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Although traffic is a challenge in many cities across the globe, it is a challenge that can be managed and solved. Governments and city officials should work with the private sector to deploy intelligent solutions that not only improve the present state of roads and highways, but also prepare for the future.

Some of the solutions to traffic challenges lie in using artificial intelligence (AI), data collection and analytics, and sophisticated hardware that automates and streamlines processes to achieve high levels of accuracy, safety, and reliability.

Coupled with existing systems comprised of network cameras and monitoring solutions, the innovation and application of AI heralds a new era in traffic management.

Cities are getting bigger and smarter

Globally, our cities are growing and becoming more populous. The migration from rural to urban areas, combined with overall population growth, will see another 2.5 billion people living in cities by 2050, with 90% of this increase taking place in Africa and Asia, as per a United Nations report. This means more people travelling on more roads, and without smart interventions, this could mean more traffic.

In some areas, with this migration comes the emergence of smart cities urban areas that use ICT, AI, and data-related processes to maximise operations and investment.

In Abu Dhabi and Dubai two cities rated 28th and 29th respectively on the IMD Smart City Index 2021 traffic congestion is not considered a problem among residents. Such a result is, in part, thanks to the adoption of AI processes to deal with traffic and road management.

Managing traffic and the challenges thereof

Traffic management centres (TMCs) are the central nervous systems used for monitoring and managing traffic in an area. Imagine sprawling rooms with hundreds of surveillance cameras watching intersections, highways, and other road infrastructure for congestion, accidents, and the like to allow for quick-time responses from officials.

However, traditional TMC setups only allow for a certain level of monitoring and oversight.

Intelligent solutions, comprising both hardware and software, can fill these gaps.

Quality hardware is the first step

The strength and effectiveness of a TMC is indicated by its ability to collect data, and the application of AI requires up-to-date and robust hardware for it to achieve its full capability.

Centres use an extensive network of video management systems (VMSs) to monitor infrastructure and capture incidents in real time. This includes a range of fixed-point, panoramic, and thermal cameras hardware thats built to perform regardless of factors such as environment, object layout, and time of day.

The application of AI

To address traffic challenges, hardware is coupled with advancements in machine learning (ML), which focuses on gathering and analysing data to identify and replicate patterns in behaviour. By design, roads are ideal for this process as users conform to its rules, producing data which can be used to track deviations and create optimised models.

Deep learning (DL) takes this a step further. A sub-field of ML, DL uses raw data to automatically determine features that distinguish different sets of data from one another. The processing of data in this way does not need human intervention.

What does this look like in the context of traffic management? It automates several processes and decreases the necessity for human intervention or oversight. Data can be used to identify weak or exposed points in infrastructure or throughout the overall scope of surveillance. Roads are made safer through quicker response times from emergency services and officials.

Edge computing and edge-based analytics

Cameras capture a lot of data. A network of cameras tied to a VMS captures even more data, and with this comes the necessity of cloud or server-based data storage and processing. Edge computing offers an alternative, more effective solution to constantly transfer data from devices to the cloud.

With edge computing, captured data is analysed closer to its point of origin rather than having to go back-and-forth between servers and central data points.[1] The localisation of this process results in less dependency on other infrastructure and less time is lost due to factors such as network latency.

Urban populations may continue to grow, but traffic problems do not need to accompany this growth. Leaders can harness the power of AI combined with smart surveillance hardware as we build the cities of the future. Cities and urban areas stand to benefit from the application of AI in this manner as we head into a smart future together.

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Artificial Intelligence key to traffic solutions in cities of the future - Construction Week Online

In this paper, a comprehensive overview on current applications of AI in oncology-related areas is provided, specifically describing the AI-based devices that have already obtained the official approval to enter into clinical practice. Starting from its birth, AI demonstrated its cross-cutting importance in all scientific branches, showing an impressive growth potential for the future. As highlighted in this study, this growth has interested also oncology and related specialties.

