D2L Brightspace presented to selected customers new generative AI tools to generate practice and quiz questions as part of its LMS platform.
With this new program, which will be in testing mode through the summer of 2024, teachers are able to set quiz questions before making them available to students, giving them more safety and control oversight.
“This automated question generation capability can make it easier for instructors to assess learners in the moment. It is the initial step in expanding our product roadmap with cutting-edge generative AI to help change the way the world learns,” said Stephen Laster, D2L president.
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Khanmigo, Khan Academy’s ChatGPT-powered tutoring bot, makes frequent calculation errors, The Wall Street Journal reported after testing it. “We tested an AI tutor for kids. It struggled with basic math,” wrote the paper.
Last year, Educator Sal Khan promised to “give every student on the planet an artificially intelligent but amazing personal tutor.”
“Asking ChatGPT to do math is sort of like asking a goldfish to ride a bicycle—it’s just not what ChatGPT is for,” said Tom McCoy, a professor at Yale University who studies AI.
According to the paper, “Khanmigo frequently made basic arithmetic errors, miscalculating subtraction problems such as 343 minus 17. It also didn’t consistently know how to round answers or calculate square roots. Khanmigo typically didn’t correct mistakes when asked to double-check solutions.”
Now being piloted by about 65,000 students in 44 school districts, Khanmigo emphasizes to students and teachers that it is imperfect.
Sal Khan said he expects “a million or two million” students to be using it by next school year at a price to schools of $35 a student.
Unlike ChatGPT, Khanmigo is trained not to give students the right answer but to guide them through problems. It offers tutoring in third grade and up in math, language arts, history and science. It can give feedback on student essays, engage in simulated dialogue as famous literary characters and debate contemporary issues.
In testing the product, the WSJ asked Khanmigo for help finding the length of the third side of a right triangle, a problem that students would likely encounter in eighth-grade math.
Khanmigo correctly identified the Pythagorean theorem, a2 + b2 = c2, as crucial to finding the answer. When asked for the solution the bot offered responses such as: “I’m here to help you learn, not just give answers!”
But Khanmigo struggled with math operations. When trying to solve a right triangle with a hypotenuse of 27 units and a side of 17, a reporter offered the wrong answer (430 rather than 440) to 272 minus 172. “Excellent!” Khanmigo responded. Later, it accepted the incorrect answer to the square root of 440.
In another instance, Khanmigo constructed its own triangle problem with a hypotenuse of 15 units and a leg of nine. But when a reporter correctly said that 152 minus 92 equals 144, Khanmigo suggested the response was wrong. “I see where you’re coming from, but let’s take another look at the subtraction,” it said.
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Amazon launched this month in beta an AI-powered shopping assistant called ‘Rufus’, trained on the company product catalog, customer reviews, community Q&As, and information from across the web. It’s available to select customers when they next update their Amazon Shopping app.
Built on an internal LLM specialized for shopping, this conversational assistant answers customer questions on a variety of shopping needs and products, provide comparisons, and make recommendations based on conversational context.
“Rufus knows Amazon’s selection inside and out, and can bring it all together with information from across the web to help them make more informed purchase decisions,” said Amazon.
“With Rufus, customers can:
Learn what to look for while shopping product categories: Customers can conduct more general product research on Amazon, asking questions such as “what to consider when buying headphones?”, “what to consider when detailing my car at home?”, or “what are clean beauty products?” and receive helpful information to guide their shopping mission.
Shop by occasion or purpose: Customers can search for and discover products based on activity, event, purpose, and other specific use cases by asking a range of questions such as “what do I need for cold weather golf?” or “I want to start an indoor garden.” Rufus suggests shoppable product categories—from golf base layers, jackets, and gloves to seed starters, potting mix, and grow lights—and related questions that customers can click on to conduct more specific searches.
Get help comparing product categories: Customers can now ask “what’s the difference between lip gloss and lip oil?” or “compare drip to pour-over coffee makers” so they can find the type of product that best suits their needs and make even more confident purchase decisions.
Find the best recommendations: Customers can ask for recommendations for exactly what they need, such as “what are good gifts for Valentine’s Day?” or “best dinosaur toys for a 5-year-old.” Rufus generates results tailored to the specific question and makes it quick and easy for customers to browse more refined results.
Ask questions about a specific product while on a product detail page: Customers can use Rufus to quickly get answers to specific questions about individual products when they are viewing the product’s detail page, such as “is this pickleball paddle good for beginners?”, or “is this jacket machine washable?”, or “is this cordless drill easy to hold?”. Rufus will generate answers based on listing details, customer reviews, and community Q&As.”
TechCrunch said that it’s worth pointing out that Amazon’s AI chatbot Q for businesses has struggled, producing hallucinations (false information) and revealing confidential data.
