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Spider-Man: Miles Morales developer Insomniac Games has revealed that the PS5 is capable of Machine Learning and AI Upscaling.

The comments come via Insomniac Games Josh DiCarlo during a series of tweets about the performance of the PS5 and its work on the Spider-Man franchise. DiCarlo revealed that its innards are ML based, and that the studio is only just scratching the service on what Sonys new console is capable of achieving.

Not sure how specific I can get with specs now for now, but you are correct in the assumption that all final deformations are resulting via ML interference at runtime on the PS5 hardware. There are no blend shapes, skin decamp, or tradition lyrics of the trade (nothing against them!)

Insomniac Games has been pretty busy with PS5 hardware, having released a remastered version of Marvels Spider-Man, a dedicated version of Spider-Man: Miles Morales alongside the PS4 edition, and is currently working on the upcoming Ratchet & Clank: Rift Apart.

Related Content Sony PS5 Complete Guide A Total Resource On PlayStation 5

[Source Joe Miller on Twitter via NeoGAF]

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PS5 Capable Of Machine Learning, AI Upscaling According To Insomniac Games - PlayStation Universe

If youve been a reader of Sprudge for any reasonable amount of time, youve no doubt by now ready multiple articles about how coffee is potentially beneficial for some particular facet of your health. The stories generally go like this: a study finds drinking coffee is associated with a X% decrease in [bad health outcome] followed shortly by the study is observational and does not prove causation.

In a new study in theAmerican Heart Associations journal Circulation: Heart Failure, researchers found a link between drinking three or more cups of coffee a day and a decreased risk of heart failure. But theres something different about this observational study. This study used machine learning to get to its conclusion, and it may significantly alter the utility of this sort of study in the future.

As reported by the New York Times, the new study isnt exactly new at all. Led by David Kao, a cardiologist at University of Colorado School of Medicine, researchers re-examined the Framingham Heart Study (FHS), a long-term, ongoing cardiovascular cohort studyof residents of the city of Framingham, Massachusetts that began in 1948 and has grown to include over 14,000 participants.

Whereas most research starts out with a hypothesis that it then seeks to prove or disprove, which can lead to false relationships being established by the sort variables researchers choose to include or exclude in their data analysis, Kao et al instead approached the FHS with no intended outcome. Instead, they utilized a powerful and increasingly popular data-analysis technique known as machine learning to find any potential links between patient characteristics captured in the FHS and the odds of the participants experiencing heart failure.

Able to analyze massive amounts of data in a short amount of timeas well as be programmed to handle uncertainties in the data, like if a reported cup of coffee is six ounces or eight ouncesmachine learning can then start to ascertain and rank which variables are most associated with incidents of heart failure, giving even observational studies more explanatory power in their findings. And indeed, when the results of the FHS machine learning analysis were compare to two other well-known studies, the Cardiovascular Heart Study (CHS) and the Atherosclerosis Risk in Communities study (ARIC), the algorithm was able to correctly predict the relationship between coffee intake and heart failure.

But, of course, there are caveats. Machine learning algorithms are only as good as the data being fed to it. If the scope is too narrow, the results may not translate more broadly and its real-world predictive utility is significantly decreased. The New York Times offers facial recognition software as an example: Trained primarily on white male subjects, the algorithms have been much less accurate in identifying women and people of color.

Still, the new study shows promise, not just for the health benefits the algorithm uncovered, but for how we undertake and interpret this sort of analysis-driven research.

Zac Cadwaladeris the managing editor at Sprudge Media Network and a staff writer based in Dallas.Read more Zac Cadwaladeron Sprudge.

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Is Machine Learning The Future Of Coffee Health Research? - Sprudge

By combining machine learning, molecular dynamics simulations and experiments it has been possible to design antimicrobial peptides from scratch.1 The approach by researchers at IBM is an important advance in a field where data is scarce and trial-and-error design is expensive and slow.

Antimicrobial peptides small molecules consisting of 12 to 50 amino acids are promising drug candidates for tackling antibiotic resistance. The co-evolution of antimicrobial peptides and bacterial phyla over millions of years suggests that resistance development against antimicrobial peptides is unlikely, but that should be taken with caution, comments Hvard Jenssen at Roskilde University in Denmark, who was not involved in the study.

Artificial intelligence (AI) tools are helpful in discovering new drugs. Payel Das from the IBM Thomas J Watson Research Centre in the US says that such methods can be broadly divided into two classes. Forward design involves screening of peptide candidates using sequenceactivity or structureactivity models, whereas the inverse approach considers targeted and de novo molecule design. IBMs AI framework, which is formulated for the inverse design problem, outperforms other de novo strategies by almost 10%, she adds.

Within 48 days, this approach enabled us to identify, synthesise and experimentally test 20 novel AI-generated antimicrobial peptide candidates, two of which displayed high potency against diverse Gram-positive and Gram-negative pathogens, including multidrug-resistant Klebsiella pneumoniae, as well as a low propensity to induce drug resistance in Escherichia coli, explains Das.