In general, the application of the FDA-approved devices has not been conceived as a substitute of classical analysis/diagnostic workflow, but is intended as an integrative tool, to be used in selected cases, potentially representing the decisive step for improving the management of cancer patients. Currently, in this field, the branches where AI is gaining a larger impact are represented by the diagnostic areas, which count for the vast majority of the approved devices (>80%), and in particular radiology and pathology.

Cancer diagnostics classically represents the necessary point of start for designing appropriate therapeutic approaches and clinical management, and its AI-based refining represents a very important achievement. Furthermore, this indicates that future developments of AI should also consider unexplored but pivotal horizons in this landscape, including drug discovery, therapy administration and follow-up strategies. In our opinion, for determining a decisive improvement in the management of cancer patients, indeed, the growth of AI should follow comprehensive and multidisciplinary patterns. This represents one of the most important opportunities provided by AI, which will allow the correct interactions and integration of oncology-related areas on a specific patient, rendering possible the challenging purposes of personalised medicine.

The specific cancer types that now are experiencing more advantages from AI-based devices in clinical practice are first of all breast cancer, lung cancer and prostate cancer. This should be seen as the direct reflection of their higher incidence compared with other tumour types, but in the future, additional tumour types should be taken into account, including rare tumours that still suffer from the lack of standardised approaches. Since AI is based on the collection and analysis of large datasets of cases, however, the improvement in the treatment of rare neoplasms will likely represent a late achievement. Notably, if together considered, rare tumours are one of the most important category in precision oncology [11]. Thus, in our opinion, ongoing strategies of AI development cannot ignore this tumour group; although the potential benefits seem far away, it is already time to start collecting data on rare neoplasms.

One of the most promising expectancy for AI is the possibility to integrate different and composite data derived from multi-omics approaches to oncologic patients. The promising tools of AI could be the only able to manage the big amount of data from different types of analysis, including information derived from DNA and RNA sequencing. Along this line, the recent release of American College of Medical Genetics standards and guidelines for the interpretation of the sequence variants [12] has fostered a new wave of AI development, with innovative opportunities in precision oncology (https://www.businesswire.com/news/home/20190401005976/en/Fabric-Genomics-Announces-AI-based-ACMG-Classification-Solution-for-Genetic-Testing-with-Hereditary-Panels; last access 09/21/2021). In our opinion, however, the lack of ground-truth information derived from protected health- data repositories still represents a bottleneck in evaluating the accuracy of AI applications for clinical decision-making.

Overall considered, AI is providing a growing impact to all scientific branches, including oncology and its related fields, as highlighted in this study. For designing new development strategies with concrete impacts, the first steps are representing by knowing its historical background and understanding its current achievements. As here highlighted, AI is already entered into the oncologic clinical practice, but continuous and increasing efforts should be warranted to allow AI expressing its entire potential. In our opinion, the creation of multidisciplinary/integrative developmental views, the immediate comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the AI-revolution in oncology.

See more here:
Artificial intelligence in oncology: current applications and future perspectives | British Journal of Cancer - Nature.com

DUBLIN, Nov. 25, 2021 /PRNewswire/ -- The "Future Growth Potential of the Global AI Market" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence (AI) is transforming organizations, industries, and the technology landscape. The world is moving to the increased adoption of AI-powered smart applications/systems, and this trend will increase exponentially over the next few years. AI technologies are maturing, and the need to leverage their capabilities is becoming a CXO priority.

As businesses make AI part of their core strategy, the transformation of business functions, measures, and controls to ensure ethical best practices will gain importance. The implementation and the governance of ethical AI practices will become a priority and a board-level concern.

The deployment of AI solutions that are ethical (from a regulatory and a legal standpoint), transparent, and without bias will become essential. As governments and industry bodies across the world articulate AI regulations, AI companies must establish their ethical frameworks until roadmaps are clearly defined.

The operationalization of ethical AI principles is challenging for enterprises, given the large volumes of user-centric data that need to be processed, the breadth of use-cases, the regulatory variations in operating markets, and the diverse stakeholder priorities.

This also opens up opportunities for technology vendors and service providers. To effectively partner with enterprises and monetize these opportunities, ICT providers need to assess potential areas impacting AI ethics and evaluate opportunities across the people-process-technology spectrum.

