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Challenges of real-time AI/ML computations

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

We are proud to announce that we have built from ground up and released our high-quality (i.e. on par with premium Google models) speech-to-text Models for the following languages:

• English;
• German;
• Spanish;

You can find all of our models in our repository together with examples, quality and performance benchmarks. Also we invested some time into making our models as accessible as possible — you can try our examples as well as PyTorch, ONNX, TensorFlow checkpoints. You can also load our model via TorchHub.

	PyTorch	ONNX	TensorFlow	Quality	Colab
English (en_v1)	✓	✓	✓	link
German (de_v1)	✓	✓	✓	link
Spanish (es_v1)	✓	✓	✓	link

Space exploration was always fascinating, and recent developments have reignited the interest to the heights never seen since the last man stood on the Moon. People argue about Mars exploration and features of spaceships as their grandparents would’ve done if the internet existed fifty years ago. I’m an electronics engineer working in the aerospace industry, so I know a thing or two about the technical background of this stuff — and I see that these things aren’t common knowledge, and people often have significantly skewed ideas about the reasons behind many things and decisions. Namely, I’d love to speak of some misconceptions about radiation hardened integrated circuits and the means of protection from radiation-induced damage.

What do these terms mean? And what is the difference?

English translation of Philip Ranzim's post at his blog page here. (With author's permission)

Once upon a time, my girlfriend started using Instagram. She asked me to sign up for it and subscribe to it. I tried — I couldn't do it because you couldn't register at Instagram from a desktop computer. I then thought — how weird people are. Well, what kind of fool would make a software product that can't be used with a normal computer? They won't succeed, and this Instagram will be a marginal service for a bunch of strange women, who for some reason don't like desktops.

It has only been a few years, the market has put everything in place — and the biggest fool was me. And the geniuses at Instagram predicted how and where everything would develop. Today, most of the user applications are made primarily for mobile platforms. At best, they include a universal web client, which is still optimized for mobile phones. Because users love iPhones, not computers. Business people sat down, counted, and made a decision — let's make more money, let's have mobile First everywhere.

• our requirements for the database, universal solutions, and the CAP theorem
• whether the database + application server in one approach is a silver bullet
• the evolution of the platform and the databases used in it
• the number of Tarantools we use and how we came to this

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1.1 Добавьте репозиторий Maven Huawei в файл на уровне проекта build.gradle