Darktrace💡
It took eight years to get the project from a concept on a whiteboard to those numbers on the Bloomberg screen.
Until then cyber security meant trying to build all-powerful digital walls around firms to keep bad people out. But they'd served on the board of the BBC, where someone was making a few quid selling data about star salaries to the papers. The offender hadn’t hacked into the Corporation; he or she was almost certainly sitting on the inside leaking data back out.
That episode sparked the realisation that drove Darktrace – they would never make organisations secure by surrounding them with ever higher virtual barriers. The challenge wasn’t creating an impregnable defence. It was realising that intruders were almost certain already inside organisations then taking action against them.
With the help of a brilliant coder called Jack Stockdale, the team realised that they had to turn the way they thought about cyber security on its head. They were creating an immune system rather than a defensive barrier – an AI-powered tool to spot and repel damaging forces from within.
You can’t build a great tech firm without great tech talent – and Invoke hoovered up the best technologists we could find long before Darktrace was launched.
Emily Orton, who became Darktrace’s Chief Marketing Officer, Nicole Eagan its chief strategy officer, and that coder Jack Stockdale were all Invoke hires, as was Poppy Gustafsson – who became the chief executive and took the company onto the London Stock Exchange.
The tough message for the UK is that all this work is normally easier in Silicon Valley. Whether you want technologists or teams of salespeople or marketeers, or funders who really understand tech, there are typically more of the people you really need in California.
https://bit.ly/2UmsMRI
What do you think about Darktrace?🤔
It took eight years to get the project from a concept on a whiteboard to those numbers on the Bloomberg screen.
Until then cyber security meant trying to build all-powerful digital walls around firms to keep bad people out. But they'd served on the board of the BBC, where someone was making a few quid selling data about star salaries to the papers. The offender hadn’t hacked into the Corporation; he or she was almost certainly sitting on the inside leaking data back out.
That episode sparked the realisation that drove Darktrace – they would never make organisations secure by surrounding them with ever higher virtual barriers. The challenge wasn’t creating an impregnable defence. It was realising that intruders were almost certain already inside organisations then taking action against them.
With the help of a brilliant coder called Jack Stockdale, the team realised that they had to turn the way they thought about cyber security on its head. They were creating an immune system rather than a defensive barrier – an AI-powered tool to spot and repel damaging forces from within.
You can’t build a great tech firm without great tech talent – and Invoke hoovered up the best technologists we could find long before Darktrace was launched.
Emily Orton, who became Darktrace’s Chief Marketing Officer, Nicole Eagan its chief strategy officer, and that coder Jack Stockdale were all Invoke hires, as was Poppy Gustafsson – who became the chief executive and took the company onto the London Stock Exchange.
The tough message for the UK is that all this work is normally easier in Silicon Valley. Whether you want technologists or teams of salespeople or marketeers, or funders who really understand tech, there are typically more of the people you really need in California.
https://bit.ly/2UmsMRI
What do you think about Darktrace?🤔
Many people don’t trust AI, which is understandable — for them artificial intelligence is a mysterious black box that does something peculiar with their personal data. What can we, engineers, do to help folks understand AI better? For instance, we could look at AI as a design problem, an integral part of real-world solutions that people can really get.
https://bit.ly/3wLtEfF
https://bit.ly/3wLtEfF
VentureBeat
AI has become a design problem
The bulk of the discussion around making AI trustworthy centers on engineering. But it should really be focused on design.
State of Computer Vision — CVPR 2021💡
Another detailed overview of the 2021 CVPR conference featuring recent trends, CV learning examples, new CV learning methods, vision language models, and more. The article ends with several specific use cases of learning on limited data.
https://bit.ly/3itUTXj
Another detailed overview of the 2021 CVPR conference featuring recent trends, CV learning examples, new CV learning methods, vision language models, and more. The article ends with several specific use cases of learning on limited data.
https://bit.ly/3itUTXj
Medium
State of Computer Vision — CVPR 2021
Computer Vision (CV) is an area of AI that focuses on enabling computers to identify and process objects in images and videos in the same…
PyCharm is one of the most popular Python IDEs
There is a few reasons for this, including the fact that it is developed by JetBrains — the developer of the popular IntelliJ IDEA IDE (one of the big three of Java IDEs) and the “smartest JavaScript IDE” WebStorm. The support of web development capabilities (thanks to Django) is yet another credible reason.
