Mediaboss Marketing

HOBOKEN, N.J., Feb. 9, 2021 /PRNewswire/ --DropShot Capital Management, an alternative investment platform, has been innovating artificial intelligence solutions to portfolio management since 2018, focusing on data-driven approaches that deliver a customer experience free of monetary lock-ups and provide a clear window into one's investment performance in a fast-changing financial environment. DropShot offers investors access to cutting-edge machine learning models with a very competitive fee structure.

Our new Fixed Income Fund (FIF) seeks to cater to investors looking for an actively managed, rates-exposed portfolio. The product will adhere to all of DropShot's core principles, and will continue to rely on our machine learning engine for its portfolio rebalancing decisions. The product will be long only and take exposure via fixed income ETFs. The FIF offers concentrated rates market exposure, in contrast to DropShot's AI Alpha Fund, launched in 2018, which trades domestic and foreign ETFs of all asset classes.

DropShot deploys strategies that use machine learning techniques to dynamically allocate to various asset classes as market conditions change. Tactical rotation between these markets on an adaptive basis allows the fund to continuously source returns from the most attractively priced assets. This enables us to strive towards our primary goal: To compete with major global benchmarks while delivering broader exposure across markets.

We don't lock up your funds and we offer monthly redemptions/liquidity. In order to give investors their desired level of detail into their investment's trading performance, we offer personalized mobile and web views, in addition to monthly statements.

Markets are more treacherous than ever, but opportunities abound for those able to invest intelligently. DropShot strives to adapt and learn through systematic, data driven trading that guides investors through their financial journeys.

Our terms are simple: Algorithmic, Liquid, Transparent.

Contact:

Chris Kramvis

Chris@dropshotcapital.com

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SOURCE DropShot Capital

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DropShot Capital Management Adds Tactical Fixed Income to Its Liquid, Artificial Intelligence Based Offerings - The Trentonian

Has artificial intelligence revolutionized recruitments? Source: Shutterstock

Though the technology has been around for over a decade now, the adoption of Artificial Intelligence (AI) in employment recruiting expanded rapidly throughout 2020 in response to Covid-19.Due to the pandemic alone, the adoption of new technological behaviors, from video-conferencing to remote working, reached levels that were not expected until 2025 or even 2030.

In fact, a report from The Economist cites research from global consultancy firm McKinsey where they acknowledge that recent data has showed that we have essentially vaulted forward five years in digital adoptions, both in consumer and business behaviors. All in the space of just eight weeks.

The accelerated use of AI and machine learning by recruitment specialists over the past year has added thousands of jobs to the global market, and most importantly become essential prerequisites for enhancing recruiting efforts for companies around the world.

Well, for starters its incredibly precise, accurate and fast. AI has an unparalleled ability to rapidly sift through millions of data points among candidates, enabling recruiters to quickly identify high-potential individuals suited for open roles.

Additionally, AI allows recruiters to tap into a wider talent pool to source potential candidates. This includes scouring through online career boards, social media platforms, and even going through agency databases.

Prior to the pandemic, some large organizations had already adopted digitally-led human resource processes and were using AI for candidate consideration. Multi-national telecommunications company, Vodafone was already using AI to sift through over 100,000 graduates applying for 1,000 jobs.With such a high volume of candidates, the companys HR department recently teamed up with HR software company HireVue to test an AI application that removes human bias from the recruitment process. The system works by extracting as many as 25,000 data points from video interviews.It examines visual and verbal cues from candidates, comparing their word choice, facial movements, body language, and tone to help identify the best ones. Utah-based HireVuus AI system uses previously recorded videos of job applicants, asking them interview questions via their laptops webcam and microphone, and using subsequent movements and answers to base their concluding points.

HireVue has shared that between its inception and September 2019, it had conducted a total of 12 million interviews. Out of these, about 20% were done using AI software. The other 80% were done using a human interviewer on the other end of a video screen. The overall figure has now risen to 19 million, with the same percentage split.

HireVue first started offering AI interviews in 2016. Users of this service include computer chip designer ARM, and travel services firm Sabre.

Another US-based firm, Pymetrics has been using AI software in the initial recruitment processes of a number of multinational companies on their client roster. These include globally-recognized names like McDonalds, JP Morgan Bank, accountancy firm PWC, and food group Kraft Heinz. If AI deems the candidate suitable, a human recruiter will then set up an in-person interview.

