Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. Multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q-learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent: advantage actor critic (A2C), within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under simulated peak-hour traffic dynamics. Results demonstrate its optimality, robustness, and sample efficiency over other state-of-the-art decentralized MARL algorithms.

The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. A social trust and preference segmentation-based matrix factorization (SPMF) recommendation system is proposed to solve the above-mentioned problems. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly higher than that of some state-of-the-art recommendation algorithms. The proposed SPMF algorithm is a more accurate and effective recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

Russian executives are at least 7.7 percent ahead when it comes to adopting the technology, Microsoft said, adding that in France, for example, the level of AI use amounts to 10 percent. The corporation unveiled the research titled "Business Leaders in the Age of AI" earlier this week. Some 800 executives from eight countries – France, Germany, Italy, the Netherlands, Russia, Switzerland, the UK and the US – took part in the survey, which was conducted back in January among companies with more than 250 employees. "We see that the interest to solutions based on artificial intelligence from businesses in Russia has increased significantly over the past year," ABBYY Russia CEO Dmitry Shushkin said. He revealed that the software company's revenues from AI-based projects rose 63 percent in 2018.

Doctors can sometimes make a diagnosis when faced with cataracts and blurry eye scans. The Google system still struggles to do this. It is trained largely on clear, unobstructed images of the retina, though Google is exploring the use of lower-quality images. Even with this limitation, Dr. Kim said, the system can augment what doctors can do on their own. Aravind already operates small vision centers in many of the cities and villages surrounding Madurai.

After revolutionizing various industry sectors, the introduction of artificial intelligence in healthcare is transforming how we diagnose and treat critical disorders. A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists. Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy. With further improvements and the support of other advanced technologies like machine learning, AI is getting smarter with time.

Writing is the foundation of communication. Not too long ago, we hand-wrote messages to friends, wrote our homework for school, our to-do lists on paper and announced our personal news the same way. Though we may not use a pen or paper as we once did, we communicate today by typing, be it messages to friends, posts on social media, preparing a PowerPoint presentation, messaging on our phones or laptops. Written communication remains a huge part of our everyday life. How we communicate in writing has evolved.

Sella Nevo, a software engineer who specializes in machine learning (ML) research and development, currently leads the Google Flood Forecasting Initiative that aims to provide flood forecasts and warnings in developing countries. He was also one of the co-creators of the ML model used in Google Duplex. In a phone interview from Israel, Nevo--a keynote speaker at the Mint Digital Innovation Summit explains why Google chose Patna in India as a pilot for his project, and how the company plans to scale up this model not only in India but globally too. This Flood Forecasting Initiative is Google's effort to provide high accuracy, high resolution flood forecasting. It's not global yet, and our focus is in using the Google's machine learning (ML) expertise and our computational power as well as our access to various types of resources and data to substantially improve flood forecasting systems, their accuracy, their lead time and so on.

Chinese companies have surged ahead of their U.S. counterparts on a Nikkei ranking of the top 50 patent filers for artificial intelligence over the past three years, expanding their presence in the world's most prominent high-tech battleground. In the three years between 2016 and 2018, China more than doubled the amount of companies in the top 50 to 19 - up from eight in the previous three-year span. Meanwhile, the U.S. kept a tight grip on the top three spots, but only had 12 companies in the top 50 - down from 19 in the previous ranking. The rankings come after U.S. President Donald Trump announced the American AI Initiative last month, a plan to increase research and development in the sector but lacked any specific funding to towards that aim. IBM led the way for the U.S. with 3,000 applications.