Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable drone pose uncertainty in determining the viewpoint. In this paper, we provide a nonlinear optimization method that yields effective and adaptive drone viewpoints for telemanipulation with an articulated manipulator. Our first key idea is to use sparse human-in-the-loop input to toggle between multiple automatically-generated drone viewpoints. Our second key idea is to introduce optimization objectives that maintain a view of the manipulator while considering drone uncertainty and the impact on viewpoint occlusion and environment collisions. We provide an instantiation of our drone viewpoint method within a drone-manipulator remote teleoperation system. Finally, we provide an initial validation of our method in tasks where we complete common household and industrial manipulations.

Recent progress on neural approaches for language processing has triggered a resurgence of interest on building intelligent open-domain chatbots. However, even the state-of-the-art neural chatbots cannot produce satisfying responses for every turn in a dialog. A practical solution is to generate multiple response candidates for the same context, and then perform response ranking/selection to determine which candidate is the best. Previous work in response selection typically trains response rankers using synthetic data that is formed from existing dialogs by using a ground truth response as the single appropriate response and constructing inappropriate responses via random selection or using adversarial methods. In this work, we curated a dataset where responses from multiple response generators produced for the same dialog context are manually annotated as appropriate (positive) and inappropriate (negative). We argue that such training data better matches the actual use case examples, enabling the models to learn to rank responses effectively. With this new dataset, we conduct a systematic evaluation of state-of-the-art methods for response selection, and demonstrate that both strategies of using multiple positive candidates and using manually verified hard negative candidates can bring in significant performance improvement in comparison to using the adversarial training data, e.g., increase of 3% and 13% in Recall@1 score, respectively.

Edge Impulse, the leading platform for enabling ML at the edge, and BrainChip, the leading provider of neuromorphic AI IP technology, announced support for deploying Edge Impulse projects on the BrainChip MetaTF platform. Edge Impulse enables developers to rapidly build enterprise-grade ML algorithms, trained on real sensor data, in a low to no code environment. These trained algorithms can now be quantized, optimized and converted to Spiking Neural Networks (SNN), which are compatible and can be deployed with BrainChip Akida devices. This capability is available for new and existing Edge Impulse projects by using the BrainChip MetaTF model deployment block integrated on the platform. This deployment block enables free-tier developers and enterprise developer users to create and validate neuromorphic models for real-world use-cases and deploy on BrainChip Akida development kits.

… accuracy of data analytics and implement new connected technologies, so as to encourage the development and use of AI and machine learning.

… collection of NFT collectables has been turned into another collection of more artistic NFTs by some seriously powerful machine learning.

For many of us, the words artificial intelligence (AI) and machine learning (ML) inspire images of a dystopian future run by computers.

… recognition, and achievements made by Artificial Intelligence, … The Global Deep Learning Market is Projected to Reach US$60.5 Billion by …

Apple shows its latest AI system GAUDI. It can generate 3D indoor scenes and is the foundation for a new generation of generative AI based on NeRFs. So-called neural rendering brings artificial intelligence to computer graphics: AI researchers at Nvidia, for example, are showing how 3D objects are created from photos, and Google is relying on Neural Radiance Fields (NeRFs) for Immersive View or developing NeRFs for rendering people. So far, NeRFs are mainly used as a kind of neural storage medium for 3D models and 3D scenes, which can then be rendered from different camera perspectives. This is how the frequently shown camera movements through a room or around an object are created.

Ultrafast pulses from X-ray lasers reveal how atoms move at timescales of a femtosecond. However, measuring the properties of the pulses themselves is challenging. While determining a pulse's maximum strength, or'amplitude,' is straightforward, the time at which the pulse reaches the maximum, or'phase,' is often hidden. A new study trains neural networks to analyze the pulse to reveal these hidden sub-components. Physicists also call these sub-components'real' and'imaginary.' Starting from low-resolution measurements, the neural networks reveal finer details with each pulse, and they can analyze pulses millions of times faster than previous methods.

Banks worldwide are expected to spend an additional $31 billion on artificial intelligence (AI) embedded in existing systems by 2025 to reduce fraud, according to a report. Similarly for banking executives worldwide, fraud management is featured strongly as a priority, the IDC report mentioned. "In the process of coming up with digital products and services, new channels, and new payment methods, businesses might be overestimating the adequacy of their current defense mechanisms against fraud," said Michael Araneta, Associate Vice President, IDC Financial Insights. "What worked well before simply would not be enough now in the more digital world of business. There needs to be a constant upgrade of fraud management capabilities," Araneta added.