Many quadrupedal robots can adeptly handle uneven or sloped terrain, but only if the ground beneath them is relatively stable. Factor in slippery or muddy surroundings and four-legged machines may quickly falter or fail completely. But one engineering team believes they found a solution in mimicking animals often found in boggy habitats. According to a study published in Bioinspiration & Biomimetics by researchers at Estonia's Tallinn University of Technology (TalTech), dog bots could soon take their cues from giant moose. "[M]ost robots cannot access a wide range of highly important terrestrial environments, including wetlands, bogs, coastal marshes, river estuaries and fields, which are abundant in nature," explained TalTech biorobotics professor and team lead, Maarja Kruusmaa, in an accompanying statement on January 2nd. Ungulates (split-hooved animals like cattle and moose), however, are evolutionarily equipped to handle these often sticky situations.

In this paper, we present an end-to-end 3D building wireframe reconstruction method to regress edges directly from aerial LiDAR point clouds.Our method, named Parametric Building Wireframe Reconstruction (PBWR), takes aerial LiDAR point clouds and initial edge entities as input, and fully uses self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarityto remove redundant edges. Additionally, a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters, where simple use of edge distance loss isn't suitable. In our experiments, we demonstrate state-of-the-art results on the Building3D dataset, achieving an improvement of approximately 36% in entry-level dataset edge accuracy and around 42% improvement in the Tallinn dataset.

This paper describes Tallinn University of Technology (TalTech) systems developed for the ASRU MADASR 2023 Challenge. The challenge focuses on automatic speech recognition of dialect-rich Indian languages with limited training audio and text data. TalTech participated in two tracks of the challenge: Track 1 that allowed using only the provided training data and Track 3 which allowed using additional audio data. In both tracks, we relied on wav2vec2.0 models. Our methodology diverges from the traditional procedure of finetuning pretrained wav2vec2.0 models in two key points: firstly, through the implementation of the aligned data augmentation technique to enhance the linguistic diversity of the training data, and secondly, via the application of deep prefix tuning for dialect adaptation of wav2vec2.0 models. In both tracks, our approach yielded significant improvements over the provided baselines, achieving the lowest word error rates across all participating teams.

Jaan Tallinn is no stranger to disruptive tech: 25 years ago he co-engineered Kazaa, which allowed for the free download of films and music. He also co-engineered Skype, which disrupted traditional voice and video communication. But when he looks at the way Big Tech and governments are pushing the boundaries of artificial intelligence, he worries about our future. Could we be fast approaching the point when machines don't need human input anymore? Host Steve Clemons asks Tallinn, who founded the Centre for the Study of Existential Risk at Cambridge University, about risks and opportunities posed by AI.

To prevent this theoretical but cataclysmic outcome, mission-driven labs like DeepMind, OpenAI and Anthropic are racing to build a good kind of AI programmed not to lie, deceive or kill us. Meanwhile, donors such as Tesla CEO Elon Musk, disgraced FTX founder Sam Bankman-Fried, Skype founder Jaan Tallinn and ethereum co-founder Vitalik Buterin -- as well as institutions like Open Philanthropy, a charitable organization started by billionaire Facebook co-founder Dustin Moskovitz -- have worked to push doomsayers from the tech industry's margins into the mainstream.

Artificial intelligence in some shape or form has been a part of everyday life for years, but the meteoric rise of ChatGPT and the resulting aggressive development pace of conversational and generative AI models is, for the first time ever, putting the underlying technology into the hands of the general public. Even though current large language models are primarily able to guess the best-fitting next word in a sentence based on the corpus of content they were fed, CEOs, researchers and AI experts are now urging the industry to pump the brakes on training and developing models more capable than OpenAI's GPT-4. The company's latest large language model is currently available in a limited capacity for ChatGPT Plus subscribers and will soon be integrated into Microsoft productivity and security products. According to an open letter signed by influential figures like Elon Musk and Stability AI CEO Emad Mostaque, "powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable." The letter was released by the Future of Life Institute, a non-governmental organization founded in 2014 by MIT professor Max Tegmark and Skype co-founder Jaan Tallinn, among others.

On the day that a London Futurists Podcast episode dedicated wholly to OpenAI's GPT-4 system dropped, the Future of Life Institute published an open letter about the underlying technology. Signed by Stuart Russell, Max Tegmark, Elon Musk, Jaan Tallinn, and hundreds of other prominent AI researchers and commentators, the letter called for a pause in the development of the large language models like OpenAI's GPT-4, and Google's Bard. It was surprising to see the name of Sam Altman, OpenAI's CEO, on the list, and indeed it soon disappeared again. At the time of writing, there were no senior signatories from either of the two AGI labs, OpenAI and DeepMind, or from any of the AI-driven tech giants, Google, Meta, or Microsoft, Amazon or Apple. There was also no representation from the Chinese tech giants, Baidu, Alibaba, or Tencent. Whatever you think of the letter's prospects for success, and even the desirability of its objective, it was a powerful demonstration of the excitement and concern being generated in AI circles about GPT-4 and the other large language models.

An open letter signed by hundreds of prominent artificial intelligence experts, tech entrepreneurs, and scientists calls for a pause on the development and testing of AI technologies more powerful than OpenAI's language model GPT-4 so that the risks it may pose can be properly studied. It warns that language models like GPT-4 can already compete with humans at a growing range of tasks and could be used to automate jobs and spread misinformation. The letter also raises the distant prospect of AI systems that could replace humans and remake civilization. "We call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4 (including the currently-being-trained GPT-5)," states the letter, whose signatories include Yoshua Bengio, a professor at the University of Montreal considered a pioneer of modern AI, historian Yuval Noah Harari, Skype cofounder Jaan Tallinn, and Twitter CEO Elon Musk. The letter, which was written by the Future of Life Institute, an organization focused on technological risks to humanity, adds that the pause should be "public and verifiable," and should involve all those working on advanced AI models like GPT-4.

We build world-changing solutions by combining data and product development expertise with a design thinking approach. Always more than just a software company, we have worked on smart machinery and industrial automation, production lines, complex device integrations, banking backbones, and management automatics: in short, advancing the new industrial revolution. Our code makes elevators move and heating systems run. Our software helps to grow useful bacteria and makes business decisions. It can analyze satellite images and is used to provide self-service to millions of people.

The use of Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications such as disruptive trends (e.g., remote working and automation) or the design and assessment of disaggregated management strategies. Still, the broad adoption of large-scale disaggregate models is not materializing due to the inherently high complexity and computational needs. Activity-based models focused on behavioral theory, for example, may involve hundreds of parameters that need to be calibrated to match the detailed socio-economical characteristics of the population for any case study. This paper tackles this issue by proposing a novel Bayesian Optimization approach incorporating a surrogate model in the form of an improved Random Forest, designed to automate the calibration process of the behavioral parameters. The proposed method is tested on a case study for the city of Tallinn, Estonia, where the model to be calibrated consists of 477 behavioral parameters, using the SimMobility MT software. Satisfactory performance is achieved in the major indicators defined for the calibration process: the error for the overall number of trips is equal to 4% and the average error in the OD matrix is 15.92 vehicles per day.