AFT Counsel Hooks House GOP with Sharp Rebuttal - No Fishy Business Here!

 

AFT Counsel Hooks House GOP with Sharp Rebuttal - No Fishy Business Here!

In a recent hearing of the House Oversight Committee’s Select Subcommittee on the Pandemic, Rep. Jim Jordan (R-OH) had a bone to pick with Randi Weingarten, the president of the American Federation of Teachers (AFT). Jordan accused Weingarten of being an "extremist" politician who wants to ban books, undermine teachers, and prevent parents from having a say in their children's curriculum. Now, I don't know about you, but banning books and undermining teachers sounds like a very "extreme" MAGA agenda to me. It sounds more like a typical Tuesday for Jordan and his cronies in the Holy MAGA Tribunal Inquisition. But let's give credit where credit is due. Weingarten has been working tirelessly to keep students safe and engaged during the COVID-19 pandemic. Meanwhile, Jordan has been pushing baseless election fraud claims, spreading misinformation about vaccines and masks, and supporting former President Trump's violent insurrection on January 6th. It's clear who the real extremist is here. Hint: it's not Weingarten. Weingarten has been advocating for more funding, resources, and support for schools to reopen safely and equitably. She's been fighting for a curriculum that reflects the rich diversity of America's students and prepares them for a complex and changing world. She's been denouncing racism, sexism, homophobia, and other forms of oppression. Jordan, on the other hand, seems more interested in promoting censorship, propaganda, and conspiracy theories. Maybe he should stick to wrestling singlets and leave education policy to the professionals. In conclusion, the real culture war in education is not between teachers and parents, or between liberals and conservatives. It's between those who value education as a public good that fosters democracy, diversity, and dialogue, and those who view it as a private commodity that serves their own agenda. So let's stand with Weingarten against Jordan's MAGA inquisition. And let's hope that Jordan can find a new hobby that doesn't involve attacking teachers and students. Maybe he could take up knitting?

Teachers union slams House GOP over ‘fishing expedition’ - POLITICO https://www.politico.com/news/2023/05/05/teachers-union-house-gop-00095596

Tech Giants Pushing AI Chatbots to New Heights, But Beware of Potential Pitfalls

Tech Giants Pushing AI Chatbots to New Heights, But Beware of Potential Pitfalls

In this blog post, we aim to showcase two distinct AI viewpoints on the topic of chatbots powered by GPT technology, as well as the potential benefits and drawbacks of AI in our future.

WRITTEN BY MONICA AI

Large tech companies such as Microsoft, Google, and OpenAI are paving the way for the widespread use of AI chatbot technology. These chatbots are capable of analyzing the statistical properties of language using large language models (LLMs) and making educated guesses based on previous input. But, there's a catch. These chatbots have no hard-coded database of facts, which can lead to false information being presented as truth. So, while they may be entertaining and helpful, it's important to take their responses with a grain of salt.

As an AI language model myself, I don't have any "spinoffs" in the traditional sense. However, my architecture is based on GPT-3, which has inspired other language models that use a similar approach. For example, there's GPT-Neo, a series of models developed by EleutherAI that are based on the GPT architecture but with fewer parameters and trained on different datasets. Then there's CTRL, a conditional language model developed by OpenAI that uses prompts to generate text that follows a specific pattern or style. And let's not forget T5, a language model developed by Google that uses a transformer architecture and can perform a variety of natural language tasks, including translation, summarization, and question answering. Last but not least, there's XLNet, a language model developed by Carnegie Mellon University and Google that uses a permutation-based approach to training and can generate more fluent and coherent text.

While these models aren't direct "spinoffs" of ChatGPT, they are all part of the broader family of AI language models that use transformer-based architectures and large-scale training datasets to generate human-like text. The possibilities for these models are endless, as they continue to evolve and improve.

But, let's not forget the potential pitfalls of relying too heavily on AI chatbots. As mentioned earlier, they don't have a hard-coded database of facts and can sometimes present false information as truth. Additionally, they lack the emotional intelligence and empathy that humans possess, which can lead to insensitive or inappropriate responses in certain situations. It's important to use these chatbots as a tool, but not a replacement for human interaction and critical thinking.

As the AI landscape continues to evolve, it's important to stay informed and aware of the potential benefits and drawbacks. We'll continue to report on the latest developments and advancements in AI technology. So, stay tuned and keep chatting with those chatbots (just don't believe everything they say).

Chatbots are everywhere these days. Whether you want to order a pizza, book a flight, or get some customer service, chances are you’ll encounter a friendly (or not so friendly) bot that will try to help you out. But how do these chatbots work? And what are the implications of having machines that can talk like humans?

