#International AI Olympiad
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Class 3 - I SSO 2022-23 | Question Paper Set 'B' with Answers | Get the Answer Key NOW!
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ChatGPT has already wreaked havoc on classrooms and changed how teachers approach writing homework, since OpenAI publicly launched the generative AI chatbot in late 2022. School administrators rushed to try to detect AI-generated essays, and in turn, students scrambled to find out how to cloak their synthetic compositions. But by focusing on writing assignments, educators let another seismic shift take place in the periphery: students using AI more often to complete math homework too.
Right now, high schoolers and college students around the country are experimenting with free smartphone apps that help complete their math homework using generative AI. One of the most popular options on campus right now is the Gauth app, with millions of downloads. It’s owned by ByteDance, which is also TikTok’s parent company.
The Gauth app first launched in 2019 with a primary focus on mathematics, but soon expanded to other subjects as well, like chemistry and physics. It’s grown in relevance, and neared the top of smartphone download lists earlier this year for the education category. Students seem to love it. With hundreds of thousands of primarily positive reviews, Gauth has a favorable 4.8 star rating in the Apple App Store and Google Play Store.
All students have to do after downloading the app is point their smartphone at a homework problem, printed or handwritten, and then make sure any relevant information is inside of the image crop. Then Gauth’s AI model generates a step-by-step guide, often with the correct answer.
From our testing on high-school-level algebra and geometry homework samples, Gauth’s AI tool didn’t deliver A+ results and particularly struggled with some graphing questions. It performed well enough to get around a low B grade or a high C average on the homework we fed it. Not perfect, but also likely good enough to satisfy bored students who'd rather spend their time after school doing literally anything else.
The app struggled more on higher levels of math, like Calculus 2 problems, so students further along in their educational journey may find less utility in this current generation of AI homework-solving apps.
Yes, generative AI tools, with a foundation in natural language processing, are known for failing to generate accurate answers when presented with complex math equations. But researchers are focused on improving AI’s abilities in this sector, and an entry-level high school math class is likely well within the reach of current AI homework apps. Will has even written about how researchers at Google DeepMind are ecstatic about recent results from testing a math-focused large language model, called AlphaProof, on problems shown at this year’s International Math Olympiad.
To be fair, Gauth positions itself as an AI study company that’s there to “ace your homework” and help with difficult problems, rather than a cheating aid. The company even goes so far as to include an “Honor Code” on its website dictating proper usage. “Resist the temptation to use Gauth in ways that go against your values or school’s expectations,” reads the company’s website. So basically, Gauth implicitly acknowledges impulsive teenagers may use the app for much more than the occasional stumper, and wants them to pinkie promise that they’ll behave.
Prior to publication, a spokesperson for ByteDance did not answer a list of questions about the Gauth app when contacted by WIRED over email.
It’s easy to focus on Gauth’s limitations, but millions of students now have a free app in their pocket that can walk them through various math problems in seconds, with decent accuracy. This concept would be almost inconceivable to students from even a few years ago.
You could argue that Gauth promotes accessibility for students who don’t have access to quality education or who process information at a slower pace than their teacher’s curriculum. It’s a perspective shared by proponents of using AI tools, like ChatGPT, in the classroom. As long as the students all make it to the same destination, who cares what path they took on the journey? And isn’t this just the next evolution in our available math tools? We moved on from the abacus to the graphing calculator, so why not envision generative AI as another critical step forward?
I see value in teachers thoughtfully employing AI in the classroom for specific lessons or to provide students with more personalized practice questions. But I can’t get out of my head how this app, if students overly rely on it, could hollow out future generations’ critical thinking skills—often gleaned from powering through frustrating math classes and tough homework assignments. (I totally get it, though, as an English major.)
Educational leaders are missing the holistic picture if they continue to focus on AI-generated essays as the primary threat that could undermine the current approach to teaching. Instead of arduous assignments to complete outside of class, maybe centering in-class math practice could continue to facilitate positive learning outcomes in the age of AI.
If Gauth and apps like it eventually lead to the demise of math homework for high schoolers, throngs of students will breathe a collective sigh of relief. How will parents and educators respond? I’m not so sure. That remains an open question, and one for which Gauth can’t calculate an answer yet either.
