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Understanding Frustration in Children

As a parent or caregiver, you’ve likely encountered the challenge of a frustrated child. This is an experience as universal as it is daunting. Children, much like the rest of us, are not born with an innate ability to manage frustration effectively. However, recognizing and addressing these feelings in children is not just about soothing the present moment; it’s about building resilience. With the further complications brought about by the pandemic, the task of guiding children through their frustrations has become even more critical. In this article, we will delve deep into the roots and management of frustration in children, empowering you to help the young ones navigate their emotions and foster lasting resilience.

Understanding Low Frustration Tolerance (LFT) in Children

Low Frustration Tolerance (LFT) is a term that describes the difficulty some children face in handling challenging situations without becoming overwhelmed by negative emotions. If your child becomes irritable, has emotional outbursts, or throws temper tantrums more frequently than seems typical, they may be experiencing LFT. Recognizing the hallmarks of LFT is the first step in helping your child overcome these hurdles.

It’s important to differentiate between high and low frustration tolerance. Children with high frustration tolerance can weather difficulties with calm and perseverance, whereas those with low tolerance may quickly resort to anger or despair. Understanding where your child falls on this spectrum can significantly affect how you approach their frustration.

The difference between high and low frustration tolerance goes beyond the immediate reactions to stressors. It can have far-reaching implications for your child’s development, influencing everything from their social interactions to their approach to problem-solving. In the following sections, we explore these aspects more closely.

Causes and Indicators of LFT

At the heart of LFT are complex brain networks and temperamental factors. Genetics and innate temperament can predispose a child to lower frustration tolerance, but so can the environment they’re growing up in. It’s crucial to comprehend the role these factors play as you navigate your child’s frustrating moments.

The influence of parenting cannot be overstated. An overly strict or permissive approach can hinder a child’s ability to self-regulate, increasing the chances of LFT. Conversely, a balanced approach can promote a healthier response to frustrations.

Knowing the signs of high frustration, such as distress at criticism, aggressive physical behaviors, or the development of irrational beliefs, is essential for early intervention. Recognizing these signs can provide you with the opportunity to step in and offer support before emotions escalate.

Consequences of Untreated LFT

If LFT is not addressed, it can lead to more severe behavioral issues like oppositional defiant disorder. Such conditions can have a negative impact on all aspects of a child’s life, from academic performance to social interactions.

Besides behavioral issues, untreated LFT can lead to problems at school and with peers. Difficulty dealing with frustration can make it hard for children to concentrate on tasks or work collaboratively with others, hindering their learning and social development.

Long-term high frustration levels can also translate into mental health risks. It is important for parents and caregivers to understand these potential consequences in order to take proactive steps in addressing LFT.

Strategies for Helping Children Cope with Frustration

Embracing Empathy and Modeling

Your empathetic response sets the tone for how a child manages frustration. Showing understanding and mirroring emotional regulation can teach them how to handle their feelings.

Co-Regulation and Connection 

It’s not just about responding to the frustration, but doing so in sync with your child’s emotional state. This process, known as co-regulation, involves you participating in your child’s emotional experiences and helping them navigate through them. By doing so, you provide a safe space for them to learn and understand their emotions.

Encouraging Problem-Solving and Proactive Approaches 

To foster a sense of control and capability in your child, encourage them to engage in problem-solving and to think proactively. This not only offers them strategies to deal with frustration at that moment but also helps them develop skills that will benefit them throughout their lives.

Warm Parenting Styles 

A warm, consistent, and responsive parenting style is key. This kind of environment supports a child’s learning to cope with frustration positively and constructively, bolstering their resilience and ability to tackle challenges head-on.

Recommendations and Supportive Measures

Often, frustration can be exacerbated by unmet physical needs. Ensuring that your child is well-rested, fed, and physically healthy can significantly decrease their levels of frustration.

Teaching your child how to identify and articulate their emotions is a fundamental aspect of emotional coaching. Additionally, providing them with a suite of coping skills will enable them to deal with frustration in a healthy manner. This also highlights the importance of parenting training to better equip caregivers to handle such emotional challenges.

For children with developmental disorders such as ADHD or Autism, the strategies may need to be tailored to fit their specific challenges and needs. In such cases, professional support might be necessary to provide additional guidance.

Activities to Help Children Manage Frustration

Activities recommended by experts include breathing exercises, creating calming spaces within the home, engaging in physical activities, and using music or art as outlets for expression. These not only serve as ways to cope, but also as avenues for children to explore and express their emotions.

For some children, sensory-focused techniques such as playing with stress balls or kinetic sand might provide the necessary comfort and distraction to deal with moments of intense frustration.

Remember that each child is different, and what works for one may not work for another. It’s important to tailor these activities to your child’s preferences and needs. Check out more activities here.

Building Strengths and Resilience in Children

Every child reacts differently to frustration, and understanding your child’s specific temperament is crucial. Your responses should be customized to their unique needs and personality.

Together with your child, work on identifying specific triggers of frustration and establish constructive responses to those situations.

Before addressing the behavior that results from frustration, it’s important to connect with your child. This means understanding their feelings and offering comfort, serving as a foundation for teaching them how to manage their emotions effectively.

Acknowledging when you need help is important. If your child’s frustration is significant and persistent, seeking additional support from child development specialists can be beneficial.

