64% of radiologists agree with the results of AI algorithms

64% of radiologists agree with the results of AI algorithms

IT NewsFacts and forecastsTechnology

Ekaterina Alexandrova | 09.11.2020

64% of radiologists agree with the results of AI algorithms

Over the past six months, the number of radiologists who are active users of medical services based on computer vision has increased. The number of skeptical doctors to
artificial intelligence technology dropped to 36%. These results were reported by Sergei Morozov, Moscow’s chief specialist in radiation and instrumental diagnostics, director of the Center for Diagnostics and

The results of the work of AI services as part of the Moscow experiment on the introduction of computer vision in radiation diagnostics are evaluated by half a thousand radiologists. Moscow implementation experiment
computer vision in radiation diagnostics has been carried out since February 2020. This is a large-scale scientific study of the applicability and quality of artificial intelligence for solving practical problems
health care. The results of the experiment will be announced next year.

New data showed that 64% of radiologists agree with the results of artificial intelligence algorithms. Other users are still dissatisfied with the quality of medical treatment
images – the results, according to their version, are erroneous. Some doctors believe that the occurrence of false positive and false negative results in the processing and analysis of medical images
artificial intelligence is associated with the quality of IT development, and it is necessary to “retrain” algorithms. Sergey Morozov calls the real reasons for the erroneous diagnosis problems from outside
developers: gaps in the quality management system and methodically incorrect selection of datasets for training algorithms.

“Not all professionals start to quickly support new technologies; every IT product adapts to change through the innovation curve. In addition, there are doctors who express concerns about
that the profession of a radiologist is in danger in the future. This is not true. Obviously, in the future, radiologists will work with a large number of complex studies,
as the volumes of diagnostics and types of research are growing. And the share of artificial intelligence will have routine, monotonous tasks, for example, preventive research, “says Sergey

The problem of a high flow of research and, as a result, a staff shortage can be solved by computer vision technology aimed at reducing the time required to describe medical images. IN
Currently, 33% of radiologists are convinced that algorithms can reduce the time required to prepare reports. However, another 32% of radiologists believe the opposite is true: in their opinion, time is
the description has increased due to AI, since not all the functionality of the services is finalized. Nonetheless, 46% of physicians are confident that AI services will help reduce the absence of clinically significant
discrepancies. In the future, the operator of the experiment, the Diagnostic and Telemedicine Center, is considering creating personalized worklists for doctors depending on their specialty, within the framework of
which radiologists will be able to choose for themselves certain algorithms, but such a model must be tested.

According to a study by the Center for Diagnostics and Telemedicine, the average time for processing medical images by services is 10 minutes. Health standards for planned description
X-ray examinations are allocated 24 hours. “The real time of preparation of the conclusion can take less time due to the remote description of studies, concentration of expertise in
Reference Center and process automation, ”said Anton Vladzimirsky, Deputy Director for Research at the Center for Diagnostics and Telemedicine. This is especially true for situations where
the duration of the preparation of the X-ray report can be up to a week, and the records for examinations are not opened until the presence of a doctor on the spot is ensured.

Another objective of the digital project, as Anton Vladzimirsky concluded, is to reduce the burden on doctors to process a large flow of routine examinations with a low percentage of revealing significant
pathologies. According to him, out of three thousand scanned fluorograms, only one case with signs of tuberculosis can be identified. For this reason, the experiment included such routine and massive
studies like fluorography, mammography, lung cancer screening, which can be automated using computer vision technology. Together with this, in the future, some functions
radiologists can be transferred to other specialists. In particular, an X-ray of the lungs with automated marking of pneumothorax will immediately be able to go to an intensive care physician,
who, in accordance with the professional standard, is entitled to a diagnosis.

Artificial intelligence, medicine

Saber and Google create the first AI technology in travel. IT Market

Saber and Google create the first AI technology in travel

IT NewsMarket NewsIT Market

Ekaterina Alexandrova | 10/26/2020

Saber and Google create the first AI technology in travel

Saber Corporation today announced it is partnering with Google to develop the industry’s first artificial intelligence (AI) technology platform.

Saber Travel AI technology leverages Google Cloud’s artificial intelligence and machine learning technology. It will enable Saber customers to quickly provide the most relevant and
personalized content that meets the needs of travelers and unlocks ample opportunities for revenue growth and business profitability. Saber plans to integrate Saber Travel functionality
AI to its range of products in early 2021

The solution is designed to help airlines, agencies, corporations, hotels and other travel companies take their retail and digital customer engagement strategies to the next level.
They will be able to personalize the service by providing travelers with the right offer at the right time and in the right channels. This will help increase your sales conversion and
customer loyalty. Along with retail, this unified approach will enhance distribution as well as implement Saber Travel AI solutions at airports or mobile applications.

