Category Archives: Artificial Intelligence

Artificial intelligence in cybersecurity is presently gaining traction to secure information, and AI technology is proficient at rapidly investigating millions of data sets and tracking down a wide-ranging variety of cyber threats such as phishing attack, malware, and viruses. Adoption of AI in cybersecurity is expected to increase among end-users to resolve the issue of security and identify new varieties of attacks that can take place anytime; therefore, the adoption rate ofartificial intelligence in cybersecurity marketis increasing consistently

Artificial intelligence endeavors to replicate human intelligence. AI has vast potential in cybersecurity. If connected correctly, artificial intelligence systems can be trained to produce alerts for malware, identify new types of threats, and defend sensitive data for organizations. Increase in number of cyber-attacks would mean that a security expert in the company would not be able deal with the issue, and some of the threats would therefore naturally go unnoticed, which can cause a huge damage to the network. Therefore, artificial intelligence in cybersecurity can play an important role to guard the companys network and sensitive data.

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Artificial Intelligence in Cybersecurity Market: Key Drivers

Artificial intelligence in cybersecurity systems can deliver the latest information of global as well as business-specific risks to formulate vigorous prioritization results based not only on what is most likely to be used to attack your system but what might be used to attack your systems

Artificial intelligence in cybersecurity technology is intelligent, and it uses its ability to advancenetwork securityover time. It employs deep learning and machine learning to learn a business networks performance over time. It identifies patterns on the network and automatically clusters them.

Artificial intelligence in cybersecurity technology helps evaluate systems rapidly to identify the business network and weak point in the computer system. It also helps businesses to focus on important security task in order to easily manage vulnerability and protect business systems in time.

Artificial intelligence in cybersecurity ensures the security of technological innovations and critical infrastructure; consequently, companies are increasing their AI cybersecurity budgets significantly, supporting business approaches with cybersecurity plans, and creating cyber alertness programs for customers and employees.

Impact of COVID-19 on Artificial Intelligence in Cybersecurity Market

Increase in cases of COVID-19 across the globe is resulting in an economic slowdown. Developed countries are strongly affected by the pandemic. Integration of artificial intelligence in cybersecurity helps data centers, cloud systems, and digital devices, so there is a huge demand for artificial intelligence in cybersecurity

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North America to Hold Major Share of Artificial Intelligence in Cybersecurity Market

In terms of region, the global artificial intelligence in cybersecurity market can be divided into North America, Europe, Asia Pacific, Middle East & Africa, and South America

North America was a dominant region of the global artificial intelligence in cybersecurity market primarily owing to the presence of developed economies, such as the U.S. and Canada. Moreover, the high rate of adoption of artificial intelligence in cybersecurity by government agencies, financial institutes, and banks in the region face various challenges related to cyber threats and several big companies are based in North America are dealing in the artificial intelligence in cybersecurity market.

The global artificial intelligence in the cybersecurity market in Asia Pacific is anticipated to expand at a rapid pace during the forecast period. Developing countries in the region are mostly focused on the implementation of new technologies, and countries like India, China, and Japan are emphasizing on cybersecurity technology in various sectors.

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Key Players Operating in Global Artificial Intelligence in Cybersecurity Market

Intel Corporation

Intel Corporation specializes in the design and manufacture of integrated processors, platforms, chipsets, circuits, and software solutions, such as Intel security, and embedded software. The company offers a wide range of products including desktops, tablets, SSDs, boards & kits, server products, Intel gateways, modems, and radio frequency transceivers. It provides solutions for embedded applications for healthcare, automotive, energy, and retail sectors.

SAP SE

SAP SE is a global technology company that provides enterprise application software for industries and companies across diverse sectors. SAP SE offers solutions for various businesses, including commerce, finance, asset management, manufacturing, human resources, supply chain, and procurement. The company also offers analytic solutions such as agile visualization, and business intelligence and cybersecurity for information and data needs.

