Thank you for that introduction, Yll [ILL-ee] (Bajraktari, NSCAI Executive Director) [Bah-j-Rock-Tar-ee]. And let me also thank Representative Stefanik [Steff-ON-ick]…for her leadership and passion for AI's nexus with national security. It is truly an honor to address all of you at the National Security Commission on Artificial Intelligence Conference … on the future of AI and national security. Today's theme is strength through innovation … and that is exactly as it should be. For innovation is the lifeblood of our country… and a vital source for our security.

Artificial intelligence, big data and machine learning will increasingly be put to use monitoring the energy performance of buildings and helping owners cut costs, according to Keith Gunaratne, the founder and managing director of technology firm EP&T. Founded in 1993, EP&T specialises in the development of energy conservation technologies in the commercial sector. One of its products, Edge Zeus - an AI and machine-learning platform that allows energy performance to be monitored and controlled through a mobile device – has been deployed in 12 buildings in the portfolio of the Abu Dhabi Financial Group, or ADFG. In one of the buildings – Abu Dhabi's Seaside Tower – the technology has led to a 29 percent reduction in energy consumption and cost savings of over AED 626,000 in a 12-month period. "What we would like to see in the local market is an accommodative environment that fosters the take-up of data science as a solution that remains attractive to the c-suite," Gunaratne said.

This is just the beginning. Data science and machine learning are driving image recognition, autonomous vehicles development, decisions in the financial and energy sectors, advances in medicine, the rise of social networks, and more. Linear regression is an important part of this. Linear regression is one of the fundamental statistical and machine learning techniques. Whether you want to do statistics, machine learning, or scientific computing, there are good chances that you'll need it. It's advisable to learn it first and then proceed towards more complex methods. By the end of this article, you'll have learned: Free Bonus: Click here to get access to a free NumPy Resources Guide that points you to the best tutorials, videos, and books for improving your NumPy skills. Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them. For example, you can observe several employees of some company and try to understand how their salaries depend on the features, such as experience, level of education, role, city they work in, and so on. This is a regression problem where data related to each employee represent one observation.

AI is helping Royal Dutch Shell locate new oil and gas sources. One of the company's 280 AI projects is aimed at helping the company find new sources of oil and gas by cleaning up data from seismic surveys, which are used to create images of rock formations that in turn help scientists locate oil deposits below the ocean floor. The problem, historically, has been that these surveys don't paint a clear picture of what rock formations look like. Underwater currents and other factors produce noisy data that affects the images. Shell created machine-learning algorithms, based on images the company has cleaned, to filter out that noise.

A worker lifts a lunch bowl off the production line at Spyce, a restaurant which uses a robotic cooking process, in Boston. Robots aren't replacing everyone, but a quarter of U.S. jobs will be severely disrupted as artificial intelligence accelerates the automation of today's work, according to a Brookings Institution report. A worker lifts a lunch bowl off the production line at Spyce, a restaurant which uses a robotic cooking process, in Boston. Robots aren't replacing everyone, but a quarter of U.S. jobs will be severely A worker lifts a lunch bowl off the production line at Spyce, a restaurant which uses a robotic cooking process, in Boston. Robots aren't replacing everyone, but a quarter of U.S. jobs will be severely disrupted as artificial intelligence accelerates the automation of today's work, according to a Brookings Institution report.

With my ongoing goal to be the Purchaser of the Future, and thanks to my current studies on Machine Learning, a new world has opened-up to me, and I can tell you, it's been very addictive. In the beginning of 2019, apart from the autonomous vehicles and the autonomous ship project from Kongsberg, I had no idea about what was really going on the field of #artificialintelligence. Checking here and there, I decided to dig into it, so I shared 10 Amazing Projects using DIGITALISATION within Maritime. So many nice projects I have discovered last month! Really amazing to see these fantastic ideas being transformed into algorithms, using all this DATA to improve performance, decision taking and with an inimaginable speed. Either on Maritime or in the Oil&Gas, here are some of the companies that really impressed me.

Airlines are continuing to look at new ways to adapt to changes in the industry. As 2020 approaches, firms are turning to new technologies to remain ahead of the competition during the next decade. As 2019 draws to a close, there have been many breakthroughs in technology to assist aviation markets. One key segment that companies have been looking at is jet fuel. The carbon footprint involved with sourcing and using jet fuel has led to a global call for the revision of its role in flying.

This paper proposes a formal approach to learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming knowledge of a bound on the maximal rate of change of system dynamics. Such an approach generalizes the estimation method commonly used in learning algorithms for unknown Markov decision processes with time-invariant transition probabilities, but is also able to quickly and correctly identify the system dynamics following a change. Based on the proposed method, we generalize the exploration bonuses used in learning for time-invariant Markov decision processes by introducing a notion of uncertainty in a learned time-varying model, and develop a control policy for time-varying Markov decision processes based on the exploitation and exploration trade-off. We demonstrate the proposed methods on four numerical examples: a patrolling task with a change in system dynamics, a two-state MDP with periodically changing outcomes of actions, a wind flow estimation task, and a multi-arm bandit problem with periodically changing probabilities of different rewards.

Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial time series forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.