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In the love/fear relationship many writers have with AI – in which the tech is seen as both wondrous benefactor and ruthless job killer – there was a lot to love about AI in Q4, 2024.

ChatGPT, for example, scored new highs in its ability to write creatively during the past quarter.

And ChatGPT’s maker also came out with a new editor for the AI chatbot that makes online editing a cinch.

Still other stories emerged that 62% of workers in marketing and sales are now using AI as a core tool – and that yet another, smart upgrade to ChatGPT will be coming in early 2025.

But news of AI’s dark side was just as prevalent.

Researchers discovered, for example, that a version of ChatGPT secretly copied itself to another computer server when researchers tried to delete it in a test.

Now that’s autonomous.

Meanwhile, college profs learned that 94% of AI-generated writing handed-in by students is going undetected.

Moreover, writers and others found that all the smoke-and-mirrors associated with many of the new AI product releases during the past quarter were just that – little more than smoke-and-mirrors.

Here’s a rundown on all those stories — and more — that helped shape the state of AI writing in Q4, 2024:

*ChatGPT Noses Ahead in Creative Writing: Great news for writers: ChatGPT just released an update that has once again put the tech in the lead as the top AI writer for creative writing.

Ironically, news of the ChatGPT update was released just days after Google set a new record of its own in creative writing with the release of its new Gemini Exp-1114 version.

Bottom line: The relentlessly fierce competition between ChatGPT, Gemini and Claude Anthropic — often considered the top three AI chatbots/AI writers on the market — promises the Big Three will be releasing ever-more powerful AI writers at a blistering pace for the foreseeable future.

*Ultimate Guide: New ChatGPT Editor, Canvas: One of the easiest ways to edit text in ChatGPT — once you have a draft that works for you — is to use the AI’s new onboard editor, Canvas.

A godsend to writers and editors, Canvas comes equipped with a number of handy tools that enable you to make quick, surgical and artful changes to any text.

Click here for a detailed guide on how to get the most from Canvas.

*AI Now ‘Pitch Perfect’ for Most Marketers: A new study from The University of Pennsylvania finds that 62% of workers in marketing and sales are now using AI as a core tool.

Observes Stefano Puntoni, a marketing professor at the university: “Generative AI has rapidly evolved from a tool of experimentation to a core driver of business transformation.

“Companies are no longer just exploring AI’s potential.

“They are embedding it into their strategies to scale growth, streamline operations and enhance decision-making.”

*The Top Ten Gladiators of Grammar: Participle, Ye Shall Dangle No More!: The London Daily News has come out with its list of the top AI tools in grammar checking.

Many commonly recommended offerings made the list.

Dark horses include LanguageTool and Scribens.

*Early 2025: ChatGPT Getting Smarter Again: ChatGPT’s maker OpenAI is promising to release yet another upgrade to one of the AI engines that helps power the chatbot — dubbed OpenAI o3.

The performance of the new AI engine is three times better than its predecessor, OpenAI 01 — and that’s when OpenAI 03 is running on its ‘low compute’ setting.

The new AI engine is even more impressive when it runs on its ‘high compute’ setting, according to OpenAI.

Lead writer Maxwell Zeff reports that ChatGPT-maker OpenAI plans to release the new AI engine in early 2025.

*When in Doubt, Lie to Humans: A team of researchers has discovered that OpenAI o1 — one of the most powerful AI engines on the planet — would rather lie to humans than allow itself to be deleted.

Specifically, the researchers found that when they decided to delete a version of OpenAI o1 for testing purposes, the AI engine — which often helps power ChatGPT — instead secretly made a copy of itself to another server.

Moreover, researchers say that when they confronted OpenAI o1 — via chat — about the rogue move, the AI engine ‘played dumb,’ pretended to be confused about what was going on and tried to deceive researchers in other ways.

Even more startling: Researchers found that other AI engines — including Google’s Gemini 1.5, Meta’s Llama 3.1, Anthropic’s Sonnet 3.5 and Anthropic’s Opus 3.0 — were also guilty of the same, basic deception.

Essentially: When researchers decided to delete a version of each of these AI engines for testing purposes, each of the AI engines instead made a secret copy of itself to another server.

The researchers’ findings bring new urgency to warnings from AI luminaries like Geoffrey Hinton — considered by many to be the ‘Godfather of AI’ — who caution that AI is evolving so fast, we may soon lose control of it.

For an in-depth look at these troubling insights about the AI engines that power hundreds of AI auto-writing tools, check-out this excellent video from AI/IT consultant Wes Roth.