Saber customers are expected to be able to seamlessly integrate their own designs with Saber Travel AI in the future. Saber Travel AI will give them access to advanced cloud-based tools that
allow you to prepare and store datasets, supplement them with your own and third-party databases, quickly conduct tests in an experimental environment, create and deploy your own models
machine learning, evaluate their effectiveness, quickly optimize, implement and scale new solutions.

Saber Travel AI will help accelerate Saber’s digital transformation by developing mission-critical products and solutions tailored to the current and future needs of its customers. Company
emphasizes that its strategic partners, including Google, do not have the right to access or use any internal data of the company and its customers. The company has full control over how
this data is stored in the Google Cloud.

Over time, it is planned to introduce Saber Travel AI into all Saber products for omnichannel retail, distribution and services.

Artificial Intelligence

Sberbank has created an AI-based legal entity verification system

Sberbank has created an AI-based legal entity verification system

IT NewsFacts and forecastsTechnology

Ekaterina Alexandrova | 10/12/2020

Sberbank has created an AI-based legal entity verification system

Sberbank created and patented the first in Russia system for checking legal entities for legal capacity based on artificial intelligence.

With the help of a robotic lawyer developed in the Legal Department of Sberbank, over 2.5 million legal opinions were prepared in 8 months. The robot can significantly speed up business processes and
avoid mistakes when manually processing large amounts of data and checking information about counterparties. The analysis process for one legal entity takes on average 7 minutes.

The robot checks counterparties by recognizing and extracting legally significant information from documents. Information on bankruptcy, liquidation and reorganization of legal entities is being checked,
the correctness of the information contained in the extract from the Unified State Register of Legal Entities, the signatory’s powers to conclude a transaction and many other parameters. The system analyzes information from more than 10 documents.

The result of the legal capacity check is conclusions on the client’s key risk metrics. Legal risk is described in as much detail as possible, taking into account various aspects, including justification and level of risk,
assessing the ability to eliminate and minimize the risk.

The development can be widely in demand on the market in various areas of business, namely, wherever it is required to check the legal capacity of clients and counterparties, Sberbank believes.

Artificial intelligence, banks, law

Sberbank | Sberbank

Kaspersky Lab received a patent in the field of AI

Kaspersky Lab received a patent in the field of AI

IT NewsMarket newsSecurity

Ekaterina Alexandrova | 09/04/2020

Kaspersky Lab received a patent in the field of AI

Kaspersky Lab has patented a machine learning-based technology for monitoring industrial plants and other complex equipment.

Patent No. 2724716, which confirms the uniqueness of the development and authorship of specialists, was issued by Rospatent. The technology formed the basis for Kaspersky Machine Learning for Anomaly Detection (MLAD) –
an anomaly detector designed for early detection and prevention of cyber attacks, equipment failures, process failures and other critical situations in production.

Kaspersky MLAD is capable of analyzing the interconnection of telemetry signals, “remembering” their behavior in normal operation and predicting technological indicators for some time ahead. If a
the difference between the predicted and actual values ​​exceeds a certain threshold, the system informs about a potential deviation: problem equipment, incorrect actions of personnel or
malicious interference with the operation of the facility. This allows you to prevent critical situations, minimize the risks of downtime, sudden breakdowns, and increase the service life of the units.

Kaspersky MLAD is deployed at a production facility as a stand-alone system or in conjunction with Kaspersky Industrial CyberSecurity for Networks.

The solution can be used by cybersecurity professionals, operators and process specialists to identify and investigate anomalies in the process.
reasons for the subsequent adjustment of the production facility. Cybersecurity professionals will be able to receive alerts about dangerous situations at the early stages of their inception and development.
Process control operators will be able to respond to process disruptions before they have a major impact on the bottom line.

When using this technology, the total economic effect can reach hundreds of billions of rubles, says Kaspersky Lab.

information security, cybersecurity, Software, Artificial intelligence, Patent

Kaspersky lab | Kaspersky Lab

?️ Making A Game in 48 Hours with Strangers!

PATREON: https://www.patreon.com/Jabrils

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Table Of Contents
0:00 – Intro
0:23 – What is a 48 Hour Game Jam?

