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Could robots become more ethical than humans?

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Could robots become more ethical than humans?

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Children or people in general may lose the ability to think about new ideas or being creative if they can just ask every time an AI tool to give them ready made ideas.

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Machines can not feel emotions but they can "know" about emotions! They "know" what it means to be happy...

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Plato said that, if you were sick, who would you ask; the many or the pathologist?

This was discussed in accordance to the existence of true democracy.

With this in mind, if most people tend to ask the AI instead of humankind in some aspects, it will have god-like reputation, resulting in humankind disregard.

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Not be able to:
1) have critical thinking
2) adapt
3) work
4) communicate
5) feel empathy
6) not have a spherical knowledge

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convenience will be transformed into comfort and in the long run into incapacity and dependency+ increase in
digital divide!

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People who don’t really achieve anything significant are usually the same people that take someone else’s idea and then they don’t develop it any further. It’s an uninspired approach, which doesn’t produce anything new, but more often than not leaves the “idea thief” satisfied with what they’ve done. Treating AI as an authority on all topics leads to humans becoming complacent and non-imaginative.

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References & Further Resources

Video: The ethical dilemma of self-driving cars - Patrick Lin

Video: “Trustworthy AI: Overview of the 7 requirements for Trustworthy AI”

Code.org: How AI Works

Code.org: AI Ethics Research Areas

Guide: “Ethics Guidelines for Trustworthy AI - A Definition of AI: Main Capabilities And Disciplines”

Guide: “Hands-On for the Classroom AI Projects - A Guide on Ethics and AI (ISTE)”

Trustworthy AI Erasmus+ Project: “Teaching Trustworthy AI Resources”

Website: AI-in-education (ISTE)

Website: DigCitCommit (for Students)

Presentation: Ehe ethical use of artificial intelligence (AI) and data in teaching and learning for Educators

Workshop: AI-Ethic @ΕΕ - Workshop of Talos Project

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What is artificial intelligence (AI)?

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.

How does AI work?

As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use it. Often, what they refer to as AI is simply a component of the technology, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No single programming language is synonymous with AI, but Python, R, Java, C++ and Julia have features popular with AI developers.

In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text can learn to generate lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. New, rapidly improving generative AI techniques can create realistic text, images, music and other media.

AI programming focuses on cognitive skills that include the following:

Learning. This aspect of AI programming focuses on acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.

Reasoning. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome.

Self-correction. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.

Creativity. This aspect of AIuses neural networks, rules-based systems, statistical methods and other AI techniques to generate new images, new text, new music and new ideas.

Why is artificial intelligence important?

AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans. Particularly when it comes to repetitive, detail-oriented tasks, such as analyzing large numbers of legal documents to ensure relevant fields are filled in properly, AI tools often complete jobs quickly and with relatively few errors. Because of the massive data sets it can process, AI can also give enterprises insights into their operations they might not have been aware of. The rapidly expanding population of generative AI tools will be important in fields ranging from education and marketing to product design.

Indeed, advances in AI techniques have not only helped fuel an explosion in efficiency, but opened the door to entirely new business opportunities for some larger enterprises. Prior to the current wave of AI, it would have been hard to imagine using computer software to connect riders to taxis, but Uber has become a Fortune 500 company by doing just that.

AI has become central to many of today's largest and most successful companies, including Alphabet, Apple, Microsoft and Meta, where AI technologies are used to improve operations and outpace competitors. At Alphabet subsidiary Google, for example, AI is central to its search engine, Waymo's self-driving cars and Google Brain, which invented the transformer neural network architecture that underpins the recent breakthroughs in natural language processing.

What are the advantages and disadvantages of artificial intelligence?

Artificial neural networks and deep learning AI technologies are quickly evolving, primarily because AI can process large amounts of data much faster and make predictions more accurately than humanly possible.

While the huge volume of data created on a daily basis would bury a human researcher, AI applications using machine learning can take that data and quickly turn it into actionable information. As of this writing, a primary disadvantage of AI is that it is expensive to process the large amounts of data AI programming requires. As AI techniques are incorporated into more products and services, organizations must also be attuned to AI's potential to create biased and discriminatory systems, intentionally or inadvertently.

Advantages of AI

The following are some advantages of AI.

Good at detail-oriented jobs. AI has proven to be as good or better than doctors at diagnosing certain cancers, including breast cancer and melanoma.

Reduced time for data-heavy tasks. AI is widely used in data-heavy industries, including banking and securities, pharma and insurance, to reduce the time it takes to analyze big data sets. Financial services, for example, routinely use AI to process loan applications and detect fraud.

Saves labor and increases productivity. An example here is the use of warehouse automation, which grew during the pandemic and is expected to increase with the integration of AI and machine learning.

Delivers consistent results. The best AI translation tools deliver high levels of consistency, offering even small businesses the ability to reach customers in their native language.

Can improve customer satisfaction through personalization. AI can personalize content, messaging, ads, recommendations and websites to individual customers.

AI-powered virtual agents are always available. AI programs do not need to sleep or take breaks, providing 24/7 service.

Disadvantages of AI

The following are some disadvantages of AI.

Expensive.

Requires deep technical expertise.

Limited supply of qualified workers to build AI tools.

Reflects the biases of its training data, at scale.

Lack of ability to generalize from one task to another.

Eliminates human jobs, increasing unemployment rates.

Strong AI vs. weak AI

AI can be categorized as weak or strong.

Weak AI, also known as narrow AI, is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI.

Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. In theory, a strong AI program should be able to pass both a Turing test and the Chinese Room argument.

What are the 4 types of artificial intelligence?

Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, explained that AI can be categorized into four types, beginning with the task-specific intelligent systems in wide use today and progressing to sentient systems, which do not yet exist. The categories are as follows.

Type 1: Reactive machines. These AI systems have no memory and are task-specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on a chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.

Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.

Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.

Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.

What are examples of AI technology and how is it used today?

AI is incorporated into a variety of different types of technology. Here are seven examples.

Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation ( RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes.

Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:

Supervised learning. Data sets are labeled so that patterns can be detected and used to label new data sets.

Unsupervised learning. Data sets aren't labeled and are sorted according to similarities or differences.

Reinforcement learning. Data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.