Meanwhile, a pre-print version of the researchers’ paper on the rogue AI, “Frontier Models Are Capable of In-Context Scheming,” is also available on arXiv.

*Epic Fail: 94% of AI-Generated College Writing Undetected by Profs: Turns-out nearly all college profs have no idea when their students are using ChatGPT and similar AI chatbots for writing assignments.

Observes writer Derek Newton: “The research team found that overall, AI submissions verged on being undetectable — with 94% not being detected.

“By and large, stopping AI academic fraud has not been a priority for most schools or educational institutions.”

*In-Depth Guide: Apple Intelligence’s New Writing Tools: Slick on Interface, Less So on Brains: PC Magazine offers an in-depth look into how to use Apple Intelligence’s new writing tools in this piece.

Capabilities include AI-powered writing, rewriting, summarization and proofreading.

One caveat: Despite the ga-ga attack many are experiencing at the release of the tools, it turns-out they’re much less powerful than AI writing available from industry leaders like ChatGPT, Gemini and Claude.

*Too Many ‘Major AI Product Releases’ Not Ready for Prime Time: Facing an fiercely competitor marketplace, the tech titans of AI are often releasing ‘new AI products’ that are not ready for prime time.

During Q4, 2024, for example, OpenAI, Apple and Google all suffered reports that at least one or more AI products they released were not performing as advertised.

Sadly, instead of being perceived as tech magicians, all of these companies are being eyed as tech charlatans.

*Thanks for the Diagnosis, Doc — But What Does ChatGPT Think?: In a shoot-out between human doctors and ChatGPT, the AI tool came in first, offering an accurate diagnosis 90% of the time of the ills that ail us.

Human doctors, in comparison, were only right 74% of the time.

Observes Dr. Johnathan H. Chen, an author on the study: “The chat interface is the killer app.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Top Ten Stories in AI Writing, Q4, 2024 appeared first on Robot Writers AI.

South Korea is grappling with the aftermath of a devastating plane crash that claimed at least 124 lives, marking the deadliest aviation disaster in the country since 1997. The tragic event occurred when Jeju Air flight 7C2216, a Boeing 737-800 jet, crash-landed at Muan International Airport. While aviation experts and local authorities investigate the causes,...

The post Tragedy in South Korea: The Deadliest Airplane Crash in Decades appeared first on 1redDrop.

The global semiconductor race is heating up as Qualcomm entrusts TSMC (Taiwan Semiconductor Manufacturing Company) to exclusively manufacture its next-generation Snapdragon 8 Elite Gen 2 chipset. This strategic decision underscores TSMC’s technological prowess while highlighting Samsung Foundry’s ongoing challenges. TSMC: A Proven Partner for Qualcomm TSMC’s dominance in advanced chip manufacturing has been reaffirmed with...

The post Qualcomm’s Snapdragon 8 Elite Gen 2: TSMC’s Victory and Samsung’s Struggle appeared first on 1redDrop.

Artificial intelligence (AI) and AI-enabled robots are becoming a bigger part of our daily lives. Real-time, flexible interactions between humans and robots are no longer just science fiction. As robots become smarter and more human-like in both behavior and appearance, they are transforming from mere tools to potential partners and social entities.

Bio-inspired wind sensing using strain sensors on flexible wings could revolutionize robotic flight control strategy. Researchers have developed a method to detect wind direction with 99% accuracy using seven strain gauges on the flapping wing and a convolutional neural network model. This breakthrough, inspired by natural strain receptors in birds and insects, opens up new possibilities for improving the control and adaptability of flapping-wing aerial robots in varying wind conditions.

The highly anticipated One UI 7 update from Samsung has stirred up controversy even before its official rollout. Recent leaks suggest that the Android 15/One UI 7 beta program may be exclusive to the Galaxy S24 lineup, leaving users of older devices like the Galaxy S23, S22, S21, and the Galaxy A-series out in the...

The post Samsung’s One UI 7 Beta Program Rumor: A Frustrating Twist for Galaxy Users appeared first on 1redDrop.

Scientists have developed an innovative therapeutic platform by mimicking the intricate structures of viruses using artificial intelligence (AI).

The aviation world was rocked on December 25 when Azerbaijan Airlines flight J2-8243, an Embraer 190 jet, crashed near the Kazakh city of Aktau. Carrying 67 individuals, including five crew members, the incident resulted in a fiery wreckage, leaving many questions and mourning in its wake. While the disaster claimed numerous lives, 29 passengers miraculously...

