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Palliative Care Nurses’ Top Priorities: Dashboards and Automation

Taylor Marlowe
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Palliative Care Nurses’ Top Priorities: Dashboards and Automation

Australian nurses in residential aged care homes wanted technology solutions that support their work in providing palliative care.

FINDINGS

According to the study, which findings have been published in the journal BMC Nursing, aged care nurses face practical challenges in using technology in residential homes, particularly with navigating multiple, fragmented digital systems or platforms (including medication management portals, incident reporting systems, rostering platforms, training portal and digital systems for clinical documentation). This resulted in them double-handling data, increasing their workload. Nurses pointed out “poor interoperability” as another major issue.

Despite these challenges, the nurses expressed openness to using digital technologies, recognising their value in palliative care. They even emphasised the need for technologies that support symptom assessment and continuous monitoring of residents’ conditions.

Requirements for Technology

Nurses also wanted consolidated dashboards integrating various clinical data to monitor residents’ decline. They also saw the potential of applying automation in error-prone areas like medication administration and documentation.

WHY IT MATTERS

“With more than a third of Australian deaths occurring in residential aged care facilities, it is critical we understand how technology can assist in end-of-life care,” study lead author Dr Priyanka Vandersman said about their study.

The perceived openness of aged care nurses to technology, according to their study, highlights the importance of addressing their challenges by developing or deploying technology that is user-friendly and that seamlessly fits into existing systems.

Larger Trend

The Australian government is pursuing reforms in the aged care sector, covering technology adoption and upgrades. For this year’s budget, it earmarked A$1.4 billion ($1 billion) for upgrading technology and digital infrastructure, meeting the recommendations of the Royal Commission into Aged Care Quality and Safety in 2021.

CONCLUSION

The study highlights the importance of understanding the needs and challenges of aged care nurses in using technology to provide palliative care. By developing intuitive digital solutions and providing nurses with the right support and education, we can ensure technology complements compassionate caregiving, enhancing residents’ choice, dignity, and quality of life in their final stages.

FAQs

Q: What are the main challenges faced by aged care nurses in using technology?
A: The main challenges faced by aged care nurses in using technology include navigating multiple, fragmented digital systems, poor interoperability, and double-handling data.

Q: What are the benefits of using technology in palliative care?
A: The benefits of using technology in palliative care include symptom assessment, continuous monitoring of residents’ conditions, and automation of error-prone areas like medication administration and documentation.

Q: What is the Australian government doing to improve technology in aged care?
A: The Australian government has earmarked A$1.4 billion ($1 billion) for upgrading technology and digital infrastructure in aged care, and has released the five-year Aged Care Data and Digital Strategy, which guides the technology reforms in the sector.

We Tested the New Entry-Level Samsung Galaxy Tab

Phil
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0
We Tested the New Entry-Level Samsung Galaxy Tab

Key Specs

  • Chipset: Mediatek Dimensity 9300+
  • RAM: 12GB (16GB for 1TB model)
  • Storage: 256GB + MicroSD (512GB and 1TB also available)
  • OS: Android 14 (One UI 6.1)
  • Screen: 12.4in Dynamic OLED, 2800 x 1752, 120Hz
  • Rear cameras: 13MP wide, 8MP ultrawide
  • Front camera: 12MP
  • Connectivity: USB 3.2 Gen 1 Type-C, Wi-Fi 6E, Bluetooth 5.3
  • Battery: 10990mAh
  • Dimensions: 185.4 x 285.4 x 5.6mm
  • Weight: 571g

Design & Build

The Galaxy Tab S10+ is a comfortable tablet to hold, even without a case, thanks to its flat sides and rounded corners. The screen is coated with Gorilla Glass, and there’s a distinct groove in the back for magnetically attaching the stylus to top up its battery. Unusually for a tablet, you get two camera lenses, though neither the wide nor ultrawide cameras are anything special.

Features & Performance

The tablet comes with an IP68 ingress resistance rating, as does the S-Pen, which is included in the box rather than being an optional extra. It doesn’t slide inside the device in the same way the Galaxy S24 Ultra’s stylus does, and the magnetic charging dock on the back of the frame is an imperfect solution that means it’s likely to get dislodged. However, if you’re carrying a tablet, you’re more likely to keep it in a bag than a pocket, so perhaps you’ll be able to keep it somewhere else when not charging.

