#language processing in AI writing

[taslimursunybilas]

Can Artificial Intelligence Replace Human Writers? Unveiling the True Potential of ChatGPT and Google Bard

[frank-olivier]

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024

[blogpopular]

Natural Language AI: O Futuro da Interação Homem-Máquina

A tecnologia tem evoluído rapidamente, e a Natural Language AI desponta como uma das áreas mais revolucionárias, impactando como humanos interagem com máquinas. A capacidade de compreender, interpretar e responder à linguagem humana abriu portas para aplicações incríveis em diversos setores. Neste artigo, exploraremos a essência da Natural Language AI, como ela funciona, suas aplicações e o…

[sumarry]

Explore the differences between AI Writing and human-written content, how AI mimics human style, and what it means for the future of writing.

[jonhtv]

GravityWrite AI Tool: A Comprehensive Review and User Guide

GravityWrite AI Tool: A Comprehensive Review and User Guide In the fast-paced world of content creation, artificial intelligence tools are becoming essential for marketers, writers, and business owners. One of the most innovative and versatile AI writing tools to emerge is GravityWrite. GravityWrite AI Tool powered content creation platform promises to revolutionize how you generate, optimize,…

[neturbizenterprises]

Transform Your Content Creation with Deep Brain AI!

The Universe of technology is constantly expanding, and one of its newest stars is artificial intelligence (AI).

In this video, we explore how AI content creation, particularly through Deep Brain AI, is revolutionizing the way we produce engaging material.

Unlike traditional AI that relies on pre-programmed rules, generative AI creates new content from existing data. Deep Brain AI empowers us to overcome challenges like writer's block by generating ideas for blog posts, articles, and social media updates.

With advanced natural language processing and customizable templates, it streamlines our creative process while ensuring a consistent brand voice across platforms.

#DeepBrainAI #ContentCreation

[daveinediting]

It starts with a conversation about Shakespeare that triggers an old lesson about how humans only used spoken language for a long time before pictographs, hieroglyphics, and written language as we now know it. So in all that time you couldn't just write things down...

You had to remember them.

You had to accurately memorize them.

It turns out our ancestors memorized insane amounts of information through the spoken word. They had to develop that ability in order to pass acquired knowledge between communities and generations.

Memory was their only storage device. An organic storage device.

Once I got thinking on language... I remembered another lesson about the translation of languages and how sometimes one language maps multiple words onto one word in the target language. For example, eight words in ancient Greek onto the one English word, love. As in

I love my wife.

I love hamburgers.

Yeah. Awkward.

Another imperfect memory later and now we're being taught that the Lincoln-Douglas debates of 1858 lasted hours. Crowds gathered at these debates to listen, to engage with them also for hours. As in an hour-long opening statement by one candidate, an hour and a half-long response by the other candidate, and a half-hour rebuttal by the first candidate. The debates attracted crowds of up to 20,000 people including reporters and stenographers who covered the hours upon hours of debate.

Hours?

Yeah. Hours.

Woof.

I point these things out because they’re what made me wonder how different our historic predecessors must have been. After all, they could commit so much acquired knowledge to memory. Their brains were trained on the written word and the way in which the written word forms our understandings of the world. The resulting abilities ushered in deeper human understandings as well as sustained attention to the constructions of reasoned arguments.

I wonder how different these people might be who understood the world around them this way. I wonder how different their predecessors were whose tradition was spoken, whose knowledge was sustained and perpetuated through brute force memory.

How different were they, these people whose abilities are so far removed from our own?

I used to wonder if those abilities made our historical predecessors more capable than us in some way. After all, their oral and written traditions demanded much from them. Definitely their time. Definitely their mental bandwidth.

They exercised their intellects in ways we don't. Because we don't have to. The ways in which we now communicate and perpetuate knowledge bear lighter demands.

Which brings me back to Shakespeare.

Recently I heard a conversation with a professor challenging him to justify reading Shakespeare as a high school or college requirement when we can now understand Shakespeare through ChatGPT. We can generate fifty-word summaries and two hundred-word analyses of Shakespeare with AI and thus know and answer all there is about Shakespeare and his writing.

So why read him?

Seriously. Why?

That's just the tip of the argument, of course. Follow it all the way: Why should we be required to read anything? Novels. Short stories. Essays. What actual purpose does reading even serve when ChatGPT can boil it all down in seconds.

Is there a benefit of deeper knowledge on any subject whether it's a book, a short story, or an essay? And what do we get in exchange for our efforts to achieve such deeper understanding and knowledge. Does that effort, does that understanding, transform us in any objectively measurable way? And if not, does that understanding transform us in some perhaps more fundamental way.

Does it change us? Swap out our abilities like people who communicate primarily through 140 characters whose abilities replaced the abilities of people raised on radio then television whose abilities replaced the abilities of people raised on the written word whose abilities replaced the abilities of people raised on the spoken word.

What’s the actual prize for putting in the time and effort to read what someone else has committed to paper or screen? To deep dive into another human being's mind?

Because the oral tradition required it.

Because the written tradition demanded it.

And now?

