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A web-based cohort study suggests that, if machine learning algorithms are provided with a sufficiently wide range of predictive data, they can be induced to analyse epidemiologic data and predict the probability of conception with a discrimination accuracy which exceeds earlier studies.

One focus for AI research has been in predicting the chance of pregnancy - with varying success. A study last year found an AI-based model outperformed clinicians in assessing embryo viability, while a poster from last years annual meeting of preliminary research into predicting embryo ploidy showed that the algorithm tended to classify embryos as aneuploid.(1,2)

Adding to this evidence base, a new large prospective study has now found that algorithms are able to forecast the probability of conception among couples trying to get pregnant if given a wide range of data on predictors of fecundability (defined as the per-cycle probability of conception).(3) Based on a study participation cohort of more than 4000 women, results showed an overall discrimination performance of around 70% for six different supervised machine-learning algorithms in distinguishing between women who were likely to conceive and those who were not.

It was an outcome which, the authors say, exceeds results from predictive models in previous studies and demonstrates that such models can be created with reasonable discrimination using self-reported data. They add that this is in the absence of more detailed medical information such as laboratory or imaging tests.

Earlier work in this area has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in sub-fertile populations but with limited power and using little or no data on lifestyle, environmental and sociodemographic factors. In contrast, a total of 163 predictors of fecundability were considered in this new study to anticipate the cumulative likelihood of pregnancy over six and 12 menstrual cycles.

The data were based on 4133 women from the ongoing Pregnancy Study Online (PRESTO), a web-based preconception cohort study which is analysing the impact of environmental and behavioural factors on fertility and pregnancy. Participants in the study were aged 2144 years, from the US or Canada, were not using fertility treatment, reported no more than one menstrual cycle of pregnancy attempt at study entry, and were actively trying to conceive at enrolment (20132019).

The female patients completed extensive questionnaires at enrolment (eg, marital status, reproductive and diet history, male partner characteristics, etc). Some of this information (eg, menstrual cycle dates) was updated via follow-up questionnaires completed bimonthly for 12 months, or until conception/cessation of pregnancy attempts or study withdrawal.

Next, the data were used to develop models to predict the probability of pregnancy. These were based on three time periods: pregnancy in fewer than 12 menstrual cycles (model I, n = 3195); pregnancy within six menstrual cycles (model II, n = 3476); and the average probability of pregnancy per menstrual cycle (model III, n = 4133). Additional models were also developed for women (n = 1957) who had never been pregnant but had no history of infertility: pregnancy in fewer than 12 menstrual cycles (model IV); pregnancy within six menstrual cycles (model V); and predicting fecundability (model VI). Six different supervised machine learning algorithms were then applied to each model to establish how each algorithm performed.

Results showed 86% of women in model I became pregnant and 69% in model II within the timeframes. For all six algorithms, the AUC (for prediction accuracy) was as follows: model I 68-70% (SD: 0.8%-1.9%); model II 65-66% (SD: 1.9%-2.6%); model III (63%); model IV 69.5% (SD: 1.4%); model V 65.6% (SD: 2.9); and model VI 60.2% concordant index.

Female age, female BMI and history of infertility were the predictors inversely associated with pregnancy in all models. The predictors positively associated with pregnancy in the first three models were having previously breastfed an infant and using multivitamins or folic acid supplements. Among the nulligravid women, the most important predictors were female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.

The authors conclude that the findings are especially relevant for couples planning a pregnancy and for clinicians caring for women coming off contraception to have a baby. However, they add that the models do need to be validated in external populations before they can become a counselling tool.

1. VerMilyea M, Hall J, Diakiw S, at al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human doi: 10.1093/humrep/deaa0132. Aparicio Ruiz B, Bori L, Paya E, et al. Applying artificial intelligence for ploidy prediction: The concentration of IL-6 in spent culture medium, blastocyst morphological grade and embryo morphokinetics as variables under consideration. Human Reprod 2021; doi.org/10.1093/humrep/deab127.0663. Yland J, Wang T, Zad Z, et al. Predictive models of pregnancy based on data from a preconception cohort study. Human Reprod 2022; 1-13; doi.org/10.1093/humrep/deab280

Excerpt from:
The algorithm will see you now: artificial intelligence in the prediction of pregnancy - ESHRE

The Ministry of Agriculture and Forestry plans to implement artificial intelligence (AI) technology to tackle forest fires, which destroyed large swaths of land last year.

