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State of AI and Ethical Issues

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State of AI and Ethical Issues


How to Regulate Artificial Intelligence the Right Way: State of AI and Ethical Issues

The current artificial intelligence (AI) systems are regulated by other existing regulations such as data protection, consumer protection and market competition laws.

It is critical for governments, leaders, and decision makers to develop a firm understanding of the fundamental differences between artificial intelligence, machine learning, and deep learning.

Artificial intelligence (AI) applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions.

Machine learning (ML) is a subset of AI that utilises advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa and Apple’s Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.

Deep learning (DL) is a subset of machine learning that uses advanced algorithms to enable an AI system to train itself to perform tasks by exposing multilayered neural networks to vast amounts of data. It then uses what it learns to recognize new patterns contained in the data. Learning can be human-supervised learningunsupervised learning, and/or reinforcement learning, like Google used with DeepMind to learn how to beat humans at the game Go.

State of Artificial Intelligence in the Pandemic Era

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Artificial intelligence (AI) is stepping up in more concrete ways in blockchain, education, internet of things, quantum computing, arm race and vaccine development.

During the Covid-19 pandemic, we have seen AI become increasingly pivotal to breakthroughs in everything from drug discovery to mission critical infrastructure like electricity grids.

AI-first approaches have taken biology by storm with faster simulations of humans’ cellular machinery (proteins and RNA). This has the potential to transform drug discovery and healthcare.

Transformers have emerged as a general purpose architecture for machine learning, beating the state of the art in many domains including natural language planning (NLP), computer vision, and even protein structure prediction.

AI is now an actual arms race rather than a figurative one. 

Organizations must learn from the mistakes made with the internet, and prepare for a safer AI.

Artificial intelligence deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment.

There are 3 stages of artificial intelligence:

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1. Artificial Narrow Intelligence (ANI), which has a limited range of capabilities. As an example: AlphaGo, IBM’s Watson, virtual assistants like Siri, disease mapping and prediction tools, self-driving cars, machine learning models like recommendation systems and deep learning translation.

2. Artificial General Intelligence (AGI), which has attributes that are on par with human capabilities. This level hasn’t been achieved yet. 

3. Artificial Super Intelligence (ASI), which has skills that surpass humans and can make them obsolete. This level hasn’t been achieved yet. 

Why Governments Need to Regulate Artificial Intelligence?

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We need to regulate artificial intelligence for two reasons.

  • First, because governments and companies use AI to make decisions that can have a significant impact on our lives. For example, algorithms that calculate school performance can have a devastating effect. 

  • Second, because whenever someone takes a decision that affects us, they have to be accountable to us. Human rights law sets out minimum standards of treatment that everyone can expect. It gives everyone the right to a remedy where those standards are not met, and you suffer harm.

Is There An International Artificial Intelligence Law?

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As of today, there is no international artificial intelligence law nor specific legislation designed to regulate its use. However, progress has been made as bills have been passed to regulate certain specific AI systems and frameworks.

Artificial intelligence has changed rapidly over the last few decades. It has made our lives so much easier and saves us valuable time to complete other tasks.

AI must be regulated to protect the positive progress of the technology. Legislators across the globe have to this day failed to design laws that specifically regulate the use of artificial intelligence. This allows profit-oriented companies to develop systems that may cause harm to individuals and to the broader society. 

National and International Artificial Intelligence Regulations

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National and local governments have started adopting strategies and working on new laws for a number of years, but no legislation has been passed yet.

China for example has developed in 2017 a strategy to become the world’s leader in AI in 2030. In the US, the White House issued ten principles for the regulation of AI. They include the promotion of “reliable, robust and trustworthy AI applications”, public participation and scientific integrity. International bodies that give advice to governments, such as the OECD or the World Economic Forum, have developed ethical guidelines. 

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The Council of Europe created a Committee dedicated to help develop a legal framework on AI. The most ambitious proposal yet comes from the EU. On 21 April 2021, the EU Commission put forward a proposal for a new AI Act. 

Ethical Concerns of Artificial Intelligence

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Police forces across the EU deploy facial recognition technologies and predictive policing systems. These systems are inevitably biased and thus perpetuate discrimination and inequality.

Crime prediction and recidivism risk are a second AI application fraught with legal problems. A ProPublica investigation into an algorithm-based criminal risk assessment tool found the formula more likely to flag black defendants as future criminals, labelling them at twice the rate as white defendants, and white defendants were mislabeled as low-risk more often than black defendants. We need to think about the way we are mass producing decisions and processing people, particularly low income and low-status individuals, through automation and their consequences for society.

