Time Series Model enables customers author and manage metadata and computations and associate it with the telemetry ingested from IoT devices. This helps convey semantics about time series, allowing them to search and understand time series in a way that relates to the physical world.
In this episode of the IoT Show, Olivier discusses the IoT data flow within Azure Time Series Insights as well as demonstrate how TSI Model and Query work and can be used.
On the second episode of Siraj Raval’s educational podcast, he interviews the CEO of the largest cryptocurrency company in the world, Brian Armstrong.
From the video description:
I knew Brian years ago when Coinbase was a relatively humble startup and it’s been mind blowing to see how far they’ve come in such a short period of time, managing the equivalent of billions of dollars in wealth in cryptocurrency. I admire Brian because he wakes up everyday determined to solve the ‘meta-problem’ of financial freedom using cryptocurrency, which he believes can indirectly alleviate poverty, increase entrepreneurship, and help reduce corruption in governments around the world. The President of the USA tweeted about Bitcoin and Facebook released it’s “Libra” cryptocurrency, so it’s an extremely exciting time in the space. I also ask him about his most ambitious ideas regarding the future of payments, and was pleasantly shocked by his answers.
Lex Fridman interviews Chris Urmson, former CTO of the Google Self-Driving Car team, a key engineer and leader behind the Carnegie Mellon autonomous vehicle entries in the DARPA grand challenges and the winner of the DARPA urban challenge. Today he is the CEO of Aurora Innovation, an autonomous vehicle software company he started with Sterling Anderson, who was the former director of Tesla Autopilot, and Drew Bagnell, Uber’s former autonomy and perception lead.
This video picks up on the continuing project that DeepLizard has been working on to build a deep Q-network to master the cart and pole problem. Learn how to manage the environment and process images that will be passed to the deep Q-network as input.
TensorFlow developers interested in Reinforcement Learning (RL) may want to take a look at Huskarl. The framework was recently introduced in a Medium blog post and is meant for easy prototyping with deep-RL algorithms.
According to its creator, software engineer Daniel Salvadori, Huskarl “abstracts away the agent-environment interaction” in a similar way “to how TensorFlow abstracts away the management of computational graphs”. Under the hood it makes use of TensorFlow 2.0, naturally, and the tf.keras API. It is also implemented in a way that facilitates the parallelisation of computation of environment dynamics across CPU cores, to help in scenarios benefitting from multiple sources.
While this is technically a press release, there could be something to DarwinAI if it really can increase neural networks performance more than 1600%. We’ll have to keep an eye on this technology. 😉
“The complexity of deep neural networks makes them a challenge to build, run and use, especially in edge-based scenarios such as autonomous vehicles and mobile devices where power and computational resources are limited,” said Sheldon Fernandez, CEO of DarwinAI. “Our Generative Synthesis platform is a key technology in enabling AI at the edge – a fact bolstered and validated by Intel’s solution brief.”