AI will be used in the Remote Smoke Detection-Early Fire Warning System developed by the ministry. It will enable a faster response to fires. Forestry Minister Bekir Pakdemirli said the technology will be used in cameras set atop watchtowers in the forests. In an interview published by Yeni afak newspaper on Wednesday, he stated that cameras can detect smoke from a distance up to 20 kilometers (12.4 miles) through smoke perception, and the new system would reduce the detection time to two minutes.

The system is currently installed in Antalya and Mula, two Mediterranean provinces that lost hundreds of acres of forests to devastating wildfires in the summer of 2021, one of the worst and deadliest outbreaks in the region. AI enables us to keep track of the smoke and deploy our teams as soon as possible, Pakdemirli said.

The ministry has 76 smart watchtowers, entirely operated without staff and 103 towers installed with cameras. Cameras, through AI and machine learning, are able to send alarm signals to authorities, via text or multimedia message, upon detection of smoke. Every tower can scan an area of up to 50,000 hectares in two minutes and can send exact coordinates of the fire.

Forest fires, worsened by the ongoing climate crisis, are a major concern for Turkey, which has expanded its forest cover in the past two decades. President Recep Tayyip Erdoan said on Monday after a Cabinet meeting that they were working to boost infrastructure to fight forest fires. We will increase the number of domestically manufactured unmanned aerial vehicles (UAVs) to eight, the number of firefighting planes to 20 and helicopters to 55, Erdoan said.

Turkey suffered from at least 2,105 forest fires last year, though the worst was in Antalya and Mula. Strong winds and extreme temperatures hampered efforts to douse the fires. The country witnessed an unprecedented surge in forest fires starting from the last week of July, a period with the highest number of almost simultaneous forest fires. It took around two weeks for authorities to put out all 240 wildfires that had raged across the country forcing the evacuation of hundreds of people.

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This new connection between the institutions will facilitate the sharing of co-advised thesis or participating on the dissertation committee for students & PhD candidates and the interchange of scholarly papers, research materials, and other information in both parties areas of interest. This cooperation involves collaborative research and activities and strong internship chances at AIISC for IIIT-Delhi students. The MoU further specifies that the parties can develop specific joint educational programmes in the future and enjoy the benefits of interchange of research, teaching, and technical personnel.

The Center for Artificial Intelligence (CAI), IIIT-Delhi and AIISC have many knowledge and skills from world-class academic experts to students. This Memorandum of Understanding will focus on productivity and a desire to bridge the knowledge gap and promote innovation. This association will provide ground breaking results that will benefit all the parties involved.

Artificial Intelligence Institute, University of South Carolina (AIISC) aspires to be a leader in Artificial Intelligence (AI) and its applications. It fosters comprehensive multidisciplinary & translational AI research across the institution, workforce and economic growth in the state through education, technology, and commercialisation, in addition to many primary research topics in AI.

Prof. Amit Sheth, Director, AIISC, commented, "Since I visited IIITD a decade ago, I have seen it build one of the best research ecosystems among Indian universities. AIISC, a university-wide institute at the state flagship, Carnegie R1, University of South Carolina, already has over 30 researchers, strong foundational research in AI complemented by equally strong translational research. I look forward to having CAI/IIITD students among the AIISC's large pool of remote and on-site interns working on world-class research, with access to faculty from both organizations and having access to our exceptional computing resources. The research collaborations will result in excellent publications and add to the eminence of both organizations.

The Centre for Artificial Intelligence (CAI) aspires to be India's primary AI development centre. It comprises basic AI algorithms for furthering research and AI applications for tackling societal problems in the Indian context.