Conclusion

Learning to manage frustration is not a luxury; it’s an essential life skill. Through your guidance and support, your child can learn to navigate their emotions and develop the resilience necessary for a healthy and successful life.

We have touched upon various strategies and insights that are crucial for helping children cope with frustration. By adopting an empathetic and proactive approach, you can guide your child towards emotional maturity and resilience. Do you have strategies that work for your child, or are you seeking advice? Feel free to share your experiences and questions in the comments below.

 

The post Understanding Frustration in Children appeared first on Marina Mele's site.

What is ChatGPT and how it compares to Bard and Claude

ChatGPT is a new artificial intelligence chatbot that has taken the world by storm. Developed by OpenAI and launched in November 2022, ChatGPT can engage in conversational dialogues and perform a variety of tasks like answering questions, explaining concepts, summarizing text passages, and even generating new content like articles, poems and computer code.

But what exactly is ChatGPT and how does this impressive new AI work? Let’s break it down.

Neural Network - ChatGPT

All the images of this post have been generated using Midjourney.

The History and Development of ChatGPT

ChatGPT is based on a large language AI model called GPT-3, which was created by OpenAI in 2020. GPT-3 uses a specialized deep learning technique called transformers to analyze massive amounts of text data and understand the patterns and structure of human language. Check this article if you want to know more about large language models.

Building on the capabilities of GPT-3, OpenAI trained ChatGPT specifically to have natural conversations and respond to a wide range of prompts and instructions. After over a year of development and training on vast datasets, ChatGPT was unveiled to the public in a beta version in November 2022, immediately capturing people’s attention and imagination.

Other tech companies like Anthropic and Google have also launched their own AI chatbots, such as Claude and Bard, to rival ChatGPT. Each model possesses its unique strengths, and a detailed comparison will be provided later in the article.

How Does ChatGPT Actually Work?

When you give ChatGPT a written prompt or question, it analyzes the text to understand the intent and context. It then generates a response by predicting the most likely next words in a sequence, based on the patterns it learned during its training.

ChatGPT progressively generates its response word-by-word, while continuously referring back to the original prompt for context. This allows it to have coherent, multi-turn conversations that stay on topic.

The key to ChatGPT’s conversational abilities lies in its training methodology. During training, ChatGPT was shown many real examples of human conversations, questions and answers from diverse sources on the internet. It uses these examples to learn the nuances of natural dialogue.

ChatGPT was also trained using a technique called reinforcement learning from human feedback. Essentially, it was rewarded when it generated responses that humans rated as satisfactory, and penalized for inadequate responses. This allowed it to keep improving the quality and relevance of its replies.

The Impressive, Yet Limited, Capabilities of ChatGPT

ChatGPT offers a wide range of useful applications and capabilities that showcase the technology’s current strengths and potential. However, as with any AI system, it also comes with significant limitations that warrant consideration.

First, we will explore some of the key ways ChatGPT can provide value and assistance. While not an exhaustive list, these applications demonstrate ChatGPT’s diverse conversational skills.

ChatGPT’s Current Skills and Use Cases

  • ChatGPT has extensive knowledge that allows users to define concepts, ask questions, and find information that may be difficult to search for directly. For example, it can identify a song based on a description when you don’t know the exact lyrics.
  • It’s now multimodal (for paid users). This means you can include an image in your prompt and ask something about the image, like which plant is that, or to plan something to eat with what you have on your fridge.
  • It excels at planning and boosting productivity by mapping out schedules and steps to achieve goals like learning new skills. For instance, it can outline an effective learning path when starting to program in a new language.
  • Generating engaging stories and ideas for kids is another aplication. Parents can use ChatGPT to create personalized bedtime stories based on a child’s interests, especially when feeling too tired to come up with them.
  • Explain complex concepts to everyone. You can ask it to explain something for a 10 year old, or 5, or 3.
  • For teachers, ChatGPT can develop interactive lessons and educational games covering course material. It can also assist with structuring and writing presentations on various topics.
  • As a language partner, ChatGPT provides customized explanations of grammar rules and gives practice exercises when learning a new language. For example, it can ask you questions and correct your answers, or quiz you some vocabulary.
  • It acts as an adept writing assistant by drafting, revising, and refining documents like emails, reports, and articles. A brief objective statement is sufficient for it to generate a draft with the desired tone.
  • ChatGPT makes for an insightful thinking partner, providing opinions and feedback when asked. It can even emulate famous individuals to offer their perspectives on ideas.
  • And finally, with various plugins and the code interpreter, its capabilities expand beyond text to include integrating with platforms like Canva and Worldfram Alpha, or analyzing data, generating graphs or running code.

These examples offer just a sample of ChatGPT’s expanding capabilities. Its conversational nature enables a wide variety of functions through natural prompts and instructions. However, ChatGPT is far from flawless. Next we will discuss key current limitations to understand its deficiencies alongside its utilities.

Limitations to Consider

The breadth of tasks ChatGPT can perform is impressive. It can hold conversations, explain concepts, summarize texts and generate new content in various styles. However, as an AI system still in development, ChatGPT has significant limitations to keep in mind.

