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Robots that act collectively: when, and how? – #ICRA2022 Day 4 interview with K. Petersen, M. A. Olivares Mendez, and T. Kaiser ( video digest)

Robohub

Attending ICRA is a great opportunity to see many state-of-the-art (and famous?) robots in a single venue. Indeed, a quick trip to the exhibitors' booths is enough to get introduced to the large and diverse group of commercial robots we have today. Yet, one can easily notice that these amazing state-of-the-art robots do not interact with each other. At least they do not do it without human mediation. Although in the exhibitions one can find two or three robots that appear to be joyfully playing together, the reality is that their operators are creating these inter-robot interactions.


How Can AI Chatbots Help Improve Customer Experience

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Chatbots are increasingly being used by businesses for numerous business applications, especially customer service. The main reason behind this is that they can help improve your customer experiences. One of the main reasons why people love to interact with chatbots for customer support is that they don†t have to wait to talk to a human agent. In today†s age, consumers have become very demanding and they want instant responses. To meet this demand, businesses might have to hire more people to work in different shifts and provide 24 7 support.


Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

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Artificial neural networks (ANNs) for material modeling have received significant interest. We recently reported an adaptation of ANNs based on Boltzmann machine (BM) architectures to an ansatz of the multiconfigurational many-electron wavefunction, designated neural-network quantum state (NQS), for quantum chemistry calculations. Here, this study presents its extended formalism to a quantum algorithm that enables the preparation of the NQS through quantum gates. The descriptors of the ANN model, which are chosen as occupancies of electronic configurations, are quantum-mechanically represented by qubits. Our algorithm may thus bring potential advantages over classical sampling-based computation employed in the previous studies.


Brain Tumor Detection using Machine Learning, Python, and GridDB

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Brain tumors are one of the most challenging diseases for clinical researchers, as it causes severe harm to patients. The brain is a central organ in the human body, and minor damage to this organ could affect the correct functioning of the human body. Brain tumors can lead to irreversible and dysfunctional damage to patients, including memory and vision loss. For these reasons, medical studies have, for a long time, focused on the study of the brain and its diseases, including brain tumors. Computer studies have contributed to medical research by offering machine learning algorithms to classify medical analysis records as brain tumors or normal clinical conditions.


Urban Rhapsody: Large-scale exploration of urban soundscapes

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Noise is one of the primary quality-of-life issues in urban environments. While low-cost sensors can be deployed to monitor ambient noise levels at high temporal resolutions, the amount of data they produce and the complexity of these data pose significant analytical challenges. One way to address these challenges is through machine listening techniques, which are used to extract features in attempts to classify the source of noise and understand temporal patterns of a city's noise situation. However, the overwhelming number of noise sources in the urban environment and the scarcity of labeled data makes it nearly impossible to create classification models with large enough vocabularies that capture the true dynamism of urban soundscapes In this paper, we first identify a set of requirements in the yet unexplored domain of urban soundscape exploration. To satisfy the requirements and tackle the identified challenges, we propose Urban Rhapsody, a framework that combines state-of-the-art audio representation, machine learning, and visual analytics to allow users to interactively create classification models, understand noise patterns of a city, and quickly retrieve and label audio excerpts in order to create a large high-precision annotated database of urban sound recordings.


AI for business users: a glossary

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When you work with IT staff and data scientists, they're going to use acronyms that you might not be familiar with. It's important to know some of the basic terms and acronyms so you can communicate. Business users should make themselves familiar with these common AI terms to communicate well with the data teams. Artificial intelligence is a form of intelligence demonstrated by a computer. A computer can be programmed with logic and business rules that will enable it to "reason" through situations and come up with a conclusion.


How do beginners make AI movies in 30 sec?

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There are several reasons why some filmmakers prefer to create their work outside of the studio structure, including those who are new to the industry or have a small budget. This kind of film is known as "indie film" because it is more controlled by the filmmaker in terms of substance and voice. Indie filmmakers have more leeway to express the tale they want to convey since they have a smaller budget and fewer crew members to work with. We introduces a new model "indie AI film". Your creativity is enough; machine learning will take care of the rest.


Making The Most Of MLOps - AI Summary

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Today, MLOps offers a fairly robust framework for operationalizing AI, says Zuccarelli, who's now innovation data scientist at CVS Health. By way of example, Zuccarelli points to a project he worked on previously to create an app that would predict adverse outcomes, such as hospital readmission or disease progression. That meant creating a mobile app that was reliable, fast, and stable, with a machine learning system on the back end connected via API. As MLOps platforms mature, they accelerate the entire model development process because companies don't have to reinvent the wheel with every project, he says. And this means developing expertise in a wide range of activities, says Meagan Gentry, national practice manager for the AI team at Insight, a Tempe-based technology consulting company.


GitHub - lucidrains/imagen-pytorch: Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch

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It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design. It appears neither CLIP nor prior network is needed after all.


Manage ML Automation Workflow with DagsHub, GitHub Action, and CML

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