There are many other factors that make PyCharm one of the most complete and comprehensive integrated development environments for working with Python.
PyCharm comes with lots of modules, packages, and tools to speed up Python development while minimizing the required effort. On top of that, PyCharmit can be easily customized based on development requirements and personal preferences.
The main reason Pycharm was used to develop the IDE was Python; i.e. the ability of this programming language to make it easier for developers to complete various dev & ops tasks across multiple platforms like Windows, Linux, and macOS. The IDE includes code analysis tools, a debugger, testing tools, and also several version control features.
PyCharm is an extremely popular Python IDE. The Integrated Development Environment features a code editor and a compiler for writing and compiling programs in one or many programming languages
https://bit.ly/3hNOkQa
There is a few reasons for this, including the fact that it is developed by JetBrains — the developer of the popular IntelliJ IDEA IDE (one of the big three of Java IDEs) and the “smartest JavaScript IDE” WebStorm. The support of web development capabilities (thanks to Django) is yet another credible reason.
There are many other factors that make PyCharm one of the most complete and comprehensive integrated development environments for working with Python.
PyCharm comes with lots of modules, packages, and tools to speed up Python development while minimizing the required effort. On top of that, PyCharmit can be easily customized based on development requirements and personal preferences.
The main reason Pycharm was used to develop the IDE was Python; i.e. the ability of this programming language to make it easier for developers to complete various dev & ops tasks across multiple platforms like Windows, Linux, and macOS. The IDE includes code analysis tools, a debugger, testing tools, and also several version control features.
PyCharm is an extremely popular Python IDE. The Integrated Development Environment features a code editor and a compiler for writing and compiling programs in one or many programming languages
https://bit.ly/3hNOkQa
🌐 Join the researchers and programmers channel (advanced level).
✅ The latest paid books and scientific articles
t.me/DataScience_Books
✅ The latest paid books and scientific articles
t.me/DataScience_Books
LOGML Summer School
Here you'll find a collection of videos from LOGML Summer School (London Geometry and Machine Learning). The videos encompass a wide range of topics, from deep 3D generative modeling to tropical support vector machines and graph ML.
Have you seen these, let us know?👇🏻
https://bit.ly/3ezqeXq
Here you'll find a collection of videos from LOGML Summer School (London Geometry and Machine Learning). The videos encompass a wide range of topics, from deep 3D generative modeling to tropical support vector machines and graph ML.
Have you seen these, let us know?👇🏻
https://bit.ly/3ezqeXq
JupyterLite: Jupyter, WebAssembly, and Python
A succinct overview of JupyterLite, a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels. The article explains why it's a nice-to-have solution and how to use it. It offers several use cases to try it out online.
https://bit.ly/2Ttx4Gy
A succinct overview of JupyterLite, a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels. The article explains why it's a nice-to-have solution and how to use it. It offers several use cases to try it out online.
https://bit.ly/2Ttx4Gy
5 types of robots you should know🦾
Mechanical bots come in all shapes and sizes to efficiently carry out the task for which they are designed. All robots vary in design, functionality and degree of autonomy. Generally, there are five types of robots:
1) Pre-Programmed Robots
Pre-programmed robots operate in a controlled environment where they do simple, monotonous tasks. An example of a pre-programmed robot would be a mechanical arm on an automotive assembly line. The arm serves one function — to weld a door on, to insert a certain part into the engine, etc. — and its job is to perform that task longer, faster and more efficiently than a human.
2) Humanoid Robots
Humanoid robots are robots that look like and/or mimic human behavior. These robots usually perform human-like activities (like running, jumping, and carrying objects), and are sometimes designed to look like us, even having human faces and expressions.
3) Autonomous Robots
Autonomous robots operate independently of human operators. These robots are usually designed to carry out tasks in open environments that do not require human supervision. They are quite unique because they use sensors to perceive the world around them, and then employ decision-making structures (usually a computer) to take the optimal next step based on their data and mission. An example of an autonomous robot would be the Roomba vacuum cleaner, which uses sensors to roam freely throughout a home.
4) Teleoperated Robots
Teleoperated robots are semi-autonomous bots that use a wireless network to enable human control from a safe distance. These robots usually work in extreme geographical conditions, weather, circumstances, etc. Examples of teleoperated robots are the human-controlled submarines used to fix underwater pipe leaks during the BP oil spill or drones used to detect landmines on a battlefield.
