Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to your applications. Today, we're excited to launch Japanese, Korean, and Brazilian Portuguese language support for Amazon Transcribe streaming. To deliver streaming transcriptions with low latency for these languages, we're also announcing availability of Amazon Transcribe streaming in the Asia Pacific (Seoul), Asia Pacific (Tokyo), and South America (São Paulo) Regions. Amazon Transcribe added support for Italian and German languages earlier in November 2020, and this launch continues to expand the service's streaming footprint. Now you can automatically generate live streaming transcriptions for a diverse set of use cases within your contact centers and media production workflows.

Back when the world seemed bright and ambitious--another century, it might have been--I managed to convince myself, despite a lot of evidence to the contrary, that what I really needed in my life was an assistant. This was December, the month when traditionally I can no longer outrun the clerical tasks that have stalked me since the middle of the year. I had weeks of crinkled receipts to expense: the year-end tax on negligence. I was halfway through the process of contesting the charge on a vaccine shot that my insurance company had refused to cover, and I had to transcribe hours of interviews before I could begin to write--the only use of my time which generates an income. As a moonless night wore on, filled with snacking and monsters, I futzed with the formulas in my sad expense spreadsheets and knew that these were hours of life I'd never get back.

Non-Intrusive Load Monitoring (NILM) is a computational technique to estimate the power loads' appliance-by-appliance from the whole consumption measured by a single meter. In this paper, we propose a conditional density estimation model, based on deep neural networks, that joins a Conditional Variational Autoencoder with a Conditional Invertible Normalizing Flow model to estimate the individual appliance's power demand. The resulting model is called Prior Flow Variational Autoencoder or, for simplicity PFVAE. Thus, instead of having one model per appliance, the resulting model is responsible for estimating the power demand, appliance-by-appliance, at once. We train and evaluate our proposed model in a publicly available dataset composed of power demand measures from a poultry feed factory located in Brazil. The proposed model's quality is evaluated by comparing the obtained normalized disaggregation error (NDE) and signal aggregated error (SAE) with the previous work values on the same dataset. Our proposal achieves highly competitive results, and for six of the eight machines belonging to the dataset, we observe consistent improvements that go from 28% up to 81% in NDE and from 27% up to 86% in SAE.

Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.

The latest Drug Developing Platforms by Artificial Intelligence (AI) market report offers a detailed analysis of growth driving factors, challenges, and opportunities that will govern the industry expansion in the ensuing years. Besides, it delivers a complete assessment of several industry segments to provide a clear picture of the top revenue prospects of this industry vertical. According to industry analysts, the market is projected to accrue notable gains while recording a CAGR of XX% over the forecast period 2020-2025. Considering the impact of Covid-19, except from healthcare industries, the global health crisis has turned out to be a nightmare for majority of businesses. While some have successfully made changes to their business model or pivoted the entire organization's mission, others continue to face an onslaught of challenges.

The latest Artificial Intelligence (AI) in Insurance market report offers a detailed analysis of growth driving factors, challenges, and opportunities that will govern the industry expansion in the ensuing years. Besides, it delivers a complete assessment of several industry segments to provide a clear picture of the top revenue prospects of this industry vertical. According to industry analysts, the market is projected to accrue notable gains while recording a CAGR of XX% over the forecast period 2020-2025. Considering the impact of Covid-19, except from healthcare industries, the global health crisis has turned out to be a nightmare for majority of businesses. While some have successfully made changes to their business model or pivoted the entire organization's mission, others continue to face an onslaught of challenges.

Ocado's warehouse in Erith, 15 miles east of London on the Thames estuary, is staffed by 1,050 "personal shoppers". Outnumbering them are 1,800 robots the size of small washing machines. You see them by climbing to the top level of the vast warehouse – at 564,000 sq ft, it is more than three times the size of St Peter's in Rome – where a sign tells you that photography is strictly prohibited. The online supermarket is paranoid that rivals will glimpse the technology it believes to be revolutionary. From the viewing platform you can watch these metal cubes endlessly whiz around, moving thousands of plastic crates as if they were playing an enormous game of chess. You occasionally sight bottles of bleach or rosé, packets of noodles and dog biscuits, before they are sent down to a lower level. "I find it quite mesmerising, like robotic ballet," says Mel Smith, CEO of Ocado Retail, the UK arm of the business. "The day I decided I wanted this job was when I went to [the warehouse] and thought, this is absolutely the future."

Markov Chain Monte Carlo (MCMC) algorithms are commonly used for their versatility in sampling from complicated probability distributions. However, as the dimension of the distribution gets larger, the computational costs for a satisfactory exploration of the sampling space become challenging. Adaptive MCMC methods employing a choice of proposal distribution can address this issue speeding up the convergence. In this paper we show an alternative way of performing adaptive MCMC, by using the outcome of Bayesian Neural Networks as the initial proposal for the Markov Chain. This combined approach increases the acceptance rate in the Metropolis-Hasting algorithm and accelerate the convergence of the MCMC while reaching the same final accuracy. Finally, we demonstrate the main advantages of this approach by constraining the cosmological parameters directly from Cosmic Microwave Background maps.

Police and border guards must combat racial profiling and ensure that their use of "big data" collected via artificial intelligence does not reinforce biases against minorities, United Nations experts said on Thursday. Companies that sell algorithmic profiling systems to public entities and private companies, often used in screening job applicants, must be regulated to prevent misuse of personal data that perpetuates prejudices, they said. "It's a rapidly developing technological means used by law enforcement to determine, using big data, who is likely to do what. And that's the danger of it," Verene Shepherd, a member of the UN Committee on the Elimination of Racial Discrimination, told Reuters. "We've heard about companies using these algorithmic methods to discriminate on the basis of skin colour," she added, speaking from Jamaica.

As e-commerce volumes soar and labor shortages continue in the wake of the COVID-19 pandemic, more enterprises are seeking automated robotics solutions to meet client demand and increase efficiency. Mech-Mind Robotics' intelligent industrial robots address key challenges facing industry players today with next-gen solutions that are accurate, precise, and cost-effective. Mech-Mind Robotics ("Mech-Mind") is a fast-growing Chinese startup backed by leading venture capital including SEQUOIA Capital China and Intel Capital with its core massive applied technologies in high-performance 3D cameras, motion planning AI algorithms and software. During the trade fair, Mech-Mind presented its core technologies and solutions in four exhibition zones: high-efficiency assorting of common goods, mixed-carton depalletizing with AI 3D vision, parcel loading with AI 3D vision, and high-precision smart camera image and recognition technology. "At Mech-Mind, we harness advanced technologies such as 3D vision and deep learning to power next-generation industrial robots. CeMAT ASIA 2020 is the ideal stage for us to demonstrate our far-reaching solutions to logistics and manufacturing customers seeking to increase efficiency, improve effectiveness, and reduce time and labor costs affordably. Smart robotics are no longer limited to labs. We are very excited to be part of this," said Tianlan Shao, CEO and Founder of Mech-Mind Robotics.