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This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
In this video we explain how neural style transfer works with TensorFlow, and how you guys can able to create an image and drawing like Picasso by yourself, you can do many amazing things with this implementation.
To know more visit us at: bit.ly/2MY9UCw
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
Jeff Dean sat for this portrait in Google’s Building 43 on an August afternoon in 2025. The room was quiet, the light falling in a way that made his eyes appear both steady and amused. He has the look of someone who has been through countless problem-solving sessions but still finds joy in the process.
Dean’s story begins in Minnesota, where he studied computer science before heading west for graduate school at the University of Washington. There he focused on compilers, writing work that was highly technical but deeply practical. It was less about abstract beauty and more about making computers run faster, squeezing efficiency out of systems that always seemed too limited. That mindset would shape the rest of his career.
His first real laboratory was Digital Equipment Corporation’s Western Research Lab in Palo Alto. At DEC he was surrounded by engineers who cared about elegance but never at the expense of reliability. Those years gave him a lasting respect for craftsmanship in software. He has spoken of how formative it was to be in a place where even the smallest decisions about performance and structure mattered.
When he arrived at Google in 1999, the company had fewer than a hundred employees. Search was already straining the capacity of existing systems. Dean and his longtime collaborator Sanjay Ghemawat began sketching solutions on whiteboards, filling them with boxes and arrows that hinted at ways to divide work across thousands of machines. Out of those sessions came MapReduce, which allowed Google to process massive data sets in a fraction of the time. Bigtable followed, giving the company a storage system that could keep pace with its ambitions. Engineers still remember the first time they saw queries finish in hours instead of days.
Stories about Dean inside Google often return to the way he approaches code reviews. He is known to mark up a colleague’s code with detailed comments, sometimes line by line, always pushing for clarity. Yet the tone is never dismissive. Younger engineers recall feeling surprised that someone of his stature took their work so seriously. It gave them confidence to tackle harder problems.
By the middle of the 2010s Dean turned his focus to artificial intelligence. As head of Google Brain, he encouraged the team to take bold steps, even when success was uncertain. TensorFlow, the open-source library they released, was built with that same spirit. It made sophisticated machine learning accessible, and within a few years it was being used by researchers in medicine, climate science, and language technology. Dean liked to point out that the best ideas often came from unexpected places once the tools were in wide circulation.
His colleagues describe a leadership style that is more invitation than command. In meetings he often starts with a quiet question that reframes the problem, steering the discussion toward fundamentals. He is patient with complexity, willing to sit through hours of debate if it means arriving at a solution that will endure. His amusement shows itself in small ways, often in a half-smile when a tough question lands on the table.
Outside recognition has come steadily. Election to the National Academy of Engineering confirmed his standing as one of the country’s most influential technologists. Awards have followed, but inside Google what people talk about most is his constancy. He shows up, he listens, and he remains deeply engaged with the work itself.
To photograph him is to see both sides at once. His arms are folded, his expression serious, yet his eyes suggest he is ready to lean forward into the next idea. The systems he has helped build are vast and nearly invisible, woven into daily life for billions of people. But in person he is disarmingly affable, a reminder that behind the abstractions of code and scale is a human being who still loves to puzzle out how things work.
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
"The (m)Otherhood of Meep (the bat translator)" is an AI interpreter for grey-headed flying foxes, drawing from scientific research on flying fox vocalizations to interpret their voices into poetic form in real-time. It aims to evoke an interspecies bridge between species at the center of human/wildlife conflicts. The artist moonlights as a registered bat rescuer, and this project has been born of those real-life experiences of interspecies care, nursing bats through to release back into the wild, and going through processes of bonding and unbonding. To make the work, a machine has been trained on a corpus of collected and categorized vocalizations, and given a visual display through TensorFlow and JavaScript, connecting to an array of wording and imagery designed by the artist. The artwork proposes a future for machine learning technologies where corpuses of human language are decentered, and AI are trained for purposes that aim to decenter human expression in preference for highlighting the voices and expressions of others.
Photo: Alinta Krauth (the artist)
Take your machine learning to the next level with these artificial intelligence technologies.
Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation, we seem to be getting increasingly addicted to them.
Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies.
Here is a list of 8 best open sources AI technologies you can use to take your machine learning projects to the next level.
1. TensorFlow
Initially released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy across a variety of platforms. It is one of the most well-maintained and extensively used frameworks for machine learning.
Created by Google for supporting its research and production objectives, TensorFlow is now widely used by several companies, including Dropbox, eBay, Intel, Twitter, and Uber.
TensorFlow is available in Python, C++, Haskell, Java, Go, Rust, and most recently, JavaScript. You can also find third-party packages for other
programming languages.
The framework allows you to develop neural networks (and even other computational models) using flowgraphs.
2. Keras
Initially released in 2015, Keras is an open source software library designed to simplify the creation of deep learning models. It is written in Python
and can be deployed on top of other AI technologies such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano.
Keras is known for its user-friendliness, modularity, and ease of extensibility. It is suitable if you need a machine learning library that allows for easy and fast prototyping, supports both convolutional and recurrent networks, and runs optimally on both CPUs (central processing units) and GPUs (graphics processing units).
3. Scikit-learn
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Initially released in 2007, sci-kit-learn is an open source library developed for machine learning. This traditional framework is written in Python and features several machine learning models including classification, regression, clustering, and dimensionality reduction.
Scikit-learn is designed on three other open source projects—Matplotlib, NumPy, and SciPy—and it focuses on data mining and data analysis.
4. Microsoft Cognitive Toolkit
Initially released in 2016, the Microsoft Cognitive Toolkit (previously referred to as CNTK), is an AI solution that can empower you to take your
machine learning projects to the next level.
Microsoft says that the open source framework is capable of "training deep learning algorithms to function like the human brain."
Some of the vital features of the Microsoft Cognitive Toolkit include highly optimized components capable of handling data from Python, C++, or BrainScript, ability to provide efficient resource usage, ease of integration with Microsoft Azure, and interoperation with NumPy.
5. Theano
Initially released in 2007, Theano is an open source Python library that allows you to easily fashion various machine learning models. Since it's one of the oldest libraries, it is regarded as an industry standard that has inspired developments in deep learning.
At its core, it enables you to simplify the process of defining, optimizing, and assessing mathematical expressions.
Theano is capable of taking your structures and transforming them into very efficient code that integrates with NumPy, efficient native libraries such as BLAS, and native code (C++).
Furthermore, it is optimized for GPUs, provides efficient symbolic differentiation, and comes with extensive code-testing capabilities.
6. Caffe
Initially released in 2017, Caffe (Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on
expressiveness, speed, and modularity. The open source framework is written in C++ and comes with a Python interface.
Caffe's main features include an expressive architecture that inspires innovation, extensive code that facilitates active development, a fast performance that accelerates industry deployment, and a vibrant community that stimulates growth.
7. Torch
Initially released in 2002, Torch is a machine learning library that offers a wide array of algorithms for deep learning. The open source framework provides you with optimized flexibility and speed when handling machine learning projects—without causing unnecessary complexities in the process.
It is written using the scripting language Lua and comes with an underlying C implementation. Some of Torch's key features include N-dimensional arrays, linear algebra routines, numeric optimization routines, efficient GPU support, and support for iOS and Android platforms.
8. Accord.NET
Initially released in 2010, Accord. NET is a machine learning framework entirely written in C#.
The open source framework is suitable for production-grade scientific computing. With its extensive range of libraries, you can build
various applications in artificial neural networks, statistical data processing, image processing, and many others.
This photo was captured at the 2018 edition of Great Indian Developer Summit (#gids18), April 24-28, Bangalore, India.