Collaborating Authors


A Beginner's Guide To Attention And Memory In Deep Learning


It might have never occurred to you how you could make sense of what your friend is blabbering at a loud party. There are all kinds of noises in a party; then how come we are perfectly able to carry out a conversation? This question is known widely as the'cocktail party problem'. Most of our cognitive processes can pay attention to only a single activity at a time. In the case of a party house, our capability of directing attention towards one set of words while ignoring other sets of words, which are often overpowering, is still a conundrum.

Are we making spacecraft too autonomous?

MIT Technology Review

Software has never played a more critical role in spaceflight. It has made it safer and more efficient, allowing a spacecraft to automatically adjust to changing conditions. According to Darrel Raines, a NASA engineer leading software development for the Orion deep space capsule, autonomy is particularly key for areas of "critical response time"--like the ascent of a rocket after liftoff, when a problem might require initiating an abort sequence in just a matter of seconds. Or in instances where the crew might be incapacitated for some reason. And increased autonomy is practically essential to making some forms of spaceflight even work.

Computational model decodes speech by predicting it


UNIGE scientists developed a neuro-computer model which helps explain how the brain identifies syllables in natural speech. The model uses the equivalent of neuronal oscillations produced by brain activity to process the continuous sound flow of connected speech. The model functions according to a theory known as predictive coding, whereby the brain optimizes perception by constantly trying to predict the sensory signals based on candidate hypotheses (syllables in this model).

How Machine Learning Impact Product Personalization


Machine learning-based personalization has gained traction over the years due to volume in the amount of data across sources and the velocity at which consumers and organizations generate new data. Traditional ways of personalization focused on deriving business rules using techniques like segmentation, which often did not address a customer uniquely. Recent progress in specialized hardware (read GPUs and cloud computing) and a burgeoning ML and DL toolkits enable us to develop 1:1 customer personalization which scales. Recommender systems are beneficial to both service providers and users. They reduce transaction costs of finding and selecting items in an online shopping environment and improves customer experience.

Recurrent Neural Networks (RNN): Deep Learning for Sequential Data


Recurrent Neural Networks (RNN) are a class of Artificial Neural Networks that can process a sequence of inputs in deep learning and retain its state while processing the next sequence of inputs. Traditional neural networks will process an input and move onto the next one disregarding its sequence. Data such as time series have a sequential order that needs to be followed in order to understand. Traditional feed-forward networks cannot comprehend this as each input is assumed to be independent of each other whereas in a time series setting each input is dependent on the previous input. In Illustration 1 we see that the neural network (hidden state) A takes an xt and outputs a value ht.

The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP) - KDnuggets


Humans have a lot of senses, and yet our sensory experiences are typically dominated by vision. With that in mind, perhaps it is unsurprising that the vanguard of modern machine learning has been led by computer vision tasks. Likewise, when humans want to communicate or receive information, the most ubiquitous and natural avenue they use is language. Language can be conveyed by spoken and written words, gestures, or some combination of modalities, but for the purposes of this article, we'll focus on the written word (although many of the lessons here overlap with verbal speech as well). Over the years we've seen the field of natural language processing (aka NLP, not to be confused with that NLP) with deep neural networks follow closely on the heels of progress in deep learning for computer vision.

Python may get pattern matching syntax


The creators of the Python language are mulling a new proposal, PEP 622, that would finally bring a pattern matching statement syntax to Python. The new pattern matching statements would give Python programmers more expressive ways of handling structured data, without having to resort to workarounds. Pattern matching is a common feature of many programming languages, such as switch/case in C. It allows one of a number of possible actions to be taken based on the value of a given variable or expression. While Python has lacked a native syntax for pattern matching, it has been possible to emulate it with if/elif/else chains or a dictionary lookup. Supported pattern match types include literals, names, constant values, sequences, a mapping (basically, the presence of a key-value pair in the expression), a class, a mixture of the above, or any of those plus conditional expressions.

The A-Z of AI and Machine Learning: Comprehensive Glossary


I don't know whether you know it or not… but there are a lot of misconceptions surrounding artificial intelligence. While some assume it means robots coming to life to interact with humans, other ones believe it is a superintelligence that soon will take over the world. Well, I consider this to be very discouraging. Not for me to explain the importance of knowing what AI is and what it can really do (especially if you are thinking about establishing your own AI expertise, or you are already using it). Today, I offer to take care of terminology and don't be so naive anymore. In this article, I'll aim to highlight some of the most necessary concepts in a clear, straightforward way. So, feel free to grab your coffee and a comfortable chair, and just dive in.

Christopher Nolan's 'Tenet' delays release again amid reported coronavirus spikes

FOX News

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Warner Bros. has once again delayed the release of the Christopher Nolan-directed film "Tenet" amid reported cases of the novel coronavirus surging. The studio announced the decision on Thursday, stressing the need for flexibility. The sci-fi thriller, which stars John David Washington and Robert Pattinson, will now be released on Wednesday, Aug. 12.

Time Series Prediction with TensorFlow


In this article, we focus on'Time Series Data' which is a part of Sequence models. In this article, we focus on'Time Series Data' which is a part of Sequence models. In essence, this represents a type of data that changes over time such as the weather of a particular place, the trend of behaviour of a group of people, the rate of change of data, the movement of body in a 2D or 3D space or the closing price for a particular stock in the markets. Analysis of time series data can be done for anything that has a'time' factor involved in it. So what can machine learning help us achieve over time series data? It can also be used to predict missing values in the data. There are certain keywords that always come up when dealing with time series data.