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Mathematics in the Age of Automation: Navigating the Opportunities and Challenges of AI
The convergence of Artificial Intelligence and mathematics, exemplified by the Alpha Proof project, heralds a transformative era for the field, yet its full potential remains contingent upon addressing inherent challenges. A recent conversation with key contributors provided invaluable insights into the application of AI in mathematical reasoning, proof verification, and discovery.
Alpha Proof's architectural lineage from Alpha Zero underscores the viability of Reinforcement Learning in navigating the vast mathematical search space, as evidenced by its solutions to a subset of International Mathematical Olympiad problems. However, the project's true transformative potential lies not merely in its problem-solving prowess, but in its capacity to facilitate collaborative mathematics by automating proof verification, thereby freeing human mathematicians to pursue more abstract and innovative endeavors.
A significant impediment to the widespread adoption of such AI tools is their inaccessibility to the broader mathematical community. The development of intuitive interfaces and educational resources, particularly in formal proof systems like Lean, is crucial for democratizing access to these technologies. By doing so, not only can the collaboration between humans and AI be enhanced, but also personalized learning experiences can be offered, thereby bridging the gap between computational mathematics and traditional mathematical practices.
The symbiotic relationship between human creativity and AI capabilities emerges as a pivotal theme. While AI excels in the structured realm of theorem-proving, human ingenuity remains indispensable in the more ephemeral domain of theory-building, where the selection of problems and the formulation of novel questions dictate the trajectory of mathematical progress. This dichotomy suggests a future where AI augments human capabilities, enabling a deeper exploration of mathematical truths, while humans continue to drive the creative impetus behind theoretical advancements.
Google's DeepMind's AlphaProof Team (No Priors, November 2024)
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Friday, November 15, 2024
#artificial intelligence#mathematics#ai in math#machine learning#mathematical discovery#human-computer collaboration#computational mathematics#interview#ai assisted writing#machine art#Youtube
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I haven't seen anyone talk yet about the fact that an AI solved 4/6 of this year's IMO problems. Is there some way they fudged it so that it's not as big a deal as it seems? (I do not count more time as fudging- you could address that by adding more compute. I also do not count giving the question already formalised as fudging, as AIs can already do that).
I ask because I really want this not to be a big deal, because the alternative is scary. I thought this would be one of the last milestones for AI surpassing human intelligence, and it seems like the same reasoning skills required for this problem would be able to solve a vast array of other important problems. And potentially it's a hop and a skip away from outperforming humans in research mathematics.
I did not think we were anywhere near this point, and I was already pretty worried about the societal upheaval that neural networks will cause.
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OpenAI's o1: The new era of reasoning in artificial intelligence
🔹 OpenAI has introduced its latest AI model, o1, which is distinguished by its advanced reasoning capabilities. This innovation enables the model to effectively handle complex tasks in fields such as science, programming, and mathematics, achieving performance levels akin to those of PhD students.
🔹 In a notable performance at the International Mathematical Olympiad, o1 achieved an impressive 83% accuracy, significantly surpassing the results of its predecessor. Sam Altman, co-founder of OpenAI, acknowledges that while the model has its limitations, it represents a significant advancement in tackling difficult challenges across various domains, including medical research and advanced physics.
🔹 The o1 model benefits from efficient training methodologies and is designed for continuous improvement in learning. This adaptability positions it to transform how professionals engage with complex problems in their everyday work.
🔹 Overall, OpenAI's o1 marks a significant leap forward in artificial intelligence, particularly in its reasoning capabilities, indicating a promising future for its application in various specialized fields.
#artificial intelligence#open ai#open ai o1#ai model#ai#ai hardware#coding#robots#robotics#chatgpt#chat
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The Toughest Math Benchmark Ever Built
New Post has been published on https://thedigitalinsider.com/the-toughest-math-benchmark-ever-built/
The Toughest Math Benchmark Ever Built
Frontier Math approach math reasoning in LLMs from a different perspective.
Created Using DALL-E
Next Week in The Sequence:
Edge 448: Discusses into adversarial distillation including some research in that area. It also reviews the LMQL framework for querying LLMs.