Machine vision. This technology gives a machine the ability to see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.

Natural language processing (NLP). This is the processing of human language by a computer program. One of the older and best-known examples of NLP is spam detection, which looks at the subject line and text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.

Robotics. This field of engineering focuses on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. For example, robots are used in car production assembly lines or by NASA to move large objects in space. Researchers also use machine learning to build robots that can interact in social settings.

Self-driving cars. Autonomous vehicles use a combination of computer vision, image recognition and deep learning to build automated skills to pilot a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.

Text, image and audio generation. Generative AI techniques, which create various types of media from text prompts, are being applied extensively across businesses to create a seemingly limitless range of content types from photorealistic art to email responses and screenplays.

What are the applications of AI?

Artificial intelligence has made its way into a wide variety of markets. Here are 11 examples.

AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster medical diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and can respond to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include using online virtual health assistants and chatbots to help patients and healthcare customers find medical information, schedule appointments, understand the billing process and complete other administrative processes. An array of AI technologies is also being used to predict, fight and understand pandemics such as COVID-19.

AI in business. Machine learning algorithms are being integrated into analytics and customer relationship management ( CRM) platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. The rapid advancement of generative AI technology such as ChatGPT is expected to have far-reaching consequences: eliminating jobs, revolutionizing product design and disrupting business models.

AI in education. AI can automate grading, giving educators more time for other tasks. It can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. The technology could also change where and how students learn, perhaps even replacing some teachers. As demonstrated by ChatGPT, Bard and other large language models, generative AI can help educators craft course work and other teaching materials and engage students in new ways. The advent of these tools also forces educators to rethink student homework and testing and revise policies on plagiarism.

AI in finance. AI in personal finance applications, such as Intuit Mint or TurboTax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, artificial intelligence software performs much of the trading on Wall Street.

AI in law. The discovery process -- sifting through documents -- in law is often overwhelming for humans. Using AI to help automate the legal industry's labor-intensive processes is saving time and improving client service. Law firms use machine learning to describe data and predict outcomes, computer vision to classify and extract information from documents, and NLP to interpret requests for information.

AI in entertainment and media. The entertainment business uses AI techniques for targeted advertising, recommending content, distribution, detecting fraud, creating scripts and making movies. Automated journalism helps newsrooms streamline media workflows reducing time, costs and complexity. Newsrooms use AI to automate routine tasks, such as data entry and proofreading; and to research topics and assist with headlines. How journalism can reliably use ChatGPT and other generative AI to generate content is open to question.

AI in software coding and IT processes. New generative AI tools can be used to produce application code based on natural language prompts, but it is early days for these tools and unlikely they will replace software engineers soon. AI is also being used to automate many IT processes, including data entry, fraud detection, customer service, and predictive maintenance and security.

Security. AI and machine learning are at the top of the buzzword list security vendors use to market their products, so buyers should approach with caution. Still, AI techniques are being successfully applied to multiple aspects of cybersecurity, including anomaly detection, solving the false-positive problem and conducting behavioral threat analytics. Organizations use machine learning in security information and event management ( SIEM) software and related areas to detect anomalies and identify suspicious activities that indicate threats. By analyzing data and using logic to identify similarities to known malicious code, AI can provide alerts to new and emerging attacks much sooner than human employees and previous technology iterations.

AI in manufacturing. Manufacturing has been at the forefront of incorporating robots into the workflow. For example, the industrial robots that were at one time programmed to perform single tasks and separated from human workers, increasingly function as cobots: Smaller, multitasking robots that collaborate with humans and take on responsibility for more parts of the job in warehouses, factory floors and other workspaces.

AI in banking. Banks are successfully employing chatbots to make their customers aware of services and offerings and to handle transactions that don't require human intervention. AI virtual assistants are used to improve and cut the costs of compliance with banking regulations. Banking organizations use AI to improve their decision-making for loans, set credit limits and identify investment opportunities.

AI in transportation. In addition to AI's fundamental role in operating autonomous vehicles, AI technologies are used in transportation to manage traffic, predict flight delays, and make ocean shipping safer and more efficient. In supply chains, AI is replacing traditional methods of forecasting demand and predicting disruptions, a trend accelerated by COVID-19 when many companies were caught off guard by the effects of a global pandemic on the supply and demand of goods.

Augmented intelligence vs. artificial intelligence

Some industry experts have argued that the term artificial intelligence is too closely linked to popular culture, which has caused the general public to have improbable expectations about how AI will change the workplace and life in general. They have suggested using the term augmented intelligence to differentiate between AI systems that act autonomously -- popular culture examples include Hal 9000 and The Terminator -- and AI tools that support humans.

Augmented intelligence. Some researchers and marketers hope the label augmented intelligence, which has a more neutral connotation, will help people understand that most implementations of AI will be weak and simply improve products and services. Examples include automatically surfacing important information in business intelligence reports or highlighting important information in legal filings. The rapid adoption of ChatGPT and Bard across industry indicates a willingness to use AI to support human decision-making.

Artificial intelligence. True AI, or AGI, is closely associated with the concept of the technological singularity -- a future ruled by an artificial superintelligence that far surpasses the human brain's ability to understand it or how it is shaping our reality. This remains within the realm of science fiction, though some developers are working on the problem. Many believe that technologies such as quantum computing could play an important role in making AGI a reality and that we should reserve the use of the term AI for this kind of general intelligence.

Ethical use of artificial intelligence

While AI tools present a range of new functionality for businesses, the use of AI also raises ethical questions because, for better or worse, an AI system will reinforce what it has already learned.

This can be problematic because machine learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human being selects what data is used to train an AI program, the potential for machine learning bias is inherent and must be monitored closely.

Anyone looking to use machine learning as part of real-world, in-production systems needs to factor ethics into their AI training processes and strive to avoid bias. This is especially true when using AI algorithms that are inherently unexplainable in deep learning and generative adversarial network ( GAN) applications.

Explainability is a potential stumbling block to using AI in industries that operate under strict regulatory compliance requirements. For example, financial institutions in the United States operate under regulations that require them to explain their credit-issuing decisions. When a decision to refuse credit is made by AI programming, however, it can be difficult to explain how the decision was arrived at because the AI tools used to make such decisions operate by teasing out subtle correlations between thousands of variables. When the decision-making process cannot be explained, the program may be referred to as black box AI.