5) Augmenting Robots
Augmenting robots either enhance current human capabilities or replace the capabilities a human may have lost. The field of robotics for human augmentation is a field where science fiction could become reality very soon, with bots that have the ability to redefine the definition of humanity by making humans faster and stronger. Some examples of current augmenting robots are robotic prosthetic limbs or exoskeletons used to lift hefty weights.
Mechanical bots come in all shapes and sizes to efficiently carry out the task for which they are designed. All robots vary in design, functionality and degree of autonomy. Generally, there are five types of robots:
1) Pre-Programmed Robots
Pre-programmed robots operate in a controlled environment where they do simple, monotonous tasks. An example of a pre-programmed robot would be a mechanical arm on an automotive assembly line. The arm serves one function — to weld a door on, to insert a certain part into the engine, etc. — and its job is to perform that task longer, faster and more efficiently than a human.
2) Humanoid Robots
Humanoid robots are robots that look like and/or mimic human behavior. These robots usually perform human-like activities (like running, jumping, and carrying objects), and are sometimes designed to look like us, even having human faces and expressions.
3) Autonomous Robots
Autonomous robots operate independently of human operators. These robots are usually designed to carry out tasks in open environments that do not require human supervision. They are quite unique because they use sensors to perceive the world around them, and then employ decision-making structures (usually a computer) to take the optimal next step based on their data and mission. An example of an autonomous robot would be the Roomba vacuum cleaner, which uses sensors to roam freely throughout a home.
4) Teleoperated Robots
Teleoperated robots are semi-autonomous bots that use a wireless network to enable human control from a safe distance. These robots usually work in extreme geographical conditions, weather, circumstances, etc. Examples of teleoperated robots are the human-controlled submarines used to fix underwater pipe leaks during the BP oil spill or drones used to detect landmines on a battlefield.
5) Augmenting Robots
Augmenting robots either enhance current human capabilities or replace the capabilities a human may have lost. The field of robotics for human augmentation is a field where science fiction could become reality very soon, with bots that have the ability to redefine the definition of humanity by making humans faster and stronger. Some examples of current augmenting robots are robotic prosthetic limbs or exoskeletons used to lift hefty weights.
Hey there! Are you waiting for our weekly digest? We can’t wait to share! If you haven’t subscribed yet, kindly do it and you won't miss a thing! 👇🏻
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Data Phoenix
Data Phoenix is your best friend in learning and growing in the data world! We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI.
Blender Bot 2.0: An Open Source Chatbot that Builds Long-Term Memory and Searches the Internet
In this article by Facebook AI, you'll learn about BlenderBot 2.0, the first chatbot that can simultaneously build long-term memory, search the internet for timely information, and have sophisticated conversations on nearly any topic.
https://bit.ly/3wXQK30
In this article by Facebook AI, you'll learn about BlenderBot 2.0, the first chatbot that can simultaneously build long-term memory, search the internet for timely information, and have sophisticated conversations on nearly any topic.
https://bit.ly/3wXQK30
Facebook
Blender Bot 2.0: An open source chatbot that builds long-term memory and searches the internet
We’ve built and open-sourced BlenderBot 2.0, the first chatbot that can store and continually access long-term memory, search the internet for timely information, and have sophisticated conversations on nearly any topic. It’s a significant advancement in…
The latest issue of the digest is already waiting for you on our website! New articles, papers, books, and more! Tap on the link and feel free to subscribe 👇🏻
https://bit.ly/36UjEGI
https://bit.ly/36UjEGI
Data Phoenix
Data Phoenix Digest - 22.07.2021
AI in gaming and energy, an overview of the CVPR 2021, CI/CD for ML online serving and models, BlenderBot 2.0, JupyterLite, TensorRT 8, Color2Style, videos from LOGML summer school, an introduction to Active Learning, datasets, jobs, and more...
