In recent years, Deep Learning (DL) has demonstrated outstanding capabilities in solving 2D-image tasks such as image classification, object detection, semantic segmentation, etc. Not an exception, DL has showed tremendous progresses in applying it to 3D graphic problems. In this post we will explore a recent attempt of extending DL to the Single image 3D reconstruction task, one of the most important and profound challenge in the field of 3D computer graphics. A single image is only a projection of 3D object into a 2D plane, so some data from the higher dimension space must be lost in the lower dimension representation. Therefore from a single-view 2D image, there will never be enough data construct its 3D component.

Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme.

The Re•Work AI in Insurance Summit in New York City was held September 5–6 and saw 60 speakers from AVIVA, Travelers, GoCompare, Prudential and other insurance-related companies cover a wide range of topics -- from detecting claims fraud to applying machine learning to underwriting and maximizing revenue. Today's specialty and commercial insurance underwriters face an overwhelming number of challenges. AXIS Capital Senior Data Scientist Min Yu believes artificial intelligence (AI) will transform the specialty and commercial insurance underwriting from a "detect and repair" mode to "predict and prevent" mode. In her talk on Machine Learning to Specialty Insurance Underwriting, Yu outlined the AI process as follows: receive a submission, retrieve data, analyze risk, automate quote and quick binding. Manual underwriting would be mainly used for review, or on complicated or emerging risks.

Successful artificial intelligence (AI) and machine learning (ML) initiatives bring value to the entire organization by delivering insights to the right person or system at the right time within the right context. But many organizations are unable to do this because they are too focused on algorithms. Data science is more than neural networks and deep learning! Organizations need to instead leverage people, processes, and technology to infuse AI and ML into business processes. It sounds simple, only four ingredients: flour, water, yeast, and a bit of salt.

I grew up in a small manufacturing town in Northeast Iowa. The factory in my hometown made tractors (no surprise given that it was Iowa), but eventually the economics of cheap foreign labor and an interconnected global economy caught up with that factory – as it did with many US-based manufacturers – and soon the factory closed, and many people were laid off. But the technology world continues to evolve – especially with respect to IoT, Data Science and AI/ML – and so comes an opportunity for manufacturing to make a big return to the US. However, tomorrow's manufacturing battles won't be fought with cheap labor. In fact, measuring a country's manufacturing strength by the number of manufacturing jobs is fighting yesteryear's battle.

Bots removed opponents' tools from the game space, and launched themselves into the air… Two teams of AI agents tasked with playing a game (or million) of hide and seek in a virtual environment developed complex strategies and counterstrategies – and exploited holes in their environment that even its creators didn't even know that it had. The game was part of an experiment by OpenAI designed to test the AI skills that emerge from multi-agent competition and standard reinforcement learning algorithms at scale. OpenAI described the outcome in a striking paper published this week. The organisation, now heavily backed by Microsoft, described the outcome as further proof that "skills, far more complex than the seed game dynamics and environment, can emerge" (from such experiments/training exercises). Some of its findings are neatly captured in the video below.

Amid the shift towards more complex chips at advanced nodes, many chipmakers are exploring or turning to advanced forms of machine learning to help solve some big challenges in IC production. A subset of artificial intelligence (AI), machine learning, uses advanced algorithms in systems to recognize patterns in data as well as to learn and make predictions about the information. In the fab, machine learning promises to provide faster and more accurate results in select areas, such as finding and classifying defects in chips. Machine learning also is used in other process steps, but there are still some challenges to deploy it. It has been used in computing and other fields for decades. It first appeared in semiconductor production in the 1990s. Some saw it as a way to help automate the steps for some manually-driven fab equipment. Over time, machine learning has made staggering progress in computing and elsewhere.

Every artificial intelligence(AI) problem is a new universe of complexities and unique challenges. Very often, the most challenging aspects of solving an AI problem is not about finding a solution but understanding the problem itself. As paradoxically as that sounds, even the most experienced AI experts have been guilty of rushing into proposing deep learning algorithms and exoteric optimization techniques without fully understanding the problem at hand. When we think about an AI problem, we tend to link our reasoning to two main aspects: datasets and models. However, that reasoning is ignoring what can be considered the most challenging aspect of an AI problem: the environment.

Executives and others are increasingly using data when assessing business policies, comparing marketing strategies, and making other decisions. In particular, they use machine learning to analyze data--and the results to make decisions. But how much can an executive trust a recommendation generated by machine learning? Recognizing that uncertainty is involved, and could produce expensive mistakes, Chicago Booth's Max Farrell, Tengyuan Liang, and Sanjog Misra have sought to quantify this uncertainty so that decision makers can take it into account. In the past few years, machine-learning methods have come to dominate data analysis in academia and industry.

Generative Adversarial Networks (GANs) have been a hot topic in Deep Learning ever since their initial invention and publication at NIPS 2014. There's a good reason for it all: GANs can create new content based on only a small bit of guidance. It's that sort of creativity which makes them so powerful. Just to name a few there. With all of this, massive resources are being poured into GAN research to figure out both how they work and how to design the absolute best GAN networks.