Distorted image

  • ChatGPT-4’s knowledge only extends to September 2021, as that’s when its training data cuts off. Anything past 2021 remains unknown to the model, creating a major blindspot.
  • Only paid users have a ChatGPT option to access the internet. And only if activated. In general, it’s not connected to the Internet.
  • ChatGPT also generates sometimes plausible-sounding but incorrect or nonsensical text due to its lack of reasoning skills and grounding in common sense. These shortcomings are known as hallucinations in AI.
  • One major limitation of ChatGPT is its struggle with mathematical and logical reasoning (except if using the code interpreter). Since its training data from the internet features small numbers far more frequently, the model is inclined to bias its numerical responses toward lower values rather than provide accurate calculations. ChatGPT does not truly comprehend or reason about mathematical concepts and relationships. Without the capability to understand numbers, it often defaults to guessing probabilities based on what values it saw most during training.
  • ChatGPT also faces contextual limitations within prolonged conversations. While it can process thousands of tokens (words) per prompt, this context resets after each response. ChatGPT does not maintain persistent memory and awareness indefinitely. As conversations continue over many prompts, ChatGPT tends to lose focus on earlier statements and facts. This can result in contradictory or irrelevant statements if an exchange exceeds the model’s contextual capacity within a given chat.
  • ChatGPT also lacks true long-term memory. Its conversations exist in isolation, with the model forgetting previous interactions once a chat session ends. Asking if it remembers past questions or specific users is ineffective, as the model has no genuine recall ability. This limitation also means any factual errors or contradictions generated by the model tend to compound within a conversation as context is lost.
  • ChatGPT was trained by analyzing enormous datasets of actual online content, and as a result, its responses aim to emulate the most common things a human might say in a given context based on that statistical training.
  • As an AI trained on internet data, ChatGPT inherently reflects some of the biases and imperfections from its training sources. So problematic responses based on inaccurate stereotypes or toxic viewpoints do occur on occasion.
  • Despite efforts by OpenAI, it remains possible for determined users to coax harmful, unethical or dangerous information out of ChatGPT. While OpenAI implemented filters to block certain topics, techniques like carefully crafted prompts can sometimes circumvent these protections. This risk of misuse continues to be an area of concern with conversational models like ChatGPT.
  • A key limitation is that ChatGPT provides no sources or citations for the information it provides. As an AI, it generates text based on patterns learned during training, not by referencing documents. All facts, quotes and data are produced from its statistical model, meaning ChatGPT cannot offer credible attribution for its content.
  • ChatGPT suffers from compounding errors during conversations. If a factual mistake or contradiction occurs, the model lacks the ability to self-correct. With no long-term memory, any initial errors persist and potentially grow worse as ChatGPT continues generating flawed text devoid of context.

Therefore, while remarkably capable, ChatGPT cannot fully replace human intellect just yet. Relying solely on it for critical advice would be unwise. That said, various plugins and integrations are expanding ChatGPT’s capabilities beyond just text generation. And for less high-stakes applications like drafting emails or summarizing concepts, ChatGPT proves very useful.

While often accurate, ChatGPT cannot be relied upon for 100% factual correctness. Any high-stakes use should involve human verification of its outputs.

While these limitations highlight areas for improvement, they do not negate ChatGPT’s impressive capabilities. Understanding its boundaries allows for responsible, informed use – avoiding overreliance or unrealistic expectations. ChatGPT was built to generate likely text continuations, not possess encyclopedic knowledge. By recognizing its strengths as a conversational model and anticipating its weaknesses, we can have productive interactions without frustration.

Moving forward, it will be insightful to explore how alternative AI systems attempt to address ChatGPT’s limitations. In the next section, we will compare ChatGPT to its two major competitors – Claude and Bard – to see how they stack up in terms of capabilities, limitations, and overall utility. Examining the contrasts and similarities with these other natural language models will provide useful perspective on the current state and trajectory of this rapidly evolving technology.

How ChatGPT Stacks Up Against Claude and Bard

ChatGPT made a big splash as the first widely available conversational AI, but it now faces competition from Claude created by Anthropic, and Bard from Google. How do these alternative natural language models compare?

A boy using a computer

Claude: A Focus on Safety

Claude is an AI assistant developed by startup Anthropic to prioritize safety and integrity. Like ChatGPT, it uses a transformer neural network architecture for natural language processing. However, Anthropic employed a technique called Constitutional AI to align Claude’s goals and values with human preferences.

As a result, Claude is said to exhibit less harmful bias and is more cautious about generating dangerous or unethical content compared to ChatGPT. It also incorporates self-supervised learning to better handle novel situations outside its training data.

It has a bigger context than ChatGPT or Bard, of about 100k tokens. This gives it the ability to read longer articles, even short books, and retain the information within a chat for much longer.

On the other hand, Claude’s capabilities lag behind ChatGPT and Bard when it comes to conversational breadth and creativity. Its responses tend to be simpler and more constrained. Though possibly safer, Claude lacks some of the expressiveness that makes ChatGPT engaging.

Bard: Google’s Alternative

Google’s Bard aims to rival ChatGPT as an AI assistant that can explain concepts, generate content, and converse naturally. It is based on Google’s LaMDA model rather than GPT, featuring deep neural networks and trillions of parameters.

In initial tests, Bard appears significantly more grounded in facts compared to ChatGPT, with Google’s extensive Knowledge Graph likely providing useful context. However, Bard’s responses are often shorter and it struggles with more complex conversational prompts.