Reducing the Computational Cost of Deep Reinforcement Learning Research
In this exploratory and research article by Google AI, you'll learn about the team's efforts to to reproduce the findings of the Rainbow paper and uncover new and interesting phenomena. The results of Google's experiments are quite interesting.
https://bit.ly/3rtT0h7
In this exploratory and research article by Google AI, you'll learn about the team's efforts to to reproduce the findings of the Rainbow paper and uncover new and interesting phenomena. The results of Google's experiments are quite interesting.
https://bit.ly/3rtT0h7
Google AI Blog
Reducing the Computational Cost of Deep Reinforcement Learning Research
Posted by Pablo Samuel Castro, Staff Software Engineer, Google Research It is widely accepted that the enormous growth of deep reinforcem...
Hi folks! We know that some of you are looking for job opportunities now. To help you out a bit, we've put together a list of 10 cool positions available this week. Let us know what you think!
1) AWS, Machine Learning Developer
https://bit.ly/3kOKlEY
2) Accenture, Research Scientist (ML: Graph Representation Learning / Explainable AI)
https://accntu.re/3BAch5m
3) Apple, ML Researcher (Speech Recognition), Siri Understanding
https://apple.co/2V7ia9a
4) Spotify, Machine Learning Engineer for Content Platform
https://bit.ly/3hV9T1c
5) Stripe, Data Scientist for Forecasting Platform
https://bit.ly/3zn8TbY
6) Data Science UA, Machine Learning Engineer (Computer Vision)
https://bit.ly/3rswcOM
7) Data Science UA, Machine Learning Optimization Engineer
https://bit.ly/3ixOVEw
8) Apostera, Senior/Middle CV/ML Engineer
https://bit.ly/3BvZ9hr
9) Lohika / Capgemini, Data Science Engineer
https://bit.ly/3BHRLQr
10) AUTODOC, Data Analyst
https://bit.ly/3BzZoIk
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
1) AWS, Machine Learning Developer
https://bit.ly/3kOKlEY
2) Accenture, Research Scientist (ML: Graph Representation Learning / Explainable AI)
https://accntu.re/3BAch5m
3) Apple, ML Researcher (Speech Recognition), Siri Understanding
https://apple.co/2V7ia9a
4) Spotify, Machine Learning Engineer for Content Platform
https://bit.ly/3hV9T1c
5) Stripe, Data Scientist for Forecasting Platform
https://bit.ly/3zn8TbY
6) Data Science UA, Machine Learning Engineer (Computer Vision)
https://bit.ly/3rswcOM
7) Data Science UA, Machine Learning Optimization Engineer
https://bit.ly/3ixOVEw
8) Apostera, Senior/Middle CV/ML Engineer
https://bit.ly/3BvZ9hr
9) Lohika / Capgemini, Data Science Engineer
https://bit.ly/3BHRLQr
10) AUTODOC, Data Analyst
https://bit.ly/3BzZoIk
Looking to feature your open positions in the digest? Kindly reach out to us at editor@dataphoenix.info for details. We'll be proud to help your business thrive!
What to watch this weekend?
Our team prepared movie which is showing how machine learning can lead in movie industry.
A.I. Artificial Intelligence
It’s possible to feed algorithms and get a machine to work but is it possible for us to instill emotions into them? This field of thought has driven many debates and arguments globally (it’s still an oft-debated topic in tech circles). A.I. Artificial Intelligence, also known as A.I., is a 2001 American science fiction drama film directed by Steven Spielberg. A.I. tells the story a robot, a childlike android uniquely programmed with the ability to love. A robot boy programmed to experience human emotions embarks on a journey of self-discovery.
Our team prepared movie which is showing how machine learning can lead in movie industry.
A.I. Artificial Intelligence
It’s possible to feed algorithms and get a machine to work but is it possible for us to instill emotions into them? This field of thought has driven many debates and arguments globally (it’s still an oft-debated topic in tech circles). A.I. Artificial Intelligence, also known as A.I., is a 2001 American science fiction drama film directed by Steven Spielberg. A.I. tells the story a robot, a childlike android uniquely programmed with the ability to love. A robot boy programmed to experience human emotions embarks on a journey of self-discovery.