In summary, AI's ethical challenges include the following: bias, due to improperly trained algorithms and human bias; misuse, due to deepfakes and phishing; legal concerns, including AI libel and copyright issues; elimination of jobs; and data privacy concerns, particularly in the banking, healthcare and legal fields.

AI governance and regulations

Despite potential risks, there are currently few regulations governing the use of AI tools, and where laws do exist, they typically pertain to AI indirectly. For example, as previously mentioned, U.S. Fair Lending regulations require financial institutions to explain credit decisions to potential customers. This limits the extent to which lenders can use deep learning algorithms, which by their nature are opaque and lack explainability.

The European Union's General Data Protection Regulation ( GDPR) is considering AI regulations. GDPR's strict limits on how enterprises can use consumer data already limits the training and functionality of many consumer-facing AI applications.

Policymakers in the U.S. have yet to issue AI legislation, but that could change soon. A "Blueprint for an AI Bill of Rights" published in October 2022 by the White House Office of Science and Technology Policy (OSTP) guides businesses on how to implement ethical AI systems. The U.S. Chamber of Commerce also called for AI regulations in a report released in March 2023.

Crafting laws to regulate AI will not be easy, in part because AI comprises a variety of technologies that companies use for different ends, and partly because regulations can come at the cost of AI progress and development. The rapid evolution of AI technologies is another obstacle to forming meaningful regulation of AI, as are the challenges presented by AI's lack of transparency that make it difficult to see how the algorithms reach their results. Moreover, technology breakthroughs and novel applications such as ChatGPT and Dall-E can make existing laws instantly obsolete. And, of course, the laws that governments do manage to craft to regulate AI don't stop criminals from using the technology with malicious intent.

What is the history of AI?

The concept of inanimate objects endowed with intelligence has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold. Engineers in ancient Egypt built statues of gods animated by priests. Throughout the centuries, thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull to René Descartes and Thomas Bayes used the tools and logic of their times to describe human thought processes as symbols, laying the foundation for AI concepts such as general knowledge representation.

The late 19th and first half of the 20th centuries brought forth the foundational work that would give rise to the modern computer. In 1836, Cambridge University mathematician Charles Babbage and Augusta Ada King, Countess of Lovelace, invented the first design for a programmable machine.

1940s. Princeton mathematician John Von Neumann conceived the architecture for the stored-program computer -- the idea that a computer's program and the data it processes can be kept in the computer's memory. And Warren McCulloch and Walter Pitts laid the foundation for neural networks.

1950s. With the advent of modern computers, scientists could test their ideas about machine intelligence. One method for determining whether a computer has intelligence was devised by the British mathematician and World War II code-breaker Alan Turing. The Turing test focused on a computer's ability to fool interrogators into believing its responses to their questions were made by a human being.

1956. The modern field of artificial intelligence is widely cited as starting this year during a summer conference at Dartmouth College. Sponsored by the Defense Advanced Research Projects Agency (DARPA), the conference was attended by 10 luminaries in the field, including AI pioneers Marvin Minsky, Oliver Selfridge and John McCarthy, who is credited with coining the term artificial intelligence. Also in attendance were Allen Newell, a computer scientist, and Herbert A. Simon, an economist, political scientist and cognitive psychologist. The two presented their groundbreaking Logic Theorist, a computer program capable of proving certain mathematical theorems and referred to as the first AI program.

1950s and 1960s. In the wake of the Dartmouth College conference, leaders in the fledgling field of AI predicted that a man-made intelligence equivalent to the human brain was around the corner, attracting major government and industry support. Indeed, nearly 20 years of well-funded basic research generated significant advances in AI: For example, in the late 1950s, Newell and Simon published the General Problem Solver (GPS) algorithm, which fell short of solving complex problems but laid the foundations for developing more sophisticated cognitive architectures; and McCarthy developed Lisp, a language for AI programming still used today. In the mid-1960s, MIT Professor Joseph Weizenbaum developed ELIZA, an early NLP program that laid the foundation for today's chatbots.

1970s and 1980s. The achievement of artificial general intelligence proved elusive, not imminent, hampered by limitations in computer processing and memory and by the complexity of the problem. Government and corporations backed away from their support of AI research, leading to a fallow period lasting from 1974 to 1980 known as the first " AI Winter." In the 1980s, research on deep learning techniques and industry's adoption of Edward Feigenbaum's expert systems sparked a new wave of AI enthusiasm, only to be followed by another collapse of government funding and industry support. The second AI winter lasted until the mid-1990s.

1990s. Increases in computational power and an explosion of data sparked an AI renaissance in the late 1990s that set the stage for the remarkable advances in AI we see today. The combination of big data and increased computational power propelled breakthroughs in NLP, computer vision, robotics, machine learning and deep learning. In 1997, as advances in AI accelerated, IBM's Deep Blue defeated Russian chess grandmaster Garry Kasparov, becoming the first computer program to beat a world chess champion.

2000s. Further advances in machine learning, deep learning, NLP, speech recognition and computer vision gave rise to products and services that have shaped the way we live today. These include the 2000 launch of Google's search engine and the 2001 launch of Amazon's recommendation engine. Netflix developed its recommendation system for movies, Facebook introduced its facial recognition system and Microsoft launched its speech recognition system for transcribing speech into text. IBM launched Watson and Google started its self-driving initiative, Waymo.

2010s. The decade between 2010 and 2020 saw a steady stream of AI developments. These include the launch of Apple's Siri and Amazon's Alexa voice assistants; IBM Watson's victories on Jeopardy; self-driving cars; the development of the first generative adversarial network; the launch of TensorFlow, Google's open source deep learning framework; the founding of research lab OpenAI, developers of the GPT-3 language model and Dall-E image generator; the defeat of world Go champion Lee Sedol by Google DeepMind's AlphaGo; and the implementation of AI-based systems that detect cancers with a high degree of accuracy.

2020s. The current decade has seen the advent of generative AI, a type of artificial intelligence technology that can produce new content. Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes or any input that the AI system can process. Various AI algorithms then return new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person. The abilities of language models such as ChatGPT-3, Google's Bard and Microsoft's Megatron-Turing NLG have wowed the world, but the technology is still in early stages, as evidenced by its tendency to hallucinate or skew answers.

