Google’s Bristlecone Quantum Computing Chip

A few days ago Google previewed its new quantum processor, Bristlecone, a quantum computing chip that will serve as a testbed for research regarding the system error rates and scalability of Google’s qubit technology. In a post on its research blog, Google said it’s “cautiously optimistic that quantum supremacy can be achieved with Bristlecone.”


72 Qubit Quantum Computing Chip

The purpose of this gate-based superconducting system is to provide a testbed for research into system error rates and scalability of their qubit technology, as well as applications in quantum simulation, optimization, and machine learning. Qubits (the quantum version of traditional bits) are very unstable and can be adversely affected by noise, and most of these systems can only hold a state for less than 100 microseconds. Google believes that quantum supremacy can be “comfortably demonstrated” with 49 qubits and a two-qubit error below 0.5 percent. Previous quantum systems by Google have given two-qubit errors of 0.6 percent, which in theory sounds like an extremely small difference, but in the world of quantum computing remains significant.

However, each Bristlecone chip features 72 qubits, which may help mitigate some of this error, but as Google says, quantum computing isn’t just about qubits. Until now, the most advanced quantum chip, built by IBM, had 50 qubits.  “Operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself,” the team writes in a blog post. “Getting this right requires careful systems engineering over several iterations.”

(via Google Research Blog, Engadget, Forbes)



Deep learning improves your computer with age

The researchers at Google have devised a new technique that could let a laptop or smartphone learn to do things better and faster over time. The researchers published a paper which focuses on a common problem. The prefetching problem. Computers process information much faster than they can pull it from memory to be processed. To avoid bottlenecks, they try to predict which information is likely to be needed and pull it in advance. As computers get more powerful, this prediction becomes progressively harder.

In the paper published, Google focuses on using deep learning to improve prefetching. “The work that we did is only the tip of the iceberg,” says Heiner Litz of the University of California, Santa Cruz, a visiting researcher on the project. Litz believes it should be possible to apply machine learning to every part of a computer, from the low-level operating system to the software that users interact with.

Such advances would be opportune. Moore’s Law is finally slowing down, and the fundamental design of computer chips hasn’t changed much in recent years. Tim Kraska, an associate professor at MIT who is also exploring how machine learning can make computers work better, says the approach could be useful for high-level algorithms, too. A database might automatically learn how to handle financial data as opposed to social-network data, for instance. Or an application could teach itself to respond to a particular user’s habits more effectively.

Paper reference:


(via: MitTechReview)

Baidu Research’s New AI Algorithm Mimics Voice With Very Few Samples

AI typically needs a plethora of data and a lot of time for something like voice cloning. It needs to listen to hours of recordings. However, a new process could get that down to one minute. Baidu researchers have unveiled an upgraded version of Deep Voice, their text-to-speech synthesis system, that can now, once trained, clone any voice after listening to a few snippets of audio. This capability was enabled by learning shared and discriminative information from speakers. Baidu calls this ‘Voice Cloning’. Voice cloning is expected to have significant applications in the direction of personalization in human-machine interfaces.


Here, Baidu focuses on two fundamental approaches (refer above figure):

  1. Speaker AdaptionSpeaker adaptation is based on fine-tuning a multi-speaker generative model with a few cloning samples, by using backpropagation-based optimization. Adaptation can be applied to the whole model or only the low-dimensional speaker embeddings. The latter enables a much lower number of parameters to represent each speaker, albeit it yields a longer cloning time and a lower audio quality.
  2. Speaker EncodingSpeaker encoding is based on training a separate model to directly infer a new speaker embedding from cloning audios that will ultimately be used with a multi-speaker generative model. The speaker encoding model has time-and-frequency-domain processing blocks to retrieve speaker identity information from each audio sample, and attention blocks to combine them in an optimal way.

For detailed information and mathematical explanations, refer the paper by Baidu Research.

However, this technology can also possibly have a downside as this could be tumultuous to people relying upon biometric voice security.