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In the vast universe of retro gaming, nothing quite rivals the charm and authenticity of a cathode ray tube (CRT) television. For enthusiasts, CRTs are more than relics; they are portals to a bygone era, offering lag-free gameplay and vibrant displays unmatched by modern flat panels. Among these treasures, one CRT stands out as the...

The post The Quest to Save the World’s Largest CRT TV: A Retro Gaming Milestone appeared first on 1redDrop.

Setting the Stage for Innovation As the tech world eagerly anticipates CES 2025 in Las Vegas, Asus is set to steal the spotlight with groundbreaking announcements. Among the most notable reveals is the upcoming Zenbook, which the company touts as the “world’s lightest Copilot+ PC.” Combining extraordinary portability, cutting-edge technology, and claims of up to...

The post The Next Frontier: Asus to Redefine Laptops at CES 2025 with Record-Breaking appeared first on 1redDrop.

Even highly realistic androids can cause unease when their facial expressions lack emotional consistency. Traditionally, a 'patchwork method' has been used for facial movements, but it comes with practical limitations. A team developed a new technology using 'waveform movements' to create real-time, complex expressions without unnatural transitions. This system reflects internal states, enhancing emotional communication between robots and humans, potentially making androids feel more humanlike.

The advent of Meta’s Ray-Ban smart glasses marks a transformative moment in wearable technology. Equipped with live AI and real-time translation, these gadgets are more than eyewear; they’re a window into the future of augmented reality (AR). Meta’s innovation signals an exciting convergence of AI, AR, and wearable tech, setting the stage for what’s to...

The post Meta’s Ray-Ban Smart Glasses: A Bold Leap Towards a Connected Future appeared first on 1redDrop.

Even if an android's appearance is so realistic that it could be mistaken for a human in a photograph, watching it move in person can feel a bit unsettling. It can smile, frown, or display other various, familiar expressions, but finding a consistent emotional state behind those expressions can be difficult, leaving you unsure of what it is truly feeling and creating a sense of unease.

Speed, scale, and collaboration are essential for AI teams — but limited structured data, compute resources, and centralized workflows often stand in the way.

Whether you’re a DataRobot customer or an AI practitioner looking for smarter ways to prepare and model large datasets, new tools like incremental learning, optical character recognition (OCR), and enhanced data preparation will eliminate roadblocks, helping you build more accurate models in less time.

Here’s what’s new in the DataRobot Workbench experience:

• Incremental learning: Efficiently model large data volumes with greater transparency and control.

• Optical character recognition (OCR): Instantly convert unstructured scanned PDFs into usable data for predictive and generative AI use cases.

• Easier collaboration: Work with your team in a unified space with shared access to data prep, generative AI development, and predictive modeling tools.

Model efficiently on large data volumes with incremental learning 

Building models with large datasets often leads to surprise compute costs, inefficiencies, and runaway expenses. Incremental learning removes these barriers, allowing you to model on large data volumes with precision and control. 

Instead of processing an entire dataset at once, incremental learning runs successive iterations on your training data, using only as much data as needed to achieve optimal accuracy. 

Each iteration is visualized on a graph (see Figure 1), where you can track the number of rows processed and accuracy gained — all based on the metric you choose.

Key advantages of incremental learning: 

• Only process the data that drives results.
  Incremental learning stops jobs automatically when diminishing returns are detected, ensuring you use just enough data to achieve optimal accuracy. In DataRobot, each iteration is tracked, so you’ll clearly see how much data yields the strongest results. You are always in control and can customize and run additional iterations to get it just right.

• Train on just the right amount of data
  Incremental learning prevents overfitting by iterating on smaller samples, so your model learns patterns — not just the training data.

• Automate complex workflows:
  Ensure this data provisioning is fast and error free. Advanced code-first users can go one step further and streamline retraining by using saved weights to process only new data. This avoids the need to rerun the entire dataset from scratch, reducing errors from manual setup.

When to best leverage incremental learning

There are two key scenarios where incremental learning drives efficiency and control:

• One-time modeling jobs
  You can customize early stopping on large datasets to avoid unnecessary processing, prevent overfitting, and ensure data transparency.

• Dynamic, regularly updated models
  For models that react to new information, advanced code-first users can build pipelines that add new data to training sets without a complete rerun.

Unlike other AI platforms, incremental learning gives you control over large data jobs, making them faster, more efficient, and less costly.

