The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. This study aimed to explore psychiatrists' opinions about the potential impact of innovations in artificial intelligence and machine learning on psychiatric practice. In Spring 2019, we conducted a web-based survey of 791 psychiatrists from 22 countries worldwide. The survey measured opinions about the likelihood future technology would fully replace physicians in performing ten key psychiatric tasks. This study involved qualitative descriptive analysis of written response to three open-ended questions in the survey. Comments were classified into four major categories in relation to the impact of future technology on patient-psychiatric interactions, the quality of patient medical care, the profession of psychiatry, and health systems. Overwhelmingly, psychiatrists were skeptical that technology could fully replace human empathy. Many predicted that 'man and machine' would increasingly collaborate in undertaking clinical decisions, with mixed opinions about the benefits and harms of such an arrangement. Participants were optimistic that technology might improve efficiencies and access to care, and reduce costs. Ethical and regulatory considerations received limited attention. This study presents timely information of psychiatrists' view about the scope of artificial intelligence and machine learning on psychiatric practice. Psychiatrists expressed divergent views about the value and impact of future technology with worrying omissions about practice guidelines, and ethical and regulatory issues.

The rapid rate of change enabled by software make this industry more vulnerable than most to the falling behind on the innovation curve. This problem has only accelerated in recent years as the number of disruptive technologies have grown at an exponential rate fueled by the growing size of the market and the number of software engineers. The open source community has been a driving source of disruptive technologies such as big data Hadoop and Spark, JavaScript frameworks like Angular and React, and machine learning frameworks like TensorFlow. Software based companies who do not embrace these disruptive technologies face the ever-increasing risk of being pushed aside by those that do. To make this even more challenging, the skills required to enhance the current product and the skills required to innovate using new disruptive technologies are different.

IBM this afternoon issued several updates to its public cloud and Watson AI portfolios, including news that Aegean Airlines, BNP Paribas, ExxonMobil, Elaw Tecnologia SA (a Brazil-based legal management company) and Home Trust "are selecting IBM public cloud as their preferred destination for mission critical workloads," Big Blue said. On the AI front, IBM announced updates to its "Watson Anywhere" strategy designed to scale AI across any cloud and to ease AI implementations, according to the company. Drift Detection – Intended to address the concerns about data privacy and algorithm accountability, IBM announced the "Drift Detection" capability within Watson OpenScale, an AI platform launched last year to detect bias and to enable understanding of how AI arrived at its results. IBM said Drift Detection Drift Detection indicates how far a model has "drifted" by comparing production and training data and the resulting predictions it creates. Alerts are issued when a user-defined drift threshold is exceeded.

Artificial Intelligence is changing banking, health, business, and the military. But so far, it has been slow to go big in K-12 education, said Scott Garrigan, a professor at Lehigh University at a session at the International Society for Technology in Education's annual conference here. But that is likely to change in the coming years, he said. No sector will be untouched by AI. It will produce changes as big as the automobile," Garrigan said. "We have no idea what's going to happen as AI rolls out massively.

Satellites could help locate stranded whales more efficiently and in real-time. Scientists have begun harnessing the power of the technology's high-resolution imagery to detect and monitor whales stranded on the shore from space. The team noted that the use of satellites will help find stranded whales in remote locations, as well as spot potentially deteriorating ocean conditions. Satellites could help locate stranded whales more efficiently and in real-time. Scientists have begun harnessing the power of the technology's high-resolution imagery to detect and monitor whales stranded on the shore from space Chile witnessed one of the largest mass mortality of baleen whales in 2015 on the remote beaches of Patagonia – at least 343 died.

Gartner, Inc. today highlighted the top strategic technology trends that organizations need to explore in 2020. Analysts presented their findings during Gartner IT Symposium/Xpo, which is taking place here through Thursday. Gartner defines a strategic technology trend as one with substantial disruptive potential that is beginning to break out of an emerging state into broader impact and use, or which is rapidly growing with a high degree of volatility reaching tipping points over the next five years. "People-centric smart spaces are the structure used to organize and evaluate the primary impact of the Gartner top strategic technology trends for 2020," said David Cearley, vice president and Gartner Fellow. "Putting people at the center of your technology strategy highlights one of the most important aspects of technology -- how it impacts customers, employees, business partners, society or other key constituencies. Arguably all actions of the organization can be attributed to how it impacts these individuals and groups either directly or indirectly. This is a people-centric approach."

Artificial Intelligence (AI) is having a broad and deep impact on the way services are exported globally. Be it for good or bad, there is no getting away from the reality that AI is an agent of disruption. One of the perennial front-runners of Nearshore outsourcing, Costa Rica, appears to be adapting to the AI opportunity faster than most countries in the region. Local companies are intensifying their AI development operations and a number of AI technologies are gaining traction there – all of which will influence Costa Rica's positioning in the next-generation of services delivery. The Latin American nation of nearly five million has long been seen as a tech epicenter of Central America ever since Intel chose it to open the biggest microchip factory in the region in 1997, with an initial investment of US$800 million.

People have more trust in robots than their managers, according to the second annual AI at Work study conducted by Oracle and Future Workplace. The study of 8 370 employees, managers and HR leaders across 10 countries, found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent. Contrary to common fears around how AI will impact jobs, employees, managers and HR leaders across the globe are reporting increased adoption of AI at work and many are welcoming AI with love and optimism. AI is becoming more prominent with 50% of workers currently using some form of AI at work compared to only 32% last year. Workers in China (77%) and India (78%) have adopted AI over two-times more than those in France (32%) and Japan (29%).

JOURNAL OF L A T EX CLASS FILES, VOL., NO., 1 Single V ersus Union: Nonparallel Support V ector Machine Frameworks Chun-Na Li, Y uan-Hai Shao, Huajun Wang, Y u-Ting Zhao, Ling-Wei Huang, Naihua Xiu and Nai-Y ang Deng Abstract --Considering the classification problem, we summarize the nonparallel support vector machines with the nonparallel hyperplanes to two types of frameworks. It solves a series of small optimization problems to obtain a series of hyperplanes, but is hard to measure the loss of each sample. The other type constructs all the hyperplanes simultaneously, and it solves one big optimization problem with the ascertained loss of each sample. We give the characteristics of each framework and compare them carefully. In addition, based on the second framework, we construct a max-min distance-based nonparallel support vector machine for multiclass classification problem, called NSVM. Experimental results on benchmark data sets and human face databases show the advantages of our NSVM. I NTRODUCTION F OR binary classification problem, the generalized eigenvalue proximal support vector machine (GEPSVM) was proposed by Mangasarian and Wild [1] in 2006, which is the first nonparallel support vector machine. It aims at generating two nonparallel hyperplanes such that each hyperplane is closer to its class and as far as possible from the other class. GEPSVM is effective, particularly when dealing with the "Xor"-type data [1]. This leads to extensive studies on nonparallel support vector machines (NSVMs) [2]-[5].

Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision is estimating the unknown accuracies and correlations of these sources without using labeled data. Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence. We propose Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data. Theoretically, we prove that Dugong, under mild conditions, can uniquely recover the unobserved accuracy and correlation parameters and use parameter sharing to improve sample complexity. Our method assigns clinician-validated labels to population-scale biomedical video repositories, helping outperform traditional supervision by 36.8 F1 points and addressing a key use case where machine learning has been severely limited by the lack of expert labeled data. On average, Dugong improves over traditional supervision by 16.0 F1 points and existing weak supervision approaches by 24.2 F1 points across several video and sensor classification tasks.