( via MitTechReview, Wiki, BaiduResearch)

Interpretable Machine Learning Through Teaching – (OpenAI)

The researchers at OpenAI have designed a method that encourages AIs to teach each other with examples that are cogent to human beings as well. Their method automatically selects the most informative examples for teaching a concept, for example, the best images to describe the concept of dogs, and this approach proved to be effective for both humans as well as AIs.

OpenAI envisions that some of the most impactful applications of AI will come from a result of collaboration between humans and machines. However, communication between the two is the barrier. Consider an example. Think about trying to guess the shape of a rectangle when you’re only shown a collection of random points inside that rectangle: it’s much faster to figure out the correct dimensions of the rectangle when you’re given points at the corners of the rectangle instead. OpenAI’s machine learning approach works as a cooperative game played between two agents, one the teacher and another the student. The goal here for the student is to guess a particular concept (i.e. “dog”, “zebra”) based on examples of that concept (such as images of dogs), and the goal of the teacher is to learn to select the most illustrative examples for the student.

In their two-stage technique: 

  1. A ‘student’ neural network is given randomly selected input examples of concepts and is trained from those examples using traditional supervised learning methods to guess the correct concept labels.
  2. The ‘teacher’ network — which has an intended concept to teach and access to labels linking concepts to examples — to test different examples on the student and see which concept labels the student assigns them, eventually converging on the smallest set of examples it needs to give to let the student guess the intended concept.

However, if they train the student and the teacher jointly, the student and teacher can collude to communicate via arbitrary examples that do not make sense to humans, digressing from the main goal.


(via: OpenAI)

Artificial Synapse could make Brain-On-A-Chip Hardware a Reality

Let’s start by understanding what does the title mean! This is a part of Neuromorphic Engineering aka Neuromorphic Computing, describing the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. Microprocessors configured more like brains than traditional chips could soon make computers far more astute about what’s going on around them.


Neuromorphic computer chips are designed to work like the human brain. Instead of being controlled by binary, on-or-off signals like most current chips, neuromorphic chips weight their outputs, mimicking the way different neurons fire at different strengths through their synapses. In this way, small neuromorphic chips could, like the brain, efficiently process millions of streams of parallel computations that are currently only possible with large banks of supercomputers. But one significant hangup on the way to such portable artificial intelligence has been the neural synapse, which has been particularly tricky to reproduce in hardware.

Now engineers at MIT have designed an artificial synapse in such a way that they can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team has built a small chip with artificial synapses, made from silicon germanium. In simulations, the researchers found that the chip and its synapses could be used to recognize samples of handwriting, with 95 percent accuracy. The design, published last month in the journal Nature Materials, is a major step towards building portable, low-power neuromorphic chips for use in pattern recognition and other learning tasks.

Most neuromorphic chip designs attempt to emulate the synaptic connection between neurons using two conductive layers separated by a “switching medium,” or synapse-like space. When a voltage is applied, ions should move in the switching medium to create conductive filaments, similarly to how the “weight” of a synapse changes.

The research was led by Jeehwan Kim, the Class of 1947 Career Development Assistant Professor in the departments of Mechanical Engineering and Materials Science and Engineering, and a principal investigator in MIT’s Research Laboratory of Electronics and Microsystems Technology Laboratories.

In conclusion, Artificial neural networks are already loosely modeled on the brain. The combination of neural nets and neuromorphic chips could let AI systems be packed into smaller devices and run a lot more efficiently.


(via ScienceDaily, MitTechReview, Wiki)

Google’s Cloud Auto-ML Vision

A new service by Google named Cloud AutoML uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The initial release of AutoML Cloud is limited to image recognition. Its simple interface lets you upload images with ease, train and manage them, and finally deploy models on Google Cloud.

The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.


In addition, rather than forcing enterprises to train their algorithms using Google’s data, Cloud AutoML ingests enterprise data assets and tunes the model accordingly. The key here is that Google helps enterprises to customize a model without having to do so de novo: There’s already a great deal of training baked in. Though initially focused on image data, Google plans to roll out the service to tackle text, video, and more.