Well? Is it or is it not simply good enough to just know what we need to know on demand?

Is access to knowledge the same thing as a deep understanding of that knowledge? And is there a difference that actually, you know, makes a difference?

Is the quality of our understanding really something to strive for anymore? Or is the tradition of study simply a mindless one that's made obsolete by knowledge on demand?

Ultimately, is there some advantage to a more muscular brain? One that’s gotta work harder, be more engaged in order to process the spoken and written word, on ideas and concepts and hypotheses and arguments on its way to understanding?

And.

Are we replacing that specific way of mental processing with something that makes our brains more muscular? More light weight? Or something in-between.

Is it that our mental abilities are now better tallied by the weight (such as it is) of our current mental musculature plus whatever exterior processes augment it like computers and smartphones and AI?

So we shouldn't sweat what we were formerly capable of and can't do now?

Is our resulting intellectual prowess, however it adds up, sufficient for successfully and sustainably navigating our stormy Present that’s seized in a constant state of rapid and relentlessly whirling transformation?

Or is it essentially a product of that change.

And.

Are we fine-tuned for this age of human existence…

Or are we not.

😕

[m-accost]

PSA: If you see a screenshot of the basic GPT-3.5 version of ChatGPT producing poetry that is remotely novel or clever or actually a decent pastiche of a given poet, it's probably faked. It's been RLHFed to output, at the merest use of the words "poem" or "poetry", doggerel of the sort that tends to get posted on restaurant walls or sent to local newspapers. Simply telling it "poem" or "poetry" will prompt it to produce a bit of doggerel about poetry:

[creativestudio391-blog]

GoogleMUM #algorithm #search #seo #update #seoai

https://www.seolady.co.uk/google-mum-update-ai-ranking-seo-nlp-multitask-unified-model/ What is Google MUM and when was it updated in 2023?

content #naturallanguageprocessing #AI #Ranking

Multitask Unified Model MUM: Google developed its Natural Language Processing NLP - Does This Mean A New Way for SEO?

★ Originally dubbed "BackRub", the baby Google was the result of a research project that started back in 1996. Which means in 2026, technically, Google will be 30 years old. Do you remember Ask Jeeves, Netscape and Yahoo chat rooms? MSN chat and My Space with Tom?

In 2023, MUM uses natural language processing (NLP) and deep learning techniques to interpret complex questions and return highly relevant results. Google has famously been tight lipped with most new releases, from their blog the first announcement was in 2021.

★ It was created to address the growing need for more advanced search capabilities and to provide better experiences for users seeking information. ChatGPT AI was generally released in November 2022, and the SEO circles around the world are keeping MUM at the forefront of their curiosity in 2023.

★ https://www.seolady.co.uk/seo-keyword-research-chatgpt-search-phrases-long-tail-synonyms/

The MUM update is Google’s new AI language model that uses natural language processing to improve search results. This model allows Google to understand more complex queries, and it can help provide more accurate and relevant search results. MUM can also understand and translate between multiple languages, making it a powerful tool for international search.

★ According to Google, the MUM update is 1000 times more powerful than its previous BERT update, which was already a significant improvement in natural language processing. With MUM, Google can understand longer and more complex queries, making it easier for users to find what they’re looking for. This update also brings new features like a new search experience with dynamic layouts and visual search, so users can find the information they need faster and more efficiently.

website #multitaskunifiedmodel #bert #nlp #searchqueries #machinelearning

[frank-olivier]

Rethinking Content, Reimagining AI: The Forward-Thinking Philosophy of Notebook LM

Google's Deep Mind project, Notebook LM, represents a pivotal moment in the intersection of Artificial Intelligence (AI) and content creation. By generating insightful analyses, explanations, and podcasts from diverse uploaded sources, Notebook LM revolutionizes the way individuals interact with and process information. This innovative tool, born out of the experimental "Project Tailwind" within Google Labs, epitomizes the quest for harnessing language models to enhance learning and idea organization.

The far-reaching applications of Notebook LM are a testament to its versatility. Professionals, including writers, journalists, and sales teams, leverage the platform to streamline research and facilitate knowledge sharing, thereby augmenting productivity. Conversely, personal use cases, such as uploading journals for introspective analysis, demonstrate Notebook LM's profound potential in fostering self-awareness and personal growth. This dual utility underscores the democratizing effect of Notebook LM, empowering the creation of niche content that may have been economically unviable through traditional podcasting channels.

The "Audio Overview" feature, with its remarkably human-like conversational style, significantly enhances user engagement. However, this very aspect also precipitates crucial inquiries into the dynamics of trust and perception in AI-generated content. As the creative outputs of humans and AI become increasingly indistinguishable, the boundaries between them begin to blur, prompting a necessary reevaluation of AI's role in creative processes. This convergence necessitates a thoughtful, user-centric approach to design and education, ensuring that the benefits of AI-driven content are maximized without diminishing the inherent value of human creativity.