How to Regulate Artificial Intelligence the Right Way

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An effective, rights-protecting AI regulation must, at a minimum, contain the following safeguards. First, artificial intelligence regulation must prohibit use cases, which violate fundamental rights, such as biometric mass surveillance or predictive policing systems. The prohibition should not contain exceptions that allow corporations or public authorities to use them “under certain conditions”.

Second, there must be clear rules setting out exactly what organizations have to make public about their products and services. Companies must provide a detailed description of the AI system itself. This includes information on the data it uses, the development process, the systems’ purpose and where and by whom it is used. It is also key that individuals exposed to AI are informed about it, for example in the case of hiring algorithms. Systems that can have a significant impact on people’s lives should face extra scrutiny and feature in a publicly accessible database. This would make it easier for researchers and journalists to make sure companies and governments are protecting our freedoms properly.

Third, individuals and organisations protecting consumers need to be able to hold governments and corporations responsible when there are problems. Existing rules on accountability must be adapted to recognise that decisions are made by an algorithm and not by the user. This could mean putting the company that developed the algorithm under an obligation to check the data with which algorithms are trained and the decisions algorithms make so they can correct problems.

Fourth, new regulations must make sure that there is a regulator that can make companies and the authorities accountable and that they are following the rules properly. This watchdog should be independent and have the resources and powers it needs to do its job.

Finally, AI regulation should also contain safeguards to protect the most vulnerable. It should set up a system that allows people who have been harmed by AI systems to make a complaint and get compensation. Workers should have the right to take action against invasive AI systems used by their employer without fear of retaliation.

Conclusion

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A trustworthy artificial intelligence should respect all applicable laws and regulations, 
as well as a series of requirements; specific assessment lists aim to help verify the application of each of the key requirements:

  • Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.

  • Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.

  • Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.

  • Transparency: The traceability of AI systems should be ensured.

  • Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.

  • Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.

  • Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.



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Radware launches a spinoff of its cloud security business

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Duncan is an award-winning editor with more than 20 years experience in journalism. Having launched his tech journalism career as editor of Arabian Computer News in Dubai, he has since edited an array of tech and digital marketing publications, including Computer Business Review, TechWeekEurope, Figaro Digital, Digit and Marketing Gazette.


Radware, a provider of cyber security and application delivery solutions, has revealed the spinoff of its Cloud Native Protector (CNP) business to form a new company called SkyHawk Security.

To accelerate Skyhawk Security’s development and growth opportunities, an affiliate of Tiger Global Management will make a $35 million strategic external investment, resulting in a valuation of $180 million. Tiger Global Management is a leading global technology investment firm focused on private and public companies in the internet, software, and financial technology sectors.

Skyhawk Security is a leader in cloud threat detection and protects dozens of the world’s leading organizations using its artificial intelligence and machine learning technologies. Its Cloud Native Protector provides comprehensive protection for workloads and applications hosted in public cloud environments. It uses a multi-layered approach that covers the overall security posture of the cloud and threats to individual workloads. Easy-to-deploy, the agentless solution identifies and prevents compliance violations, cloud security misconfigurations, excessive permissions, and malicious activity in the cloud.

“We recognize the growing opportunities in the public cloud security market and are planning to capitalize on them,” said Roy Zisapel, Radware’s president and CEO. “We look forward to partnering with Tiger Global Management to scale the business, unlock even more security value for customers, and position Skyhawk Security for long-term success.”

The spinoff, which adds to Radware’s recently announced strategic cloud services initiative, further demonstrates the company’s ongoing commitment to innovation. Skyhawk Security will have the ability to operate with even greater sales, marketing, and product focus as well as speed and flexibility. Current and new CNP customers will benefit from future product development efforts, while CNP services for existing customers will continue without interruption.

Radware does not expect the deal to materially affect operating results for the second quarter or full year of 2022.

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How Sports Organizations Are Using AR, VR and AI to Bring Fans to The Game

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How Sports Organizations Are Using AR, VR and AI to Bring Fans to The Game

AR, VR, and AI in sports are changing how fans experience and engage with their favorite games.

That’s why various organizations in the sports industry are leveraging these technologies to provide more personalized and immersive digital experiences.

How do you get a sports fan’s attention when there are so many other entertainment options? By using emerging technologies to create unforgettable experiences for them! Innovative organizations in the sports industry are integrating AR, VR and AI in sports marketing and fan engagement strategies. Read on to discover how these innovative technologies are being leveraged to enhance the game-day experience for sports fans.  

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AUGMENTED REALITY IN SPORTS

AR is computer-generated imagery (CGI) that superimposes digitally created visuals onto real-world environments. Common examples of AR include heads-up displays in cars, navigation apps and weather forecasts. AR has been around for decades, but only recently has it become widely available to consumers through mobile devices. One of the best ways sports organizations can use AR is to bring historical moments to life. This can help fans connect to the past in new ways, increase brand affinity and encourage them to visit stadiums to see these experiences in person. INDE has done just that, creating an augmented reality experience that lets fans meet their favorite players at the NFL Draft.