Benchmark Results

Benchmark Result
Geekbench 6 2169 (single-core), 7255 (multi-core)
GPU (OpenCL) 12408
PCMark 10 (Work 3.0) 15555
Battery Life 8h 20m

Display

The 16:10 AMOLED display has good brightness and resolution that turn it into a decent digital-art platform if you have a favoured Android painting app. Its (variable) 120Hz refresh rate helps keep things smooth, and an anti-reflective coating has been applied that does a good job of keeping stray light at bay without making the surface feel rough.

Camera

Scoring less well are the tablet’s cameras. We’re not sure who’s using the larger slates for photography rather than a smartphone, but they are useful for simple visual notes and snapshots when there’s nothing else at hand. You don’t get wide apertures or high pixel resolutions here, and while Android’s portrait mode can create soft backgrounds, the camera that’s going to get the most use here is the 12MP front-facing lens, which can record video up to 4K/30fps.

Battery Life

With a battery life of over eight hours (with the screen on and apps running), the S10+ easily has enough juice to keep going all day. It beats plenty of other tablets and can juice up again at 45W, though there’s no charger included in the box so you’ll either want to use a laptop charger or put up with a slower charge from a phone charger.

Price

At $/£999, this is not a budget or even mid-priced tablet, but with the Samsung Galaxy name attached, you’ll approach it knowing it’s not going to be a cheap device. While it lags behind similarly priced Apple devices in terms of raw performance, it never feels slow or laggy, and if you’re invested in the Android ecosystem, it makes a great upgrade from an ageing tablet.

Buy It If

  • You want a top productivity tablet
  • A stylus would come in useful
  • You want to do more than just browse or read

Don’t Buy It If

  • You just want to browse or read
  • You don’t want to spend that much
  • You really want an iPad

Also Consider

  • Other high-end Android tablets
  • Samsung’s own Galaxy S and Note series
  • Apple’s iPad and iPad Pro series

AI Company Warns: Stop Hiring Humans

Emerson Blake
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AI Company Warns: Stop Hiring Humans

The Rise of Artisan: A New Era of AI-Powered Sales

Artisan’s Controversial Marketing Campaign

Artisan CEO Jaspar Carmichael-Jack has been defending the company’s recent marketing campaign, which has sparked a mix of curiosity and concern among the public. The campaign features billboards in San Francisco, showcasing dystopian scenarios with messages like "The End of Human Employment" and "Goodbye Humans, Hello Artisans". In an interview with SFGate, Carmichael-Jack stated, "They are somewhat dystopian, but so is AI. The way the world works is changing." He added, "We wanted something that would draw eyes – you don’t draw eyes with boring messaging."

What is Artisan?

So, what exactly does Artisan do? The company’s main product is an AI "sales agent" called Ava, which automates the process of finding and messaging potential customers. The company claims that Ava works with "no human input" and costs 96% less than hiring a human for the same role. While this may seem promising, it’s essential to be skeptical of such claims, given the current state of AI technology.

Artisan’s Expansion Plans

Artisan has plans to expand its AI tools beyond sales into areas like marketing, recruitment, finance, and design. Its sales agent, Ava, appears to be its only existing product so far. The company’s ultimate goal is to revolutionize various industries by replacing human workers with AI-powered agents.

Billboards and Public Reaction

The billboards featuring Artisan’s campaign have been generating significant attention, with some featuring additional messages like "Hire Artisans, not humans" and "Artisan’s Zoom cameras will never ‘not be working’ today," which seems to be poking fun at the frustration many people experience with remote work. While some may find the campaign thought-provoking, others are left feeling uneasy about the future of work and the potential impact on human employment.

FAQs

Q: What is Artisan’s main product?
A: Artisan’s main product is an AI "sales agent" called Ava, which automates the process of finding and messaging potential customers.

Q: How does Ava work?
A: According to Artisan, Ava works with "no human input" and costs 96% less than hiring a human for the same role.

