Ukraine's president says he met with Danish Prime Minister Mette Frederiksen in the southern Ukrainian region of Mykolaiv and discussed the effect of Russian missile and drone strikes with regional officials. Finland's foreign minister says it is maintaining its plan to join NATO at the same time as Nordic neighbour Sweden despite a potential Turkish block on the latter's bid. NATO Secretary-General Jens Stoltenberg has urged South Korea to increase military support to Ukraine, citing other countries that have changed their policy of not providing weapons to countries in conflict following Russia's invasion. The Kremlin has accused Boris Johnson of lying after the former British prime minister said President Vladimir Putin had threatened the United Kingdom with a missile attack during a phone call in the run-up to the invasion of Ukraine. Iran has summoned Ukraine's charge d'affaires in Tehran over comments by a Ukrainian official on a drone attack on a military factory in the central Iranian province of Isfahan, according to the semiofficial Tasnim news agency.

ChatGPT, the AI-driven chatbot that produces remarkable results from simple queries, has been the sensation of the tech world over the past few months, since launching in November. And unless you've been living in a cave without wifi you are likely to have read a flurry of articles on what impact it may have. Some people believe it marks a technology inflection point; and points to the redefining of many knowledge jobs, beginning with lawyers, journalists, marketers, teachers, lecturers, software coders and possibly even doctors. Others have speculated that it points to a post-Google world, leapfrogging the familiar search paradigm of the past 20 years, or will transform personal business and productivity tools so that emails, spreadsheets, reports and even software may all be generated by AI tools. GPT-3, or Generative Pre-trained Transformer, from San Francisco start-up OpenAI, is a type of artificial intelligence that has the unerring ability to generate remarkably human-like text, from a short query or input text.

Armis is looking for a few of the very best people in their field to join our A-team of big thinkers, doers, movers, and shakers. This unique opportunity truly offers the best of all worlds--start up culture, enterprise level benefits and security, and top pay for the industry. Good, keep reading, it only gets better. Ok, so what exactly does Armis do? Connected assets are growing at an explosive rate, across every industry and every geo.

At ServiceNow, our technology makes the world work for everyone, and our people make it possible. We move fast because the world can't wait, and we innovate in ways no one else can for our customers and communities. By joining ServiceNow, you are part of an ambitious team of change makers who have a restless curiosity and a drive for ingenuity. We know that your best work happens when you live your best life and share your unique talents, so we do everything we can to make that possible. We dream big together, supporting each other to make our individual and collective dreams come true.

We might think that the conception of robots, AI, and automated machines is a modern phenomenon, but, in fact, the idea had already appeared in Western literature nearly 3,000 years ago. Long before Isaac Asimov conceived the Laws of Robotics (1942) and John McCarthy coined the term "Artificial Intelligence" (1995), Ancient Greeks myths were full of stories about intelligent humanoids. The fact that these mythical humanoids meet the criteria of modern definitions on robotics and AI is impressive in itself. But what's even more astonishing is that these old tales can provide us with valuable teachings and insights into our modern discourse on artificial intelligence. Such stories "perpetuated over millennia, are a testament to the persistence of thinking and talking about what it is to be human and what it means to simulate life," historian Adrienne Mayor, writes in her book Gods and Robots: Myths, Machines, and Ancient Dreams of Technology.

In an effort to prevent the spread of destructive wildfires, Colorado lawmakers are considering investing in artificial intelligence technology to monitor video footage and warn firefighters when smoke is detected. The Colorado Senate Committee on Agriculture and Natural Resources unanimously passed a $2 million bill Thursday that would establish one or more remote camera technology pilot programs that "may include the use of artificial intelligence technologies." The bill advanced to the Colorado Senate Appropriations Committee for further consideration. If approved, a pilot program would install 40 camera stations and six mobile stations that could be moved to monitor active fires. The results of the pilot would be used to evaluate whether the technology should be implemented more widely across the state.

