Preferred Networks has released a beta version of an open-source, automatic hyperparameter optimization framework called Optuna. In this blog, we will introduce the motivation behind the development of Optuna as well as its features. A hyperparameter is a parameter to control how a machine learning algorithm behaves. In deep learning, the learning rate, batch size, and number of training iterations are hyperparameters. Hyperparameters also include the numbers of neural network layers and channels.

Researchers at the University of Zurich's Brain Research Institute have recently developed a technique to automatically detect neurons of different types in a variety of brain regions at different developmental stages. They presented this deep learning-based tool, called DeNeRD, in a paper published in Nature Scientific Reports. Mapping the structure of the mammalian brain at the cellular level is an important, yet demanding task, which typically involves capturing specific anatomical features and analyzing them. In the past, researchers were able to gather several interesting observations and insights about the mammalian brain's structure using classical histological and stereological techniques. Although these methods have proved to be very useful for studying the anatomy of the brain, carrying out a truly brain-wide analysis typically requires a different approach.

Retailers today are leveraging Artificial Intelligence-powered solutions to revolutionize the retail industry. However, it is proven that AI technology has made significant and interesting transformations across multiple industries over the past few years. But due to its high costs, inaccessibility, and proprietary systems, not every retailer has been utilizing it. As customers or shoppers are taking a multichannel approach while purchasing, offline retailers are also turning to AI to level the key players in the field. From assisting customers to explore the items using visual search to personalizing their entertainment experience through virtual assistants or chatbots, AI is transforming the retail industry in a number of ways.

LOL, How can #Deepmind #AI decipher anything while most humans are systematically mislead from knowing about, understanding their own true history??? .. or is this system exploited by those who continuously want to cover up humanities real past (timelines)pic.twitter.com/mh4wBY2IhS

Epigraphy, the study of written matter recorded on hard or durable material, which -as a term- is derived from the Classical Greek epigraphein ("to write upon, incise") and epigraphē ("inscription"), is a prime tool in recovering much of the firsthand record of antiquity and thus, an essential adjunct of the study of ancient peoples. However, inscriptions –as records of ancient cultural heritage- are often incomplete due to deliberate destruction, or erosion and fragmentation over the centuries. Illegible parts of the text must then be restored by specialists, known as epigraphists. However one of the problems with discerning meaning from incomplete fragments of text is that there are often multiple possible solutions. Now, researchers at Oxford University - a Greek, Yannis Assael, among them- and Google's DeepMind -a London-based Artificial Intelligence (AI) company- have created Pythia. Bringing together the disciplines of ancient history and deep learning, AI Pythia -which takes its name from the woman who delivered the god Apollo's oracular responses at the Greek sanctuary of Delphi- is the first ancient text restoration model aiming to recover missing characters from a damaged text input using deep neural networks.

In a recent paper [1] published by Google researchers and presented at RecSys 2019 (Copenhagen, Denmark) insight was provided in how their video platform Youtube recommends which videos to watch. In this blogpost I will try to summarise my findings after reading this paper. When users are watching videos on Youtube, a list of recommended videos are displayed which the user might like in a certain order. How to effectively and efficiently learn to reduce such biases is an open question. The described model in this paper focuses on the two main objectives.

Artificial intelligence (AI) is growing every day at a raging pace. The technology has already acquired immense success in 2018 by streamlining work procedures in the healthcare industry, curtailing overhead costs in the manufacturing, reduction of man-hours in administrative tasks in the education sector. Now in 2019, it looks like there is emergence of a new AI startup every day which is bent on changing the way we interact and work. In healthcare industry, AI can provide astounding help for analyzing complex medical data in regards to CT scans, X-rays along with other screenings and tests. Patient's data and external knowledge sources such as clinical research can be used to construct a personalized treatment path for everyone.

The 21st century has opened up a boundless mass of headlines, articles, and stories. This information influx, however, is partially contaminated: Alongside factual, truthful content is fallacious, deliberately manipulated material from dubious sources. According to research by the European Research Council, one in four Americans visited at least one fake news article during the 2016 presidential campaign. This problem has recently been exacerbated by something called "automatic text generators." Advanced artificial intelligence software, like OpenAI's GPT-2 language model, is now being used for things like auto-completion, writing assistance, summarization, and more, and it can also be used to produce large amounts of false information -- fast.

HELIXRE announces the launch of Enhanced Cloud Technology, an application of geometric deep learning (GDL) to greatly increase the benefits of digital building plans. HELIXRE now applies GDL to distinguish contents of multi-billion point clouds to allow semantic understanding of building spaces, identifying specific elements like walls, floors, ceilings, furniture, and typical office clutter. Defining and isolating these elements means no one creates more accurate building clouds faster and more cost efficiently than HELIXRE. GDL is a relatively new and cutting-edge form of machine learning, overcoming limitations of convolutional neural networks that solve 2D problems like image recognition, but are not well suited to the complexity of 3D spaces. By creating superpoints, HELIXRE reduces the problem domain to a smaller set of points to which graph convolutions are applied.

Deep Learning is the most exciting and powerful branch of Machine Learning. It's a technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.