Q: What are Artisan’s expansion plans?
A: Artisan plans to expand its AI tools beyond sales into areas like marketing, recruitment, finance, and design.

Q: What is the purpose of Artisan’s marketing campaign?
A: The campaign aims to draw attention to Artisan’s innovative products and services, while also sparking a conversation about the future of work and the role of AI in various industries.

Autonomous Robotic Surgery on the Horizon

Jordan Waverly
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0
Autonomous Robotic Surgery on the Horizon

Autonomous Surgical Robots: A New Era in Surgery

Researchers Achieve Milestone in Surgical Automation

Researchers at Johns Hopkins and Stanford Universities have made a groundbreaking discovery in the field of surgical robotics. They have successfully integrated a vision-language model (VLM) with the widely-used da Vinci robotic surgical system, enabling it to perform key surgical tasks autonomously with precision, rivaling that of human surgeons.

How it Works

The VLM was trained on hours of surgical videos and can now autonomously perform three critical surgical tasks: lifting body tissue, using a surgical needle, and suturing a wound. This is achieved through imitation learning, where the robot imitates what doctors have done in surgical videos, eliminating the need for detailed programming.

Training the Robot

To train the VLM, the researchers used NVIDIA GeForce RTX 4090 GPUs, PyTorch, and NVIDIA CUDA-X libraries for AI. They recorded around 20 hours of video of a researcher manipulating the da Vinci’s grippers to perform three procedures, including lifting a facsimile of human tissue, manipulating a surgical needle, and tying knots with surgical thread. The researcher also recorded kinematic data correlated with manual manipulation of the grippers.

Results

The researchers connected the VLM with the da Vinci robots and instructed them to perform the three surgical tasks on pieces of chicken and pork, mimicking human tissue. The results were remarkable, with the robot performing the procedures nearly flawlessly in a zero-shot environment.

Surprising Capabilities

One of the surprises was how the robot autonomously problem-solved unanticipated challenges. At one point, the grippers accidentally dropped a surgical needle, and despite not being explicitly trained to do so, the robot picked it up and continued with its task.

Future Development

The researchers are already working on a new paper outlining the results of more recent experiments deploying the robots on animal cadavers. They are also developing additional training data that can be used to expand the capabilities of the da Vinci robots.

Conclusion

The integration of VLM with the da Vinci robotic surgical system marks a significant milestone in the development of autonomous surgical robots. This technology has the potential to revolutionize the field of surgery, allowing for more precise and efficient procedures.

FAQs

Q: How does the VLM work?
A: The VLM is trained on hours of surgical videos and can imitate what doctors have done in surgical videos, eliminating the need for detailed programming.

Q: What are the potential applications of this technology?
A: This technology has the potential to revolutionize the field of surgery, allowing for more precise and efficient procedures.

Q: How does the robot problem-solve unanticipated challenges?
A: The robot can autonomously problem-solve unanticipated challenges, as demonstrated by its ability to pick up a dropped surgical needle despite not being explicitly trained to do so.

Data Management Key for AI Success

Parker Lennox
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Data Management Key for AI Success

The AI Boom and the Importance of Data Management

The AI boom has cast fresh light on the critical importance of high-quality data and solid data management practices. Three recent studies provide more grist for that mill.

NetApp’s Data Complexity Report

NetApp’s second annual Data Complexity Report surveyed 1,300 tech and data executives at organizations around the world to gauge the state of their data estates and their preparedness for AI. The report found that organizations with higher investment in data unification say they’re better prepared to reach their AI goals.

Nearly 80% of executives surveyed “recognize the importance of unifying data to achieve optimal AI outcomes,” NetApp says. The report also found that two-thirds of companies worldwide say their data is “either fully or mostly optimized for AI–meaning their data is accessible, accurate, and well-documented for AI-use cases,” NetApp says.

Qlik’s Survey on AI Success

Qlik’s survey identified several reasons for the lack of AI progress and success, with the lack of AI skills and data governance challenges being identified by survey-takers as the number one challenge.

Qlik says 37% of senior managers lack trust in AI, 42% feel that less senior employees don’t trust it, and 21% say their customers don’t trust it. Three out of five (61%) say this lack of trust is reducing AI investments in their businesses.

