analysis, statistics, and occasionally professional poker. Here, we share an interview with Alex Mentch, a graduate from our Winter 2014 Cohort. Alex hails originally from Idaho, and studied electrical engineering at Washington University in St. Louis. Looking for a career transition into data science, Alex attended our Winter 2014 cohort where he built a search engine for state legislation.