We present a data-driven modeling and control framework for physics-based building emulators. Our approach comprises: (a) Offline training of differentiable surrogate models that speed up model evaluations, provide cheap gradients, and have good predictive accuracy for the receding horizon in Model Predictive Control (MPC) and (b) Formulating and solving nonlinear building HVAC MPC problems. We extensively verify the modeling and control performance using multiple surrogate models and optimization frameworks for different available test cases in the Building Optimization Testing Framework (BOPTEST). The framework is compatible with other modeling techniques and customizable with different control formulations. The modularity makes the approach future-proof for test cases currently in development for physics-based building emulators and provides a path toward prototyping predictive controllers in large buildings.

Chatbots have long been capable of answering basic questions and even responding to obscure prompts, but recently their improvements have been far more significant. Modern chatbots like Open AIs ChatGPT3 not only have the ability to answer basic questions but can write code and movie scripts and imitate well-known people. In this paper, we analyze ChatGPTs' responses to various questions from a dataset of queries from the popular Quora forum. We submitted sixty questions to ChatGPT and scored the answers based on three industry-standard metrics for grading machine translation: BLEU, METEOR, and ROUGE. These metrics allow us to compare the machine responses with the most upvoted human answer to the same question to assess ChatGPT's ability to submit a humanistic reply. The results showed that while the responses and translation abilities of ChatGPT are remarkable, they still fall short of what a typical human reaction would be.

AIM AND GENERAL PHILOSOPHY The general profile and overarching goal of our proposed mission is to pioneer potentially highly beneficial, or even vital, and cost-effective techniques for the future human colonization of Mars. Adopting radically new and disruptive solutions untested in the Martian context, our approach is one of high risk and high reward. The real possibility of such a solution failing has prompted us to base our mission architecture around a rover carrying a set of 6 distinct experimental payloads, each capable of operating independently on the others, thus substantially increasing the chances of the mission yielding some valuable findings. At the same time, we sought to exploit available synergies by assembling a combination of payloads that would together form a coherent experimental ecosystem, with each payload providing potential value to the others. Apart from providing such a testbed for evaluation of novel technological solutions, another aim of our proposed mission is to help generate scientific know-how enhancing our understanding of the Red Planet. Mars has been attracting scientific attention predominantly as the most likely planet to provide direct indication of life beyond Earth [1] as well as for its potential habitability [2]. While several robotic missions seeking to find signs of Martian life have already taken place (e.g., Curiosity), substantial areas of the Martian landscape remain unexplored. Chiefly, research indicates that lava tubes on Mars might provide conditions particularly conducive to life, due to stable temperatures and shielding from radiation [3]. Of equal interest is the exploration of conditions that might support life on Mars in the future. Developing reliable strategies for plant growth, for instance, will likely prove crucial for future Martian outposts. By way of example, studies on Earth have shown that certain species of fungi can thrive in extreme environments and even develop resilience to high levels of radiation [4]. Our ability to understand and take advantage of such opportunities might prove indispensable for humanity's future colonization of Mars. To this end, our mission takes aim at the Nili-Fossae region, rich in natural resources (and carbonates in particular), past water repositories and signs of volcanic activity. With our proposed experimental payloads, we intend to explore existing lava -tubes, search for signs of past life and assess their potentially valuable geological features for future base building. We will evaluate biomatter in the form of plants and fungi as possible food and base-building materials respectively.

We propose a novel supervised learning approach for political ideology prediction (PIP) that is capable of predicting out-of-distribution inputs. This problem is motivated by the fact that manual data-labeling is expensive, while self-reported labels are often scarce and exhibit significant selection bias. We propose a novel statistical model that decomposes the document embeddings into a linear superposition of two vectors; a latent neutral \emph{context} vector independent of ideology, and a latent \emph{position} vector aligned with ideology. We train an end-to-end model that has intermediate contextual and positional vectors as outputs. At deployment time, our model predicts labels for input documents by exclusively leveraging the predicted positional vectors. On two benchmark datasets we show that our model is capable of outputting predictions even when trained with as little as 5\% biased data, and is significantly more accurate than the state-of-the-art. Through crowd-sourcing we validate the neutrality of contextual vectors, and show that context filtering results in ideological concentration, allowing for prediction on out-of-distribution examples.