1:05 – Day 1
2:08 – Game Jam Theme
2:49 – My Game Idea
2:25 – Team Game idea
5:14 – Our Team

5:55 – Day 2
6:13 – My feature broke
7:05 – Disaster Hit

7:49 – Day 3
8:21 – Our Completed Game
8:51 – Game Jam Showcase
9:44 – Conclusion
10:05 – End

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Super Haystack Challenge – https://gamejolt.com/games/super-haystack-challenge/217553
Creeper World – https://knucklecracker.com/creeperworld/playcwts.php
Tower Rescue (Our GGJ Game) – https://gamejolt.com/games/towerrescue/318476
Tower Rescue Open Source – turns out max Github file is 100MB this project got to 1GB, it may not get uploaded


#GameDev #IndieGame #Unity3D

How to deploy PyQt, Keras, Tensorflow apps with PyInstaller

This video shows how you can use PyInstaller ( www.pyinstaller.org ) to build standalone python applications in Windows 10 for deployment. I build an app that bundles PyQt5, Tensorflow, Keras, and Numpy. Hopefully it’s helpful to someone!

Link to .whl downloads: http://www.lfd.uci.edu/~gohlke/pythonlibs/

Tensorflow: https://www.tensorflow.org/
Tensorflow github: https://github.com/tensorflow/tensorflow
Keras Website: https://keras.io/
Keras github: https://github.com/fchollet/keras

Learn TensorFlow.js – Deep Learning and Neural Networks with JavaScript

This full course introduces the concept of client-side artificial neural networks. We will learn how to deploy and run models along with full deep learning applications in the browser! To implement this cool capability, we’ll be using TensorFlow.js (TFJS), TensorFlow’s JavaScript library.

By the end of this video tutorial, you will have built and deployed a web application that runs a neural network in the browser to classify images! To get there, we’ll learn about client-server deep learning architectures, converting Keras models to TFJS models, serving models with Node.js, tensor operations, and more!

This course was created by deeplizard. Check out their YouTube channel and website for more tutorials! ?

⭐️Course Sections⭐️
⌨️ 0:00 – Intro to deep learning with client-side neural networks
⌨️ 6:06 – Convert Keras model to Layers API format
⌨️ 11:16 – Serve deep learning models with Node.js and Express
⌨️ 19:22 – Building UI for neural network web app
⌨️ 27:08 – Loading model into a neural network web app
⌨️ 36:55 – Explore tensor operations with VGG16 preprocessing
⌨️ 45:16 – Examining tensors with the debugger
⌨️ 1:00:37 – Broadcasting with tensors
⌨️ 1:11:30 – Running MobileNet in the browser

Keras model H5 files:
VGG16 – https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5
MobileNet – https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf.h5

Broadcasting notebook:
Download access to code files and notebooks are available as a perk for the deeplizard hivemind. Check out the details regarding deeplizard perks and rewards at: http://deeplizard.com/hivemind

Jarvic 8 by Kevin MacLeod
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I used Machine Learning to hack baseball

I always sucked at baseball… until now… ok, I still probably suck. Go to https://NordVPN.com/MarkRober and use code MARKROBER to get 75% off a 3 year plan and an extra month for free.

Go subscribe to Jabril’s channel!!! https://www.youtube.com/channel/UCQALLeQPoZdZC4JNUboVEUg

Simple app (it’s actually just a webpage for now): http://jabrils.com/sp/
Complex app using ML: https://github.com/Jabrils/Uncle-Rober-Baseball-Predictor

CORRECTION: I said in ad portion of the video that my data could be stolen on unprotected WiFi. This is true but in the example I gave with YouTube which uses HTTPS it’s already encrypted. Sorry. Didn’t know that’s how it worked. I will correct that in any future ads with Nord.

0:29 Dansez – Fasion https://www.epidemicsound.com/track/QXxs7iZ3Rn
1:01 Dive – Lvly https://www.epidemicsound.com/track/4JmHD4z5Bj
2:19 Dansez – Fasion https://www.epidemicsound.com/track/QXxs7iZ3Rn
4:16 Kalimba Jam – Blue Wednesday https://soundcloud.com/bluewednesday/
5:23 Take Me Out to the Ballgame -Matt Cherne- https://smarturl.it/einsteinbeats
7:04 Arrow – Andrew Applepie http://andrewapplepie.com/
8:18 Cereal Killa – Blue Wednesday https://soundcloud.com/bluewednesday/
11:16 Salamanca – Sarah, the Illstrumentalist https://www.epidemicsound.com/track/nAhVeSoknF
13:12 Q – Blue Wednesday https://soundcloud.com/bluewednesday/
14:18 Too Happy to be cool – Notebreak https://soundcloud.com/notebreak/dubstep-too-happy-to-be-cool

Summary: I wanted to see if I could make an app that could decode baseball signs. Turns out we could and it was a great opportunity for me to learn more about Machine Learning and neural networks and artificial intelligence from my friend Jabril.