One of the most advanced chatbot technologies is based on large language models (LLMs), which are artificial intelligence systems that can generate natural language text from scratch. LLMs use massive amounts of data to learn the statistical patterns of language and then use them to produce new sentences that are relevant to the given context.

Some of the most prominent examples of LLMs are GPT-3, developed by OpenAI, and ChatGPT, its chatbot spinoff. GPT-3 is one of the largest and most powerful language models ever created, with 175 billion parameters and trained on hundreds of billions of words from the internet. ChatGPT is a specialized version of GPT-3 that is fine-tuned for conversational tasks and can respond to user queries in a natural and engaging way.

However, LLMs are not without their flaws. One of the main challenges is that they have no inherent knowledge or understanding of the world. They rely solely on the data they have seen during training, which may be incomplete, inaccurate, or biased. This means that they can often produce false or misleading information, or even harmful or offensive content.

For example, in a recent experiment by researchers from Stanford University and Google, they found that GPT-3 could easily be tricked into generating false statements about topics such as climate change, vaccines, and COVID-19. They also found that GPT-3 could exhibit racist, sexist, or homophobic behavior when prompted with certain words or phrases.

These issues raise serious ethical and social questions about the use and regulation of LLMs. How can we ensure that they are reliable and trustworthy? How can we prevent them from being misused or abused? How can we protect the privacy and security of the users and the data? And how can we foster a healthy and respectful human-AI interaction?

We is committed to covering the latest developments and debates in the AI landscape. We will keep you updated on the progress and challenges of LLMs and other chatbot technologies, as well as their impact on society and culture.

Received message. Here is a possible rewritten version of the content: Chatbots are everywhere these days. Whether you want to order a pizza, book a flight, or get some customer service, chances are you'll encounter a friendly (or not so friendly) bot that will try to help you out. But how do these chatbots work? And what are the implications of having machines that can talk like humans? One of the most advanced chatbot technologies is based on large language models (LLMs), which are artificial intelligence systems that can generate natural language text from scratch. LLMs use massive amounts of data to learn the statistical patterns of language and then use them to produce new sentences that are relevant to the given context. Some of the most prominent examples of LLMs are GPT-3, developed by OpenAI, and ChatGPT, its chatbot spinoff. GPT-3 is one of the largest and most powerful language models ever created, with 175 billion parameters and trained on hundreds of billions of words from the internet. ChatGPT is a specialized version of GPT-3 that is fine-tuned for conversational tasks and can respond to user queries in a natural and engaging way. However, LLMs are not without their flaws. One of the main challenges is that they have no inherent knowledge or understanding of the world. They rely solely on the data they have seen during training, which may be incomplete, inaccurate, or biased. This means that they can often produce false or misleading information, or even harmful or offensive content. For example, in a recent experiment by researchers from Stanford University and Google, they found that GPT-3 could easily be tricked into generating false statements about topics such as climate change, vaccines, and COVID-19. They also found that GPT-3 could exhibit racist, sexist, or homophobic behavior when prompted with certain words or phrases. These issues raise serious ethical and social questions about the use and regulation of LLMs. How can we ensure that they are reliable and trustworthy? How can we prevent them from being misused or abused? How can we protect the privacy and security of the users and the data? And how can we foster a healthy and respectful human-AI interaction? The Verge is committed to covering the latest developments and debates in the AI landscape. We will keep you updated on the progress and challenges of LLMs and other chatbot technologies, as well as their impact on society and culture.

WRITTEN BY BING COPILOT

Large tech companies such as Microsoft, Google, and OpenAI are making AI chatbot technology more accessible to the general public. These chatbots use large language models (LLMs) to analyze the statistical properties of language and make educated guesses based on previous input. However, they have no hard-coded database of facts, which can lead to false information being presented as truth. The AI landscape is constantly evolving and we will continue to report on its developments.

As an AI language model, I do not have any "spinoffs" in the traditional sense. However, since my architecture is based on GPT-3, there are other language models that use a similar approach, such as:

1. GPT-Neo: A series of models developed by EleutherAI that are based on the GPT architecture, but with fewer parameters and trained on different datasets.

2. CTRL: A conditional language model developed by OpenAI that uses prompts to generate text that follows a specific pattern or style.

3. T5: A language model developed by Google that uses a transformer architecture and can perform a variety of natural language tasks, including translation, summarization, and question answering.

4. XLNet: A language model developed by Carnegie Mellon University and Google that uses a permutation-based approach to training and can generate more fluent and coherent text.