AI tools and services

AI tools and services are evolving at a rapid rate. Current innovations in AI tools and services can be traced to the 2012 AlexNet neural network that ushered in a new era of high-performance AI built on GPUs and large data sets. The key change was the ability to train neural networks on massive amounts of data across multiple GPU cores in parallel in a more scalable way.

Over the last several years, the symbiotic relationship between AI discoveries at Google, Microsoft, and OpenAI, and the hardware innovations pioneered by Nvidia have enabled running ever-larger AI models on more connected GPUs, driving game-changing improvements in performance and scalability.

The collaboration among these AI luminaries was crucial for the recent success of ChatGPT, not to mention dozens of other breakout AI services. Here is a rundown of important innovations in AI tools and services.

Transformers. Google, for example, led the way in finding a more efficient process for provisioning AI training across a large cluster of commodity PCs with GPUs. This paved the way for the discovery of transformers that automate many aspects of training AI on unlabeled data.

Hardware optimization. Just as important, hardware vendors like Nvidia are also optimizing the microcode for running across multiple GPU cores in parallel for the most popular algorithms. Nvidia claimed the combination of faster hardware, more efficient AI algorithms, fine-tuning GPU instructions and better data center integration is driving a million-fold improvement in AI performance. Nvidia is also working with all cloud center providers to make this capability more accessible as AI-as-a-Service through IaaS, SaaS and PaaS models.

Generative pre-trained transformers. The AI stack has also evolved rapidly over the last few years. Previously enterprises would have to train their AI models from scratch. Increasingly vendors such as OpenAI, Nvidia, Microsoft, Google, and others provide generative pre-trained transformers (GPTs), which can be fine-tuned for a specific task at a dramatically reduced cost, expertise and time. Whereas some of the largest models are estimated to cost $5 million to $10 million per run, enterprises can fine-tune the resulting models for a few thousand dollars. This results in faster time to market and reduces risk.

AI cloud services. Among the biggest roadblocks that prevent enterprises from effectively using AI in their businesses are the data engineering and data science tasks required to weave AI capabilities into new apps or to develop new ones. All the leading cloud providers are rolling out their own branded AI as service offerings to streamline data prep, model development and application deployment. Top examples include AWS AI Services, Google Cloud AI, Microsoft Azure AI platform, IBM AI solutions and Oracle Cloud Infrastructure AI Services.

Cutting-edge AI models as a service. Leading AI model developers also offer cutting-edge AI models on top of these cloud services. OpenAI has dozens of large language models optimized for chat, NLP, image generation and code generation that are provisioned through Azure. Nvidia has pursued a more cloud-agnostic approach by selling AI infrastructure and foundational models optimized for text, images and medical data available across all cloud providers. Hundreds of other players are offering models customized for various industries and use cases as well.
George Lawton also contributed to this article.

Continue Reading About artificial intelligence (AI)

Dig Deeper on AI technologies

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What is Artificial Intelligence and How Does AI Work? TechTarget

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Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Read the full definition.

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AI stands for Artificial Intelligence. It refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems are designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, problem-solving, and language translation.

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The science and engineering of making intelligent machines that can think, perceive and act under unpredictable circumstances

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Module 1: What is intelligence and what is artificial intelligence

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PhotoMath

Symbolab

Wolframalpha

Seek by iNaturalist

Verse by Verse

Duolingo

MuseNet

Socratic

Gradescope

Century Tech

Knewton’s Alta

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1. How would you describe “AI ethics” to a family member or friend who didn’t participate in today’s activities?

2. What are guidelines we can use to create ethical machine learning apps?

3. Can AI systems develop an ethical behaviour in the same way that humans grow to be ethical?

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Google Duplex: We need to talk about the ethics

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The ethical dilemma of self-driving cars - Patrick Lin

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Wildlife Insights: Saving Biodiversity with Tech and AI

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What’s Going On With Facial Recognition? | Untangled

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How Facebook Data Mining, And Your Info, Is Influencing The 2016 Election | TODAY

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Background

Video: Interview with a humanoid robot [link]

Video: How Alexa works [link]

Video: Creating an “AI baby” [link]

Ethical Issues

Article: Why voice assistants are female [link]

Article: AI, race, and customer service [link]

Video: Social media “virtual teenager” experiment [link]

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Each student participates in one of 5 groups of AI experts. Each of the groups specialises in one of the 5 Areas of AI ethics.

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Each student participates in one of 5 groups of AI experts. Each of the groups specialises in one of the 5 Areas of AI ethics.

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1. Think of a specific ethical issue related to this technology. Why is this issue important? Whom does it impact? How?

2. How can this issue be addressed while maintaining the social benefits of the technology?

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Background

Video: Top AI algorithms in healthcare [link]

Video: Why we don’t have self-driving cars yet [link]

Article: AI solves a 50 year old science problem [link]

Ethical Issues

Article/Video: Racial biases in healthcare AI [link]

Article: AI and COVID-19 [link]

Video: AI and the future of healthcare [link]

Article: How AI bias happens and how to fix it [link]

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Environment, Natural Resources, and Wildlife

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Environment, Natural Resources, and Wildlife

# data[].attributes.body
Background

Video: AI and sustainability [link]

Article: Detecting plastic litter in natural environments [link]

Ethical Issues

Article: Environmental costs of AI [link]

Article: Renewable energy for AI [link]

Video: How will AI impact society and the environment [link]

Article: AI and treatment of animals [link]

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We build a data profile of ourselves based on the types of data trails we frequently leave behind when we are online

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We build a data profile of ourselves based on the types of data trails we frequently leave behind when we are online

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Each student get the worksheet: “My profile” with the following categories (that will you will write 3 examples for every category), without writing your names:

Text keywords I have used for online searches:

1. photos I have searched online (e.g., people, location, things)

2. online songs I have listened

3. online videos I have watched

4. online games I play regularly

5. websites I have visited

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Were the learning objectives clear/align to you?

Did you feel motivated throughout the lesson?

What was your favourite activity or learning experience in this lesson?