High Fidelity Image Generation Using Diffusion Models
Join Google AI's team to push the performance of diffusion models to state-of-the-art on super-resolution and class-conditional ImageNet generation benchmarks. Learn the results of their tests and how they managed to limit the diffusion models for generative modeling problems.
https://bit.ly/3BzIV79
Join Google AI's team to push the performance of diffusion models to state-of-the-art on super-resolution and class-conditional ImageNet generation benchmarks. Learn the results of their tests and how they managed to limit the diffusion models for generative modeling problems.
https://bit.ly/3BzIV79
Googleblog
High Fidelity Image Generation Using Diffusion Models
Speeding Up Reinforcement Learning with a New Physics Simulation Engine
In this article, the researchers at Google AI invite all to check the results of their work and learn how to perform a more qualitative measure of Brax’s physics fidelity by training their own policies in the Brax Training Colab.
https://bit.ly/2UO6d8L
In this article, the researchers at Google AI invite all to check the results of their work and learn how to perform a more qualitative measure of Brax’s physics fidelity by training their own policies in the Brax Training Colab.
https://bit.ly/2UO6d8L
Googleblog
Speeding Up Reinforcement Learning with a New Physics Simulation Engine
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
In this paper by Eric Heiden et al., you'll learn about DiSECt, the first differentiable simulator for cutting soft materials. Through various experiments, the team evaluates the performance of the simulator, demostrating the potential for optimization in robotic cutting of soft materials.
https://bit.ly/3yicDeA
In this paper by Eric Heiden et al., you'll learn about DiSECt, the first differentiable simulator for cutting soft materials. Through various experiments, the team evaluates the performance of the simulator, demostrating the potential for optimization in robotic cutting of soft materials.
https://bit.ly/3yicDeA
Differentiable Cutting Simulator
DiSECt - Differentiable Cutting Simulator
A differentiable simulator for robotic cutting, enabling efficient inference of simulation parameters, and optimization of cutting motions.
Patrick van der Smagt is director of AI research at Volkswagen Group and the lead of its Machine Learning Research Lab in Munich. The lab focuses on probabilistic deep learning for time series modelling, optimal control, robotics, and quantum machine learning. He is also a faculty member of the LMU Graduate School of Systemic Neurosciences and research professor at Eötvös Loránd University Budapest. He is the founding head of a European industry initiative on certification of ethics in AI applications and member of the AI Council of the State of Bavaria.
Patrick van der Smagt also directed a lab as a professor for machine learning and biomimetic robotics at the Technical University of Munich, while leading the machine learning group at the Fortiss research institute. He founded and headed the Assistive Robotics and Bionics Lab at the DLR Oberpfaffenhofen.
He did his PhD and MSc on neural networks in robotics and vision at several universities in Amsterdam. In addition to publishing numerous papers and patents on machine learning, robotics, and motor control, he has won a number of awards, including the 2013 Helmholtz-Association Erwin Schrödinger Award, the 2014 King-Sun Fu Memorial Award, the 2013 Harvard Medical School/MGH Martin Research Prize, and best-paper awards at machine learning and robotics conferences and journals. He was founding chairman of a non-for-profit organization for Assistive Robotics for tetraplegics and is a co-founder of various technology companies.
Did you know about Patrick van der Smagt?
Patrick van der Smagt also directed a lab as a professor for machine learning and biomimetic robotics at the Technical University of Munich, while leading the machine learning group at the Fortiss research institute. He founded and headed the Assistive Robotics and Bionics Lab at the DLR Oberpfaffenhofen.
He did his PhD and MSc on neural networks in robotics and vision at several universities in Amsterdam. In addition to publishing numerous papers and patents on machine learning, robotics, and motor control, he has won a number of awards, including the 2013 Helmholtz-Association Erwin Schrödinger Award, the 2014 King-Sun Fu Memorial Award, the 2013 Harvard Medical School/MGH Martin Research Prize, and best-paper awards at machine learning and robotics conferences and journals. He was founding chairman of a non-for-profit organization for Assistive Robotics for tetraplegics and is a co-founder of various technology companies.
Did you know about Patrick van der Smagt?
Towards Real-World Blind Face Restoration with Generative Facial Prior
Xintao Wang et al. present a GFP-GAN that leverages a pretrained face GAN for blind face restoration. It is incorporated into the face restoration process via novel channel-split spatial feature transform layers, to achieve a good balance of realness and fidelity.
https://bit.ly/3i3B89M
Xintao Wang et al. present a GFP-GAN that leverages a pretrained face GAN for blind face restoration. It is incorporated into the face restoration process via novel channel-split spatial feature transform layers, to achieve a good balance of realness and fidelity.
https://bit.ly/3i3B89M