Ataccama’s Data Trust Report

Ataccama’s Data Trust Report surveyed 300 executives in the US, Canada, and the UK for a report on the state of their data and AI initiatives. The results show that data management is a top issue for would-be AI practitioners.

51% of respondents cited improving data quality and accuracy as an immediate priority, and 30% reported that managing large volumes of data is among the top challenges CDOs face in their organizations today.

Conclusion

Having a well-designed data management system that yields high-quality, trusted data clearly is important for succeeding with AI. There are obviously other challenges too, related to skills, deployment, trust, and budget, among others. But since AI essentially is a distillation of data, there is not a clear path to succeed with AI when you’re starting with bad data.

FAQs
Q: Why is data management important for AI success?

A: Having a well-designed data management system that yields high-quality, trusted data is critical for succeeding with AI. Bad data can lead to inaccurate results and a lack of trust in AI outcomes.

Q: What are the top challenges to AI success?

A: The top challenges to AI success identified in the studies include a lack of AI skills, data governance challenges, and a lack of trust in AI. Additionally, budget and a lack of trusted data can also be barriers to success.

Q: How can organizations overcome these challenges?

A: Organizations can overcome these challenges by prioritizing data quality and accuracy, investing in AI skills and training, and implementing effective data governance practices. Additionally, building trust in AI by providing clear use cases and measurable outcomes can also be key to success.

AI Pioneers Claim Nobel Prizes

Emerson Blake
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AI Pioneers Claim Nobel Prizes

Artificial Intelligence, Once the Realm of Science Fiction, Claims Its Place at the Pinnacle of Scientific Achievement

Hopfield’s Legacy and the Foundations of Neural Networks

In the 1980s, Hopfield, a physicist with a knack for asking big questions, brought a new perspective to neural networks. He introduced energy landscapes — borrowed from physics — to explain how neural networks solve problems by finding stable, low-energy states. His ideas, abstract yet elegant, laid the foundation for AI by showing how complex systems optimize themselves.

Fast forward to the early 2000s, when Geoffrey Hinton — a British cognitive psychologist with a penchant for radical ideas — picked up the baton. Hinton believed neural networks could revolutionize AI, but training these systems required enormous computational power.

AlphaFold: Biology’s AI Revolution

A decade after AlexNet, AI moved to biology. Hassabis and Jumper led the development of AlphaFold to solve a problem that had stumped scientists for years: predicting the shape of proteins.

The GPU Factor: Enabling AI’s Potential

GPUs, the indispensable engines of modern AI, are at the heart of these achievements. Originally designed to make video games look good, GPUs were perfect for the massive parallel processing demands of neural networks. NVIDIA GPUs, in particular, became the engine driving breakthroughs like AlexNet and AlphaFold. Their ability to process vast datasets with extraordinary speed allowed AI to tackle problems on a scale and complexity never before possible.

Redefining Science and Industry

The Nobel-winning breakthroughs of 2024 aren’t just rewriting textbooks — they’re optimizing global supply chains, accelerating drug development, and helping farmers adapt to changing climates. Hopfield’s energy-based optimization principles now inform AI-powered logistics systems. Hinton’s architectures underpin self-driving cars and language models like ChatGPT. AlphaFold’s success is inspiring AI-driven approaches to climate modeling, sustainable agriculture, and even materials science.

Conclusion

The recognition of AI in physics and chemistry signals a shift in how we think about science. These tools are no longer confined to the digital realm. They’re reshaping the physical and biological worlds.

FAQs

Q: What is the Nobel Prize in Physics?
A: The Nobel Prize in Physics is an annual international award that recognizes outstanding contributions in the field of physics.

Q: Who won the Nobel Prize in Physics in 2024?
A: John Hopfield and Geoffrey Hinton won the Nobel Prize in Physics in 2024 for their pioneering work on neural networks.

Q: What is AlphaFold?
A: AlphaFold is a system that uses AI to predict the shape of proteins, a feat with profound implications for medicine and biotechnology.

Q: What role do GPUs play in AI?
A: GPUs are the indispensable engines of modern AI, providing the massive parallel processing power needed to tackle complex problems.

