Sign in to report inappropriate content. Graham MackIntosh is a pioneer in the field of advanced analytics, working as an AI consultant for NASA and the SETI Institute. Hear how NASA is using AI to understand solar events, detect failures and expand our understanding of the universe.

Google-owned DeepMind is to collaborate with a UK hospital to help doctors spot more than 50 different eye diseases using AI. When it isn't creating artificial intelligence (AI) capable of destroying human opponents in a game of Go, Google-owned DeepMind is trying to build other systems that could transform healthcare, among other things. Now, the UK-based company has revealed a joint research partnership with Moorfields Eye Hospital that could help spot sight-threatening eye diseases much quicker than before. Publishing its findings in Nature Medicine, the company said that its latest AI can quickly run through eye scans taken from routine clinical practice and identify more than 50 serious diseases as accurately as world-leading expert doctors. Under existing systems, ophthalmologists use 3D images called optical coherence tomography (OCT) to create a detailed map of a person's eye.

Paige, a NYC-based leader in computational pathology transforming the diagnosis and treatment of cancer, today announced it has closed its Series B funding round of $45 million, bringing the Company's total capital raised to over $70 million. Healthcare Venture Partners brought the largest contribution to the round, with Breyer Capital, Kenan Turnacioglu, and other funds participating. Paige will use this new capital to drive FDA clearance of its products and expand its portfolio, delving deeper into cancer pathology, novel biomarkers, and prognostic capabilities. Additionally, the Company will accelerate commercial efforts in the U.S. and expansion in Europe, Brazil, and Canada. Pathology is the cornerstone of cancer diagnoses.

Intel is pushing for Responsibility-Sensitive Safety (RSS), a mathematical model for autonomous-vehicle safety conceived by Mobileye (now an Intel company), to become an IEEE standard. The company is spearheading a new working group, IEEE P2846, to pursue "A Formal Model for Safety Considerations in Automated Vehicle Decision Making." The group's first meeting is scheduled for late January in San Jose, Calif. More specifically, the working group seeks to enable industry and government to "align on a common definition of what it means for an automated vehicle to drive safely balancing safety and practicability." Intel sees the initiative as a way to encourage autonomous-vehicle industry to ask -- and grapple with answering -- the hardest question of all in the AV era: How safe is safe enough?

Like electricity or the internet, artificial intelligence (AI) is considered a general purpose technology with the potential to transform productivity, accelerate economic growth and improve wellbeing across the whole of society. It has started, and will continue to, drastically transform the way we work and live. At least, this is what the report'Towards Our Intelligent Future' published by New Zealand AI Forum earlier this year affirms. The report represents over nine months of collaborative work on parallel streams exploring AI adoption, policy and strategy in New Zealand and around the world. It highlights the value of AI for achieving New Zealand's wellbeing, sustainability and economic goals.

The bookstore manager looked at me and said that the computer program that I had developed to analyze the books database was going to run "perpetually" and he was quite steamed about how long it was taking to execute. Well, hold on, let's start this story at the beginning so you'll have some context about what was happening. Back in my college days, I was a gun-for-hire in terms of a willingness to whip together off-the-cuff computer programs for anyone that needed a quick-and-dirty programmable task done, doing so to earn a few extra bucks for those large pepperoni pizzas and kegs of beer that I kept ordering with my classmates. The college bookstore manager had asked me to craft a program that would generate some reports for him. Without taking much time to analyze the situation (that's when I was young and headstrong), I wrote a brute force algorithm that would sort the voluminous data and produce the reports. On a Monday morning, I launched the program and let it fly. In that era, the amount of data involved was considered rather large since it was data for all 30,000 students and included their classes, the books required for their classes, etc. When the bookstore manager asked me how long it would take for the program to run, I hedged and said it would take about a day.

TAIPEI, Taiwan–(BUSINESS WIRE)–CyberLink Corp. (5203.TW), a pioneer of AI and facial recognition technologies, participated in the Intel Edge Computing Solution Summit. The summit brought together leaders from the IoT industry who shared insights on AI edge computing's latest breakthroughs and the opportunities that this technology will bring in the future. Dr. Jau Huang, CyberLink's founder and CEO, was invited to speak about the benefits of edge computing and how it enables precise, fast, affordable and secure AIoT use cases including facial recognition, such as the company's FaceMe AI-based engine. With FaceMe, CyberLink has leveraged edge-based technology and AI to deliver one of the world's most precise, flexible and best performing facial recognition engines. Compared with cloud-based solutions, edge computing is much cheaper, greatly enhances flexibility and provides real-time response, helping system integrators quickly develop and add new functionalities into existing systems and new AIoT products.

--Operating envelope is an important concept in industrial operations. Accurate identification for operating envelope can be extremely beneficial to stakeholders as it provides a set of operational parameters that optimizes some key performance indicators (KPI) such as product quality, operational safety, equipment efficiency, environmental impact, etc. Given the importance, data-driven approaches for computing the operating envelope are gaining popularity. These approaches typically use classifiers such as support vector machines, to set the operating envelope by learning the boundary in the operational parameter spaces between the manually assigned'large KPI' and'small KPI' groups. One challenge to these approaches is that the assignment to these groups is often ad-hoc and hence arbitrary. However, a bigger challenge with these approaches is that they don't take into account two key features that are needed to operationalize operating envelopes: (i) interpretability of the envelope by the operator and (ii) implementability of the envelope from a practical standpoint. In this work, we propose a new definition for operating envelope which directly targets the expected magnitude of KPI (i.e., no need to arbitrarily bin the data instances into groups) and accounts for the interpretability and the implementability. We then propose a regularized'GA penalty' algorithm that outputs an envelope where the user can tradeoff between bias and variance. The validity of our proposed algorithm is demonstrated by two sets of simulation studies and an application to a real-world challenge in the mining processes of a flotation plant. In industrial operations, an important concept is that of the operating envelope. Conceptually, the operating envelope is a set of operational parameters, such that some KPI is optimized. In the industrial context, typical KPIs include product quality, operational safety, equipment efficiency, environmental impact, etc [1]-[4]. The operating envelope has wide application since it directly targets the business outcome and yields actionable recommendations in the operations space.

Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the main focus of such analysis has been on achieving high prediction accuracy while little effort has been spent on the important task of understanding the association between the two data modalities. Performance on the time series hence receives little explanation though human-understandable textual information is available. In this work, we address the problem of given a numerical time series, and a general corpus of textual stories collected in the same period of the time series, the task is to timely discover a succinct set of textual stories associated with that time series. Towards this goal, we propose a novel multi-modal neural model called MSIN that jointly learns both numerical time series and categorical text articles in order to unearth the association between them. Through multiple steps of data interrelation between the two data modalities, MSIN learns to focus on a small subset of text articles that best align with the performance in the time series. This succinct set is timely discovered and presented as recommended documents, acting as automated information filtering, for the given time series. We empirically evaluate the performance of our model on discovering relevant news articles for two stock time series from Apple and Google companies, along with the daily news articles collected from the Thomson Reuters over a period of seven consecutive years. The experimental results demonstrate that MSIN achieves up to 84.9% and 87.2% in recalling the ground truth articles respectively to the two examined time series, far more superior to state-of-the-art algorithms that rely on conventional attention mechanism in deep learning.

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning models including Random Forest (RF), M5P, Random Committee (RC), KStar and Additive Regression Model (AR) implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models, RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.