We consider first order gradient methods for effectively optimizing a composite objective in the form of a sum of smooth and, potentially, non-smooth functions. We present accelerated and adaptive gradient methods, called FLAG and FLARE, which can offer the best of both worlds. They can achieve the optimal convergence rate by attaining the optimal first-order oracle complexity for smooth convex optimization. Additionally, they can adaptively and non-uniformly re-scale the gradient direction to adapt to the limited curvature available and conform to the geometry of the domain. We show theoretically and empirically that, through the compounding effects of acceleration and adaptivity, FLAG and FLARE can be highly effective for many data fitting and machine learning applications.

Using Keras to train deep neural networks with multiple GPUs (Photo credit: Nor-Tech.com). Keras is undoubtedly my favorite deep learning Python framework, especially for image classification. I use Keras in production applications, in my personal deep learning projects, and here on the PyImageSearch blog. I've even based over two-thirds of my new book, Deep Learning for Computer Vision with Python on Keras. However, one of my biggest hangups with Keras is that it can be a pain to perform multi-GPU training.

Approximately 658 corporate decision makers have been surveyed for their confidence in their own decision-making skills and their acceptance of the importance of Artificial Intelligence (A.I.) in general as well as in augmenting (or replacing) their decision making. Furthermore, the survey reveals the general perception of the corporate data-driven environment available to decision maker, e.g., the structure and perceived quality of available data. A comprehensive overview and analysis of our AI sentiments as it relates to corporate decision making is provided as a function Gender, Age, Job-level, Work area and Education. You don't need to make an effort to find articles, blogs, social media postings, books and insights in general on how Artificial Intelligence (hereafter abbreviated A.I.) will provide wonders for all human beings, society and leapfrog corporate efficiencies and shareholder values for the ones adapting to A.I. (of which you would be pretty silly not too of course).

The Shenzhen-listed iFlyTek said on Thursday its intelligent doctor's assistant, which works similarly to IBM's Watson, has become the first AI robot to pass the exam taken by medical students training to become licensed doctors in China. "We have leapfrogged IBM's Watson in becoming the first AI [robot] to qualify as a human doctor. Watson hasn't passed such a licensing exam in the United States," said Liu Qingfeng, the chairman of iFlyTek, which is headquartered in central China's Anhui province. The robot, called the iFlyTek Smart Doctor Assistant, achieved a score of 456, higher than the mark of 360 required to pass the Clinical Practitioner Examination. After showing AI's power in the exam, Liu said the company was going to use the technology, which allows machines to talk and even think like humans, to "empower the world" by starting to change the education, medical care and law industries.

While SQL Server 2017 continues to get attention for opening up to Linux, many of Microsoft's database advances revolve around various ways the company is opening up analytics on its flagship database. Trends come and go, but your DB strategy shouldn't be a flavor of the month. Learn why you shouldn't get distracted by new DB technology, how Facebook is using a RDBMS to do the data slicing and dicing they can't in Hadoop, and more. You forgot to provide an Email Address. This email address doesn't appear to be valid.

Anoop Dawar oversees global product management and product marketing at MapR Data Technologies. He comes to MapR with over a decade of experience leading product management and development teams at Aerohive (HIVE) and Cisco (CSCO). His scientific approach to product management and marketing stems from his background in business and technology, as both a practitioner and student. Anoop holds an MS degree in Computer Science from the University of Texas Austin, and an MBA from The Wharton School at the University of Pennsylvania.

Dr. Iraj Sadegh Amiri received his B. Sc (Applied Physics) from Public University of Urmia, Iran in 2001 and a gold medalist M. Sc. in optics from University Technology Malaysia (UTM), in 2009. He was awarded a PhD degree in photonics in Jan 2014. He has published well over 350 academic publications since the 2012s in optical soliton communications, laser physics, photonics, optics and nanotechnology engineering. Currently he is a senior lecturer in University of Malaysia (UM), Kuala Lumpur, Malaysia. O.A. Akanbi received his B. Sc. (Hons, Information Technology - Software Engineering) from Kuala Lumpur Metropolitan University, Malaysia, M. Sc. in Information Security from University Teknologi Malaysia (UTM), and he is presently a graduate student in Computer Science at Texas Tech University His area of research is in CyberSecurity.

I cannot emphasise how much that last point has contributed to my motivation to keep going. I'm personally not the type of learner inclined to spend months getting to know the basics before i get my hands dirty, so figuring out a small project on which i can start working right away proved to be an immense help in keeping me motivated throughout my learning journey. Let's be clear: you will not know how to do most of the things you want to do; but the simple fact that you WANT to get something done will help provide the motivation to search for more info on how to get it done. That will then lead you to other questions, then other questions, towards something that will soon feel like a bottomless rabbit hole! Keep going though, as many others have also gone into that deep pit and came out safely at the other end!

How soon do you need to prepare for artificial intelligence? Artificial intelligence is already here – it's no longer a futuristic promise. And it's been here for years. Companies should already be thinking about how they can automate many of their ordinary marketing processes. This is the basic step that every company should take to make themselves more efficient.

In a usual scenario today, a salesperson might receive a draft of a nondisclosure agreement from a potential customer and forward it to a company's in-house lawyers. It could take a couple days for the legal team to review the contract and send it back--or a couple of weeks. As the salesperson waits, he or she loses the ability to move the deal forward. "There are only a few pieces or items that you care about, but there's a labyrinth of clauses, so you don't know what will trip it up," said Andrew (Ondřej) Antos, Klarity's CEO. "We decided to use natural language processing and AI to accelerate review."