Did the lesson provide opportunities for you to develop new competences?

What improvements or changes would you suggest for this lesson plan?

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Background

Video: How computer vision works [link]

Article: Five exciting computer vision applications [link]

Video: AI, Sensors, and Data  [link]

Ethical Issues

Article/Video: AI, gender, and race [link]

Video: AI and policing [link]

Video: AI in the Classroom [link]

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Evaluation of Module 3.9: Elements of AI Ethics

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Social Media, Shopping, and Entertainment

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Social Media, Shopping, and Entertainment

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Background

Video: 5 technologies that will change the way you shop [link]

Article: How AI can help brands align with social media influencers [link]

Ethical Issues

Article/Videos: How AI recommends videos [link]

Article: Pros and cons of AI in social media [link]

Article: Social-philosophical impacts of video game AI [link]

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Artificial Intelligence - the ability of machines to learn and problem-solve

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Artificial Intelligence - the ability of machines to learn and problem-solve

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What comes to mind when you hear the term artificial intelligence? What are examples of Artificial Intelligence you've seen either in real life or in fiction, like movies or music or television? Can you think of times when these examples were used for good and/or for bad, even if unintended? Are there things they do we might consider "right" or "wrong"?

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Ethics & AI: Equal Access and Algorithmic Bias

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DigCompEdu

DigComp 2.2

Data Literacy Education Framework

Conceptualizing AI literacy

Designing Digital Literacy Activities

Online Testing Tool

What Is a Dataset in Machine Learning

Cartoon Emotions

Betaface API

Luxand – Face Recognition for Developers

Search what you see

AI Experiments

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Avoid creating or reinforcing unfair bias.

AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief.

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Each student will be given someone else's profile to analyse and specify the type of person he/she believes would have produced this profile

Each student suggests a product for that specific profile that has been analysed that believes it would be possible to be interested in.

Ask the students who created the profile to verify or not if he would be interested in that product.

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1. Which face is real?

2. Teachable Machine (train to recognize your own images-sounds-poses)

3. Animated Drawings

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ChatGPT can be asked for the ethical issues about AI and what it suggests about them (communicate with ChatGPT like a conversation)

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Ethics Guidelines for Trustworthy Artificial Intelligence

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Share examples of an AI code of ethics, such as those by Google Responsibility.

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The ethics of AI: Are we going to be good parents?

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The ethics of AI: Are we going to be good parents?

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The way we are chatting with AI today is the way they will build their ethics and values system

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EMERGENCY EPISODE: Ex-Google Officer Finally Speaks Out On The Dangers Of AI! - Mo Gawdat | E252

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Τι περιλαμβάνει το προσχέδιο του ΑΙ Act που ψηφίστηκε από το Ευρωπαϊκό Κοινοβούλιο; Ποιους κανόνες προσπαθεί να θέσει η Ευρωπαϊκή Ένωση στην τεχνητή νοημοσύνη; Όλα τα συμπεράσματά μας από την πρόσφατη επίσκεψή μας στο Ευρωπαϊκό Κοινοβούλιο.

"Σήμερα γράφουμε ιστορία, διαμορφώνοντας το μέλλον μας" ανέφερε στο μήνυμά της η Ρομπέρτα Μέτσολα, πρόεδρος του Ευρωπαϊκού Κοινοβουλίου, λίγα λεπτά μετά την ψήφιση με ευρεία πλειοψηφία του προσχεδίου νομοθεσίας για την τεχνητή νοημοσύνη, γνωστό και ως ΑΙ Act.

Making history by shaping our future.

This is what today's groundbreaking vote on the world's first ever #AI legislation is all about.

It is about Europe taking the lead in digital innovation. pic.twitter.com/jICNwcX9hy
- Roberta Metsola (@EP_President) June 14, 2023

Για πρώτη φορά ένας συνασπισμός κρατών (ή ένα κράτος μεμονωμένα) επιχειρεί να βάλει όρια στην κακόβουλη ανάπτυξη και χρήση των συστημάτων τεχνητής νοημοσύνης. Με νόμους, όχι με συστάσεις ή οδηγίες, περιμένοντας την αυτορρύθμιση των εταιρειών. Αυτό από μόνο του είναι ένα σημείο που καθιστά το σχετικό ψήφισμα του Ευρωπαϊκού Κοινοβουλίου ως ορόσημο (το τελικό κείμενο μπορείτε να το βρείτε εδώ). "Ένα ιστορικό γεγονός, σήμερα όλος ο κόσμος στρέφει το βλέμμα του στην Ευρώπη", ανέφεραν κάποιοι εκ των ευρωβουλευτών στις δικές τους τοποθετήσεις τους. Μια φράση που αντικατοπτρίζει ένα ακόμα στόχο, μια φιλοδοξία της Ευρώπης: να αποτελέσει το παράδειγμα για τον υπόλοιπο κόσμο -τουλάχιστον τον δυτικό- όπως έγινε και στην περίπτωση του Γενικού Κανονισμού Προσωπικών Δεδομένων (GDPR).

" Η νομοθετική πράξη για την τεχνητή νοημοσύνη θα ορίσει τη βάση της παγκόσμιας συζήτησης για την ανάπτυξη και τη διακυβέρνηση της τεχνητής νοημοσύνης, διασφαλίζοντας ότι η τεχνολογία αυτή, η οποία είναι έτοιμη να αλλάξει ριζικά τις κοινωνίες μας μέσω των τεράστιων οφελών που μπορεί να προσφέρει, θα αναπτυχθεί και θα χρησιμοποιηθεί σύμφωνα με τις ευρωπαϊκές αξίες της δημοκρατίας, των θεμελιωδών δικαιωμάτων και του κράτους δικαίου" σημείωσε ο Ντράγκος Τουντοράκε, ένας εκ των βασικών εισηγητών του ΑΙ Act.

Προφανώς για να έχουμε την τελική μορφή που θα λάβει η νομοθεσία θα χρειαστεί χρόνος. Όμως, πρόθεση της Ευρωπαϊκής Ένωσης είναι να "τρέξει" η διαδικασία, έτσι ώστε να έχει ολοκληρωθεί πριν τις επόμενες ευρωεκλογές, τον Ιούνιο του 2024. Πρόθεση που δείχνει και την αποφασιστικότητα της Ευρωπαϊκής Ένωσης να επέμβει, αλλά και τη σημασία που αποδίδει στην Τεχνητή Νοημοσύνη και στις επιπτώσεις της κακόβουλης χρήσης της.