Cloud AutoML Vision is built on Google’s transfer learning and neural architecture search technologies (among others). Disney has already started using the technology to annotate their products to improve the customer’s experience on their shop-Disney site. The Zoological Society of London is also using AutoML Vision to recognize and track wildlife in order to understand their distribution and how humans are impacting the species.

The video below simplifies and formulates the working of Cloud AutoML Vision.

Capsule Nets

A few months ago, Geoffrey Hinton and his team published two papers that introduced a completely new type of a neural network based on Capsules, further to in support of those Capsule Networks, the team published an algorithm called dynamic routing between capsules for the training of such networks.

With Hinton’s capsule network, layers are comprised not of individual Artificial Neural Networks (ANNs), but rather of small groups of ANNs arranged in functional pods, or “capsules.” Each capsule is programmed to detect a particular attribute of the object being classified, thus getting around the need for massive input data sets. This makes capsule networks a departure from the “let them teach themselves” approach of traditional neural nets.

A layer is assigned the task of verifying the presence of some characteristic, and when enough capsules are in agreement on the meaning of their input data, the layer passes on its prediction to the next layer.



Capsule Net Architecture


A capsule is a nested set of neural layers. So in a regular neural network, you keep on adding more layers. In CapsNet you would add more layers inside a single layer. Or in other words, nesting a neural layer inside another. The state of the neurons inside a capsule capture the above properties of one entity inside an image. A capsule outputs a vector to represent the existence of the entity. The orientation of the vector represents the properties of the entity. The vector is sent to all possible parents in the neural network. For each possible parent, a capsule can find a prediction vector. Prediction vector is calculated based on multiplying its own weight and a weight matrix. Whichever parent has the largest scalar prediction vector product, increases the capsule bond. Rest of the parents decrease their bond. This routing by agreement method is superior to the current mechanism like max-pooling. Max pooling routes based on the strongest feature detected in the lower layer. Apart from dynamic routing, CapsNet talks about adding squashing to a capsule. Squashing is a non-linearity. So instead of adding squashing to each layer like how you do in CNN, you add the squashing to a nested set of layers. So the squashing function gets applied to the vector output of each capsule.

So far, capsule nets have proven equally adept at as traditional neural nets at understanding handwriting, and cut the error rate in half for identifying toy cars and trucks. Impressive, but it’s just a start. The current implantation of capsule networks is, according to Hinton, slower than it will have to be in the end.


(via arxiv, medium blogs, i-programmer, bigthink)


DCGANs stand for Deep Convolutional Generative Adversarial Networks. It is quite the contrary to a Convolutional Neural Network (CNN). It works in an opposite direction compared to a CNN. What CNN does is that it transforms an image to class labels, that is a list of probabilities, whereas DCGAN generates an image from random parameters.



Some of you might wonder what are convolutions. Convolutions are the operations or the modifications we perform on an image. We perform modifications on the image kernel by multiplying it with the matrix of the operation we want to perform. For detailed information on image kernel and convolutions please visit here.

Moving on, what CNN does is it applies a lot of filters to extract various features from a single image. CNN applies multi-layered filters to a single image to extract features moving deeper into the layers.


Now the typical working on CNNs is that it starts from a single RGB image on the right, multiple filtering layers are applied to produce smaller and large number of images.


Flow of CNNs


Image generation flow of DCGANs


Flow of DCGANs


Now, the filters that we previously mentioned are convolutional in CNNs and transposed-convolutional in DCGANs, both of them work in the opposite direction.


Illustration of their working

Convolution: (Bottom Up) 3×3 blue pixels contribute to generating a single green pixel. Each of 3×3 blue pixels multiplied by the corresponding filter value, and the results from different blue pixels summed up to be a single green pixel.

Transposed-Convolutions: (Top Down) A single green pixel contributes to generating 3×3 blue pixels. Each green pixel is multiplied by each 3×3 filter values and the results from different green pixels are summed up to be a single blue pixel.