Telemedicine Launches in Regional Taiwanese Hospital

Taylor Marlowe
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0
Telemedicine Launches in Regional Taiwanese Hospital

Enterprise Taxonomy

Introduction

In today’s rapidly evolving healthcare landscape, managing and organizing patient data has become a crucial aspect of healthcare operations. Enterprise taxonomy is a vital tool in this process, enabling healthcare organizations to categorize and standardize patient data, improving patient access to care, and streamlining business operations. This article will delve into the world of enterprise taxonomy, exploring its benefits, challenges, and best practices for implementation.

Benefits of Enterprise Taxonomy

  1. Improved Patient Care: By standardizing patient data, healthcare organizations can quickly locate and retrieve patient records, ensuring that healthcare providers have access to the most up-to-date information, resulting in better patient care.
  2. Enhanced Efficiency: Automating data categorization and retrieval reduces the time spent searching for patient records, freeing up staff to focus on more critical tasks, such as patient care.
  3. Better Decision Making: With a standardized and organized data structure, healthcare organizations can make data-driven decisions, leading to improved patient outcomes and reduced costs.
  4. Compliance and Audit Readiness: A well-structured taxonomy ensures that healthcare organizations are compliant with regulatory requirements, reducing the risk of non-compliance and associated penalties.

Challenges of Enterprise Taxonomy

  1. Data Complexity: Handling large volumes of unstructured and semi-structured data can be a significant challenge, requiring significant resources and expertise.
  2. Scalability: As healthcare organizations grow, their data taxonomies must be able to scale to accommodate increasing volumes of data, without compromising performance.
  3. Stakeholder Buy-In: Gaining support from multiple stakeholders, including IT, clinical, and business teams, is crucial for successful taxonomy implementation.
  4. Change Management: Implementing a new taxonomy requires careful planning and communication to minimize disruption to daily operations.

Best Practices for Implementation

  1. Establish a Governance Model: Set up a clear governance model to ensure ownership, decision-making, and accountability throughout the taxonomy development process.
  2. Conduct a Thorough Needs Assessment: Engage stakeholders to identify business needs, pain points, and goals to inform the development of the taxonomy.
  3. Develop a Scalable Architecture: Design a taxonomy architecture that can accommodate growth, flexibility, and adaptability.
  4. Use Standardized Vocabularies: Utilize standardized vocabularies, such as SNOMED-CT, to ensure consistency and interoperability.
  5. Monitor and Evaluate: Continuously monitor and evaluate the taxonomy’s effectiveness, making adjustments as needed to ensure optimal performance.

Conclusion

Enterprise taxonomy is a critical component of healthcare operations, offering numerous benefits, including improved patient care, enhanced efficiency, and better decision making. While there are challenges to implementation, by following best practices and adopting a governance model, healthcare organizations can successfully navigate these challenges and reap the rewards of a well-structured taxonomy. By doing so, they can improve patient outcomes, reduce costs, and stay ahead of the competition.

FAQs

Q: What is enterprise taxonomy?
A: Enterprise taxonomy is a structured system of categorizing and organizing patient data, enabling healthcare organizations to efficiently manage and retrieve patient information.

Q: What are the benefits of enterprise taxonomy?
A: The benefits of enterprise taxonomy include improved patient care, enhanced efficiency, better decision making, and compliance and audit readiness.

Q: What are the challenges of enterprise taxonomy?
A: The challenges of enterprise taxonomy include data complexity, scalability, stakeholder buy-in, and change management.

Q: How can healthcare organizations implement a successful enterprise taxonomy?
A: Healthcare organizations can implement a successful enterprise taxonomy by establishing a governance model, conducting a thorough needs assessment, developing a scalable architecture, using standardized vocabularies, and monitoring and evaluating the taxonomy’s effectiveness.

Public Diffusion Model

Phil
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Public Diffusion Model

Public Diffusion – The Truly Open-Source AI Model

The AI Model Built by the Community, for the Community!

The Public Diffusion AI model is an incredible project that has been trained on public domain images and Creative Commons images that don’t require attribution. This ensures a fully open, transparent, and ethical approach to AI.

Why Choose Public Diffusion?