OpenAI plans to sell existing shares in a tender offer led by venture firm Thrive Capital, in a deal that values the San Francisco-based company at $80 million, tripling its valuation in less than ten months, The New York Times reported.
Under this deal, employees will be able to cash out their shares.
Early last year, the venture capital firms Thrive Capital, Sequoia Capital, Andreessen Horowitz, and K2 Global agreed to buy OpenAI shares in a tender offer, valuing the company at around $29 billion.
Last January, Microsoft invested $10 billion in OpenAI, bringing its total investment in the San Francisco start-up to $13 billion.
Since then, Anthropic, an OpenAI rival, has raised $6 billion from Google and Amazon. Cohere, a start-up founded by former Google researchers, raised $270 million, bringing its total funding to more than $440 million. Inflection AI, founded by a former Google executive, also raised a $1.3 billion round, bringing its total to $1.5 billion.
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OpenAI’s new video-generating AI model ‘Sora’, which is able to generate up to a minute of 1080p video, can render video games, too.
A research paper published by OpenAI yesterday points to the ability of Sora to simulate digital worlds and pave the way for more realistic games from text descriptions.
In an experiment, OpenAI fed Sora prompts containing the word Minecraft and had it render a convincingly a game.
Senior Nvidia researcher Jim Fan defined Sora as a “data-driven physics engine.” He added, “It is a simulation of many worlds, real or fantastical. The simulator learns intricate rendering, intuitive physics, long-horizon reasoning, and semantic grounding, all by some denoising and gradient maths.”
In a webpage full of examples, Open AI said, “We believe the capabilities Sora has today demonstrate that continued scaling of video models is a promising path towards the development of capable simulators of the physical and digital world, and the objects, animals, and people that live within them.”
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If you think OpenAI Sora is a creative toy like DALLE, … think again. Sora is a data-driven physics engine. It is a simulation of many worlds, real or fantastical. The simulator learns intricate rendering, “intuitive” physics, long-horizon reasoning, and semantic grounding, all… pic.twitter.com/pRuiXhUqYR
OpenAI shared yesterday a new AI technology called ‘Sora’ that instantly generates eye-popping videos and can speed up the work of moviemakers while replacing less experienced digital artists.
The San Francisco-based start-up shared this tool with a small group of academics and researchers.
In an interview with The New York Times, the company said that it had not yet released Sora to the public because it was still working to understand the system’s dangers.
OpenAI calls its new system Sora, after the Japanese word for sky. It chose the name because it “evokes the idea of limitless creative potential.”
In April 2023, a New York start-up called Runway AI unveiled technology that lets people generate videos simply by typing a prompt. Ten months later, OpenAI has unveiled a similar system that creates videos with a significantly higher quality.
A demonstration included short videos created in minutes, like the ones shown below.
OpenAI, which owns the still-image generator DALL-E, is now in the race to improve the AI video generator. Google and Meta are in this business, too.
OpenAI declined to say how many videos the system learned from or where they came from, except to say the training included both publicly available videos and videos that were licensed from copyright holders. The company says little about the data used to train its technologies, most likely because it wants to maintain an advantage over competitors — and has been sued multiple times for using copyrighted material.
The company is already tagging videos produced by the system with watermarks that identify them as being generated by AI. But it acknowledges that these can be removed.
DALL-E, Midjourney, and other still-image generators have improved so quickly that they are now producing images nearly indistinguishable from photographs. Many digital artists are complaining that it has made it harder for them to find work.
Examples:
An AI-generated video by OpenAI was created with the following prompt: “Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field.”
This video’s AI prompt: “Beautiful, snowy Tokyo city is bustling. The camera moves through the bustling city street, following several people enjoying the beautiful snowy weather and shopping at nearby stalls. Gorgeous sakura petals are flying through the wind along with snowflakes.”
“Animated scene features a close-up of a short fluffy monster kneeling beside a melting red candle. The art style is 3D and realistic, with a focus on lighting and texture. The mood of the painting is one of wonder and curiosity, as the monster gazes at the flame with wide eyes and open mouth. Its pose and expression convey a sense of innocence and playfulness, as if it is exploring the world around it for the first time. The use of warm colors and dramatic lighting further enhances the cozy atmosphere of the image.”
This video’s AI prompt: “A gorgeously rendered papercraft world of a coral reef, rife with colorful fish and sea creatures.”
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OpenAI: Launched Sora
Meta: Launched V-JEPA
Google: Launched Gemini 1.5 PRO
Perplexity AI: Launching on Arc Browser
Magic Dev: Launching AI Software Engineer
and these sophomore kids are still looking for HTML, CSS, and Javascript roadmaps.
OpenAI announced that it started to test a memory feature that powers ChatGPT to remember things users discuss across all chats.
This feature, which saves users from having to repeat information, will be applied to GPTs, too.