The Sequence Chat: Discusses the provocative topic of the data walls in generative AI.
Edge 490: Dives into Anthropic’s crazy research about how LLMs can sabotage human evalautions.
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📝 Editorial: The Toughest Math Benchmark Ever Built
Mathematical reasoning is often considered one of the most critical abilities of foundational AI models and serves as a proxy for general problem-solving. Over the past few years, we have witnessed large language models (LLMs) push the boundaries of math benchmarks, scoring competitively on International Math Olympiad (IMO) problems and advancing discoveries in various areas of mathematics. From this perspective, it might seem as though LLMs are inching towards “super math powers,” but that is not entirely the case.
Much of AI’s impressive performance in math benchmarks relies on scenarios where the problem is perfectly articulated within a prompt. However, most foundational models struggle when they need to combine different ideas creatively or use “common sense” to structure and solve a problem. Can we develop benchmarks that measure these deeper reasoning capabilities?
Frontier Math, a new benchmark developed by Epoch AI, is designed to test the boundaries of artificial intelligence in advanced mathematics. Unlike traditional math benchmarks such as GSM-8K and MATH, where AI models now score over 90%, Frontier Math presents a significantly more challenging test. This higher difficulty stems from the originality of its problems, which are unpublished and crafted to resist shortcuts, requiring deep reasoning and creativity—skills that AI currently lacks.
From an AI standpoint, Frontier Math stands out by emphasizing the capacity for complex reasoning. The benchmark comprises hundreds of intricate math problems spanning diverse fields of modern mathematics, from computational number theory to abstract algebraic geometry. These problems cannot be solved through simple memorization or pattern recognition, as is often the case with existing benchmarks. Instead, they demand multi-step, logical thinking akin to research-level mathematics, often requiring hours or even days for human mathematicians to solve.
The problems within Frontier Math are specifically designed to test genuine mathematical understanding, making them “guess-proof.” This means that AI models cannot rely on pattern matching or brute-force approaches to arrive at the correct answer. The solutions, which often involve large numerical values or complex mathematical constructs, have less than a 1% chance of being guessed correctly without proper reasoning. This focus on “guess-proof” problems ensures that Frontier Math serves as a robust and meaningful test of an AI model’s ability to truly engage with advanced mathematical concepts.
Despite being equipped with tools like Python to aid in problem-solving, leading AI models—including GPT-4o and Gemini 1.5 Pro—have managed to solve fewer than 2% of the Frontier Math problems. This stands in stark contrast to their high performance on traditional benchmarks and highlights the significant gap between current AI capabilities and true mathematical reasoning.
Frontier Math provides a critical benchmark for measuring progress in AI reasoning as these systems continue to evolve. The results underscore the long journey ahead in developing AI that can genuinely rival the complex reasoning abilities of human mathematicians.
⭐️ Save your spot for SmallCon: A free virtual conference for GenAI builders! ⭐️
it’s bringing together AI leaders from Meta, Mistral AI, Salesforce, Harvey AI, Upstage, Nubank, Nvidia, and more for deep-dive tech talks, interactive panel discussions, and live demos on the latest tech and trends in GenAI. You’ll learn firsthand how to build big with small models and architect the GenAI stack of the future.
🔎 ML Research
Modular Models
This paper examines the potential of modular AI models, particularly focusing on the MoErging approach, which combines independently trained expert models to solve complex tasks. The authors, working at Microsoft Research Lab – New York City and Microsoft Research Lab – Montréal, propose a taxonomy for categorizing and comparing different MoErging methods, which can facilitate collaborative AI development and address challenges related to data privacy, model accountability, and continuous learning —> Read more.
Sematic Hub Hypothesis
This paper, authored by researchers from MIT, Allen Institute for AI and University of Southern California, propose the semantic hub hypothesis, suggesting that language models represent semantically similar inputs from various modalities close together in their intermediate layers. The authors provide evidence for this by showing that interventions in the dominant language (usually English) in this shared semantic space can predictably alter model behavior when processing other data types like Chinese text or Python code —> Read more.