However, Bard also has access to the internet. This means that it can keep its knowledge up-to-date by reading the latest news articles, blog posts, and social media posts. This gives Bard a significant advantage over ChatGPT, which in general, it’s not able to access the internet (only paid users and with the browsing on).

For example, if you ask Bard about the latest news, it will be able to tell you about the most recent events. It will also be able to provide you with links to articles and other sources of information so that you can learn more.

What do I use

In general, I use

  • Bard when I need some factual information or recent news
  • Claude when I need help to write an article for my blog like this one, as I usually need larger context.
  • ChatGPT for creative ideas, brainstorming, bedtime stories for my kids

But my recommendation is that you should try the different tools and discover which one works better for your specific needs. I sometimes ask the same question to two different chats, to compare, and even discover new functionalities. You can have a browser window specifically for your AI tools like Bard, ChatGPT or Claude.

And you, which of them do you use in your daily live? Leave a comment with your specific use cases. I’d like to know!

Follow me to stay tuned to the latest news regarding AI. You can subscribe to my blog here, or to my weekly AI newsletter at marinamele.substack.com.

The post What is ChatGPT and how it compares to Bard and Claude appeared first on Marina Mele's site.

The Incredible Journey of AI Image Generation

Do you ever wish you could just describe an image in your mind and have it magically appear on your screen? Well, thanks to recent advances in artificial intelligence, we’re getting closer than ever to making that sci-fi dream a reality. In this post, we’ll explore the history of how AI has evolved to generate increasingly stunning and creative images from text descriptions alone.

dreamy unicorn flying above a kid

The rise of GANs for Image Generation

It all started with an exciting breakthrough in 2014 from researcher Ian Goodfellow and his colleagues. They introduced an AI technique called generative adversarial networks, or GANs for short. GANs pit two neural networks against each other in a competitive game of counterfeiting. One AI generates fake images while the other tries to detect the fakes. Through this adversarial training process, the “generator” keeps getting better at producing realistic images that can fool its partner AI. GANs were like a creative spark that ignited the field of neural art generation.

Suddenly, GANs were creating photorealistic pictures of everything from human faces to stunning landscapes. Researchers also adapted GANs for applications like transferring artistic styles from one image to another. However, GANs had their limitations. They were tricky to train properly and often got stuck churning out a limited variety of similar-looking images.

Goodfellow article - Images generated by GENs
The paper “Generative Adversarial Nets” by Goodfellow et al. (2013) presents original training samples and results, with GAN model outputs highlighted in yellow.

Transformers & CLIP

The AI community turned to a different technique to overcome these challenges – Transformers. Originally created for processing language in applications like chatbots, Transformers proved to be a key ingredient in the next generation of text-to-image models. In 2018, OpenAI introduced GPT-2 which used the Transformer architecture to generate remarkably human-like text.

Researchers soon realized they could train Transformers like GPT-2 on massive datasets of image and caption pairs. The result was DALL-E in 2021, which could generate diverse and realistic images from text prompts. DALL-E’s outputs were still a bit rough around the edges though.

OpenAI Dall-e first examples.
First images generated by Dall-E from OpenAI (source: https://openai.com/research/dall-e)

This brings us to CLIP, another pivotal model in text-to-image generation by OpenAI. CLIP provided the missing link between understanding text and images. By training on captioned images, CLIP learned to embed text and images into a common mathematical space. This enabled better alignment between text descriptions and the generated image results.

CLIP acted as a guiding hand for image generation AIs like DALL-E, dramatically improving the quality and accuracy of the images produced from text prompts. But CLIP was still hungry for more data and processing power to reach its full potential.

Diffusion Models for Image Generation

That’s where diffusion models came to the rescue! Diffusion models simulate the natural process of particles diffusing and coalescing to gradually transform random noise into coherent images. Researchers discovered that running the diffusion process in a compressed latent space made image generation much more efficient.

Latent Diffusion combined with guidance from CLIP resulted in huge leaps in quality and creativity. Now high-resolution images poured out of the models with incredible detail and precision tailored to the text prompts. Services like DALL-E 2 from OpenAI brought these advanced text-to-image models directly into the hands of everyday users through intuitive apps and websites.

Dall·E 2 Image of an astronaut riding a horse in photorealistic style.
Dall·E 2 Image of an astronaut riding a horse in photorealistic style. (source: https://openai.com/dall-e-2)

Of course, the story doesn’t end here. Generative AI is advancing rapidly with new techniques like Stable Diffusion making high-quality image generation widely accessible and customizable. There are still challenges around consistency, coherence and photorealism, but the future looks bright as research continues.

Midjourney: an astronaut riding a horse in photorealistic style
Midjourney: an astronaut riding a horse in photorealistic style
Midjourney: an armchair in the shape of an avocado
Midjourney: an armchair in the shape of an avocado

The journey so far has been remarkable. In less than a decade, AI has evolved from simply classifying images to creatively synthesizing them. Who knows what new innovations and applications the next decade may bring as generative models continue to mature. But one thing’s for sure – the worlds of art, media and communication will never be the same!

Which part of this incredible AI image journey excites you the most? Let me know in the comments! I’d love to hear your thoughts.

The post The Incredible Journey of AI Image Generation appeared first on Marina Mele's site.