  • 100% Free and Open-Source: No hidden licenses or restrictions.
  • Powered by Community Contributions: Encouraging collaboration and innovation.
  • Perfect for Creative Projects, Education, and More: Ideal for use in various fields, including art, education, and more.

Let’s Make AI Accessible and Ethical for Everyone!

You can join the Public Diffusion community and support the project by:

Source

FAQs

  • Q: What is Public Diffusion?
    A: Public Diffusion is an AI model built by the community, for the community.
  • Q: What is the purpose of Public Diffusion?
    A: To make AI accessible and ethical for everyone.
  • Q: How can I support Public Diffusion?
    A: You can buy me a coffee, join my social media groups, subscribe to my newsletter, or support me on Patreon.
  • Q: What are the benefits of using Public Diffusion?
    A: It’s 100% free and open-source, powered by community contributions, and perfect for creative projects, education, and more.

YouTube’s AI-Powered Dubbing

Avery Winslow
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YouTube’s AI-Powered Dubbing

YouTube Expands AI-Powered Auto-Dubbing to Hundreds of Thousands of Channels

YouTube Partners with Creators to Offer AI-Powered Auto-Dubbing

YouTube has announced that it has expanded its AI-powered auto-dubbing feature to "hundreds of thousands of channels" in the YouTube Partner Program that are "focused on knowledge and information." The feature, which initially tested with "hundreds" of creators in June 2023, will be rolled out to other types of content soon.

How the Dubs Work

The dubs consist of translations of the original video’s language into other languages. If the original video was in English, it will be translated into French, German, Hindi, Italian, Spanish, Indonesian, Japanese, and Portuguese. If the starting video was made in one of those languages, YouTube will only produce an English dub.

Automated Dubbing Process

For channels that have the feature, AI-dubbed videos are created automatically when the original video is uploaded. Creators can opt to preview them before they’re published. YouTube also provides options to unpublish or delete dubs, according to a support document for the feature.

Improving Dubbing Quality

The dubs aren’t very natural-sounding now, but YouTube promises they’ll get better at emulating "tone, emotion, and even the ambiance of the surroundings" with later updates. However, the company cautions that "this technology is still pretty new, and it won’t always be perfect." It’s "working hard to make it as accurate as possible, but there might be times when the translation isn’t quite right or the dubbed voice doesn’t accurately represent the original speaker."

Conclusion

YouTube’s expansion of its AI-powered auto-dubbing feature is a significant step towards making content more accessible to a broader audience. While the technology is still developing, it has the potential to revolutionize the way we consume and interact with content online.

Frequently Asked Questions

Q: How many channels will have access to AI-powered auto-dubbing?
A: Hundreds of thousands of channels in the YouTube Partner Program will have access to the feature.

Q: What languages will the dubs be translated into?
A: The dubs will be translated into French, German, Hindi, Italian, Spanish, Indonesian, Japanese, and Portuguese.

Q: Can creators opt out of the feature?
A: Yes, creators can opt to unpublish or delete dubs, according to a support document for the feature.

Q: Will the dubs get better with time?
A: Yes, YouTube promises the dubs will get better at emulating "tone, emotion, and even the ambiance of the surroundings" with later updates.

OpenAI Introduces Canvas for All ChatGPT Users

Emerson Blake
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0
OpenAI Introduces Canvas for All ChatGPT Users

What are the ’12 days of OpenAI’?

OpenAI, a leading artificial intelligence (AI) company, has announced a 12-day event series called "12 Days of OpenAI" to celebrate the holiday season. The event started on December 5 and will run until December 16, with a live stream featuring a launch or demo each day.

What has been dropped so far?

Here’s a summary of the releases so far:

Tuesday, December 10

  • Canvas is now available to all web users, regardless of plan, in GPT-4o.
  • Canvas has been built into GPT-4o natively, allowing users to call on Canvas instead of going to the toggle on the model selector.
  • The Canvas interface is the same as what users saw in beta in ChatGPT Plus, with a table on the left-hand side showing the Q&A exchange and a right-hand tab showing the project, displaying all edits as they go, as well as shortcuts.
  • Canvas can also be used with custom GPTs. It is turned on by default when creating a new one, and there is an option to add Canvas to existing GPTs.
  • Canvas also has the ability to run Python code directly in Canvas, allowing ChatGPT to execute coding tasks such as fixing bugs.