Other key players operating in the global artificial intelligence in cybersecurity market include NVIDIA Corporation, Xilinx, Inc., Microsoft Corporation, International Business Machines Corporation, Samsung Electronics Co., Ltd., ThreatMetrix, Inc., Vectra AI, Inc., Palo Alto Networks, Inc., SparkCognition, Inc., Acalvio Technologies, Amazon Web Services, Inc., and Securonix, Inc.

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Artificial Intelligence in Cybersecurity Market Sales to Witness Significant Growth in the Near Future, TMR - Digital Journal

HUMBOLDT COUNTY, Calif.(KIEM)- During extreme weather, determining wildfire smoke from fog and other false indicators is crucial. This is why PG&E is testing artificial intelligence and machine-learning capabilities. In the growing network of high-definition cameras across Northern and Central California to see how it can enhance fire-watch and response capabilities.

One of the tools that PG&E have is in mitigating wildfire threats and producing the threat of wildfires are these HD fire watch cameras. We have 487 of them across our service territory, and 11 of them are in Humboldt County. Anybody can go on alertwildfire.org and see the live picture of these cameras, said Deanna Contreras, Spokesperson with PG&E.

None of the11 PG&E cameras located in mountain tops in Humboldt Countyhave the artificial intelligence software setup. They have collected the data from the ones with the software and are trying to figure out how to make the current IA software smarter and better.

We know it works. We know it rules out false positives, haze, fog, and smoke, and it lets us know that it spots smoke, said Deanna Contreras

The cameras provide 360-degree views and can be viewed by anyone through alertwildfire.org. By the end of 2022, the PG&E plans to have approximately 600 cameras installed to provide an ability to see in real-time more than90% of the high fire-risk areas.

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PG&E is using artificial intelligence to enhance fire-watch and response capabilities - KIEM

Prof. Anubha Gupta, Professor IIIT-Delhi talks about the role of artificial intelligence in cancer detection and highlights her teams research on this

Multiple Myeloma (MM) is a type of blood cancer that is owing to the malignancy of plasma cells. The overall survival of patients after being diagnosed with MM ranges from 6 months to more than 10 years. The variability in the outcome is an implication of the underlying biological heterogeneity. The current risk predictors of MM have been established on western populations and do not integrate ethnicity-specific information, the impact of which on disease biology cannot be overlooked.

India is ethnically diverse and has wide disparity in its healthcare infrastructure. A large number of cancer patients are initially diagnosed at peripheral hospitals and then seek specialized cancer care at advanced cancer centres. Staging of cancer is important in assessing the risks of progression, morbidity, mortality and to decide the appropriate treatment. The investigations done at the initial presentation of the disease are crucial in staging of the cancers. It is, therefore, important to develop staging systems that are based on simple tests that are widely available and yet have strong impact on disease so as to be informative of the cancer stage.

In this context, a team of researchers led by Dr Ritu Gupta, Professor, Laboratory Oncology Unit, Dr B.R.A. IRCH, AIIMS, New Delhi and Prof. Anubha Gupta, Deptt. of ECE and member, Centre of Excellence in Healthcare (CoEHe) IIIT-Delhi, did a systematic evaluation of more than 1000 Indian patients of MM. The team established the impact of ethnicity on MM risk prediction and developed two efficient and robust Artificial Intelligence (AI)-enabled risk-staging systems, namely, 1) Modified risk staging (MRS) system for patients in whom high-risk cytogenetic aberrations (HRCA) based on genomic data is not available and 2) Consensus based risk-staging system (CRSS) to establish the biological relevance of the risk predictions in patients for whom the genomic data is available. The team identified disease-specific parameters and assigned them weightage using AI and incorporated them into the risk stage prediction based on their ability to contribute to the risk of the disease.

