Artificial Intelligence has been in play for almost a decade which has fruition intelligent products that we are using or at least have the test prototypes in hand yet there is a lot to be achieved now. All that we have achieved till date is AI development code libraries which mostly works with supervised learning. Now tech giants Microsoft, Facebook & Google are working to create programs that can work over present AI development libraries for cross-platform libraries and unsupervised learning support. AI development will leverage big data, quantum computing, distributed computing and 5G communication for unsupervised learning based AI products development. Artificial Intelligence is cognitive thinking for computers just like humans do.

Recently, I started up with an NLP competition on Kaggle called Quora Question insincerity challenge. It is an NLP Challenge on text classification and as the problem has become more clear after working through the competition as well as by going through the invaluable kernels put up by the kaggle experts, I thought of sharing the knowledge. Since we have a large amount of material to cover, I am splitting this post into a series of posts. The first post i.e. this one will be based on preprocessing techniques that work with Deep learning models and we will also talk about increasing embeddings coverage. In the second post, I will try to take you through some basic conventional models like TFIDF, Count Vectorizer, Hashing etc. that have been used in text classification and try to access their performance to create a baseline. We will delve deeper into Deep learning models in the third post which will focus on different architectures for solving the text classification problem. We will try to use various other models which we were not able to use in this competition like ULMFit transfer learning approaches in the fourth post in the series.

One of the most intriguing artificial intelligence techniques was conceived when a few computer scientists where discussing deep learning and photorealistic images at a Montreal pub in 2014. Called generative adversarial networks (GAN), the concept has enabled the AI industry to take huge leaps toward creativity, generating images and sounds that are very close to their natural counterparts. However, like other AI techniques that use deep learning and neural networks, GANs are opaque, which means there's very little visibility or control on how they work. As a result, engineers find it hard to troubleshoot them, and users find it hard to trust them. To overcome these limitations, researchers at IBM and MIT have developed a technique called "GAN Dissection" that helps explore the inner workings of GANs and better understand the reasoning that results in their output.

Modern deep learning libraries such as Keras allow you to define and start fitting a wide range of neural network models in minutes with just a few lines of code. Nevertheless, it is still challenging to configure a neural network to get good performance on a new predictive modeling problem. The challenge of getting good performance can be broken down into three main areas: problems with learning, problems with generalization, and problems with predictions. Once you have diagnosed the specific type of problem that you are having with a network, a suite of classical and modern techniques can then be selected to address the issue and improve performance. In this post, you will discover a framework for diagnosing performance problems with deep learning models and techniques that you can use to target and improve each specific performance problem.

Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops. Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells.

Artificial intelligence (AI) systems' rapid advances are continually crossing rows off the list of things humans do better than our computer compatriots. AI has bested us at board games like chess and Go, and set astronomically high scores in classic computer games like Ms. Pacman. More complex games form part of AI's next frontier. While a team of AI bots developed by OpenAI, known as the OpenAI Five, ultimately lost to a team of professional players last year, they have since been running rampant against human opponents in Dota 2. Not to be outdone, Google's DeepMind AI recently took on--and beat--several professional players at StarCraft II.

It is increasingly making it easier for developers to build Machine Learning capabilities into their applications while testing their code is real time. In this piece about Pytorch Tutorial, I talk about the new platform in Deep Learning. The latest version of the platform brings a lot of new capabilities to the table and is clocking vibrant support from the whole industry. It is remarkable how Pytorch is being touted as a serious contender to Google's Tensorflow just within a couple of years of its release. Its popularity is mainly being driven by a smoother learning curve and a cleaner interface, which is providing developers with a more intuitive approach to build neural networks.

Could deep learning help paleontologists and geneticists hunt for ghosts? When modern humans first migrated out of Africa 70,000 years ago, at least two related species, now extinct, were already waiting for them on the Eurasian landmass. These were the Neanderthals and Denisovans, archaic humans who interbred with those early moderns, leaving bits of their DNA behind today in the genomes of people of non-African descent. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. But there have been growing hints of an even more convoluted and colorful history: A team of researchers reported in Nature last summer, for instance, that a bone fragment found in a Siberian cave belonged to the daughter of a Neanderthal mother and a Denisovan father.

To say artificial intelligence (AI) is transforming healthcare would be an understatement. Thanks to enormous advancements in computer processing power, as well as the increase of data collection at the patient, clinician and institutional level, AI is now driving the digital healthcare revolution. This transformation has been visibly apparent within the fields of medical diagnoses, drug discovery, e-health, and electronic health records. In particular, the field of medical diagnoses is where AI and deep learning have shown the most use cases. This is largely due to the recent developments in computer vision and object recognition, especially from 2012 when AlexNet, a convolutional network, won the ImageNet Large Scale Visual Recognition Challenge.

In 2018, the world saw the rise of automated machine learning, deep learning, and - best of all - real-life applications of these technologies, all of which have started to pave the path to Enterprise AI. But there's still a long way to go: here are our top four trends to watch for AI in the Enterprise in 2019: Following our $101 million Series C funding round and 2019 commitment to data education and responsible AI, plus after talking to our expert data scientists, we've also put together a quiz to help you gauge your readiness - take the quiz here.