Τι περιλαμβάνει το προσχέδιο που ψηφίστηκε

Στους νέους κανόνες που ψηφίστηκαν είναι εμφανής η προτεραιότητα του Ευρωκοινοβουλίου να διασφαλίσει ότι τα συστήματα τεχνητής νοημοσύνης που χρησιμοποιούνται στην ΕΕ είναι ασφαλή, διαφανή, ανιχνεύσιμα, χωρίς να δημιουργούν διακρίσεις (και φιλικά προς το περιβάλλον, όπως προσθέτει η ανακοίνωση). Τόσο ο Ντράγκος Τουντοράκε και ο Μπράντο Μπενιφέι, (ο έτερος εκ των δύο κύριων εισηγητών της ρύθμισης) στάθηκαν ιδιαίτερα στα παραπάνω σημεία και στην προσπάθεια που έγινε έτσι ώστε οι κανόνες να καλύπτουν τις παραπάνω παραμέτρους, που αποτελούν και πτυχές των βασικών αξιών του οικοδομήματος της Ευρωπαϊκής Ένωσης.

Επιπλέον, "τα συστήματα τεχνητής νοημοσύνης θα πρέπει να επιβλέπονται από ανθρώπους και όχι από αυτοματισμούς, για την πρόληψη επιβλαβών αποτελεσμάτων", αναφέρει σε ένα σημείο της η ανακοίνωση του Ευρωπαϊκού Κοινοβουλίου, προσθέτοντας ότι πρόθεσή του είναι να καθιερώσει έναν τεχνολογικά ουδέτερο, ενιαίο ορισμό για την τεχνητή νοημοσύνη που θα μπορούσε να εφαρμοστεί σε μελλοντικά ΑΙ συστήματα.

Βασική πτυχή του ΑΙ Act είναι η θέσπιση κανόνων (για παρόχους και χρήστες) ανάλογα με το επίπεδο κινδύνου από την τεχνητή νοημοσύνη. Υπάρχει δηλαδή μια διαβάθμιση κινδύνου και με βάση αυτή επιτρέπεται η ανάπτυξη και χρήση συστημάτων τεχνητής νοημοσύνης. Ειδικότερα:
Απαράδεκτος κίνδυνος
Σ' αυτά τα συστήματα που θεωρούνται απειλή για τον άνθρωπο, υπάρχει γενική απαγόρευση. Σ' αυτό το επίπεδο περιλαμβάνονται συστήματα γνωστικής συμπεριφορικής χειραγώγησης ατόμων ή συγκεκριμένων ευάλωτων ομάδων (παιχνίδια που ενθαρρύνουν επικίνδυνη συμπεριφορά σε παιδιά), κοινωνικής βαθμολογίας (ταξινόμηση ατόμων με βάση τη συμπεριφορά, την οικονομική κατάσταση κ.α.), εξ' αποστάσεως βιομετρικής αναγνώρισης σε πραγματικό χρόνο (αναγνώριση προσώπου). Ως προς τα τελευταία προβλέπεται η δυνατότητα εξαίρεσης, όπου η ταυτοποίηση πραγματοποιείται μετά από σημαντική καθυστέρηση και η οποία θα επιτρέπεται για τη δίωξη σοβαρών εγκλημάτων, αλλά μόνο μετά από έγκριση δικαστηρίου.
Υψηλού κινδύνου
Σ' αυτήν την κατηγορία εμπίπτουν τα συστήματα τεχνητής νοημοσύνης που επηρεάζουν αρνητικά την ασφάλεια ή τα θεμελιώδη δικαιώματα. Το ΑΙ Act προβλέπει δύο κατηγορίες:

Συστήματα τεχνητής νοημοσύνης που χρησιμοποιούνται σε προϊόντα που εμπίπτουν στη νομοθεσία της ΕΕ για την ασφάλεια των προϊόντων (παιχνίδια, αυτοκίνητα, ιατρικές συσκευές κ.α.)

Συστήματα τεχνητής νοημοσύνης που εμπίπτουν σε οκτώ συγκεκριμένους τομείς που θα καταχωρούνται σε μια βάση δεδομένων της ΕΕ. Ειδικότερα:

1. Βιομετρική αναγνώριση και κατηγοριοποίηση φυσικών προσώπων

2. Διαχείριση και λειτουργία υποδομών ζωτικής σημασίας

3. Εκπαίδευση και επαγγελματική κατάρτιση

4. Απασχόληση, διαχείριση εργαζομένων και πρόσβαση στην αυτοαπασχόληση

5. Πρόσβαση και απόλαυση βασικών ιδιωτικών υπηρεσιών και δημόσιων υπηρεσιών και παροχών

6. Επιβολή του νόμου

7. Διαχείριση μετανάστευσης, ασύλου και ελέγχου των συνόρων

8. Βοήθεια στη νομική ερμηνεία και εφαρμογή του νόμου

Όλα τα συστήματα τεχνητής νοημοσύνης υψηλού κινδύνου θα αξιολογούνται όχι μόνο πριν διατεθούν στην αγορά, αλλά και καθ' όλη τη διάρκεια του κύκλου ζωής τους.
Generative AI
Για το Generative AI, όπως το ChatGPT, έχουν προβλεφθεί συγκεκριμένες απαιτήσεις διαφάνειας, που περιλαμβάνουν τη ρητή αναφορά ότι το περιεχόμενο δημιουργήθηκε από ένα ΑΙ σύστημα, την αποτροπή δημιουργίας παράνομου περιεχομένου και τη δημοσίευση περιλήψεων για τα δεδομένα (που έχουν χρησιμοποιηθεί) που προστατεύονται από πνευματικά δικαιώματα που χρησιμοποιούνται για εκπαίδευση.
Περιορισμένος κίνδυνος
Σ' αυτό το επίπεδο περιλαμβάνονται συστήματα τα οποία θα πρέπει να είναι σύμφωνα με ελάχιστες απαιτήσεις διαφάνειας, που θα επιτρέπουν στους χρήστες να λαμβάνουν τεκμηριωμένες αποφάσεις. Ειδικότερα, αφού αλληλεπιδράσουν με τέτοιες εφαρμογές, οι χρήστες θα αποφασίζουν αν θέλουν να συνεχίσουν να το χρησιμοποιούν, ενώ επιπλέον θα πρέπει να ενημερώνονται (οι χρήστες) όταν αλληλεπιδρούν με ΑΙ συστήματα. Σ' αυτά περιλαμβάνονται εκείνα που δημιουργούν ή διαχειρίζονται αρχεία εικόνας, ήχου ή βίντεο, όπως τα deepfakes.