The MRS is based on patient and disease-specific parameters of age, serum levels of albumin, creatinine, beta 2 microglobulin, calcium, and haemoglobin. All of these six parameters are tested on blood and are widely available at the level of district hospitals. The simplicity of the method allows for staging of the disease for almost all the patients diagnosed with multiple myeloma in our country. The CRSS is an advanced model which includes additional three genetic parameters and can be used in patients in whom cancer genetic testing is available.

Both these MRS and CRSS works have been published in renowned peer-reviewed international journals and were compared with the current international staging system, i.e., the revised International Staging System (R-ISS). Full details are available for MRS work in the publication in the journal of Translational Oncology, Elsevier and for CRSS work in the publication in the journal of in Frontiers in Oncology.

We used curated dataset of more than 1000 patients of multiple myeloma collected over a period of five years and another dataset of 900 patients from the American population curated by the Multiple Myeloma Research Foundation (MMRF), USA. Both the risk prediction and staging systems developed by us performed better than RISS in Indian cohort and also improved risk stratification in the American dataset.

We have designed simple online tools to allow automated calculation of MRS and CRSS. One can find out the stage of the disease by feeding the values of the laboratory test results and age of the patient; and generates predictions for survival for the particular patient case.

Our CRSS work discovers changes in cut-offs in Indian patients from the established cut-offs of prognostic features and highlights the need for focused research to identify the differences and unique features of Indian patients with cancers for better risk stratification to decide on appropriate treatments. This work establishes novel robust risk-staging models that can be widely employed in India with its existing diversity and disparity in the health care infrastructure. As of now, the proposed calculators are validated for Indian population. In future, this concept can be used to develop risk stratification models for specific ethnic groups across the globe.

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Artificial intelligence as an efficient tool in cancer detection - Express Healthcare

Imagine youre planning a vacation for your family. Youve spent a generous amount of time researching destinations, booking flights, securing a car rental, and finally, the hotel. After thoroughly researching all of your options, you settled on a property and pick up the phone. After a few rings, you hear a voice on the other end of the line, and you immediately tense upa voice bot. What could have been a quick and painless phone call turns into a one-sided conversation that seems to take you in circles until finally, youre able to talk to a human on the other end of the line.

In 2021, customers value a personal connection, but the convenience of a voice bot is hard to beat for some businesses that just dont have the manpower to handle their current call volume. Is there a better solution for voice bots? To fully understand how Artificial Intelligence can fit into the hospitality industry, we must first understand how it has failed us thus far.

Voice bots are software used in call centers of large companies to help customers navigate to their desired representative more naturally than a voice recording with keyed responses. Voice bots are powered by artificial intelligence and are known as Interactive Voice Response Systems or IVR for short.

Despite vast improvements in technology from the previous iterations of IVR that had customers listen to menus and press corresponding numbers on their keypads, the majority of consumers still seek to avoid voice bots whenever possible.

IVR systems seemingly appeared overnight and forced customers into a loop of long wait times and incoherent call and answer scenarios. However, IVR systems are widely used by industries across the globe to help companies cope with massive call volumes. It doesnt take an industry expert to point out whats wrong with current IVR systems.

Because IVR systems can't differentiate between types of calls, customers are forced into a cycle of repeating menus to help narrow down their reason for calling. This is an incredibly frustrating situation to be in for any customer, but older customers find it especially difficult to follow.

Unlike humans, IVR systems cannot provide a personalized call experience. Completely unaware of customers purchasing history, previous needs, or customer journey, callers are all forced to jump through the same hoops, again and again, each time they call.

IVR systems can only collect and store a limited amount of data, so returning callers will not have their progress saved. Additionally, the failure of IVR systems to collect data cripples a companys ability to make data-driven decisions based on their customers call experiences.

According to Vonage, an industry leader in cloud communication, 61% of customers feel that IVRs make for a poor experience. Additionally, the State of IVR in 2018 asserts that 83% of customers have abandoned a company altogether after reaching an IVRs menu of options. Customer service experts have since identified the error of mass implementation of IVR.