VIRTUAL REALITY IN SPORTS

VR is a computer-generated simulation of an artificial environment that lets you interact with that environment. You experience VR by wearing a headset that transports you to a computer-generated environment and lets you see, hear, smell, taste, and touch it as if you were actually there. VR can be especially impactful for sports because it lets fans experience something they would normally not be able to do. Fans can feel what it’s like to be a quarterback on the field, a skier in a race, a trapeze artist, or any other scenario they’d like. The VR experience is fully immersive, and the user is able to interact with the content using hand-held controllers. This enables users to move around and explore their virtual environment as if they were actually present in it.

ARTIFICIAL INTELLIGENCE IN SPORTS

Artificial intelligence is machine intelligence implemented in software or hardware and designed to complete tasks that humans usually do. AI tools can manage large amounts of data, identify patterns and make predictions based on that data. AI is already influencing all aspects of sports, from fan experience to talent management. Organizations are using AI to power better digital experiences for fans. They’re also using it to collect and analyze data about fan behavior and preferences, which helps organizers better understand what their customers want. AI is also changing the game on the field, with organizations using it to make better decisions in real time, improve training and manage player health. Much of this AI is powered by machine learning, which is a type of AI that uses data to train computer systems to learn without being programmed. Machine learning is the reason why AI is able to evolve and get better over time — it allows AI systems to adjust and improve based on new data.

MERGING THE REAL AND VIRTUAL

VR and AR are both incredible technologies that offer unique benefits. VR, for example, is an immersive experience that allows you to fully imagine and explore another virtual space. AR, on the other hand, is a technology that allows you to see and interact with the real world while also being able to see digital content superimposed on top of it. VR and AR are both rapidly evolving and can have a significant impact on sports marketing. By using both technologies, brands and sporting organizations can create experiences that bridge the real and virtual. This can help sports marketers create more engaging experiences that truly immerse their customers in the game.

Technologies like AR, VR and AI in sports are making it possible for fans to enjoy their favorite games in entirely new ways. AR, for example, can help sports lovers experience historical moments, VR lets them immerse themselves in the game, and AI brings them more personalized and immersive digital experiences. The best part is that sports fans can also use these technologies to interact with one another and feel even more connected. 

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The Dark Side of Wearable Technology

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The Dark Side of Wearable Technology

Wearable technology, such as smartwatches, fitness trackers, and other devices, has become increasingly popular in recent years.

These devices can provide a wealth of information about our health and activity levels, and can even help us stay connected with our loved ones. However, there is also a dark side to wearable technology, including issues related to privacy, security, and addiction. In this article, we will explore some of the darker aspects of wearable technology and the potential risks associated with these devices.

1. Privacy Concerns

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Source: Deloitte

Wearable technology can collect and transmit a significant amount of personal data, including location, health information, and more. This data is often shared with third parties, such as app developers and advertisers, and can be used to track and target users with personalized advertising. Additionally, many wearable devices lack robust security measures, making them vulnerable to hacking and data breaches. This can put users’ personal information at risk and expose them to identity theft and other cybercrimes.

2. Security Risks

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Source: MDPI

Wearable technology can also pose security risks, both to the individual user and to organizations. For example, hackers can use wearable devices to gain access to sensitive information, such as financial data or personal contacts, and use this information for malicious purposes. Additionally, wearable technology can be used to gain unauthorized access to secure areas, such as buildings or computer systems, which can be a major concern for organizations and governments.

3. Addiction Issues

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Source: Very Well Mind

The constant connectivity and access to information provided by wearable technology can also lead to addiction. The constant notifications and the ability to check social media, emails and other apps can create a constant need to check the device, leading to addiction-like symptoms such as anxiety, insomnia and depression.

4. Health Risks

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Source: RSSB 

Wearable technology can also pose health risks, such as skin irritation and allergic reactions caused by the materials used in the device. Additionally, the constant use of wearable technology can lead to poor posture and repetitive stress injuries, such as carpal tunnel syndrome. It is important for users to be aware of these risks and to take steps to protect their health, such as taking regular breaks from using the device and practicing good ergonomics.

Conclusion

Wearable technology has the potential to be a powerful tool for improving our health, fitness, and overall well-being. However, it is important to be aware of the darker aspects of wearable technology and the potential risks associated with these devices. By understanding the privacy, security, addiction, and health risks associated with wearable technology, users can take steps to protect themselves and their personal information. Additionally, by being aware of these risks, organizations can take steps to protect their employees and customers from the potential negative effects of wearable technology.

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