I've been talking in recent posts about how our typical methods of testing AI systems are inadequate and potentially unsafe. In particular, I've complained that all of the headline-grabbing papers so far only do controlled experiments, so we don't how the AI systems will perform on real patients. Today I am going to highlight a piece of work that has not received much attention, but actually went "all the way" and tested an AI system in clinical practice, assessing clinical outcomes. They did an actual clinical trial! Big news … so why haven't you heard about it?

Today's Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from AnalySwift, Blue Prism, Microsoft, Wego, OneLogin and Extreme Reach. An innovation that helps speed the design of fishing rods, skis and cell phone electronics soon will help NASA do its work in space. AnalySwift LLC, a Purdue University-affiliated commercial software provider, has received a $125,000 Small Business Technology Transfer (STTR) grant from NASA. Blue Prism, a global leader in Robotic Process Automation (RPA), announces an expanded technical partnership with IBM. The collaboration integrates three core capabilities of IBM Cloud Pak for Automation, which includes Workflow, Capture and Decisions, with Blue Prism's Digital Workforce.

Today's Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from AnalySwift, Blue Prism, Microsoft, Wego, OneLogin and Extreme Reach. An innovation that helps speed the design of fishing rods, skis and cell phone electronics soon will help NASA do its work in space. AnalySwift LLC, a Purdue University-affiliated commercial software provider, has received a $125,000 Small Business Technology Transfer (STTR) grant from NASA. Blue Prism, a global leader in Robotic Process Automation (RPA), announces an expanded technical partnership with IBM. The collaboration integrates three core capabilities of IBM Cloud Pak for Automation, which includes Workflow, Capture and Decisions, with Blue Prism's Digital Workforce.

Will the proliferation of AI and machine learning reinforce the worldwide digital divide? It's one of the questions the Global Talent Competitiveness Index (GTCI) and Global Cities Talent Competitiveness Index (GCTCI) seek to answer by benchmarking the ability of countries and cities to compete for talent. An answer has historically proven elusive, but the 7th annual reports published by Insead, Adecco Group, and Google suggest it might instead provide "significant" opportunities despite the fact that AI skills are "scarce" and "unequally distributed" across nations. "AI is changing many facets of business and society and, if properly used and governed, has potential to foster sustainable development," said Katell Le Goulven, executive director of the Insead Hoffmann Global Institute for Business and Society. "The GTCI report argues that with multi-stakeholder cooperation the technology could help achieve some of the SDGs [the United Nations' Sustainable Development Goals] such as those related to health (via personalized remote diagnosis and big data analysis to track and reduce endemic disease). But it also points to the imperative of closing the global digital skills gap to harness the potential of AI for good."

How to assess the potential of liking a city or a neighborhood before ever having been there. The concept of urban quality has until now pertained to global city ranking, where cities are evaluated under a grid of given parameters, or either to empirical and sociological approaches, often constrained by the amount of available information. Using state of the art machine learning techniques and thousands of geotagged satellite and perspective images from diverse urban cultures, this research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Indexes.

ABOUT A CENTURY ago, engineers created a new sort of space: the control room. Before then, things that needed control were controlled by people on the spot. But as district heating systems, railway networks, electric grids and the like grew more complex, it began to make sense to put the controls all in one place. Dials and light bulbs brought the way the world was working into the room. Levers, stopcocks, switches and buttons sent decisions back out. By the 1960s control rooms had become a powerful icon of the modern. At Mission Control in Houston, young men in horn rimmed glasses and crewcuts sent commands to spacecraft heading for the Moon. In the space seen through television sets, travellers exploring strange new worlds did so within an iconic control room of their own: the bridge of Star Trek's USS Enterprise. A hexagonal room built in Santiago de Chile a decade later fitted right into the same philosophy--and aesthetic. It had an array of screens full of numbers and arrows. It was linked to a powerful computer. It had futuristic swivel chairs, complete with geometric buttons in the armrests to control the displays. Unlike the Johnson Space Centre and the Enterprise, it even had a small bar where occupants could serve themselves drinks after a hard day's controlling.

In 1985 the US pulled the plug on a computer-controlled anti-aircraft tank after a series of debacles in which its electronic brain locked guns onto a stand packed with top generals reviewing the device. Mercifully it didn't fire, but did subsequently attack a portable toilet instead of a target drone. The M247 Sergeant York (pictured above) may have been an embarrassing failure, but digital technology and artificial intelligence (AI) have changed the game since then. Today defence contractors around the world are competing to introduce small unmanned tracked vehicles into military service. Just like an army on the move, there are contrasting views about how far and how fast this technology will advance.

In July 1950, a small group of American soldiers called Task Force Smith were all that stood in the way of an advance of North Korean armor. The soldiers' only anti-armor weapons were bazookas left over from World War II. The soldiers of Task Force Smith quickly found themselves firing round after round of bazooka ammunition into advancing North Korean T-34s only to see them explode harmlessly on the heavily armored tanks. Within seven hours, 40 percent of Task Force Smith were killed or wounded, and the North Korean advance rolled on.1 The shortcomings of the bazooka were no surprise. However, budget cutbacks after World War II scuttled adoption of an improved design.

In the era of Smart Cities, social media and the Internet itself offer a constant flow of people's concerns and desires, a piece of information that can help decision-makers to act faster and more efficiently. People have become their vital core, but sometimes thousands and thousands of data can be difficult to read. Citibeats is the fastest and most efficient social intelligence and speech analytics platform in the market. Based on natural language processing (NPL) and machine learning, Citibeats organizes unstructured data. Our platform allows for the gathering of relevant information in changing social contexts, which permits public organizations and financial institutions to react more quickly and efficiently to the needs of citizens.

The global deep learning market is expected to grow at a CAGR of 51.1% from forecast period 2019 to 2026 and expected to reach the value of around US$ 56,427.2 Deep learning is a subdivision of machine learning in artificial intelligence (AI) concerned with the algorithm inspired by the functioning of human brain termed as artificial neural networks. It is also termed as deep neural learning or deep neural network. Deep learning is evolved with the increasing amount of unstructured data due to digitalization. The available amount of data is utilized in deep learning to process or understand that data for effective decision making in various industry verticals including healthcare, manufacturing, automotive, agriculture, retail, security, human resources, marketing, law, and fintech.