AI and Fundamental Rights: How the AI Act Aims to Protect Individuals

Artificial Intelligence (AI) is increasingly becoming a part of our lives. From facial recognition systems to self-driving cars, AI technologies are changing the way we live and work. But with this increasing presence of AI in our lives comes a need to ensure that it is used in a safe, ethical, and responsible way.

In response to this need, the European Union has proposed the Artificial Intelligence Act (AI Act). This proposed regulation seeks to ensure that AI is developed and used in a way that protects the fundamental rights and freedoms of individuals and society. It sets out a number of requirements for AI systems, such as requiring human oversight, fairness, non-discrimination, privacy, data protection, safety, and robustness.

This blog post will look at the AI Act in more detail, exploring its purpose, its categories, and its main concerns. We will also look at how the Act is designed to protect individuals fundamental rights and how it can be implemented in a way that ensures AI is used for good.

What is the AI Act?

The Artificial Intelligence Act (AI Act) is a proposed regulation of the European Union that aims to introduce a common regulatory and legal framework for artificial intelligence. It was proposed by the European Commission on 21 April 2021 and is currently being negotiated by the European Parliament and the Council of the European Union.

The purpose of the AI Act is to ensure that AI is developed and used in a way that is safe, ethical, and responsible. The Act sets out a number of requirements for AI systems, including requirements for human oversight, fairness, non-discrimination, privacy, data protection, safety, and robustness.

The AI Act is a complex piece of legislation, but it has the potential to ensure that AI is used in a way that benefits society. The Act is still under negotiation, but it is expected to come into force in 2026.

Categories of the AI Act

The AI Act defines three categories of AI systems:

  • Unacceptable risk: These systems are banned, such as those that use AI for social scoring or for mass surveillance.
  • High risk: These systems are subject to specific legal requirements, such as those that use AI for facial recognition or for hiring decisions.
  • Minimal risk: These systems are largely unregulated, but they must still comply with general EU law, such as the General Data Protection Regulation (GDPR).

Let’s see each of these categories in more detail.

Unacceptable risk

The AI Act defines unacceptable risk systems as those that pose a serious threat to the fundamental rights and freedoms of natural persons, such as their right to privacy, non-discrimination, or physical integrity.

Some examples of unacceptable risk systems include:

  • Social scoring systems: These systems use AI to assign a score to individuals based on their behavior, such as their spending habits or their social media activity. These systems can be used to discriminate against individuals or to restrict their access to services.
  • Mass surveillance systems: These systems use AI to collect and analyze large amounts of data about individuals, such as their location, their communications, and their online activity. These systems can be used to violate individuals’ privacy and to target them with discrimination or violence.
  • Biometric identification systems: These systems use AI to identify individuals based on their biometric data, such as their fingerprints, their facial features, or their voice. These systems can be used to track individuals without their consent and to deny them access to services.

The AI Act prohibits the development and use of unacceptable risk systems. This means that companies and organizations cannot develop or use these systems in the European Union.

There are a few exceptions to the prohibition on unacceptable risk systems. For example, the prohibition does not apply to systems that are used by law enforcement agencies for the prevention or detection of crime. However, even in these cases, the systems must be used in a way that complies with the law and that does not violate individuals’ fundamental rights.

The prohibition on unacceptable risk systems is an important part of the AI Act. It is designed to protect individuals’ fundamental rights and to ensure that AI is used in a way that is safe and ethical.

High Risk

The AI Act defines high-risk systems as those that pose a significant threat to the safety or fundamental rights of natural persons, such as their right to life, health, or property.

Some examples of high-risk systems include:

  • Facial recognition systems: These systems use AI to identify individuals based on their facial features. These systems can be used to track individuals without their consent, to deny them access to services, or to target them with discrimination or violence.
  • Hiring decision systems: These systems use AI to make hiring decisions. These systems can be used to discriminate against individuals on the basis of their race, gender, or other protected characteristics.
  • Credit scoring systems: These systems use AI to assess the creditworthiness of individuals. These systems can be used to deny individuals access to credit or to charge them higher interest rates.
  • Medical diagnosis systems: These systems use AI to diagnose medical conditions. These systems can be used to make mistakes that could have serious consequences for patients’ health.

The AI Act sets out specific requirements for high-risk AI systems. These requirements include:

  • Human oversight: High-risk AI systems must be designed in a way that allows for human oversight. This means that there must be a way for humans to understand how the system works and to intervene if necessary.
  • Fairness and non-discrimination: High-risk AI systems must not be used in a way that discriminates against individuals or groups of people.
  • Privacy and data protection: High-risk AI systems must comply with the GDPR and other EU data protection laws.
  • Safety and robustness: High-risk AI systems must be designed in a way that minimizes the risk of harm to individuals or society.

The AI Act also requires providers of high-risk AI systems to register their systems with a central EU database. This will allow the authorities to monitor the use of these systems and to take action if they are used in a way that violates the law.

The requirements for high-risk AI systems are designed to ensure that these systems are used in a safe and ethical way. They will help to protect individuals’ fundamental rights and to ensure that AI is used for good.

Minimal Risk

The AI Act defines minimal risk systems as those that do not pose any significant threat to the safety or fundamental rights of natural persons. This means that they are considered to be relatively safe and ethical.