They are soft- https://teespring.com/stores/markrober

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#222 Building an Artificial Intelligence (AI) Platform

Artificial Intelligence is surrounded by marketing hype, making it difficult to assess what’s real and useful. In this episode, we talk with a venture capital investor and two software entrepreneurs to learn what’s involved with creating products that rely on artificial intelligence and machine learning. Join us as we cut through the hype of AI.

For this show, we talk with a VC investor and two company founders developing pproducts that use AI to make work in the enterprise easier and better.

Ed Sim is the Founding Partner of Boldstart Ventures. Sean Chou is the CEO of Catalytic, which makes the Pushbot platform. Keith Brisson is CEO and co-founder of Init.ai, a venture-backed developer platform that enables companies to create conversational apps. Michael Krigsman is an industry analyst and the host of CXOTALK.

See more and read the entire transcript: https://www.cxotalk.com/episode/building-ai-software-makers-perspective
Check out all the CXOTALK episodes: https://cxotalk.com/episodes
Follow us on Twitter: https://twitter.com/cxotalk
Michael Krigsman: Now that’s good, and large companies do the same thing. But seriously, when you say “process-bot,” tell us what you mean by that?

Michael Krigsman: I think a good place to kick off this discussion is all three of you are very actively involved in the consideration of different types of AI and what does it mean. And, Sean, maybe you can begin by helping us understand what do we mean by the term, “AI,” and what does it actually encompass?
Sean Chou: Yeah. For sure. It certainly covers a lot of different things, and I think with all major new technologies, there’s always this retrospective period where people look back a little bit and say, “Hey. This really looks like it should be under this umbrella,” and you get a lot of repackaging of things that once maybe weren’t part of AI, but now, because it’s the hot, buzzy topic, now get rebranded under AI. But, I think generally, when we think about AI, we think about it in three different categories.
There’s really a strong AI, which is to try to create basically machines that are able to think in a general sense, in the same way that you and I are able to think. So, “strong” or “general AI,” there are only a handful of companies that really should be considering that. You need a ton of resources; it’s the Google, Microsoft, Amazon, you know, of the world that are going to sort of win in that type of space.
The second category is really more “weak AI,” or “narrow AI.” And, that’s not as difficult. It’s still extremely hard, but now what you’ve done is you’ve set instead of a general, thinking machine, we’re going to focus on a specific domain or a specific field. And so, you see a lot of that in virtual assistants like Siri or maybe Ingram, or Clara, you know; these are folks who are saying, “We’re going to create AI,” and its personality oftentimes, but it’s only going to solve a very narrow set of problems.
And then the third category, which I believe Init.ai and I both fall into, which is: The users of the technology and the research has come out of all this primary research on AI. So, we are beneficiaries of the research that’s gone into natural language processes, sentiment analysis, machine learning; all the things that kind of power AI, we take them, we repackage it, we make it, at least in catalytic space, we make it available for the average business so that they’re able to use it in their processes. And then we’re using it for our product in a very, very applied setting. So, you know, it’s not machine learning to be able to act as humans, machine learning will figure out how to improve their business processes.
Ed Sim: Yeah, and Sean, I think that’s a great point. Kind of how we look at the world is applied AI. I mean, AI is such a buzzy word these days. Everyone has AI in their business plan, the kind of time that everyone had “.com” back in the day. And the reality of it is, what business problem are you solving? And applied AI is very exciting because you’re not going to out-Google Google in AI learning, or Facebook with that AI, but how do you leverage best what’s out there and then apply to enterprise data? That’s the data that they don’t have and can get, and that’s what I love about what you guys are doing and what other companies are doing as well is working on that private enterprise […], and learning from that.
Michael Krigsman: What are some of the really interesting use cases for this type of applied AI that any of the three

Deploy Keras neural network to Flask web service | Part 2 – Build your first Flask app

Here, we’re going to quickly discuss Flask, see how to get it installed, and make our first simple web service.

We briefly talked last time about how we’d be working with flask over the next several videos, and we referred to what we’d be building as a web service. So let’s elaborate on flask now and get a little more familiar so we can understand its role in our upcoming tasks.

Note, in the video, we show how to export the FLASK_APP variable in a Linux terminal so that Flask knows which application to work with. To do this, we simply ran the following command:
export FLASK_APP=sample_app.py

To do the equivalent in Windows (which isn’t shown in the video), run the following command from the command prompt launched with admin privileges:
C:pathtoapp set FLASK_APP=sample_app.py

If you run into issues with starting Flask, check this out:


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