Forward-thinking technology and service companies, including large ICT providers and start-ups, are working with enterprises and industry stakeholders to leverage potential opportunities. Ethical challenges will continue to be discovered and remediated to create sustained growth in potential advisory services.

As enterprises define goals, values, strategic outcomes, and key performance metrics, the time is right for technology companies to strategically partner with enterprises in the detection and the mitigation of ethical AI concerns.

Key Topics Covered:

1. Strategic Imperatives

2. Growth Environment

3. Growth Opportunity Analysis

4. Growth Opportunity Universe

For more information about this report visit https://www.researchandmarkets.com/r/l7isqw

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

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Global AI (Artificial Intelligence) Market Report 2021: Ethical AI Practices and Advisory will be Incorporated in AI Technology Growth Strategy to...

Artificial intelligence (AI) was once the stuff of science fiction. But its becoming widespread. It is used in mobile phone technology and motor vehicles. It powers tools for agriculture and healthcare.

But concerns have emerged about the accountability of AI and related technologies like machine learning. In December 2020 a computer scientist, Timnit Gebru, was fired from Googles Ethical AI team. She had previously raised the alarm about the social effects of bias in AI technologies. For instance, in a 2018 paper Gebru and another researcher, Joy Buolamwini, had showed how facial recognition software was less accurate in identifying women and people of colour than white men. Biases in training data can have far-reaching and unintended effects.

There is already a substantial body of research about ethics in AI. This highlights the importance of principles to ensure technologies do not simply worsen biases or even introduce new social harms. As the UNESCO draft recommendation on the ethics of AI states:

We need international and national policies and regulatory frameworks to ensure that these emerging technologies benefit humanity as a whole.

In recent years, many frameworks and guidelines have been created that identify objectives and priorities for ethical AI.

This is certainly a step in the right direction. But its also critical to look beyond technical solutions when addressing issues of bias or inclusivity. Biases can enter at the level of who frames the objectives and balances the priorities.

In a recent paper, we argue that inclusivity and diversity also need to be at the level of identifying values and defining frameworks of what counts as ethical AI in the first place. This is especially pertinent when considering the growth of AI research and machine learning across the African continent.

Research and development of AI and machine learning technologies is growing in African countries. Programmes such as Data Science Africa, Data Science Nigeria, and the Deep Learning Indaba with its satellite IndabaX events, which have so far been held in 27 different African countries, illustrate the interest and human investment in the fields.

The potential of AI and related technologies to promote opportunities for growth, development and democratisation in Africa is a key driver of this research.

Yet very few African voices have so far been involved in the international ethical frameworks that aim to guide the research. This might not be a problem if the principles and values in those frameworks have universal application. But its not clear that they do.

For instance, the European AI4People framework offers a synthesis of six other ethical frameworks. It identifies respect for autonomy as one of its key principles. This principle has been criticised within the applied ethical field of bioethics. It is seen as failing to do justice to the communitarian values common across Africa. These focus less on the individual and more on community, even requiring that exceptions are made to upholding such a principle to allow for effective interventions.

Challenges like these or even acknowledgement that there could be such challenges are largely absent from the discussions and frameworks for ethical AI.

Just like training data can entrench existing inequalities and injustices, so can failing to recognise the possibility of diverse sets of values that can vary across social, cultural and political contexts.

In addition, failing to take into account social, cultural and political contexts can mean that even a seemingly perfect ethical technical solution can be ineffective or misguided once implemented.

For machine learning to be effective at making useful predictions, any learning system needs access to training data. This involves samples of the data of interest: inputs in the form of multiple features or measurements, and outputs which are the labels scientists want to predict. In most cases, both these features and labels require human knowledge of the problem. But a failure to correctly account for the local context could result in underperforming systems.

For example, mobile phone call records have been used to estimate population sizes before and after disasters. However, vulnerable populations are less likely to have access to mobile devices. So, this kind of approach could yield results that arent useful.