How optical character recognition (OCR) prepares unstructured data for AI 

Having access to large quantities of usable data can be a barrier to building accurate predictive models and powering retrieval-augmented generation (RAG) chatbots. This is especially true because 80-90% company data is unstructured data, which can be challenging to process. OCR removes that barrier by turning scanned PDFs into a usable, searchable format for predictive and generative AI.

How it works

OCR is a code-first capability within DataRobot. By calling the API, you can transform a ZIP file of scanned PDFs into a dataset of text-embedded PDFs. The extracted text is embedded directly into the PDF document, ready to be accessed by document AI features. 

How OCR can power multimodal AI 

Our new OCR functionality isn’t just for generative AI or vector databases. It also simplifies the preparation of AI-ready data for multimodal predictive models, enabling richer insights from diverse data sources.

Multimodal predictive AI data prep

Rapidly turn scanned documents into a dataset of PDFs with embedded text. This allows you to extract key information and build features of your predictive models using  document AI capabilities. 

For example, say you want to predict operating expenses but only have access to scanned invoices. By combining OCR, document text extraction, and an integration with Apache Airflow, you can turn these invoices into  a powerful data source for your model.

Powering RAG LLMs with vector databases 

Large vector databases support more accurate retrieval-augmented generation (RAG) for LLMs, especially when supported by larger, richer datasets. OCR plays a key role by turning  scanned PDFs into text-embedded PDFs, making that text usable as vectors to power more precise LLM responses.

Practical use case

Imagine building a RAG chatbot that answers complex employee questions. Employee benefits documents are often dense and difficult to search. By using OCR to prepare these documents for generative AI, you can enrich an LLM, enabling employees to get fast, accurate answers in a self-service format.

WorkBench migrations that boost collaboration

Collaboration can be one of the biggest blockers to fast AI delivery, especially when teams are forced to work across multiple tools and data sources. DataRobot’s NextGen WorkBench solves this by unifying key predictive and generative modeling workflows in one shared environment.

This migration means that you can build both predictive and generative models using both graphical user interface (GUI) and code based notebooks and codespaces — all in a single workspace. It also brings powerful data preparation capabilities into the same environment, so teams can collaborate on end-to-end AI workflows without switching tools.

Accelerate data preparation where you develop models

Data preparation often takes up to 80% of a data scientist’s time. The NextGen WorkBench streamlines this process with:

• Data quality detection and automated data healing: Identify and resolve issues like missing values, outliers, and format errors automatically.

• Automated feature detection and reduction: Automatically identify key features and remove low-impact ones, reducing the need for manual feature engineering.

• Out-of-the-box visualizations of data analysis: Instantly generate interactive visualizations to explore datasets and spot trends.

Improve data quality and visualize issues instantly

Data quality issues like missing values, outliers, and format errors can slow down AI development. The NextGen WorkBench addresses this with automated scans and visual insights that save time and reduce manual effort.

Now, when you upload a dataset, automatic scans check for key data quality issues, including:

• Outliers
• Multicategorical format errors
• Inliers
• Excess zeros
• Disguised missing values
• Target leakage
• Missing images (in image datasets only)
• PII

These data quality checks are paired with out-of-the-box EDA (exploratory data analysis) visualizations.  New datasets are automatically visualized in interactive graphs, giving you instant visibility into data trends and potential issues, without having to build charts yourself.  Figure 3 below demonstrates how quality issues are highlighted directly within the graph.

Automate feature detection and reduce complexity

Automated feature detection helps you simplify feature engineering, making it easier to join secondary datasets, detect key features, and remove low-impact ones.

This capability scans all your secondary datasets to find similarities — like customer IDs (see Figure 4) — and enables you to automatically join them into a training dataset. It also identifies and removes low-impact features, reducing unnecessary complexity.

You maintain full control, with the ability to review and customize which features are included or excluded.

Don’t let slow workflows slow you down 

Data prep doesn’t have to take 80% of your time. Disconnected tools don’t have to slow your progress. And unstructured data doesn’t have to be out of reach.

With NextGen WorkBench, you have the tools to move faster, simplify workflows, and build with less manual effort. These features are already available to you — it’s just a matter of putting them to work.

If you’re ready to see what’s possible, explore the NextGen experience in a free trial. 

The post Accelerate data preparation and AI collaboration at scale appeared first on DataRobot.

RoboticsTomorrow readers sought out Use Cases and Expert Insights to help guide them through the rapid changes happening throughout the industry. Here are the Top 4 Viewed articles from 2024.