A report from 2019 said that the growth in the use of AI will replace 16% of recruitment sector jobs before 2029.

Even global research and advisory firm Gartner predict AI-related job creation will reach two million net-new jobs in the next few years.

The World Economic Forum in its recent report went on to identify data analysts and scientists, AI and machine-learning specialists, big data specialists, and digital marketing and strategy specialists as the top four roles seeing increased demand.

While China and the US are competing for the first position in the AI race, small and big businesses throughout Southeast Asia also have developed some innovative AI solutions, too, but not without some challenges.

Even a McKinsey Global Institute study on AI reveals that those companies that have a clear plan to apply machine intelligence in their business are seeing a significant impact on their business. That same study reported that more than 30% of Southeast Asias biggest companies mentioned terms such as machine learning and AI in their most recent annual reports, compared to only 6% in 2011 illustrating that AI is moving to the forefront of businesses strategies.

According to Vervoe, SMEs can use AI in hiring in three specific parts of the process; sourcing: to find and connect with talent faster; screening: to identify and select the best applicants quickly; interviewing: to schedule interviews and save time.

There are also tools that use AI to improve recruiting results on a small business budget such as Vervoe, SmartRecruiters, ZipRecruiter, Workable, Breezy, and BambooHR.

Dashveenjit Kaur| @DashveenjitK

Dashveen writes for Tech Wire Asia and TechHQ, providing research-based commentary on the exciting world of technology in business. Previously, she reported on the ground of Malaysia's fast-paced political arena and stock market.

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Has artificial intelligence revolutionized recruitment? - Tech Wire Asia

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Researchers have developed a new method to counter emergent mutations of COVID-19 and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining economies.

Using Artificial Intelligence (AI), the research team at the University of Southern California's Viterbi School of Engineering developed a method to speed the analysis of vaccines and zero in on the best potential preventive medical therapy.

According to the researchers, the method is easily adaptable to analyze potential mutations of the virus, ensuring the best possible vaccines are quickly identifiedsolutions that give humans a big advantage over the evolving contagion.

"This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety," said researcher Paul Bogdan from the varsity.

"Moreover, this can be adapted to help us stay ahead of the coronavirus as it mutates around the world," Bogdan added, in the study published in the journal Scientific Reports.

When applied to SARS-CoV-2the virus that causes COVID-19the computer model quickly eliminated 95% of the compounds that could've possibly treated the pathogen and pinpointed the best options, the study said.

The AI-assisted method predicted 26 potential vaccines that would work against the coronavirus. From those, the researchers identified the best 11 from which to construct a multi-epitope vaccine, which can attack the spike proteins that the coronavirus uses to bind and penetrate a host cell.

Vaccines target the regionor epitopeof the contagion to disrupt the spike protein, neutralizing the ability of the virus to replicate, the team said. Moreover, the engineers can construct a new multi-epitope vaccine for a new virus in less than a minute and validate its quality within an hour, they added.

By contrast, current processes to control the virus require growing the pathogen in the lab, deactivating it and injecting the virus that caused a disease. The process is time-consuming and takes more than one year; meanwhile, the disease spreads.

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The above article has been published from a wire agency with minimal modifications to the headline and text

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Researchers Develop Artificial Intelligence Based Tool to Tackle Mutant Variants of Coronavirus | The Weather Channel - Articles from The Weather...

The 35th AAAI Conference on Artificial Intelligence (AAAI-21), held virtually this year, saw more than 9,000 paper submissions, of which, only 1,692 research papers made the cut.

The Association for the Advancement of Artificial Intelligence (AAAI) committee has announced the Best Paper and Runners Up awards. Lets take a look at the papers that won the awards.

About: Informer is an efficient transformer-based model for Long Sequence Time-series Forecasting (LSTF). A team of researchers from UC Berkeley introduced this Transformer model to predict long sequences. Informer has three distinctive characteristics:

Read the paper here.

About: Exploration-exploitation is a powerful tool in multi-agent learning (MAL). A team of researchers from Singapore University of Technology studied a variant of stateless Q-learning, with softmax or Boltzmann exploration, also termed as Boltzmann Q-learning or smooth Q-learning (SQL). Boltzmann Q-learning is one of the most fundamental models of exploration-exploitation in MAS.