Similarly, computer vision technologies for identifying different kinds of structures in an area will likely underperform where different construction materials are used. In both of these cases, as we and other colleagues discuss in another recent paper, not accounting for regional differences may have profound effects on anything from the delivery of disaster aid, to the performance of autonomous systems.

AI technologies must not simply worsen or incorporate the problematic aspects of current human societies.

Being sensitive to and inclusive of different contexts is vital for designing effective technical solutions. It is equally important not to assume that values are universal. Those developing AI need to start including people of different backgrounds: not just in the technical aspects of designing data sets and the like but also in defining the values that can be called upon to frame and set objectives and priorities.

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Defining what's ethical in artificial intelligence needs input from Africans - The Conversation CA

Published on November 25, 2021

Bengaluru IEEE, the worlds largest technical professional organization committed to advancing technology for humanity, today concluded its virtual roundtable focused on The Next Big Thing in Technology, the top technologies that will have a massive impact in 2022 and beyond. With the ongoing COVID-19 pandemic where digitization and technology have become increasingly powerful drivers for innovation, IEEE curated this roundtable to discuss how AI, ML, and advanced security mechanisms are fuelling industries to drastically increase productivity, automate systems to achieve better accuracy, and help workforces outperform while minimizing tedious repetitive tasks. AI-driven learning systems are generating more opportunities for intertwining technology trends which will only continue in 2022.

Speaking in the roundtable about The Impact of Technology in 2022, Sukanya Mandal, IEEE Member, and Founder and Data Science Professional explained, AI and ML are creating strides for technological advancements and will be extremely vital for our future to increase output, bring specialization into job roles, and increase the importance of human skills such as problem-solving, quantitative skills, and creativity. I strongly believe the future will consist of people and machines working together to improve and adapt to a modern way of working. AI will also play a critical role in all aspects of e-commerce, from customer experiences and marketing to fulfillment and distribution.

Recently published research on Artificial Intelligence and the Future of Work conducted by MIT Work of The Future, highlights that AI continues to push large-scale innovation, create more jobs, advance labor processes, and holds the immense potential to impact various sectors. Furthermore, a Gartner report predicts that half of data centers around the world will deploy advanced robotics with AI and ML capabilities by 2025, which is estimated to lead to 30% higher operating efficiencies.

Industry 4.0 is all about interconnecting machines, processes, and systems for maximum process optimization. Along the same lines, Industry 5.0 will be focused on the interaction between humans and machines. It is all about recognizing human expertise and creatively interconnecting with machine intelligence for process optimization. It is true to say that we are not far away from the 5th industrial revolution. Over this decade and the next, we will witness applications of IoT and smart systems adhering to the principles of the 5th industrial revolution across various sectors., she further added.

The roundtable also focused on Redefining the Future of Biometric Security Technology. AI-Machine Learning-based systems, in collaboration with the latest technologies such as IoT, Cloud Computing, and Data Science, have successfully advanced Biometrics. Biometric systems generate huge volumes of data that can be managed with Machine Learning techniques for better handling and space management. Deep learning can also play a vital role in analyzing data to build automated systems that achieve better accuracy. A report by Carnegie Endowment for International Peace stated that 75 countries, representing 43 percent of a total of 176 countries, are actively leveraging AI capabilities for biometric purposes, including facial recognition systems, smart cities, and others.

Commenting on this, Sambit Bakshi, Senior IEEE Member, said, During the pandemic, we all saw the increased use of technology in public places such as airports, train stations, etc., not only to monitor body temperatures but also to help maintain COVID protocols. Biometric technologies are rapidly becoming a part of the daily lives of people around the world.

Biometric authentication is likely to expand in the coming years. Multimodal authentication exercises a combination of similar biometric technologies to authenticate someone. Cues from different platforms can be integrated through cloud computing and IoT-based architecture to verify someones identity. These can include gait features or anthropometric signatures. The future of biometric security lies in simplicity. Improving modern techniques is the simplest way to offer a high level of protection.

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Artificial Intelligence, Machine Learning, and Biometric Security Technology will be Drivers of Digital Transformation in 2022 And Beyond: IEEE...