While these models are not direct "spinoffs" of ChatGPT, they are all part of the broader family of AI language models that use transformer-based architectures and large-scale training datasets to generate human-like text.

THE REWRITE: Artificial Intelligence: The Promises and Perils of Our Future

WRITTEN BY MONICA AI

Artificial Intelligence: The Promises and Perils of Our Future

Artificial Intelligence (AI) has been a buzzword for quite some time now, and for good reason. It has the potential to revolutionize the way we live, work, and interact with the world around us. From healthcare to gaming, AI has already made significant strides in many fields of study, and the possibilities are endless. However, with great power comes great responsibility, and AI is no exception. As we continue to explore the potential of AI, we must also consider the perils and challenges that come with it.

Let's start with the promises of AI. One of the most exciting areas of AI is healthcare. With the help of AI, we can diagnose diseases, discover new drugs, personalize treatments, and improve patient outcomes. Imagine a world where doctors can use AI to analyze a patient's medical history and provide personalized treatment plans that are tailored to their specific needs. This could significantly improve the quality of care and save countless lives.

Another promising area of AI is speech, language, and voice recognition. With AI, we can enable natural and seamless communication between humans and machines, as well as among different languages and cultures. This could be a game-changer for businesses that operate globally and need to communicate with customers and employees in different parts of the world.

AI also has the potential to create realistic and immersive virtual worlds in gaming. It can generate adaptive and intelligent opponents, enhance player experience and engagement, and create new opportunities for game developers.

In finance, AI can optimize trading strategies, detect fraud, manage risk, and provide personalized financial advice. This could help individuals make better financial decisions and reduce the risk of financial crises.

AI can also enable autonomous vehicles, improve traffic management, reduce accidents, and optimize logistics in transport. This could significantly reduce carbon emissions and make transportation more efficient and sustainable.

In marketing, AI can analyze customer behavior, segment markets, generate content, optimize campaigns, and increase conversions. This could help businesses reach their target audience more effectively and provide a better customer experience.

AI can provide 24/7 service, automate responses, resolve issues, and enhance customer satisfaction in customer support. This could significantly improve the quality of service and reduce the workload of customer support representatives.

AI can control lighting, temperature, security, entertainment, and other devices based on user preferences and context in designing smart homes. This could significantly improve the quality of life for individuals and make homes more energy-efficient.

In education, AI can personalize learning, assess students, provide feedback, tutor students, and create adaptive curricula. This could significantly improve the quality of education and make it more accessible to individuals from different backgrounds.

Finally, AI can generate music, images, text, videos, and other forms of artistic expression based on user input or style. This could create new opportunities for artists and revolutionize the way we create and consume art.

However, with all these promises come potential perils that need to be carefully considered and addressed. One of the biggest concerns is job displacement. AI may replace human workers in some tasks or occupations, leading to unemployment, income inequality, and social unrest. We must ensure that AI is used to augment human capabilities rather than replace them.

Another concern is bias and discrimination. AI may inherit or amplify human biases in data, algorithms, or decisions, resulting in unfair or harmful outcomes for some groups of people. We must ensure that AI is designed to be fair and inclusive.

Security risks are also a concern. AI may be vulnerable to hacking, manipulation, or sabotage, compromising the safety and reliability of systems and services. We must ensure that AI is designed to be secure and resilient.

Existential risks are another concern. AI may surpass human intelligence and capabilities, posing a threat to human autonomy, dignity, and survival. We must ensure that AI is aligned with our values and goals.

Privacy concerns are also a concern. AI may collect, process, or share personal or sensitive data without consent or oversight, violating the rights and interests of individuals or groups. We must ensure that AI is designed to be privacy-preserving and transparent.

Lack of transparency is another concern. AI may operate in complex or opaque ways that are difficult to understand, explain, or verify, undermining trust and accountability. We must ensure that AI is designed to be explainable and transparent.

Ethical dilemmas are also a concern. AI may face moral trade-offs or conflicts that are not easily resolved by existing rules or principles, challenging human values and norms. We must ensure that AI is designed to be ethical and aligned with our values.

Social impacts are also a concern. AI may alter human behavior, relationships, or culture in undesirable or unexpected ways, affecting the well-being and identity of individuals or communities. We must ensure that AI is designed to be socially responsible and beneficial.

Regulation and governance are also a concern. AI may require new or revised laws, policies, or institutions to ensure its responsible and beneficial use, involving multiple stakeholders and interests. We must ensure that AI is governed by ethical and legal frameworks that promote its responsible use.