Some examples of minimal risk systems include:

  • Chatbots: These systems use AI to simulate conversation with humans. They are often used in customer service applications.
  • Online recommendation systems: These systems use AI to recommend products or services to users. They are often used in e-commerce applications.
  • Spam filters: These systems use AI to identify and filter out spam emails.
  • Fraud detection systems: These systems use AI to identify and prevent fraudulent transactions.

The AI Act does not impose any specific requirements on minimal risk systems. However, they must still comply with general EU law, such as the General Data Protection Regulation (GDPR).

The AI Act also requires providers of minimal risk systems to make certain information publicly available, such as the purpose of the system and the data that it uses. This will allow users to make informed decisions about whether or not to use these systems.

The minimal risk category is designed to ensure that AI systems that are considered to be relatively safe and ethical are not overregulated. This will help to promote the development and use of these systems, which can benefit society in a number of ways.

Main concerns of the AI Act

The AI Act is a complex piece of legislation that has been met with mixed reactions from the AI community. Some people have praised the Act for its ambitious approach to regulating AI, while others have criticized it for being too complex and burdensome.

Here are some of the main concerns that have been raised about the AI Act:

  • The definition of AI is too broad. The AI Act defines AI as “a system that can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.” This definition is so broad that it could include a wide range of systems, from simple chatbots to complex self-driving cars. This has led to concerns that the Act could be overreaching and could stifle innovation.
  • The requirements for high-risk AI systems are too burdensome. The AI Act requires providers of high-risk AI systems to register their systems with a central EU database, to have human oversight, and to carry out impact assessments. These requirements are seen by some as being too burdensome, especially for small businesses.
  • The penalties for non-compliance are too weak. The AI Act provides for fines of up to €20 million or 4% of global turnover for non-compliance. However, some people have argued that these penalties are not enough to deter companies from breaking the law.

These are just some of the main concerns that have been raised about the AI Act. It is important to note that the Act is still under negotiation, so it is possible that some of these concerns will be addressed before it comes into force. However, the Act is a complex piece of legislation, and it is likely that there will be further debate about it in the coming months and years.

Conclusions

The AI Act is a proposed regulation from the European Union that aims to ensure that AI is developed and used in a safe, ethical, and responsible way. It sets out a number of requirements for AI systems, such as requiring human oversight, fairness, non-discrimination, privacy, data protection, safety, and robustness. The Act is still under negotiation, but it is expected to come into force in 2026.

The AI Act is an ambitious attempt to regulate AI in the European Union. It sets out a number of requirements that are designed to ensure that AI is used in a way that benefits society. However, the Act has also been met with mixed reactions, and there are still a number of concerns that need to be addressed before it comes into force.

For example, some people have expressed concern that the definition of AI is too broad, and that the requirements for high-risk AI systems are too burdensome. Others have argued that the penalties for non-compliance are too weak.

Ultimately, the AI Act is a complex piece of legislation that will have a significant impact on the development and use of AI in the European Union. It is important that these concerns are addressed, and that the Act is implemented in a way that ensures that AI is used in a safe, ethical, and responsible way.

 

The post AI and Fundamental Rights: How the AI Act Aims to Protect Individuals appeared first on Marina Mele's site.

Assertiveness: A Crucial Skill for Children’s Personal Growth

Raising children is no easy task. It requires patience, consistency, and a lot of guidance. As parents, we have the responsibility to teach our children the important skills they will need to live successful and rewarding lives. One such skill is assertiveness.

Assertiveness is a form of communication which involves expressing oneself openly and honestly, with consideration for the rights of others. It is an important skill for children to develop as it helps them become more confident and secure in their self-expression. It encourages children to have healthy relationships with others, respect boundaries and stand up for themselves.

In this blog post, we will explore why assertiveness is important for children, the main benefits of being assertive, and tips on how to teach assertiveness.

What is assertiveness and why is it important for children?

Assertiveness entails expressing one’s opinions or perspectives confidently and respectfully. And I consider it a crucial skill for children as it allows them to communicate their thoughts, emotions, and ideas clearly while also considering the feelings of others.

But let me emphasize that assertiveness is not the same as aggression. It is important to differentiate between making your point in a confident and respectful way, versus using forceful and intimidating language that can be off-putting to others. Additionally, assertiveness should be expressed in a way that is honest and direct, but not rude or condescending.

What are the main benefits of being assertive?

Assertiveness can be useful to resolve problems, as it helps children to be aware of their own needs, as well as the needs of others. This awareness can then be used to come up with creative solutions to conflicts or disagreements. For example, if your child is facing a problem with a friend, they can use assertiveness to communicate what they need and come up with a plan that works for everyone.

This skill also helps children to effectively communicate with others. Through assertive communication, children are able to express their thoughts and feelings in a way that is direct but not aggressive, and allows them to be understood and taken seriously by their peers. Additionally, it can help to build trust between the involved parties, as each person is able to trust that the other is being honest and trustworthy in their interactions.

Lastly, assertiveness encourages children to take an active role in advocating for their needs and wants. It helps them to recognize their own value and to recognize that their voice is important. This can be especially beneficial for shy or introverted children, as it gives them confidence to speak up and make their opinions known.

Tips on how to teach assertiveness

The first tip I want to share is to model assertive behaviour. This means that I express my own feelings in a respectful and direct manner, without being aggressive or passive. I also ensure that I listen to my children and allow their opinions, feelings and wishes to be heard. By doing this, I hope to teach my children that they can express themselves in a confident, non-aggressive manner too.