"I firmly believe that this MOU will open up huge opportunities for joint collaboration in terms of not only research but also several academic activities, exchange programs, and so on, stated Dr. Tanmay Chakraborty, Head, CAI, IIIT-Delhi, in response to the collaboration. He added, "AIISC, a recent university-wide institute at the University of South Carolina founded in 1801, has grown massively in the last few years. I, myself, have witnessed the growth. The Center for AI at IIITD (CAI) is also one of the old AI centres in India established by the generous funding of Infosys Foundation with the goal of advancing AI-related Interdisciplinary research. Both the institutions have unique skillsets and would bring in complementary expertise. I am super excited to witness the success of this collaboration."

Indraprastha Institute of Information Technology, Delhi (IIIT-Delhi) has a strong engineering background and connections to researchers and medical professionals from several Indian universities, including AIIMS and others. The Delhi Government established IIIT-Delhi as a state university in 2008, allowing it to conduct research and award academic degrees. IIIT-Delhi has risen to become one of India's most promising new institutions, with world-class professors and an atmosphere that strives to encourage state-of-the-art research and innovation while enabling entrepreneurial activities that bring deep-tech benefits to society.

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Center for AI at IIIT-Delhi and Artificial Intelligence Institute, University of South Carolina Sign MoU to Set Academic Cooperation and Research...

Artificial Intelligence creates new opportunities but also raises new risks for businesses and society. The AI C-suite Toolkit aims to help executives take advantage of opportunities and understand the risks of AI by asking a series of key questions.

The World Economic Forum has published the AI C-suite Toolkit to support executives in their Artificial Intelligence implementation decision making.

The toolkit provides a holistic approach to AI, covering multiple dimensions businesses need to consider when making investments in AI. Emphasis is given on potential risks these technologies create and how to ensure the ethical and responsible use of them.

The key skill executives need to develop is the ability to understand the art of the possible with AI while identifying the main risks it creates, said Kay Firth-Butterfield, Head of AI and Machine Learning at the World Economic Forum.

Furthermore, Theos Evgeniou, Professor at INSEAD and Co-Founder of Tremau, said: Organizations need to adopt new data and AI risk management practices, processes and tools in order to both comply with upcoming regulations and to ensure customer trust.

The new toolkit is the result of a collaboration among several AI experts and executives across companies and industries. It also builds on the previous World Economic Forum guide thats targeted at boards of directors.

Simon Greenman, Partner at Best Practice AI and Member of the World Economic Forums Global AI Council, said: AI is like the Internet: it feels optional until its too late. We were delighted to contribute Best Practice AIs practical digital strategy and transformation experience working with executives globally to this world class effort.

C-suite leadership is key to deliver data-enabled business model transformation and senior management learning critical to ensure that this is done ethically and sustainably. The toolkit provides both.

The AI C-suite toolkit raises and discusses key questions that company executives need to consider when making investments in AI. These questions cover aspects around AI and business strategy, the impact of AI on an organization, AI maturity and organizational change, best practices for implementing AI, understanding and managing AI risks, and adoption of ethical and responsible AI practices and processes.

Efe Erdem, MEXT Group Director and Head of C4IR Turkey, said: With our strong expertise in manufacturing consultancy and plus100 SIRI maturity assessments, we see that the foundational knowledge of AI in operationalizing the strategy is visible as a common need.

Global research on the subject confirms that using AI has benefits like providing cost reduction, inventory minimization, quality increase, profit optimization etc, and potential risks like strengthening inequalities.

Creating a platform for understanding the benefits and mitigating the risks is required, especially at the executive level. With the modularity and extensive understanding of AI, this toolkit will be a reference guide for all leaders.

We are excited for this toolkit to come to life and serve as a critical guideline for the industry.

Organizations at various levels of AI maturity can benefit from the steps laid out in the AI C-suite Toolkit to leverage.

Nihar Dalmia, Canada Government and Public Services Leader, Omnia AI, Deloitte, said: As an advisor to C-suite executives of organizations aspiring to become AI and Data-driven, we have observed firsthand how fundamental it is for leaders to understand how to make informed decisions such that their organizations can truly reap the benefits of AI in the coming years.

We believe this guide will be instrumental in helping executives identify the right opportunities to solve problems using AI and overcome the challenges and barriers they will face on their journey.

The toolkit states: A culture of large volume experimentation, data-driven decision making, and ethical AI distinguishes market leaders. The authors and contributors urge organizations to pilot this toolkit and share their learnings of using it.