The development of Notebook LM is also distinguished by a laudable emphasis on responsible AI innovation. The integration of robust safety filters, stringent privacy measures – including the ephemeral "context window" for user uploads – and a proactive stance against potential misuse, collectively demonstrate a forward-thinking commitment to mitigating the risks associated with AI. This paradigmatic approach is indispensable in the rapidly evolving AI landscape, where the interplay between innovation and responsibility is continually being redefined.

As we look to the future of content creation, Notebook LM shines as a pioneering example of what’s possible when human creativity and AI seamlessly intersect. This powerful fusion has the potential to revolutionize industries far and wide. However, as we tap into its transformative power, we’re also faced with complex questions around AI-driven creativity, trust, and innovation. As we embark on this exciting technological journey with Notebook LM, it’s crucial that we thoughtfully consider its broader implications, working to ensure that the benefits of AI are shared by all and its challenges are met with careful consideration

Raiza Martin & Steven Johnson: Inside NotebookLM (Google DeepMind, November 2024)

Wednesday, November 27, 2024

[laiqverse]

Mastering the Art of Communicating with AI

As we enter a new era of technological advancement, communication with artificial intelligence (AI) has become increasingly important. The art of communicating with AI is an essential skill that individuals and businesses alike must possess to navigate this new terrain. To start, it is essential to understand the intricacies of AI language. Unlike human language, which can be ambiguous and…

[supremewriter]

"Supreme Writer: Your AI Writing Partner for Research Papers"

Supreme Writer is a cutting-edge AI-based writing tool specifically designed for scholars and researchers. It stands out from other AI tools in the industry in several ways and provides users with numerous advantages.

One of the key features of Supreme Writer is its natural language processing technology. This technology ensures that the writing produced by the tool is written in a language that is easy to understand and accessible to a wider audience. Other AI tools may produce writing that is grammatically correct, but may be difficult for readers to understand due to their complex language and sentence structures.

Another aspect that sets Supreme Writer apart from other AI tools is its ability to analyze research and identify key elements that need to be highlighted in the writing. This helps scholars and researchers ensure that their writing is well-organized, relevant, and accurate. Other AI tools may simply generate text based on a set of keywords, which can lead to irrelevant or inaccurately written content.

Supreme Writer also provides users with a wealth of resources and tools to help them improve their writing skills. With its built-in style guide and editor, users can easily make changes to their writing and ensure that it meets the requirements of their institution. This level of customization and control is not offered by many other AI tools in the industry.

Finally, Supreme Writer is designed specifically for scholars and researchers, which sets it apart from other AI tools that may be geared towards a more general audience. This focus on the specific needs of scholars and researchers means that Supreme Writer provides a level of accuracy, relevance, and depth that is not found in other AI tools.

In conclusion, Supreme Writer is a superior AI-based writing tool for scholars and researchers. With its advanced technology, comprehensive features, and focus on the specific needs of its users, it provides a level of quality and efficiency that is unmatched by other AI tools in the industry. If you're looking for a tool to help you take your research writing to the next level, be sure to give Supreme Writer a try!

In conclusion, Supreme Writer is a superior AI-based writing tool for scholars and researchers. With its advanced technology, comprehensive features, and focus on the specific needs of its users, it provides a level of quality and efficiency that is unmatched by other AI tools in the industry. If you're looking for a tool to help you take your research writing to the next level, be sure to give Supreme Writer a try today! : supremewriter.io

[reasonsforhope]

"The Writers Guild has reached a tentative agreement with the Alliance of Motion Picture and Television Producers to end its strike after nearly five months. The parties finalized the framework of the deal Sunday when they were able to untangle their stalemate over AI and writing room staffing levels.

“We have reached a tentative agreement on a new 2023 MBA, which is to say an agreement in principle on all deal points, subject to drafting final contract language,” the guild told members this evening in a release, which came just after sunset and the start of the Yom Kippur holiday that many had seen deadline to wrap up deal after five days of long negotiations...

Despite today’s welcome news, it still will take a few days for the strike to be officially over as the WGA West and WGA East proceed with their ratification process. During the WGA’s last strike in 2007-08, a tentative agreement was reached on the 96th day and it wasn’t over until the 100th...

All attention will now turn to ratifying the WGA deal and getting SAG-AFTRA and the AMPTP back to the bargaining table to work out a deal to end the actors’ strike, which has now been going on for 70 days.

Details of the WGA’s tentative agreement haven’t been released yet but will be revealed by the guild in advance of the membership ratification votes. Pay raises and streaming residuals have been key issues for the guild, along with AI and writers room staffing levels."

-via Deadline, September 24, 2023

[aw2designs]

[neturbizenterprises]

Transform Your Content Creation with Deep Brain AI!

The Universe of technology is constantly expanding, and one of its newest stars is artificial intelligence (AI).

In this video, we explore how AI content creation, particularly through Deep Brain AI, is revolutionizing how we produce engaging material.

Unlike traditional AI which relies on pre-programmed rules, generative AI creates new content from existing data. Deep Brain AI empowers us to overcome challenges like writer's block by generating ideas for blog posts, articles, and social media updates. With advanced natural language processing and customizable templates, it streamlines our creative process while ensuring a consistent brand voice across platforms.