Another tip is to practice role-play. This can be done by creating scenarios in which children can practice expressing their own needs, while also respecting the needs of others. Here are some examples:

  • Ordering food at a restaurant: Have your child pretend to be the customer and you play the role of the server. Encourage your child to practice ordering politely and assertively, making sure to speak clearly and make eye contact.

  • Standing up to a bully: Create a scenario in which your child is being bullied by another child. Encourage your child to assertively communicate their feelings, telling the other child how their actions are making them feel and standing up for themselves.

  • Negotiating with a friend: Set up a scenario in which your child and a friend have a disagreement over something, such as sharing toys or playing a game. Encourage your child to assertively communicate their needs and wants, while also being willing to compromise and find a solution that works for both parties.

  • Asking for help: Create a scenario in which your child needs help with something, such as completing a task or understanding a lesson. Encourage your child to assertively ask for help, making sure to clearly communicate what they need assistance with and why.

And the last tip is that we should try to use positive assertive language when disciplining children. This means that we must explain why a certain behaviour is not acceptable and provide alternatives. Here are some examples:

  • I understand that you want to keep playing, but it’s time to clean up now.

  • I can see that you’re upset, but hitting your brother is not okay. Let’s find a better way to express your feelings.

  • I understand that you’re frustrated, but we don’t yell in our house. Let’s take a deep breath and try to communicate calmly.

  • I know you didn’t mean to spill the juice, but it’s important to clean up after ourselves. Let’s work together to clean it up.

  • I understand that you’re feeling angry right now, but it’s not okay to throw things. Let’s take a break and come back to this when we’re both feeling calmer.

The key is to use “I” statements, acknowledge the child’s feelings, and offer alternatives or solutions that promote positive behaviour.

Use assertiveness to set boundaries

As a mother and a professional, I understand the importance of setting boundaries for children. Establishing clear limits helps children learn self-control, emotional regulation, and respect for others. By using assertiveness, we can communicate our expectations and model the behavior we’d like our children to adopt. Additionally, setting boundaries helps create a sense of security for children, as they know what to expect and can navigate their environment with confidence.

Again, role-playing can be an excellent tool for teaching boundary-setting skills to children. By engaging in various scenarios, children can practice expressing their needs and standing up for themselves in a safe and controlled environment. Here are some examples:

  • Role-play a situation where a friend wants to borrow a favourite toy, but your child isn’t comfortable sharing it. Encourage your child to practice saying “no” assertively and calmly, explaining their reasoning without becoming defensive or aggressive.

  • In a role-play scenario involving peer pressure, you could have your child pretend they are at a party where friends are pressuring them to engage in activities they are uncomfortable with, such as trying a new risky game or consuming unhealthy snacks. Encourage your child to practice assertively expressing their feelings and standing their ground, while also suggesting alternative activities that align with their values.

  • Another role-play situation could involve addressing bullying at school. Have your child imagine they are witnessing someone being teased or mistreated. Help them practice assertively intervening by telling the bully to stop, standing up for the victim, and reporting the incident to a trusted adult. This will help them develop the confidence and skills needed to handle such situations in real life.

  • A scenario involving personal space boundaries could involve your child pretending to play with a friend who is consistently invading their personal space or touching them without permission. Encourage your child to practice assertively communicating their discomfort and setting clear limits on physical contact, while remaining respectful and empathetic towards their friend.

  • To practice assertiveness in the context of family dynamics, create a role-play scenario in which a sibling or relative is continually borrowing your child’s belongings without asking. Help your child practice expressing their feelings about the situation and setting clear boundaries around the use of their possessions, emphasizing the importance of mutual respect and communication.

  • Lastly, consider a role-play situation where your child needs to advocate for themselves in an academic setting. For example, they could pretend they’ve been given an unfair grade on a project. Encourage your child to practice assertively discussing their concerns with the “teacher” (played by you or another family member), presenting evidence to support their case and respectfully asking for a grade reconsideration. This will help them develop the skills needed to navigate challenging situations in their educational journey.

In conclusion, assertiveness is an important skill for children to develop. It helps to foster healthy relationships, encourages honest communication, and allows children to stand up for themselves in challenging situations. By modeling assertive behavior and engaging in role-play scenarios, we can teach children how to express themselves confidently and respectfully. This, in turn, will help them lead more meaningful and fulfilling lives.

The post Assertiveness: A Crucial Skill for Children’s Personal Growth appeared first on Marina Mele's site.

Selenium Tutorial: Web Scraping with Selenium and Python

Imagine what would you do if you could automate all the repetitive and boring activities you perform using internet, like checking every day the first results of Google for a given keyword, or download a bunch of files from different websites.

In this post you’ll learn to use Selenium with Python, a Web Scraping tool that simulates a user surfing the Internet. For example, you can use it to automatically look for Google queries and read the results, log in to your social accounts, simulate a user to test your web application, and anything you find in your daily live that it’s repetitive. The possibilities are infinite! 🙂

*All the code in this post has been tested with Python 2.7 and Python 3.4.

Install and use Selenium

Selenium is a python package that can be installed via pip. I recommend that you install it in a virtual environment (using virtualenv and virtualenvwrapper).

No virtualenv or virtualenvwrapper?

