DeLighT transformer library gives us a new look at the most popular model in NLP -- the transformer. The new architecture helps reduce parameter size in addition to making models deeper. Which means this new architecture can match or achieve better results with the traditional transformer architecture but is much lighter. As of right now, the architecture can help with language modeling and machine translation. According to the authors, more tasks are on the way.

We're introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision "model organisms" which are often studied in interpretability. Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems. The abilities of modern neural networks are the result of the interactions of thousands of neurons (sometimes tens of thousands or more!). In order to understand their behavior, we'd like to be able to quickly and easily investigate these neurons interactions in detail, and share those observations. This is especially true in collaborative environments.

In the previous installment of this series, I showed that – contrary to assumptions made by pioneers of artificial intelligence such as John von Neumann – the human brain bears virtually no resemblance to a digital computer. Von Neumann in particular should have realized this. He had a broad knowledge of physical science and was in contact with leading researchers in the areas of biophysics and neurophysiology. But he and others banked on the discrete, “all-or-nothing” nature of neural impulses, which superficially appear analogous to the “0” and “1” of digital computers. He assumed this would allow researchers to ignore the complicated biology and biophysics of real, live neurons and treat the brain as a digital system. If the goal is to understand how the brain actually works, that was a stupid mistake. But it did inspire early successes in developing computers and primitive AI systems, including the artificial neural network approach that eventually led to today’s “deep learning” systems.

When the chief executive of a San Francisco artificial intelligence company tries to damp down the hype surrounding his own technology then you know that some people have become rather excitable. But that is exactly what Sam Altman attempted to do last month in response to the ecstatic reaction to OpenAI's latest GPT-3 program. "The GPT-3 hype is way too much," Mr Altman tweeted. "It's impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes." GPT-3, which stands for generative pre-trained transformer version three, is, in essence, a super-sophisticated auto-complete function, which sounds less than exciting.

In this blog post we are going to ask the first and most important question there is when discovering Artificial Intelligence, we will dive into what it represents, how it is created, and the general public perception of this amazing new technology. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. This is the textbook definition of Machine Learning AI, the most popular amongst programmers, engineers, and people working in the field of data science. It is what we do every day at Cortex, to be more specific it's deep learning, but we won't get into that right now. Machine learning is the activity in which a statistical model "learns" useful patterns in data. It is quite similar to a toddler who absorbs all the information surrounding him like a sponge.

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods.

Machine learning, a sub-component of artificial intelligence, is not new to the enterprise. But with techniques like deep learning, emulating human brain actions, increasingly gaining traction, businesses are identifying new and potentially transformative deployments of digitally disruptive technologies. According to Algorithmia's 2020 report, the main use cases for machine learning translate to customer service (i.e. But machine learning has applications far and wide. Dynamic pricing or surge pricing is essentially ML models that learn from corresponding factors that include customer interest, demand and history to adjust prices and entice purchases.

I have struggled in the past to really understand the real meaning behind the use of activation functions in the CNN architecture. Let's take a 3 layer neural Network architecture (below figure), w1, w2, w3, b1, b2, b3 are the weight vectors and bias vectors between the layers. Assume X [x1, x2, x3] is the input to the network. As we know the output after a layer (neuron) is multiplication between weight and "output from the last layer" and then added bias i.e (Y WX b) Let's focus on the first row (w11,b11,w21,b21 ..)of the network. Y2 holds same linear combination with the input x1, weight (newConstant1) and bias (newConstant2) i.e W*X b.

With the high prevalence of breast cancer, it is urgent to find out the intrinsic difference between various subtypes, so as to infer the underlying mechanisms. Given the available multi-omics data, their proper integration can improve the accuracy of breast cancer subtype recognition. In this study, DeepMO, a model using deep neural networks based on multi-omics data, was employed for classifying breast cancer subtypes. Three types of omics data including mRNA data, DNA methylation data, and copy number variation (CNV) data were collected from The Cancer Genome Atlas (TCGA). After data preprocessing and feature selection, each type of omics data was input into the deep neural network, which consists of an encoding subnetwork and a classification subnetwork. The results of DeepMO based on multi-omics on binary classification are better than other methods in terms of accuracy and area under the curve (AUC). Moreover, compared with other methods using single omics data and multi-omics data, DeepMO also had a higher prediction accuracy on multi-classification. We also validated the effect of feature selection on DeepMO. Finally, we analyzed the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which were discovered during the feature selection process. We believe that the proposed model is useful for multi-omics data analysis.

Computer vision is one of the most trending subfields in Artificial Intelligence because of its wide variety of applications. In some domains, they even suppress human intelligence in recognising images with speed and accuracy. In this article, we will demonstrate one of the most popular computer vision applications -- multiclass image classification problems using fastAI library and TPU as the hardware accelerator. TPU, or Tensor Processing Unit accelerates the training process of heavy deep learning models. We use image classification for recognising objects in the image and can be used in detecting brand names using logo, classifying objects, etc.