Many research papers cover the prediction of financial time series but only a small number of them speak about the application in a real trading strategy. Most of the time the research only gives the performance metrics of the model (accuracy, RMSE, …) but without trying to transform it into a profitable strategy. When we talk about financial time series, we talk about stochastic processes, meaning it deals with a lot of randomness. For this reason, it's unrealistic to expect getting an accuracy similar to the ones obtained in many other applications of Deep Learning. So, don't expect getting a 80% accuracy when predicting the market to go up or down for a given time horizon.

GAN's is a revolution in the field of deep learning .It has been introduced by the Ian Goodfellow and others in the paper titled "Generative Adversarial Networks" which is available at https://arxiv.org/abs/1406.2661 Let's try to understand what is GAN and How it works? In direction to understand GAN's, we need to understand difference between Supervised and Unsupervised learning techniques and issues with them . Supervised learning are developed based on large quantities of "labeled" samples . The requirement for the supervised learning is large data-sets containing the explainable feature with respect to its labels.

Quantum computing has received significant attention as a next-generation computing technology due to its potential speed and ability to solve problems considered too difficult for classical computers, as reflected in the recent discussion on Quantum Supremacy. Grid sees quantum computing not only as a tool for solving optimization and quantum chemical computation problems, but also as a tool for AI (Machine Learning, Deep Learning, etc.) calculations, such as feature extraction. Previous works have announced the successful implementation of machine learning-related algorithms, such as principal component analysis and auto-encoders, on quantum computers. This work announces the development of a gradient descent (backpropagation) algorithm, a method commonly used in machine learning for neural network parameter optimization, for use on NISQ quantum computers. Due to the non-linear nature of quantum bits (qubits), Grid proposes that this algorithm can be used to perform the feature extraction and representation calculations that deep learning methods employ.

Artificial Intelligence, Deep Learning, Machine Learning, and NLP are all search terms that are popular as human technology continues to advance. But what are these advanced technologies and how do they differ? Artificial intelligence is a technology or computer system designed to function in a way that simulates how the human brain thinks. The term is an overarching field of computer science that encompasses a broad range of categories, including natural language processing, machine learning, deep learning, neural nets, content abstraction, decision-making and more. It was first coined by scientist John McCarthy at Dartmouth University in 1956.

Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of artificial intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and causality of the models. I see dozens of "Data Science Institutes" erected across the country, I read their manifestos and I check their advisory boards. Causality does not seem to be on their agenda. Which makes one doubt whether the Ladder has been internalized and where this hype will end.

In addition, recent years witnessed significant progress in virtual reality, augmented reality, and smart wearable devices, creating challenges in deploying deep learning systems to portable devices with limited resources (e.g. Now let's take a look at a few papers that introduced novel compression models: In this paper, the authors propose two novel network quantization approaches single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ). The network quantization is considered from both width and depth level. In this paper the authors proposed an efficient method for obtaining the rank configuration of the whole network. Unlike previous methods which consider each layer separately, this method considers the whole network to choose the right rank configuration. It combines three techniques -- value quantization with sparsity multiplication, base encoding, and zero-run encoding.

At present, it is mainly applied to deep learning.

Rio Tinto's boss of ports and rail Ivan Vella says the increasing bank of data the industry is generating is the greatest untapped "enabler and disrupter" available to the sector as he reveals expanding artifical intelligence across the global miner's business. Mr Vella, managing director of port, rail and core services at Rio Tinto, told the Resources Technology Showcase today that as the miner had moved to remote operations and asset automation it had generated a huge amount of data across its business. "Today, we track everything, our team is swimming in an ocean of data, which will be crucial to ensuring the efficiency and ongoing health of autonomous assets and systems," he said. "Without a doubt, it is the greatest untapped enabler and disrupter available to our industry." Mr Vella highlighted in his speech, 'Project Tempo', which Rio developed with EY data and analytics, Monash University and Strukton Rail.

The South Korean Go champion Lee Se-dol has retired from professional play, telling Yonhap news agency that his decision was motivated by the ascendancy of AI. "With the debut of AI in Go games, I've realized that I'm not at the top even if I become the number one through frantic efforts," Lee told Yonhap. "Even if I become the number one, there is an entity that cannot be defeated." For years, Go was considered beyond the reach of even the most sophisticated computer programs. The ancient board game is famously complex, with more possible configurations for pieces than atoms in the observable universe. This reputation took a knock in 2016 when the Google-owned artificial intelligence company DeepMind shocked the world by defeating Se-dol four matches to one with its AlphaGo AI system.

As AI becomes more prevalent in industry, more engineers and scientists – not just data scientists – will work on AI projects. They now have access to existing deep learning models and accessible research from the community, which allows a significant advantage than starting from scratch. While AI models were once majority image-based, most are also incorporating more sensor data, including time-series data, text and radar. Engineers and scientists will greatly influence the success of a project because of their inherent knowledge of the data, which is an advantage over data scientists not as familiar with the domain area. With tools such as automated labeling, they can use their domain knowledge to rapidly curate large, high-quality datasets.