How do humans move? The quest to understand human motion has broad applications in numerous fields, ranging from computer animation and motion synthesis to neuroscience, human prosthetics and rehabilitation. Although advances in reinforcement learning (RL) have produced impressive results in capturing human motion using simplified humanoids, controlling physiologically accurate models of the body remains an open challenge. In this work, we present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control. Using a musculoskeletal model of the lower body with 80 muscle actuators and 20 DoF, we demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data, is controllable by natural language through pre-trained text-to-motion generative models, and can be fine-tuned to carry out high-level tasks such as target goal reaching. Importantly, KINESIS generates muscle activity patterns that correlate well with human EMG activity. The physiological plausibility makes KINESIS a promising model for tackling challenging problems in human motor control theory, which we highlight by investigating Bernstein's redundancy problem in the context of locomotion. Code, videos and benchmarks will be available at https://github.com/amathislab/Kinesis.

Are you an engineer who loves to make things that just work better? Do you love to work with cutting edge technologies and think about how can this run faster, be deployed quicker or fail less and deliver killer streaming applications that add business value and stick with customers? DAZN is a tech-first sport streaming platform that reaches millions of users every week. We are challenging a traditional industry and giving power back to the fans. Our new Hyderabad tech hub will be the engine that drives us forward to the future.

Our journey with AWS Glue was a bit of a struggle once we started to dig deeper into the streaming functionality of it, the orchestration of so many layers added a huge overhead that we weren't expecting and whilst most of that is handled within the AWS suite of products, there are just too many benefits to switching our pipelines over to GCP and BigQuery to be ignored. Next steps are to finalise our deployment by using Cloud Composer (Airflow) to orchestrate the creation of each of the tables and provide a monitoring dashboard to help us detect failures and act on them. I will say that AWS got in touch with me after my previous article and I got on a call with the AWS Glue product team, in their words I had "hit pretty much every sharp edge possible" (seems to be a running theme with me -- perhaps I should switch careers to QA engineer?),

Aircall is on a mission to revolutionize the business phone industry! We are an advanced, cloud-based business phone system and call center software -- all wrapped up in one single tool (no hardware, 100% integrated). But behind our product are the people driving it . Ambition, Community, Teamwork and Transparency – these are the values we live by at Aircall. We know that success comes from smart work and deserves to be recognized and rewarded If you love a good challenge, enjoy solving meaningful problems, and want to be a part of one of the fastest growing B2B startups -- then Aircall is the company you are looking for!

Data streaming is the next wave in the analytics and machine learning landscape as it assists organisations in quick decision-making through real-time analytics. With the increased adoption of cloud computing, data streaming in the cloud is on the rise as it provides agility in data pipeline for various applications and caters to different business needs. Understanding the importance of data streaming, organisations are embracing hybrid platforms in a way that they can leverage the advantages of both batch and streaming data analytics. To assist firms in determining the best data streaming tools, Analytics India Magazine has compiled the most feature-rich tools for instant analytics. Through Amazon Kinesis, organisations can build streaming applications using SQL editor, and open-source Java libraries.

A Data Model for Training and Scoring 5 Members Events Event RSVPs Model Member Predicted RSVP Offline Training Real-Time Scoring 6. Component Integration and Serving 6 Kinesis Producer AWS S3 Spark Model Training Spark Structured Streaming Meetup Stream Meetup Member API Meetup Prediction 7. Producing the Kinesis Firehose Stream 7 requests.get() Save the model to disk for scoringmodel.write.overwrite().save(...) 10. Scoring the Model in Real-time 10 Load the trained modelval model PipelineModel.load(...) Stream meetup event data Score the model val events spark.readStream ML Limitations in Structured Streaming 11 •Structured streaming does not support operations needed by ML methods –count, collect, round, aggregate*, etc. • Many models, transformers, and estimators are not supported –K-Means, SVM, CountVectorizer, VectorAssembler, StringIndexer, etc. 12.

Microsoft is hoping to boost the accessibility of machine learning technology with a new predictive analytics cloud service, Microsoft Azure Machine Learning. The service is part of a movement that includes a new breed of business intelligence vendor that aims to help businesses take advantage of the increasing volume of data they collect. The new service is one of several machine learning technologies that have emerged in the past few years. It shows the need to manage data and the predictive analytics technologies that are increasingly required to analyze complex data structures. Microsoft said it built the new service using technologies that it developed for other products like Xbox and Bing.

Today the Zillow Group is a public company with 645 million in revenue that also operates websites for mortgage and real estate professionals -- and completed the acquisition of its nearest competitor, Trulia, last year. From the start, Zillow offered the "Zestimate," its value-forecasting feature for homes in locations across the United States. Currently, Zillow claims to have Zestimates for more than 100 million homes, with 100-plus attributes tracked for each property. The technology powering Zestimates and other features has advanced steadily over the years, with open source and cloud computing playing increasingly important roles. Last week I interviewed Stan Humphries, chief analytics officer at Zillow, along with Jasjeet Thind, senior director of data science and engineering.