The last year and a half put IVR systems to the ultimate stress test, especially in the travel industry. When flights are canceled in mass, call volumes for airlines surge, and its clear that IVR systems are hurting the customer experience rather than simplifying it.

Its 2021, automated customer service experiences don't have to be so painstakingly miserable. Many corporations need some type of automated system to help process and sort callers. An investment in technology to create a customer-focused, alternative intelligence-powered voice bot is a feasible solution.

A voice bot with a focus on increasing response time, decreasing total call time, and quickly redirecting callers with an added component of a humanlike interaction is now a reality. Keep your eye out for our next story when we introduce Bella, The Virtual Hotel Agent.

Given its progressive approach to the voice channel in terms of performance, training, transparency, testing and the tools used to measure performance Travel Outlook Premium Hotel Call Center has become the premier voice reservations team in hospitality. Travel Outlook"s valued client list includes Viceroy Hotel Group, Outrigger, KSL Resorts, Proper Hospitality Group, Pacific Hospitality Group, Highgate Hotels, The Irvine Company, Catalina Island and many others. Travel Outlook"s team and approach increases sales conversion and helps to create more effective voice communication between hotels and their guests, resulting in improved social scores in addition to increased voice channel revenue. For more information, visit http://www.traveloutlook.com.

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Does Artificial Intelligence Have a Place in the Travel Industry? | By John Smallwood Hospitality Net - Hospitality Net

Starting today (14 December) in Moscow, the WHO European conference on tackling noncommunicable diseases through digital solutions brings together decision-makers and experts from across the WHO European Region to identify innovative ways to tackle chronic diseases that affect millions of people.

The growing burden of noncommunicable diseases (NCDs) in the European Region has called for new approaches to managing chronic conditions. COVID-19 has limited access to health services and placed a huge burden on economies; inspiring countries to look for digital solutions to improve the quality of health services, making them more responsive to peoples needs.

At the same time, decision-makers across the Region are searching for new ways to improve the prevention of NCDs and promote healthier lifestyles in general an area that requires further exploration.

A selection of stories from countries shows how digital solutions can benefit prevention and treatment of NCDs.

A national diabetes registry was first established in 2000 in Croatia. Called CroDiab, the registry is a web-based system for the collection of information on diabetic patients, which allows health professionals to focus on their individual needs and choose better treatment options.

CroDiabs data is collected from government registries and primary care and hospital reports. Since 2004, use of this digital database has been mandatory for all primary and secondary health-care physicians who have patients with diabetes in their care.

A national electronic cancer data collection system in Georgia makes the cancer screening, diagnosis and treatment process more efficient for patients and doctors, and allows the government to better devise cancer management strategies.

The Unified Electronic System for Cancer Data Collection registers every step in the cancer case management process. As a result, patients do not have to carry around their diagnosis papers when seeing different specialists everything is already in the system. Using this innovative tool, the countrys health professionals and authorities are able to better plan cancer management and choose the best practices.

In Slovakia, a new technology helps reduce the average time spent by a radiation oncologist in planning radiation therapy for patients by at least 30%.

The software tool uses artificial intelligence to automatically generate images within seconds from computerized tomography (CT) scans. This helps oncologists ensure that radiation therapy planning is optimal, with the least possible impact on the patient.

Many people with chronic conditions find it makes a huge difference to get support from others dealing with the same challenges. Recognizing this, the Elsa Science app was developed in Sweden to link up patients who wish to share their experiences, gain knowledge about their condition, and play an active part in their health care.

The first chronic condition the Elsa Science app is focusing on is rheumatoid arthritis. While using the app, people with this condition can share their health information with their rheumatology specialists or health facilities, and get support from their families and friends.

In the European Region, digital solutions are helping more and more people to enjoy and share the benefits of quality health care and to learn more about healthier choices and lifestyles.