Πρόστιμα
Σύμφωνα με τους αξιωματούχους της ΕΕ η μεγάλη πλειονότητα των συστημάτων τεχνητής νοημοσύνης δεν εμπίπτουν στη ρύθμιση. Μόλις το 10% του συνόλου των συστημάτων σε όλο τον κόσμο εκτιμάται ότι επηρεάζονται από το ΑΙ Act, το οποίο προβλέπει και σημαντικά πρόστιμα σε περιπτώσεις ενασχόλησης με απαγορευμένες πρακτικές. Ενδεικτικά, για την κατηγορία απαράδεκτου κινδύνου η ενασχόληση μιας εταιρείας θα μπορούσε να οδηγήσει σε πρόστιμο έως και 40 εκατομμύρια ευρώ ή έως και το 7% του παγκοσμίου κύκλου εργασιών της, όποιο από τα δύο ποσά είναι μεγαλύτερο. Για λόγους σύγκρισης, η παραβίαση των κανόνων GDPR προβλέπει πρόστιμο ύψους 10 εκατομμυρίων ευρώ ή 2% του ετήσιου κύκλου εργασιών. Οι διαφορές στο ύψος των προστίμων δείχνουν και τη μεγάλη σημασία που αποδίδει η ΕΕ στην ορθή ανάπτυξη και χρήση της τεχνητής νοημοσύνης. Πάντως σε περιπτώσεις startups πιθανώς να υπάρξει μια επιείκεια στην επιβολή προστίμων.

Οι πρώτοι κανόνες στον κόσμο

"Τη στιγμή που οι μεγάλες εταιρείες τεχνολογίας κρούουν το καμπανάκι κινδύνου για τα δικά τους συστήματα, η Ευρώπη προχώρησε και προτείνει μια συγκεκριμένη απάντηση στους κινδύνους που έχει αρχίσει να εγκυμονεί η τεχνητή νοημοσύνη" σχολίασε μεταξύ άλλων ο Μπράντο Μπενιφέι, ένας εκ των εισηγητών του ΑΙ Act.

Μόλις μια εβδομάδα πριν την ψήφιση του ΑΙ Act, το 42% των CEOs αρκετών από τις μεγαλύτερες εταιρείες στον κόσμο εξέφραζαν την άποψη ότι η τεχνητή νοημοσύνη έχει τη δυνατότητα να καταστρέψει την ανθρωπότητα σε 5 με 10 χρόνια, ζητώντας περισσότερη ρύθμιση. Δεν ήταν η πρώτη φορά που εκφράστηκε μια τέτοια άποψη.

Το ΑΙ Act αποτελεί για την Ευρώπη την απάντηση, έτσι ώστε και να περιοριστούν οι κίνδυνοι, αλλά και να εμπεδωθεί στο κοινό ένα αίσθημα ασφάλειας, όπως και εμπιστοσύνης για τα "νόμιμα" συστήματα τεχνητής νοημοσύνης.

Για τους συντάκτες του ΑΙ Act, το προσχέδιο είναι ένα καλά ισορροπημένο κείμενο μεταξύ της προστασίας θεμελιωδών αξιών και δικαιωμάτων των Ευρωπαίων πολιτών, εξασφαλίζοντας παράλληλα την προώθηση της καινοτομίας, ως προς την τεχνητή νοημοσύνη. Άποψη που εξέφρασε και ο Πίτερ Βιτάνοφ (σκιώδης εισηγητής της επιτροπής LIBE για το ΑΙ Act) στη συνάντηση που είχαμε μαζί του, ο οποίος στάθηκε και στο γεγονός ότι στο ΑΙ Αct όλα τα συστήματα τεχνητής νοημοσύνης υπόκεινται στην ανθρώπινη εποπτεία.

"Με αυτό το κείμενο θέλαμε να επιτύχουμε την ισορροπία", ανέφερε μεταξύ άλλων και ο βασικός εισηγητής του ΑΙ Act, Ντράγκος Τουντοράτσε. Ο νόμος προστατεύει τους πολίτες, ενώ παράλληλα "προωθεί την καινοτομία, χωρίς να εμποδίζει τη δημιουργικότητα και την ανάπτυξη της τεχνητής νοημοσύνης στην Ευρώπη", πρόσθεσε. Ως προς το τελευταίο, το ΑΙ Act απαιτεί από τα κράτη-μέλη τη δημιουργία ενός τουλάχιστον sandbox, για τη δοκιμή συστημάτων τεχνητής νοημοσύνης πριν από το λανσάρισμά τους, ενώ ως προς την προστασία των πολιτών, αυτοί έχουν το δικαίωμα να υποβάλλουν καταγγελίες κατά παρόχων συστημάτων τεχνητής νοημοσύνης. Επιπλέον, προβλέπεται ένα κεντρικό γραφείο τεχνητής νοημοσύνης της ΕΕ για την παρακολούθηση της επιβολής της νομοθεσίας, ενώ και πάλι τα κράτη-μέλη υποχρεώνονται να ορίσουν εθνικές εποπτικές αρχές για την τεχνητή νοημοσύνη.