It is suggested that the input parameters could use a semantic structure as in the following example-


Interpretation of Input Parameters


Training Strategies:

CNN: Classifying authentic and fake images. Here, authentic images are provided as training data to the model.

DCGAN: It is trained to generate images classified as authentic by the CNN. It works by trying to fool the CNN, DCGAN learns to generate images similar to the training data.


What is Meta-Learning in Machine Learning?

Meta-Learning is a subfield of machine learning where automatic learning algorithms are applied on meta-data. In brief, it means Learning to Learn. The main goal is to use meta-data to understand how automatic learning can become flexible in solving different kinds of learning problems, hence to improve the performance of existing learning algorithms. Which means that how effectively we can increase the learning rate of our algorithms.

Meta-Learning affects the hypothesis space for the learning algorithm by either:

  • Changing the hypothesis space of the learning algorithms (hyper-parameter tuning, feature selection)
  • Changing the way the hypothesis space is searched by the learning algorithms (learning rules)

Variations of Meta-Learning: 

  • Algorithm Learning (selection) – Select learning algorithms according to the characteristics of the instance.
  • Hyper-parameter Optimization – Select hyper-parameters for learning algorithms. The choice of the hyper-parameters influences how well you learn.
  • Ensemble Methods – Learn to learn “collectively” – Bagging, Boosting, Stacked Generalization.

Flexibility is very important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. This means that it will only learn well if the bias matches the data in the learning problem. A learning algorithm may perform very well on one learning problem, but very badly on the next. From a non-expert point of view, this poses strong restrictions on the use of machine learning or data mining techniques, since the relationship between the learning problem (often some kind of database) and the effectiveness of different learning algorithms is not yet understood.

By using different kinds of meta-data, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to select, alter or combine different learning algorithms to effectively solve a given learning problem. Critiques of meta-learning approaches bear a strong resemblance to the critique of metaheuristic, which can be said to be a related problem.

Metalearning may be the most ambitious but also the most rewarding goal of machine learning. There are few limits to what a good meta-learner will learn. Where appropriate it will learn to learn by analogy, by chunking, by planning, by subgoal generation, etc.

OpenAI’s Virtual Wrestling Bots

OpenAI, a firm backed by Elon Musk, has currently revealed one of it’s latest developments in the fields of Machine Learning, demonstrated using the technology of virtual sumo wrestlers.


These are the bots inside the virtual world of RoboSumo controlled my machine learning. They (The Bots) taught themselves through trial and error using Reinforcement Learning, a technique inspired by the way animals learn through feedback. It has proved useful for training computers to play games and to control robots. The virtual wrestlers might look slightly ridiculous, but they are using a very clever approach to learning in a fast-changing environment while dealing with an opponent. This game and it’s virtual world were created at OpenAI to show how forcing AI systems to compete can spur them to become more intelligent.

However, one of the disadvantages of reinforcement learning is that doesn’t work well in realistic situations, or where things are more dynamic. OpenAI devised a solution to this problem by creating its own reinforcement algorithm called proximal policy optimization (PPO), which is especially well suited to changing environments.

The latest work, done in collaboration with researchers from Carnegie Mellon University and UC Berkeley, demonstrates a way for AI agents to apply what the researchers call a “meta-learning” framework. This means the agents can take what they have already learned and apply it to a new situation.

Inside the RoboSumo environment (see video above), the agents started out behaving randomly. Through thousands of iterations of trial and error, they gradually developed the ability to move—and, eventually, to fight. Through further iterations, the wrestlers developed the ability to avoid each other, and even to question their own actions. This learning happened on the fly, with the agents adapting even they wrestled each other.

Flexible learning is a very important part of human intelligence, and it will be crucial if machines are going to become capable of performing anything other than very narrow tasks in the real world. This kind of learning is very difficult to implement in machines, and the latest work is a small but significant step in that direction.


(sources: MitTechReview, OpenAI Blog, Wired)