Today NVIDIA announced the NVIDIA EGX Edge Supercomputing Platform – a high-performance, cloud-native platform that lets organizations harness rapidly streaming data from factory floors, manufacturing inspection lines and city streets to securely deliver next-generation AI, IoT and 5G-based services at scale, with low latency. Early adopters of the platform – which combines NVIDIA CUDA-X software with NVIDIA-certified GPU servers and devices – include Walmart, BMW, Procter & Gamble, Samsung Electronics and NTT East, as well as the cities of San Francisco and Las Vegas. We've entered a new era, where billions of always-on IoT sensors will be connected by 5G and processed by AI," said Jensen Huang, NVIDIA founder and CEO, at a keynote at the start of MWC Los Angeles. "Its foundation requires a new class of highly secure, networked computers operated with ease from far away. "We've created the NVIDIA EGX Edge Supercomputing Platform for this world, where computing moves beyond personal and beyond the cloud to operate at planetary scale," he said.

Measures to boost trade and investment and cooperation in new areas such as artificial intelligence are expected to be on the agenda during German Chancellor Angela Merkel's meeting with Prime Minister Narendra Modi in New Delhi on November 1. Merkel will arrive in India on October 31 for the day-long visit at Modi's invitation. She will be accompanied by several ministers and state secretaries of the German government and a high-level business delegation. The two Leaders will also have a separate engagement with CEOs and business leaders of both sides. As part of the inter-governmental consultations, Indian ministers and their German counterparts will hold initial discussions in their respective areas, and the outcome of these talks will be reported on at the meet co-chaired by Modi and Merkel. During the consultations, the two sides will discuss ways to deepen cooperation in traditional sectors such as transport, skill development and energy, and explore possibilities for working together in new areas such as artificial intelligence and green urban mobility, the external affairs ministry said on Friday.

In machine learning, Feature Selection is the process of choosing features that are most useful for your prediction. Although it sounds simple it is one of the most complex problems in the work of creating a new machine learning model. In this post, I will share with you some of the approaches that were researched during the last project I led at Fiverr. You will get some ideas on the basic method I tried and also the more complex approach, which got the best results -- removing over 60% of the features, while maintaining accuracy and achieving more stability for our model. I'll also be sharing our improvement to this algorithm.

Editor's Note: At ODSC Europe 2019, Sray Agarwal will conduct a workshop on fairness and accountability demonstrating how to detect bias and remove bias from ML models. The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE, MAPE, AUC, ROC, Gini, etc are considered to be high performing models. While such accuracy metrics important, are there other metrics that the data science community has been ignoring so far? The answer is yes--in the pursuit of accuracy, most models sacrifice "fairness" and "interpretability."

These topics are described in separate white papers prepared jointly by the organizers. The breakout sessions will produce reports summarizing results. Almost 100 experts are expected to participate.

The days of construction projects running behind schedule and over budget could soon be over as AI technology tries to solve the U.S. productivity puzzle. Disperse, an AI-powered construction firm, has raised fresh finance to expand into the U.S. in a bid to tackle inefficiencies on building sites. The company's technology uses visual snapshots of construction projects to alert managers about potential problems before they happen. The construction sector has been grappling with low levels of productivity for decades, with underinvestment in technology one of the key factors. Closing the productivity gap in global construction could be worth $1.6 trillion a year, with a third of that coming in the U.S., according to the McKinsey Global Institute.

In July 2017, China's State Council released the country's strategy for developing artificial intelligence (AI), entitled'New Generation Artificial Intelligence Development Plan' (新一代人工智能发展规划). This strategy outlined China's aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan ($150 billion) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China's AI policies or have assessed the country's technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China's AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use.

Businesses in the UK need to move beyond tinkering with artificial intelligence (AI) and get on with seriously harnessing the technology at scale to compete on the global stage, a report by Microsoft has said. The tech giant has found that organisations already using AI at scale are performing better than those which are not, by making them more productive, showing higher profitability and producing better business outcomes. But it warned that there is a clear and widening gap between companies using it fully and those still in the early testing phase or simply not implementing it at all. It fears firms being cautious because of the current political uncertainty could miss out, with the retail sector among the weakest adopter surveyed, while financial services have ramped up their approach to AI. Some 43% of the finance-related companies who participated in the study said they used AI for more automation this year, compared with 28% the year before.

Artificial intelligence, in any meaningful sense, doesn't exist. Every example of what is sometimes taken to be AI is in fact a case of the'robotic fallacy'1. This fallacy is to mistake an instance of seemingly intelligent behaviour for the existence of an underlying faculty of intelligence. That is, one sees or hears an AI program or robot say or do something which, if it were human, would be associated with a general level of intelligence. And one tends to assume it also has – or could come to possess – that intelligence. But in fact the behaviour fell into what, by human standards, is a very narrow and customised domain. There is little, if anything, else that the AI can offer in the way of apparently intelligent actions. And it's not a question of waiting a little while until researchers have worked out how to attain AI. There is a vast chasm they need to cross. And that chasm, it will be argued here, exists in part because of a failure to recognise the nature of symbolic systems. To make things more concrete, here is pseudocode for a type of'AI' program2 typified by Alexa, Siri and other virtual assistants or bots: Technology called machine learning turns the human's spoken words into text and performs'natural language processing' to break them down to fit a template of (greeting, name).

It is no surprise that following the massive success of deep learning technology in solving complicated tasks, there is a growing demand for automated deep learning. Even though deep learning is a highly effective technology, there is a tremendous amount of human effort that goes into designing a deep learning algorithm (Figure 1). The field of automated deep learning is concerned with automating this process by finding suitable preprocessing techniques and architecture designs along with training routines and configurations required to obtain a well performing deep learning model. This drive for automation has led to a lot of interesting research work. Recently IBM launched their service of NeuNetS to automatically synthesize deep neural network models for various business applications.