The AI C-suite Toolkit on Empowering AI Leadership is a timely report for C-suite executives as more organizations embrace AI across their enterprise, said Anand Rao, Global Leader, Artificial Intelligence, PwC, USA.

PwC is delighted to collaborate with the World Economic Forum on this toolkit to provide a practical and operational framework to implement AI in a responsible manner. The holistic and enterprise-wide end-to-end governance will enable C-suite executives to take advantage of the benefits of AI while also addressing the societal and ethical risks.

Automation is key

Following the release of the World Economic Forums Global Risks Report, Chuck Everette, Director of Cybersecurity Advocacy at Deep Instinct, gave us his opinions on its contents, stressing the importance AI will play in the future of cybersecurity:

The World Economic Forums report into cybersecurity highlights that Artificial Intelligence (AI) solutions are the next step in cybersecurity, with 48% of respondents believing automation and Machine Learning will have the greatest influence on transforming the cybersecurity landscape in the next two years.

As the rise in cyberattacks continue, more people are looking towards automation to help deal with threats. Solutions that only mitigate the impacts of a cyberattack are no longer enough to fully protect organizations, therefore businesses will naturally look towards solutions that can prevent cyberattacks altogether. Deep Learning, a more advanced subset of AI, enables the prevention of cyberattacks and it is already being used to transform the cybersecurity landscape.

Deep Learning mimics the workings of the human brain in order to learn whether activity is malicious or benign. Unlike Machine Learning which requires security teams to manually input data, Deep Learning is fully autonomous.

This means it is able to prevent and predict cyberattacks. Some of the fastest ransomware only needs 15 seconds to encrypt a network, but security teams dont have time to wait for attacks to be executed to then check if they are malicious.

Deep Learning helpsdelivera sub-20 millisecond response timestoppinga cyberattack, pre-execution, before it can take hold. This means security teams are proactively stopping cyberattacks rather than waiting for them to happen.

The independent nature of Deep Learning means that it is able to spot threats with a greater accuracy than Machine Learning, including zero-day threats. Due to the existence of Deep Learning within cybersecurity and its ability to actually predict cyberattacks, there is no doubt that automation will indeed have the greatest influence on transforming the cybersecurity landscape.

Tackling the skills shortage

Following the release of the World Economic Forums Global Risks Report, Jawahar Sivasankaran, President and COO at Appgate, discusses the skills shortage that urgently needs to be tackled in all regions across the globe.Every cybersecurity expert is constantly worrying about the threats their organization is exposed to and how they can stop them. However, the other great challenge for cybersecurity leaders is how to stop the current skills shortage seen in the industry.

The World Economic Forums report into the cybersecurity landscape stated that less than 25% of companies with 5,000 to 50,000 employees, have the people and skills [they] need today. This skill shortage can leave organizations vulnerable to cyberattacks with security teams no longer having the knowledge and experience to deal with certain situations.

Individuals within security teams have the constant pressure of not feeling prepared, ultimately leading to stress and the eventual departure from the industry altogether. According to the report 88% of security-focused executives describe being moderately or tremendously stressed. Those who are stressed are not going to stay in the industry for long and therefore the skills gap is exacerbated.

Part of the reason why so many companies have this shortage is due to the mass exodus of baby boomers taking early retirement due to the pandemic, which has forced younger generations to step up and take their place without necessarily having the skills or experience to do so. Organizations must find the right balance in security teams and ensure that different generations are working together.

Appgates research into generational differences showed 80% of baby boomers were willing to re-enter the industry as paid consultants. This would result in security teams having more experience; therefore, they would feel more confident in dealing with high pressured scenarios and be able to transfer important skills to younger generations.

Younger members of security teams who are confident that they have the right skills and training are less likely to feel stressed within their job. Naturally if employees are less stressed, they are more likely to enjoy their work, which leads to fewer people leaving the cybersecurity industry and an eventual narrowing of the skills gap.