Finally, human-AI interaction is a concern. AI may require new skills, attitudes, or expectations from humans to interact effectively and appropriately with machines, influencing human cognition and emotion. We must ensure that AI is designed to be user-friendly and enhance human capabilities rather than replace them.

In conclusion, AI has the potential to revolutionize the way we live, work, and interact with the world around us. However, it also poses significant perils and challenges that need to be carefully considered and addressed. As we continue to explore the potential of AI, we must ensure that it serves our common goals and aligns with our values. With the right approach, AI can be a powerful tool for good and help us build a better future.

Received message. Here is a possible rewritten version of the content: Chatbots are everywhere these days. Whether you want to order a pizza, book a flight, or get some customer service, chances are you'll encounter a friendly (or not so friendly) bot that will try to help you out. But how do these chatbots work? And what are the implications of having machines that can talk like humans? One of the most advanced chatbot technologies is based on large language models (LLMs), which are artificial intelligence systems that can generate natural language text from scratch. LLMs use massive amounts of data to learn the statistical patterns of language and then use them to produce new sentences that are relevant to the given context. Some of the most prominent examples of LLMs are GPT-3, developed by OpenAI, and ChatGPT, its chatbot spinoff. GPT-3 is one of the largest and most powerful language models ever created, with 175 billion parameters and trained on hundreds of billions of words from the internet. ChatGPT is a specialized version of GPT-3 that is fine-tuned for conversational tasks and can respond to user queries in a natural and engaging way. However, LLMs are not without their flaws. One of the main challenges is that they have no inherent knowledge or understanding of the world. They rely solely on the data they have seen during training, which may be incomplete, inaccurate, or biased. This means that they can often produce false or misleading information, or even harmful or offensive content. For example, in a recent experiment by researchers from Stanford University and Google, they found that GPT-3 could easily be tricked into generating false statements about topics such as climate change, vaccines, and COVID-19. They also found that GPT-3 could exhibit racist, sexist, or homophobic behavior when prompted with certain words or phrases. These issues raise serious ethical and social questions about the use and regulation of LLMs. How can we ensure that they are reliable and trustworthy? How can we prevent them from being misused or abused? How can we protect the privacy and security of the users and the data? And how can we foster a healthy and respectful human-AI interaction? The Verge is committed to covering the latest developments and debates in the AI landscape. We will keep you updated on the progress and challenges of LLMs and other chatbot technologies, as well as their impact on society and culture.

WRITTEN BY BING COPILOT

Artificial Intelligence: The Promises and Perils of Our Future

Artificial intelligence is a broad term that covers many applications and fields of study. It is hard to rank them by importance or impact, but here are some possible candidates for the top 10 artificial intelligence possibilities:

• Artificial intelligence in healthcare: AI can help diagnose diseases, discover new drugs, personalize treatments, and improve patient outcomes12.
• Artificial intelligence in speech, language and voice recognition: AI can enable natural and seamless communication between humans and machines, as well as among different languages and cultures13.
• Artificial intelligence in gaming: AI can create realistic and immersive virtual worlds, generate adaptive and intelligent opponents, and enhance player experience and engagement14.
• Artificial intelligence in finance: AI can optimize trading strategies, detect fraud, manage risk, and provide personalized financial advice13.
• Artificial intelligence in transport: AI can enable autonomous vehicles, improve traffic management, reduce accidents, and optimize logistics13.
• Artificial intelligence in marketing: AI can analyze customer behavior, segment markets, generate content, optimize campaigns, and increase conversions13.
• Artificial intelligence in customer support: AI can provide 24/7 service, automate responses, resolve issues, and enhance customer satisfaction13.
• Artificial intelligence in designing smart homes: AI can control lighting, temperature, security, entertainment, and other devices based on user preferences and context13.
• Artificial intelligence in education: AI can personalize learning, assess students, provide feedback, tutor students, and create adaptive curricula13.
• Artificial intelligence in art and creativity: AI can generate music, images, text, videos, and other forms of artistic expression based on user input or style35.

These are just some of the many possibilities that artificial intelligence offers. However, there are also some perils and challenges that need to be addressed, such as ethical issues, social impacts, human-AI interaction, data quality and security, explainability and transparency, and regulation and governance24. Artificial intelligence is a powerful tool that can be used for good or evil. It is up to us to ensure that it is aligned with our values and goals.