Πρόθεση της Ευρώπης και των οργάνων είναι να ψηφιστεί η τελική μορφή που θα λάβει η νομοθεσία πριν από τη λήξη της θητείας του τρέχοντος Ευρωπαϊκού Κοινοβουλίου. Ήδη, όπως ανέφερε ο κ. Τουντοράτσε οι διαπραγματεύσεις με το Συμβούλιο των κρατών και την Επιτροπή (τριμερής διάλογος) πάνω στο προσχέδιο έχει ξεκινήσει από το ίδιο απόγευμα της ψήφισής του (14 Ιουνίου). Επιπλέον, όπως έχει συμβεί και σε άλλα ψηφίσματα στο παρελθόν, το ΑΙ Act θα προβλέπει και μια περίοδο δύο ετών στη διάρκεια των οποίων όλοι οι ενδιαφερόμενοι θα πρέπει να έχουν προσαρμοστεί στους κανόνες. Αυτό σημαίνει ότι με βάση το υπάρχον χρονοδιάγραμμα η εφαρμογή του ΑΙ Act θα ξεκινήσει το 2026.

Θα είναι αποτελεσματική η ρύθμιση;

Όπως σημείωσαν πλήθος Ευρωπαίων αξιωματούχων που συμμετείχαν στις διαβουλεύσεις για το ΑΙ Act πραγματοποιήθηκαν εκατοντάδες συναντήσεις και με εταιρείες που αναπτύσσουν συστήματα τεχνητής νοημοσύνης, προκειμένου η ρύθμιση να λαμβάνει υπόψη και τη συγκεκριμένη πλευρά.

Αμέσως μετά την ψήφιση του προσχεδίου αρκετές εταιρείες χαιρέτησαν την πρόοδο που επιτεύχθηκε, σημειώνοντας από την πλευρά τους τις βελτιώσεις που θα ήθελαν να δουν στο τελικό κείμενο. Ενδεικτικά, η ανακοίνωση της IBM, προτείνει βελτιώσεις, όπως η μεγαλύτερη σαφήνεια για την τεχνητή νοημοσύνη υψηλού κινδύνου, "έτσι ώστε να καταγράφονται μόνο περιπτώσεις χρήσης πραγματικά υψηλού κινδύνου".

Αρκετοί παράγοντες της αγοράς και αναλυτές εξέφρασαν ερωτηματικά σχετικά με την αποτελεσματικότητα της ρύθμισης, με την έννοια ότι οι νέες δυνατότητες της τεχνητής νοημοσύνης αναδύονται πιο γρήγορα από το χρόνο στον οποίο μπορούν να ανταποκριθούν οι νομοθέτες. Ήδη στο κείμενο που ψηφίστηκε έχουν γίνει πλήθος αλλαγών σε σχέση με το πρώτο προσχέδιο που παρουσιάστηκε το 2021. Το πεδίο εφαρμογής του νόμου θα διευρύνεται καθώς αναπτύσσεται η τεχνολογία εκτιμούν, σημειώνοντας το επίπεδο δυσκολίας μιας αποτελεσματικής ρύθμισης, καθώς οι ίδιες οι εταιρείες που αναπτύσσουν ΑΙ συστήματα δεν είναι απολύτως σαφείς για την εξέλιξή τους.

Από την άλλη πλευρά, όπως αναφέρουν οι υπερασπιστές της ρύθμισης, θα ήταν σίγουρα χειρότερο για όλους μας να λειτουργούμε χωρίς κάποιον επαρκή κανονισμό.

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Φανούρης Δρακάκης

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Κανόνες στην Τεχνητή Νοημοσύνη, η Ευρώπη δείχνει το δρόμο - 2045.gr

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Τι περιλαμβάνει το προσχέδιο του ΑΙ Act που ψηφίστηκε από το Ευρωπαϊκό Κοινοβούλιο; Ποιους κανόνες προσπαθεί να θέσει η Ευρωπαϊκή Ένωση στην τεχνητή νοημοσύνη;

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Robots cannot always answer sensitive questions such as politics.

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Pepper’s role is to assist whenever it can and answer to any question asked to it.

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The roots of mental health, which has started to significantly evolve in the 21st century has been a huge argument in the field of social sciences, such as the one of psychology. As it is suggested that the practice of mindfulness and the state of spaciousness is a way to increase one's mentality, I believe that AI can develop the database in the field of therapy. However, is artificial intelligence "emotional intelligent" in order to comprehend such a topic?

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By uploading data and information to an online system, the privacy of the end user may be compromised due to data selling to third parties or cubersecurity attacks

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Putting the main issues of generalized ethics aside, as this era is represented by our generation; youngsters, it is of high importance to view AI through a more "youth-related perspective". We cannot deny that the majority of modern teenagers feel alone, lonely and expose themselves in platforms like Instagram, TikTok, Facebook, Reddit, Twitter, Snapchat, BeReal and so on... Furthermore, I've observed that teens yearn to belong in a group, since they don't find social acceptance in the already-existing community. Hence, they join various columns and groups to find the desired company. With the creation of AI, the computer starts evolving into the new "friend" or "partner", who will "help us treat loneliness", when in fact, no one is there. Right?

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Other than the significant issue of privacy and confidentiality, the concept of artificial intelligence has been entirely covered by the fear of fully developing human consciousness, through the applications of human behavior in its data processing. The question is; “Is the “mutation” of such forms of intelligence created by no other than humans, into a new form of “conscious beings” a threat to mankind?”. After all, it is strongly believed that with the conscious mind comes new types of questions, the roots of which are the human existential dread; something that could have an impact on the AI’s actions and view upon its creators/everyday users.

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Facial recognition can enhance surveillance, which is very unethical and restrictive. To resolve this, a regulatory framework must be established that prevents the exploitation of personal data and its monetization by taking legal action against it and monitoring databases.

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As it is widely believed, in order for one to be ethical, logic, understanding of society's circumstances, as well as empathy/emotional intelligence are needed. When it comes to artificial intelligence, other than logic and understanding due to unlimited access to the internet, there's still a great lack of emotional intelligence. Therefore, even though AI is still not capable of fully grasping the idea of ethics, one could say that the needed form of intelligence will grow simultaneously as AI adapts to human behaviour.

# data[].attributes.body
One of the main issues that increases the possibility of one's private pieces of information being accessed by third parties, is the lack of privacy and confidentiality within the 21st century's artificial intelligence. Facial recognition, tracking, cookies are some of the ways the web gains access to the entirety of our daily life (activities, family, relationships, job...). Is AI simply a guide that reassures a more efficient journey in the digital era or a threat to our precious privacy?