“You’re in control of ChatGPT’s memory. You can explicitly tell it to remember something, ask it what it remembers, and tell it to forget conversationally or through settings. You can also turn it off entirely,” said the company.
Users can turn off memory at any time (Settings > Personalization > Memory). While memory is off, memories won’t be used or created.
OpenAI put these examples:
You’ve explained that you prefer meeting notes to have headlines, bullets, and action items summarized at the bottom. ChatGPT remembers this and recaps meetings this way.
You’ve told ChatGPT you own a neighborhood coffee shop. When brainstorming messaging for a social post celebrating a new location, ChatGPT knows where to start.
You mention that you have a toddler and that she loves jellyfish. When you ask ChatGPT to help create her birthday card, it suggests a jellyfish wearing a party hat.
As a kindergarten teacher with 25 students, you prefer 50-minute lessons with follow-up activities. ChatGPT remembers this when helping you create lesson plans.
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NVIDIA introduced yesterday a personalized demo chatbot app called Chat With RTX that runs locally on RTX-Powered Windows PCs providing fast and secure results.
This early version allows users to personalize a LLM connected to their own content—docs, notes, videos, or other data. It leverages retrieval-augmented generation (RAG), TensorRT-LLM, and RTX acceleration so users can query a custom chatbot to quickly get contextually relevant answers.
Available to download, with 35GB’s installer, NVIDIA’s Chat With RTX requires Windows 11 and a GPU with NVIDIA GeForce RTX 30 or 40 Series GPU or NVIDIA RTX Ampere or Ada Generation GPU with at least 8GB of VRAM.
With this app tailored for searching local documents and personal files, users can feed it YouTube videos and their own documents to create summaries and get relevant answers based on their own data analyzing collection of documents as well as scanning through PDFs.
Chat with RTX essentially installs a web server and Python instance on a PC, which then leverages Mistral or Llama 2 models to query the data. It doesn’t remember context, so follow-up questions can’t be based on the context of a previous question.
Online learning platform company 2U / edX warned yesterday of substantial doubt about its ability to continue as a going concern.
Referring to its liquidity and cash flow, the Lanham, Md.-based company said:
“The company expects that if it does not amend or refinance its term loan, or raise capital to reduce its debt in the short term, and in the event the obligations under its term loan accelerate or come due within twelve months from the date of its financial statement issuance in accordance with its current terms, there is substantial doubt about its ability to continue as a going concern.”
2U Inc., now under the leadership of a new CEO, presented its results for the fourth quarter and the full year of 2023. “We are resetting and enhancing our operations with renewed financial discipline,” said Paul Lalljie, Chief Executive Officer of 2U.
“Looking ahead, we believe this renewed focus, along with our market-proven offerings, robust partner network, and scalable technology and services, will allow us to take advantage of increasing demand for high-quality online education and continue to deliver on our mission.”
“Our immediate focus in 2024 is to strengthen the fundamentals of our business in order to extend our debt maturities and restore a healthy balance sheet,” added Matthew Norden, Chief Financial Officer of 2U.On the results of 2023 compared to 2022, revenue decreased 2% to $946.0 million and net loss was $317.6 million. Costs and expenses for the year totaled $1.17 billion, a 4% decrease from $1.22 billion in 2022.
The results for the fourth quarter of 2023 compared to fourth quarter 2022 showed a revenue increased of 8% to $255.7 million, while degree program segment revenue increased 19% to $163.5 million and alternative credential segment revenue decreased 7% to $92.2 million.
Looking forward, the company expects to increase its revenue in the first quarter of 2024 from $195 million to $198 million with a net loss ranging from $60 million to $55 million and adjusted EBITDA to range from $10 million to $12 million.
For the full year of 2024, it expects revenue to range from $805 million to $815 million, net loss to range from $90 million to $85 million, and adjusted EBITDA to range from $120 million to $125 million.
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Apple released last week MGIE (MLLM-Guided Image Editing), a new open-source AI model that edits images based on natural language instructions. It leverages multimodal large language models (MLLMs) to interpret user commands and perform pixel-level manipulations.
Experts agreed that MGIE represents a major breakthrough, highlighting that the pace of progress in multimodal AI systems is accelerating quickly.
The model can handle a wide range of editing scenarios, such as simple color and brightness adjustments, photo optimization, object manipulations, and Photoshop-style modification, such as cropping, resizing, rotating, flipping, and adding filters.
For example, an instruction can make the sky more blue, and MGIE produces the instruction to increase the saturation of the sky region by 20%.
MGIE — which was presented in a paper accepted at the International Conference on Learning Representations (ICLR) 2024 — is the result of a collaboration between Apple and researchers from the University of California, Santa Barbara.
MGIE is available as an open-source project on GitHub. The project also provides a demo notebook that shows how to use MGIE for various editing tasks. Users can also try out MGIE online through a web demo hosted on Hugging Face Spaces.
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