Learn how to create one here, for Python 2.7 and for Python 3. It’s really useful, once you start using them you won’t stop! 🙂

Remember that to create the environment in Python 2.7, just type:

$ mkvirtualenv selenium_env

and in Python 3:

$ mkvirtualenv --python=/usr/local/bin/python3 selenium_env

where you should use your own Python 3 path.

Note: if you don’t want to use a virtual environment, you can still install the packages directly on you computer.

To install selenium, you just need to type:

$ pip install selenium

In this post we are going to initialize a Firefox driver — you can install it by visiting their website. However, if you want to work with Chrome or IE, you can find more information here.

Once you have Selenium and Firefox installed, create a python file, selenium_script.py. We are going to initialize a browser using Selenium:

import time
from selenium import webdriver

driver = webdriver.Firefox()
time.sleep(5)
driver.quit()

This just initializes a Firefox instance, waits for 5 seconds, and closes it.

Well, that was not very useful…

How about if we go to Google and search for something?

Web Scraping Google with Selenium

Let’s make a script that loads the main Google search page and makes a query to look for “Selenium”:

import time
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import TimeoutException


def init_driver():
    driver = webdriver.Firefox()
    driver.wait = WebDriverWait(driver, 5)
    return driver


def lookup(driver, query):
    driver.get("http://www.google.com")
    try:
        box = driver.wait.until(EC.presence_of_element_located(
            (By.NAME, "q")))
        button = driver.wait.until(EC.element_to_be_clickable(
            (By.NAME, "btnK")))
        box.send_keys(query)
        button.click()
    except TimeoutException:
        print("Box or Button not found in google.com")


if __name__ == "__main__":
    driver = init_driver()
    lookup(driver, "Selenium")
    time.sleep(5)
    driver.quit()

In the previous code:

  • the function init_driver  initializes a driver instance.
    • creates the driver instance
    • adds the WebDriverWait function as an attribute to the driver, so it can be accessed more easily. This function is used to make the driver wait a certain amount of time (here 5 seconds) for an event to occur.
  • the function lookup  takes two arguments: a driver instance and a query lookup (a string).
    • it loads the Google search page
    • it waits for the query box element to be located and for the button to be clickable. Note that we are using the WebDriverWait function to wait for these elements to appear.
    • Both elements are located by name. Other options would be to locate them by ID, XPATH, TAG_NAME, CLASS_NAME, CSS_SELECTOR , etc (see table below). You can find more information here.
    • Next, it sends the query into the box element and clicks the search button.
    • If either the box or button are not located during the time established in the wait function (here, 5 seconds), the TimeoutException  is raised.
  • the next statement is a conditional that is true only when the script is run directly. This prevents the next statements to run when this file is imported.
    • it initializes the driver and calls the lookup function to look for “Selenium”.
    • it waits for 5 seconds to see the results and quits the driver

Finally, run your code with:

$ python selenium_script.py

Did it work? If you got an ElementNotVisibleException , keep reading!

How to catch an ElementNotVisibleExcpetion

Google search has recently changed so that initially, Google shows this page:

Google-Search

and when you start writing your query, the search button moves into the upper part of the screen.

Query-Google-Search

Well, actually it doesn’t move. The old button becomes invisible and the new one visible (and thus the exception when you click the old one: it’s not visible to click!).

We can update the lookup function in our code so that it catches this exception:

from selenium.common.exceptions import ElementNotVisibleException

def lookup(driver, query):
    driver.get("http://www.google.com")
    try:
        box = driver.wait.until(EC.presence_of_element_located(
            (By.NAME, "q")))
        button = driver.wait.until(EC.element_to_be_clickable(
            (By.NAME, "btnK")))
        box.send_keys(query)
        try:
            button.click()
        except ElementNotVisibleException:
            button = driver.wait.until(EC.visibility_of_element_located(
                (By.NAME, "btnG")))
            button.click()
    except TimeoutException:
        print("Box or Button not found in google.com")

  • the element that raised the exception, button.click() is inside a try statement.
  • if the exception is raised, we look for the second button, using visibility_of_element_located to make sure the element is visible, and then click this button.
  • if at any time, some element is not found within the 5 second period, the TimeoutException  is raised and caught by the two end lines of code.
  • Note that the initial button name is “btnK” and the new one is “btnG”.

Method list in Selenium

To sum up, I’ve created a table with the main methods used here.

Note: it’s not a python file — don’t try to run/import it 🙂

# INITIALIZE DRIVER
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait

driver = webdriver.Firefox()
driver.wait = WebDriverWait(driver, 5)


# WAIT FOR ELEMENTS
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC

element = driver.wait.until(
    EC.presence_of_element_located(
    EC.element_to_be_clickable(
    EC.visibility_of_element_located(
        (By.NAME, "name")
        (By.ID, "id")
        (By.LINK_TEXT, "link text")
        (By.PARTIAL_LINK_TEXT, "partial link text")
        (By.TAG_NAME, "tag name")
        (By.CLASS_NAME, "class name")
        (By.CSS_SELECTOR, "css selector")
        (By.XPATH, "xpath")
    )
)


# CATCH EXCEPTIONS
from selenium.common.exceptions import
    TimeoutException
    ElementNotVisibleException

That’s all! Hope it was useful! 🙂

Don’t forget to share it with your friends!

The post Selenium Tutorial: Web Scraping with Selenium and Python appeared first on Marina Mele's site.

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