The team first used a machine learning system called a deep generative autoencoder to capture information about different peptide sequences and then applied controlled latent attribute space sampling, a new computational method for generating peptide molecules with custom properties. This created a pool of 90,000 possible sequences. We further screened those molecules using deep learning classifiers for additional key attributes such as toxicity and broad-spectrum activity, Das says. The researchers then carried out peptidemembrane binding simulations on the pre-screened candidates and finally selected 20 peptides, which were tested in lab experiments and in mice. Their studies indicated that the new peptides work by disrupting pathogen membranes.

The authors created an exciting way of producing new lead compounds, but theyre not the best compounds that have ever been made, says Robert Hancock from the University of British Columbia in Canada, who discovered other peptides with antimicrobial activity in 2009.2 Jenssen participated in that study too and agrees. The identified sequences are novel and cover a new avenue of the classical chemical space, but to flag them as interesting from a drug development point of view, the activities need to be optimised.

Das points out that IBMs tool looks for new peptides from scratch and doesnt depend on engineered input features. This line of earlier work relies on the forward design problem, that is, screening of pre-defined peptide libraries designed using an existing antimicrobial sequence, she says.

Hancock agrees that this makes the new approach challenging. The problem they were trying to solve was much more complex because we narrowed down to a modest number of amino acids whereas they just took anything that came up in nature, he says. That could represent a significant advance, but the output at this stage isnt optimal. Hancock adds that the strategy does find some good sequences to start with, so he thinks it could be combined with other methods to improve on those leads and come up with really good molecules.

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Machine learning tool sets out to find new antimicrobial peptides - Chemistry World

This article was originally published here

PLoS One. 2021 Apr 1;16(4):e0249285. doi: 10.1371/journal.pone.0249285. eCollection 2021.

ABSTRACT

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.

OBJECTIVES: To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted.

METHODS: Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort.

RESULTS: Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%.

CONCLUSION: Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

PMID:33793600 | DOI:10.1371/journal.pone.0249285

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Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19 - DocWire News

The Global Machine Learning Operationalization Software Market report dissects the complex fragments of the market in an easy to read manner. This report covers drivers, restraints, challenges, and threats in the Machine Learning Operationalization Software market to understand the overall scope of the market in a detailed yet concise manner. Additionally, the market report covers the top-winning strategies implemented by major industry players and technological advancements that steers the growth of the market.

Key Players Landscape in the Machine Learning Operationalization Software Report

MathWorksSASMicrosoftParallelMAlgorithmiaH20.aiTIBCO SoftwareSAPIBMDominoSeldonDatmoActicoRapidMinerKNIME

Note: Additional or any specific company of the market can be added in the list at no extra cost.

Here below are some of the details that are included in the competitive landscape part of the market report:

This market research report enlists the governments and regulations that can provide remunerative opportunities and even create pitfalls for the Machine Learning Operationalization Software market. The report confers details on the supply & demand scenario in the market while covering details about the product pricing factors, trends, and profit margins that helps a business/company to make crucial business decisions such as engaging in creative strategies, product development, mergers, collaborations, partnerships, and agreements to expand the market share of the company.

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An Episode of Impact of COVID-19 Pandemic in the Machine Learning Operationalization Software Market

The COVID-19 pandemic had disrupted the global economy. This is due to the fact that the government bodies had imposed lockdown on commercial and industrial spaces. However, the market is anticipated to recover soon and is anticipated to reach the pre-COVID level by the end of 2021 if no further lockdown is imposed across the globe.

In this chapter of the report, DataIntelo has provided in-depth insights on the impact of COVID-19 on the market. This chapter covers the long-term challenges ought to be faced due to the pandemic while highlights the explored opportunities that benefited the industry players globally. The market research report confers details about the strategies implemented by industry players to survive the pandemic. Meanwhile, it also provides details on the creative strategies that companies implemented to benefit out of pandemic. Furthermore, it lays out information about the technological advancements that were carried out during the pandemic to combat the situation.

What are the prime fragments of the market report?

The Machine Learning Operationalization Software report can be segmented into products, applications, and regions. Here below are the details that are going to get covered in the report:

Products

Cloud BasedOn Premises

Applications

BFSIEnergy and Natural ResourcesConsumer IndustriesMechanical IndustriesService IndustriesPublice SectorsOther

Regions

North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Note: A country of your own choice can be added to the list at no extra cost. If more than one country needs to be added, the research quote varies accordingly.

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Below is the TOC of the report:

Executive Summary

Assumptions and Acronyms Used

Research Methodology

Machine Learning Operationalization Software Market Overview

Global Machine Learning Operationalization Software Market Analysis and Forecast by Type

Global Machine Learning Operationalization Software Market Analysis and Forecast by Application

Global Machine Learning Operationalization Software Market Analysis and Forecast by Sales Channel

Global Machine Learning Operationalization Software Market Analysis and Forecast by Region

North America Machine Learning Operationalization Software Market Analysis and Forecast

Latin America Machine Learning Operationalization Software Market Analysis and Forecast

Europe Machine Learning Operationalization Software Market Analysis and Forecast

Asia Pacific Machine Learning Operationalization Software Market Analysis and Forecast

Asia Pacific Machine Learning Operationalization Software Market Size and Volume Forecast by Application

Middle East & Africa Machine Learning Operationalization Software Market Analysis and Forecast

Competition Landscape

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Machine Learning Operationalization Software Market 2021 Is Booming Across the Globe by Share, Size, Growth, Segments and Forecast to 2027 | Top...