The Moscow conference on digital solutions to tackle NCDs reflects the vision of the WHO European Programme of Work 20202025, and shares the hope that even struggling with the challenges of COVID-19, we are creating a better and healthier world to live in.

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Artificial intelligence and data technology provide smarter health care 4 solutions that have made a difference for noncommunicable diseases - World...

On 7 December 2021, the Council of Europe Project Strengthening Media Freedom, Internet Governance and Personal Data Protection SMIP-GE launched the Project Action in the field of Artificial Intelligence and Personal Data Protection, which will be implemented in cooperation with the State Inspectors Service of Georgia (national data protection authority). The Project Action envisages use of the Council of Europe expertise to build the capacity of the State Inspectors Service in the field of Artificial Intelligence. This involves research, training and policy development for the institution. As a result of the series of activities, the State Inspectors Service will have improved skills and knowledge to monitor Artificial Intellige nce tools vis--vis personal data protection in Georgia.

The Project Action launch was organised in the framework of the Council of Europe Project Strengthening Media Freedom, Internet Governance and Personal Data Protection financed under the Council of Europe Action Plan for Georgia 2021-2023.

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Launch of the Project Action in the field of Artificial Intelligence and Personal Data Protection - Council of Europe

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...

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

Last week, the Dutch Scientific Council for Government Policy (WRR) found that the Netherlands is not well prepared for the consequences of artificial intelligence (AI). In Challenge AI, The New Systems Technology (in Dutch), the council calls for regulation of technology and data, its use, and social implications. And rightly so. Machines will have more computing power than humans in a few decades. If devices with artificial intelligence then start to think and decide for themselves, it is to be hoped that they will observe a number of commandments.

AI is also entering mobility, and the problems the WRR refers to are also at play there. The most imaginative AI appearance in mobility is the autonomous car. It is potentially much safer and more comfortable, but there are tricky liability issues if an accident occurs. Should you as a human always be able to override the system? And what would it take for a self-driving car to interpret the law flexibly when necessary? This is something we, as humans, do every minute in daily traffic, precisely in the service of safety.

One day, when I was driving along with traffic at 120 km/h on the E25 through the Ardennes, my automatic cruise control suddenly lowered the speed limit to 70 km/h because the road workers had forgotten to remove a speed sign. Fortunately, I was able to override that and not adhere to that officially legal speed limit. Despite this example, however, in the future, we should not start allowing extremely smart machines to be flexible with the rules, just like us, without any ethical or moral framework. That could lead to dystopian states where machines, perhaps unintentionally, start endangering humanity.

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But the AI issues in mobility go far beyond the self-driving car. What if Google or TomTom takes over traffic management from the road authority? What if the big tech giants take over the entire planning of public transport once people plan their journeys solely through their services? What if those platforms, after a friendly free initial period, start abusing their achieved monopolies? Who will guarantee availability and safety? Cab services like Uber are more popular than the classic taxi, but who can oblige them, as with regulated cab transport, to also accept guide dogs and wheelchairs, for example, so that a significant part of society is not left aside?

Artificial intelligence will make mobility better, safer, and more comfortable. But these systems need ethical and moral frameworks within which they can achieve this. In the Netherlands, companies, and knowledge institutions have already united in the Dutch AI Coalition. They received 276 million from the growth fund earlier this year to strengthen the Dutch position internationally. Wisely, the first part of that goes to so-called Elsa labs: Ethical, Legal & Societal aspects of AI, in which consortia focus on these aspects. Just as in mobility, AI will help steer other areas as well, but we still want to be able to take the wheel ourselves.

Maarten Steinbuch and Carlo van de Weijer are alternately writingthis weekly column, originally published (in Dutch)in FD. Did you like it? Theres more to enjoy: a book with a selection of these columns has just been published by24U and distributed byLecturis.

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Artificial intelligence and mobility, who's at the wheel? - Innovation Origins