Monday, December 9

  • OpenAI teased the third-day announcement as "something you’ve been waiting for" and dropped its video model, Sora.
  • Sora is known as Sora Turbo and is smarter than the February model that was previewed.
  • Access is coming in the US later today; users need only ChatGPT Plus and Pro.
  • Sora can generate video-to-video, text-to-video, and more.
  • ChatGPT Plus users can generate up to 50 videos per month at 480p resolution or fewer videos at 720p. The Pro Plan offers 10x more usage.
  • The new model is smarter and cheaper than the previewed February model.

Friday, December 6

  • On the second day of "shipmas," OpenAI expanded access to its Reinforcement Fine-Tuning Research Program.
  • The Reinforcement Fine-Tuning program allows developers and machine learning engineers to fine-tune OpenAI models to "excel at specific sets of complex, domain-specific tasks," according to OpenAI.
  • OpenAI encourages research institutes, universities, and enterprises to apply to the program, particularly those that perform narrow sets of complex tasks, could benefit from the assistance of AI, and perform tasks that have an objectively correct answer.
  • Spots are limited; interested applicants can apply by filling out this form.
  • OpenAI aims to make Reinforcement Fine-Tuning publicly available in early 2025.

Thursday, December 5

  • OpenAI started with a bang, unveiling two major upgrades to its chatbot: a new tier of ChatGPT subscription, ChatGPT Pro, and the full version of the company’s o1 model.
  • The full version of o1:
    • Will be better for all kinds of prompts, beyond math and science
    • Will make major mistakes about 34% less often than o1-preview, while thinking about 50% faster
    • Rolls out today, replacing o1-preview to all ChatGPT Plus and now Pro users
    • Lets users input images, as seen in the demo, to provide multi-modal reasoning (reasoning on both text and images)
  • ChatGPT Pro:
    • Is meant for ChatGPT Plus superusers, granting them unlimited access to the best OpenAI has to offer, including unlimited access to OpenAI o1-mini, GPT-4o, and Advanced Mode
    • Features o1 pro mode, which uses more computing to reason through the hardest science and math problems
    • Costs $200 per month

Where can you access the live stream?

The live streams are held on the OpenAI website, and posted to its YouTube channel immediately after. To make access easier, OpenAI will also post a link to the live stream on its X account 10 minutes before it starts, which will be at approximately 10 a.m. PT/1 p.m. ET daily.

What can you expect?

The releases remain a surprise, but many anticipate that Sora, OpenAI’s video model initially announced last February, will be launched as part of one of the bigger drops. Since that first announcement, the model has been available to a select group of red teamers and testers and was leaked last week by some testers over grievances about "unpaid labor," according to reports.

Conclusion

The "12 Days of OpenAI" event series has been an exciting ride, with daily releases that have left the AI community eager for more. With the possibility of Sora and other rumored releases on the horizon, it’s clear that OpenAI is committed to pushing the boundaries of AI technology. As the event continues, it’s exciting to see what the future holds for this innovative company.

FAQs

Q: What is the "12 Days of OpenAI" event series?
A: The "12 Days of OpenAI" event series is a 12-day event series where OpenAI will host live streams and release new features, models, and updates.

Q: What has been released so far?
A: Canvas, Sora, Reinforcement Fine-Tuning Research Program, and two major upgrades to its chatbot: ChatGPT Pro and the full version of the company’s o1 model.

Q: What is Sora?
A: Sora is a video model that can generate video-to-video, text-to-video, and more, and is smarter than the February model that was previewed.

Q: How can I access the live stream?
A: The live streams are held on the OpenAI website, and posted to its YouTube channel immediately after. A link to the live stream will also be posted on OpenAI’s X account 10 minutes before it starts.

Q: What can I expect from the rest of the event?
A: The releases remain a surprise, but many anticipate that Sora and other rumored releases, including a new, fuller version of the company’s o1 LLM with more advanced reasoning capabilities, and a Santa voice for OpenAI’s Advanced Voice Mode, will be launched as part of the bigger drops.

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