Science fiction is becoming reality as increasingly intelligent machines are gradually emerging -- ones that not only specialize in things like chess, but that can also carry out higher-level reasoning, or even answer deep philosophical questions. For the past few decades, experts have been collectively bending their efforts toward the creation of such a human-like artificial intelligence, or a so-called "strong" or artificial general intelligence (AGI), which can learn to perform a wide range of tasks as easily as a human might. But while current AI development may take some inspiration from the neuroscience of the human brain, is it actually appropriate to compare the way AI processes information with the way humans do it? The answer to that question depends on how experiments are set up, and how AI models are structured and trained, according to new research from a team of German researchers from the University of Tübingen and other research institutes. The team's study suggests that because of the differences between the way AI and humans arrive at such decisions, any generalizations from such a comparison may not be completely reliable, especially if machines are used to automate critical tasks.

TinyML is the latest from the world of deep learning and artificial intelligence. It brings the capability to run machine learning models in a ubiquitous microcontroller - the smallest electronic chip present almost everywhere. Microcontrollers are the brain for many devices that we use almost every day. From a TV remote controller to the elevator to the smart speaker, they are everywhere. Multiple sensors that can emit telemetry data are connected to a microcontroller.

"Which topic can frequently be found in these documents?" Void of any categories or labels I am forced to look into unsupervised techniques to extract these topics, namely Topic Modeling. Although topic models such as LDA and NMF have shown to be good starting points, I always felt it took quite some effort through hyperparameter tuning to create meaningful topics. Moreover, I wanted to use transformer-based models such as BERT as they have shown amazing results in various NLP tasks over the last few years. Pre-trained models are especially helpful as they are supposed to contain more accurate representations of words and sentences.

Efforts have been underway for forty years to build computers that might emulate some of the structure of the brain in the way they solve problems. To date, they have shown few practical successes. But hope for so-called neuromorphic computing springs eternal, and lately, the endeavor has gained some surprising champions. The research lab of Terry Sejnowski at The Salk Institute in La Jolla this year proposed a new way to train "spiking" neurons using standard forms of machine learning, called "recurrent neural networks," or "RNNs." And Hava Siegelmann, who has been doing pioneering work on alternative computer designs for decades, proposed along with colleagues a system of spiking neurons that would perform what's called "unsupervised" learning.

Sentiment Analysis can be defined as the process of analyzing text data and categorizing them into Positive, Negative, or Neutral sentiments. Sentiment Analysis is used in many cases like Social Media Monitoring, Customer service, Brand Monitoring, political campaigns, etc. Analyzing customer feedback such as social media conversations, product reviews, and survey responses allows companies to understand the customer's emotions better which is becoming more essential to meet their needs. It is almost impossible to manually sort thousands of social media conversations, customer reviews, and surveys. So we have to use either ML/DL to build a model that analyzes the text data and performs the required operations. The problem I am trying to solve here is part of this Kaggle competition.

So let's get started training a logistic classifier. A logistic classifier is what's called the linear classifier. It takes the input, for example, the pixels in an image, and applies a linear function to them to generate its predictions. A linear function is just a giant matrix multiplier. It takes all the inputs as a big vector that will denote x and multiplies them with a matrix to generate its predictions, one per output class.

Reinforcement learning models are trained, using a similar concept by animal researchers to train animals. For a very long period, artificial intelligence agents were trained on machine learning models to perform tasks that are usually done by humans. The neural networks of machine learning models are designed and trained in such a format that they perform the tasks without any human intervention or supervision. However, ever since its inception, the researchers and scientists are curious to induce cognitive abilities into artificial intelligence agents. For a decade, despite the experiments designed to train the artificial neural network by utilizing the human cognitive ability for adopting common sense, the researchers were unable to reach into a reasonable conclusion. The researchers were resorting to behavioral science and neuroscience earlier to induce common sense into the artificial intelligence agents.

The modern AI revolution began during an obscure research contest. It was 2012, the third year of the annual ImageNet competition, which challenged teams to build computer vision systems that would recognize 1,000 objects, from animals to landscapes to people. In the first two years, the best teams had failed to reach even 75% accuracy. But in the third, a band of three researchers--a professor and his students--suddenly blew past this ceiling. They won the competition by a staggering 10.8 percentage points. That professor was Geoffrey Hinton, and the technique they used was called deep learning.

I am Dweep, a deep learning enthusiast. I have finished lots of ML and AI courses, and I felt it was time for me to give back to the community. If you're interested in Space discovery, I've also been involved in leading a Helium powered space project named Mission Vimaan. To know more, mail here. So I started a blog on Blogger named LightSpeedAC, to share my knowledge to the thousands willing to get into artificial intelligence, machine learning or deep learning.

In deep learning, convolutional layers have been major building blocks in many deep neural networks. The design was inspired by the visual cortex, where individual neurons respond to a restricted region of the visual field known as the receptive field. A collection of such fields overlap to cover the entire visible area. Though convolutional layers were initially applied in computer vision, its shift-invariant characteristics have allowed convolutional layers to be applied in natural language processing, time series, recommender systems, and signal processing. The easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix.