Brand Aware

SAN FRANCISCO–(BUSINESS WIRE)–INDUS.AI, a construction software company using computer vision to track and analyze construction project performance in real-time, announced today that it has received an $8 million Series A investment led by Millennium New Horizons with additional participation from strategic investors Foundamental and Groundbreak Ventures. Previous investors Spero Ventures, UP2398, and Bootstrap Labs also joined the round. The new funding will be used to accelerate product development and expand sales, marketing and customer success services. "The construction industry has been a neglected space for too long. This country expends extraordinary energy and capital to build amazing things – tunnels, train stations, skyscrapers and stadiums. But the sad truth is that the vast majority of those projects are painfully over-budget and delayed," said Matt Man, CEO and co-founder.

A growing number of countries are following China's lead in deploying artificial intelligence to track citizens, according to a research group's report published Tuesday. And US tech companies IBM as well as Cisco are among firms whose technology is used most. The Carnegie Endowment for International Peace report says at least 75 countries are actively using AI tools such as facial recognition for surveillance. IBM and Cisco are among the technology firms whose technology is used most, according to the report. The index of countries where some form of AI surveillance is used includes liberal democracies such as the United States and France as well as more autocratic regimes.

Just 10 months after launching its first voice-controlled, video-calling smart displays in the US, Facebook is trying again with new Portal, Portal Mini and Portal TV – and now they are heading for the UK and Europe. The basic premise is very similar to the smart displays sold by Amazon, Google and others. Portal and Portal Mini look like digital photo frames complete with an actual black or white frame around the outside. They display your photos and calendar events, play videos and generally entertain, including streaming Spotify and Amazon Prime Video. They listen out for two hotwords depending on your settings.

The global Machine Learning Chips market reached $ 2,425.6 million in 2017 and is expected to reach $ 37,500 million with a CAGR of 40% by …

The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scale data could substantially expand the clinical inferences derived from the ECG while at the same time preserving interpretability for medical decision-making. We identified 36 186 ECGs from the University of California, San Francisco database that would enable training of models for estimation of cardiac structure or function or detection of disease. We segmented the ECG into standard component waveforms and intervals using a novel combination of convolutional neural networks and hidden Markov models and evaluated this segmentation by comparing resulting electrical intervals against 141 864 measurements produced during the clinical workflow. We then built a patient-level ECG profile, a 725-element feature vector and used this profile to train and interpret machine learning models for examples of cardiac structure (left ventricular mass, left atrial volume, and mitral annulus e-prime) and disease (pulmonary arterial hypertension, hypertrophic cardiomyopathy, cardiac amyloid, and mitral valve prolapse).

Critics of such an idea have, in the past, argued that any tax on a machine, be it a robot, a car or an AI tax, would be anti-business -- but then it would be stretching credibility to suggest Bill Gates is anti-business. "You ought to be willing to raise the tax level and even slow down the speed of automation," said the co-founder of Microsoft. He warned: "You cross the threshold of job replacement of certain activities all sort of at once. If you want to do [something about] inequality… government's got a big role to play." There are multiple problems with the idea.

With the advent of cloud computing, e-commerce, and social media, it's difficult to keep tabs on who has access to our data, and harder still to know how much care they're taking with it -- barely a day goes by without some form of data-breach, lapse, or privacy scandal coming to the fore. But what constitutes "data-misuse" is covered by a broad gamut of scenarios that reach beyond poor security hygiene. Online tracking and profiling is rife -- it turns out there is a heap of money to be made from knowing where you are, what you do, and what you like. It all comes down to personalization: selling things, be it products, playlists, or a political ideology, based on who you are. The Facebook and Cambridge Analytical, which highlighted how social networks armed with vast banks of personal data could be leveraged to profile voters and micro-target with personalized political ads, was something of a watershed moment in terms of elevating the issue of data-privacy and abuse into the public consciousness.

Shopping centre owner Hammerson is utilising its CCTV cameras for more than just security, as it looks to better manage its space and support business growth for its tenants and itself. Working with Deepnorth – a company that says it provides artificial intelligence (AI) for a physical world – the property group is tracking anonymised footfall and counting people going in and out of particular zones. The aim is to gain deeper insights into flow and traffic numbers around its centres, using AI-enabled technology that grows in sophistication the more it is used. The group said in July that it had started a trial at Westquay shopping centre to monitor customer behaviour, but Kathyrn Malloch, head of customer experience at Hammerson, has provided Essential Retail with more details. Against the backdrop of some high-profile controversial deployments of facial recognition systems in the UK, she points out Hammerson's decision not to use such technology despite its availability.

It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. The classification rule must be reformulated if costs/utilities or sampling criteria change. Predictions are separate from decisions and can be used by any decision maker. Classification is best used with non-stochastic/deterministic outcomes that occur frequently, and not when two individuals with identical inputs can easily have different outcomes.