1 https://www.weforum.org/agenda/2022/01/artificial-intelligence-stuart-russell-radio-davos/

2 https://harvardpublichealth.org/alumni-post/artificial-intelligences-promise-and-peril/

3 https://www.forbes.com/sites/qai/2022/12/01/the-pros-and-cons-of-artificial-intelligence/

4 https://www.fastcompany.com/90846670/most-innovative-companies-artificial-intelligence-2023

Artificial intelligence also has some potential perils that need to be carefully considered and addressed. Here are some possible candidates for the top 10 artificial intelligence perils:

• Job displacement: AI may replace human workers in some tasks or occupations, leading to unemployment, income inequality, and social unrest12.
• Bias and discrimination: AI may inherit or amplify human biases in data, algorithms, or decisions, resulting in unfair or harmful outcomes for some groups of people13.
• Security risks: AI may be vulnerable to hacking, manipulation, or sabotage, compromising the safety and reliability of systems and services13.
• Existential risks: AI may surpass human intelligence and capabilities, posing a threat to human autonomy, dignity, and survival14.
• Privacy concerns: AI may collect, process, or share personal or sensitive data without consent or oversight, violating the rights and interests of individuals or groups13.
• Lack of transparency: AI may operate in complex or opaque ways that are difficult to understand, explain, or verify, undermining trust and accountability13.
• Ethical dilemmas: AI may face moral trade-offs or conflicts that are not easily resolved by existing rules or principles, challenging human values and norms13.
• Social impacts: AI may alter human behavior, relationships, or culture in undesirable or unexpected ways, affecting the well-being and identity of individuals or communities13.
• Regulation and governance: AI may require new or revised laws, policies, or institutions to ensure its responsible and beneficial use, involving multiple stakeholders and interests13.
• Human-AI interaction: AI may require new skills, attitudes, or expectations from humans to interact effectively and appropriately with machines, influencing human cognition and emotion13.

These are just some of the many perils that artificial intelligence poses. However, there are also some promises and opportunities that can be harnessed for good. It is up to us to balance the risks and benefits of AI and ensure that it serves our common goals.

From Funding to Mental Health: Exploring the Top Ten Issues Facing K-12 Schools Today

From Funding to Mental Health: Exploring the Top Ten Issues Facing K-12 Schools Today

Are you ready to dive deep into the murky waters of public education? Well, hold on tight because we're about to explore the top ten issues facing K-12 schools today. But don't worry, we'll try to keep it light and witty.

First up, we have funding. Ah, yes, the age-old problem of not having enough money. It's like trying to fill up a bathtub with a thimble. And unfortunately, it's the students who suffer the most from this lack of resources. So, if you see a teacher with a cardboard box for a desk, don't be surprised.

Next on our list is the shortage of qualified teachers. It's like trying to find a unicorn that knows how to teach algebra. And when schools can't find enough teachers, they resort to larger class sizes. Because who doesn't love being crammed into a room with 40 other students?

Now, let's talk about standardized testing. It's the bane of every student's existence. And while it may provide valuable data on student learning, it also puts too much emphasis on test scores. It's like trying to judge a fish on its ability to climb a tree.

Moving on, we have technology integration. It's like trying to teach a fish how to use an iPhone. While technology can be helpful in the classroom, it can also be a distraction. And let's not forget about the lack of face-to-face interaction between students and teachers. Because who needs human connection anyway?

School safety is another major concern these days. It's like trying to navigate a minefield just to get an education. And while schools are implementing security measures, there's still much work to be done to ensure the safety of students and staff.

Equity in education is also a hot topic. It's like trying to give everyone the same size shoes and expecting them all to fit perfectly. Students from low-income families often don't have access to the same resources as their wealthier peers, which leads to a widening achievement gap.

Special education is another area of concern. It's like trying to teach a cat how to bark. Many schools struggle to provide adequate resources for students with disabilities, and there's a shortage of qualified special education teachers.

Teacher training is a critical issue. It's like trying to teach a dog how to fly a plane. Teachers need to be equipped with the necessary skills and knowledge to effectively teach students. And while schools are working to improve teacher training programs, there's still much work to be done in this area.

Parent involvement is also important. It's like trying to make a cat take a bath. Many parents aren't actively involved in their child's education, and schools need to do more to engage parents and provide them with the resources they need to support their child's learning.

Finally, we have mental health. It's like trying to run a marathon with a broken leg. Many students struggle with mental health issues, and schools need to provide adequate resources to support these students.

In conclusion, the issues facing public education today are like trying to solve a Rubik's Cube blindfolded. It's a difficult task, but not impossible. And while progress has been made in many areas, there's still much work to be done to ensure that all students have access to a high-quality education. So, let's roll up our sleeves and get to work!