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C-suite Toolkit helps executives navigate the Artificial Intelligence landscape - Intelligent CIO ME

Artificial Intelligence in the Transportation Market report contains detailed information on factors influencing demand, growth, opportunities, challenges, and restraints. It provides detailed information about the structure and prospects for global and regional industries. In addition, the report includes data on research & development, new product launches, product responses from the global and local markets by leading players. The structured analysis offers a graphical representation and a diagrammatic breakdown of the Artificial Intelligence in the Transportation Market by region.

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The global artificial intelligence in the transportation market size was US$ 1.45 billion in 2020. The global artificial intelligence in the transportation market is forecast to reach the value of US$ 17.9 billion by 2030 by growing at a compound annual growth rate (CAGR) of 18.5% during the forecast period from 2021-2030.

COVID-19 Impact Analysis

The COVID-19 outbreak has majorly affected the transportation industry mainly because of the shortage of laborers, raw materials, and decline in trade activities. Artificial Intelligence (AI) witnessed significant growth across various verticals. Artificial intelligence has helped the healthcare sector and scientists to track the pattern of the vaccine. However, the transportation sector witnessed a significant decline which hampered the growth of global artificial intelligence in the transportation market.

Factors Influencing

The stringent government regulation mainly to enhance vehicle safety and security would primarily contribute to the market growth. Moreover, the increasing adoption and demand for advanced driver assistance systems are forecast to drive market growth.

The global artificial intelligence in the transportation market would gain traction, owing to the growing demand for traffic management and increasing deployment of self-driving vehicles among the population.

Due to rising demand for enhanced logistics, the market players are forecast to witness various favorable opportunities.

Advancements in autonomous vehicles with the implementation of safety features, including collision warning, adaptive cruise control (ACC), advanced driver assistance system (ADAS), and lane-keep assist are forecast to fuel the market growth. These features reduce the risk associated with drug-impaired drivers.

The high cost associated with the implementation of artificial intelligence systems may hamper the growth of global artificial intelligence in the transportation market.

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Geographic Analysis

Geographically, North America is dominating the global artificial intelligence in the transportation market and is forecast to remain dominant in terms of revenue during the forecast period. It is due to the trending integration of self-driving vehicles and government funding to boost the safety of vehicles. In addition, the presence of prominent companies in the region is forecast to fuel the industry expansion in the coming years. Furthermore, the shortage of truck drivers and growing investment in autonomous trucks may create significant growth opportunities for the market players in the region.

The Asia-Pacific region is forecast to emerge as a rapidly growing region due to the increasing population and growing adoption of self-driving vehicles. Moreover, government policies pertaining to robust economic growth are propelling the growth of Asia-Pacific artificial intelligence in the transportation market.

Competitors in the Market

Volvo Group

Scania Group

Man SE

Daimler AG

PACCAR Inc.

Magna

Robert Bosch GmbH

Continental AG

Valeo SA

Alphabet Inc.

NVIDIA

Microsoft Corporation

ZF Friedrichshafen AG

Intel Corporation

Other prominent players

Market Segmentation

By Application

Autonomous Trucks

HMI in Trucks

Semi-Autonomous Trucks

By Offering

Hardware

Software

By Machine Learning Technology

Deep Learning

Computer Vision

Context Awareness

Natural Language Processing

By Process

Signal Recognition

Object Recognition

Data Mining

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By Region

North America

The U.S.

Canada

Mexico

Europe

Western Europe

The UK

Germany

France

Italy

Spain

Rest of Western Europe

Eastern Europe

Poland

Russia

Rest of Eastern Europe

Asia Pacific

China

India

Japan

Australia & New Zealand

ASEAN

Rest of Asia Pacific

Middle East & Africa (MEA)

UAE

Saudi Arabia

South Africa

Rest of MEA

South America

Brazil

Argentina

Rest of South America

What is the goal of the report?The market report presents the estimated size of the ICT market at the end of the forecast period. The report also examines historical and current market sizes.During the forecast period, the report analyzes the growth rate, market size, and market valuation.The report presents current trends in the industry and the future potential of the North America, Asia Pacific, Europe, Latin America, and the Middle East and Africa markets.The report offers a comprehensive view of the market based on geographic scope, market segmentation, and key player financial performance.

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Artificial Intelligence in the Transportation Market By Top Manufacturers, Production, Consumption, Trade Statistics, And Growth Analysis 2021-2030 -...