Read the paper here.

About: Researchers from Dartmouth College, University of Texas and ProtagoLabs described metrics for measuring political bias in GPT-2 generation and proposed a reinforcement learning (RL) framework to reduce political biases in the generated text. Using rewards from word embeddings or a classifier, the RL framework guided the debiased generation without having access to the training data or requiring the model to be retrained. The researchers also proposed two bias metrics (indirect bias and direct bias) to quantify the political bias in language model generation.

Read the paper here.

About: Researchers from Amazon and UC Berkeley studied the problem of batch learning from bandit feedback in extremely large action spaces. They introduced a selective importance sampling estimator (sIS) operating in a significantly more favorable bias-variance regime. The sIS estimator is obtained by performing importance sampling on the conditional expectation of the reward concerning a small subset of actions for each instance.

Read the paper here.

About: Researchers from Microsoft and Beihang University proposed a self-attention attribution algorithm to interpret the information interactions inside the Transformer. As part of the research, the scientists first extracted the most salient dependencies in each layer to construct an attribution graph, which reveals the hierarchical interactions inside the Transformer. Next, they applied self attention attribution to identify the important attention head. Finally, they showed that the attribution results can be used as adversarial patterns to implement non-targeted attacks towards BERT.

Read the paper here.

About: Researchers from Harvard University and Carnegie Mellon University introduced LIZARD, an algorithm that accounts for decomposability of the reward function, smoothness of the decomposed reward function across features, monotonicity of rewards as patrollers exert more effort, and availability of historical data. According to them, LIZARD leverages both decomposability and Lipschitz continuity simultaneously, bridging the gap between combinatorial and Lipschitz bandits.

Read the paper here.

A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact: [emailprotected]

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Which Papers Won At 35th AAAI Conference On Artificial Intelligence? - Analytics India Magazine

Global Hospital Artificial Intelligence Market report presents the market analysis on the basis of several factors. Report gives the in-depth analysis on the major countries of key regions where the market is growing. In addition, report describes the wide-ranging knowledge about the major companies in this industry and the key strategies accepted by them to survive and rise in the studied industry.

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Intel (US)NVIDIA (US)Siemens Healthineers (Germany)Medtronic (Ireland)Micron Technology (US)IBM (US)Microsoft (US)Google Inc (US)Amazon Web Services (US)Medtronic (Ireland)Micron Technology (US)

AI in Hospital Management Breakdown Data by TypeHardwareSoftwareService

AI in Hospital Management Breakdown Data by ApplicationHealthcare ProviderPharmaceutical & Biotechnology CompanyPatientOthers

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In this study, the years considered to estimate the market size of Hospital Artificial Intelligence Market are as follows:

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Table of Contents

1 Study Coverage

2 Executive Summary

3 Breakdown Data by Manufacturers

4 Breakdown Data by Type

4.1 Global Hospital Artificial Intelligence Market Sales by Type

4.2 Global Hospital Artificial Intelligence Market Revenue by Type

4.3Hospital Artificial Intelligence Market Price by Type

5 Breakdown Data by Application

5.1 Overview

5.2 Global Hospital Artificial Intelligence Market Breakdown Data by Application

6 North America

7 Europe

8 Asia Pacific

9 Central & South America

10 Middle East and Africa

11 Company Profiles

12 Future Forecast

13 Market Opportunities, Challenges, Risks and Influences Factors Analysis

14 Value Chain and Sales Channels Analysis

15 Research Findings and Conclusion

16 Appendix

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At HealthCare Intelligence Markets, we supply markets intelligence reports in the domain of personalized drugs & diagnostics after going through a rigorous research process. The healthcare industry is constantly evolving as trends are getting replaced at a rapid pace. These new trends along with the changing demands of patients and healthcare organizations, are collectively contributing to the development of the global healthcare industry. The reports made by us are updated on a regular basis to cover the latest developments in the industry. Our workforce is comprised of seasoned market research professionals who can also provide customized report as per the exclusive needs. HealthCare helps clients decode the future to be more successful and innovative.

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Hospital Artificial Intelligence Industry: 2021 Global Market Size, Share, Uses, Benefits, Trends, Growth Application, Key Manufacturers and 2028...