GitChameleon
This work from researchers at Mila and the Max Planck Institute for Intelligent Systems presents GitChameleon, a benchmark of 116 Python-based problems that evaluate the capacity of large language models to generate code that correctly accounts for version changes in APIs. Analysis of several models on GitChameleon suggests a correlation between model size and performance on these tasks, indicating a need for future work on version-aware code generation methods —> Read more.
Stronger Models are not Stronger Teachers
This paper, written by authors from the University of Washington and the Allen Institute for AI, investigates the impact of different “teacher” models used to generate responses for synthetic instruction tuning datasets. Contrary to common assumptions, larger teacher models don’t necessarily lead to better instruction-following abilities in the tuned “student” models, a phenomenon the authors call the “Larger Models’ Paradox”. They propose a new metric called Compatibility-Adjusted Reward (CAR) to better select teacher models suited to a given student model for instruction tuning —> Read more.
Counterfactual Generation in LLMs
Researchers from the ETH AI Center and the University of Copenhagen introduce a framework in this paper for generating counterfactual strings from language models by treating them as Generalized Structural-equation Models using the Gumbel-max trick. Applying their technique to evaluate existing intervention methods like knowledge editing and steering, they find that these methods often cause unintended semantic shifts, illustrating the difficulty of making precise, isolated modifications to language model behavior —> Read more.
Watermarking Anything
This work by authors at Meta presents WAM, a new deep learning model that treats invisible image watermarking as a segmentation problem. The model excels at detecting, localizing, and extracting multiple watermarks embedded in high-resolution images while maintaining invisibility to the human eye and resisting attempts to remove or alter the watermarks —> Read more.
🤖 AI Tech Releases
Stripe for AI Agents
Stripe released an SDK for AI agents —> Read more.
Frontier Math
FrontierMath is, arguably, the toughest math benchmark ever created —> Read more.
AlphaFold 3
Google DeepMind open sourced a new version of its Alpha Fold model for molecular biology —> Read more.
🛠 Real World AI
Airbnb’s Photo Tours
Airbnb discusses their use of vision transformers to enable their photo tour feature —> Read more.
📡AI Radar
AI legend Francois Chollet announced he will be leaving Google.
Cogna raised $15 million to build AI that can write enterprise software.
OpenAI seems to be inching closer to launch an AI agent for task automation.
Perplexity is experimenting with ads.
AMDis laying off 4% of its global staff, approximately 1,000 employees, in an effort to gain a stronger foothold in the expanding AI chip market dominated by Nvidia.
Tessl.io, a company focused on AI-driven software development, has raised $125 million in funding to develop a new, open platform for AI Native Software.
Lume, a company that leverages AI to automate data integration, has secured $4.2 million in seed funding to address the persistent challenge of moving data seamlessly between systems.
Magic Story, launched a children’s media platform that utilizes AI to create personalized stories with the goal of nurturing confidence and growth in children.
ServiceNow, a digital workflow company, is releasing over 150 new generative AI features to its Now Platform, which includes enhancements for Now Assist and an AI Governance offering to ensure secure and compliant AI practices.
Red Hat is acquiring Neural Magicto bolster its hybrid cloud AI portfolio and make generative AI more accessible to enterprises.
Snowflake announced a series of key updates at its BUILD conference, focused on improving its AI capabilities and security, with notable additions including enhancements to Cortex AI, the launch of Snowflake Intelligence, and new threat prevention measures.
Sema4.ai has introduced its Enterprise AI Agent Platform, designed to empower business users with the ability to create and manage AI agents, ultimately aiming to automate complex tasks and streamline workflows.
DataRobot launched a new platform for creating generative AI applications. Specifically, the platform focuses on AI agents and collaborative AI.
Perplexity is experimenting with incorporating advertising on its platform to generate revenue for publisher partners and ensure the long-term sustainability of its services while emphasizing its commitment to providing unbiased answers.
Writer, a company focused on generative AI for enterprises, has successfully raised $200 million in Series C funding, reaching a valuation of $1.9 billion, with plans to utilize the new capital to further develop its full-stack generative AI platform and its agentic AI capabilities.
TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.
#000#8K#Accounts#advertising#agent#Agentic AI#agents#ai#ai agent#AI AGENTS#AI chip#AI development#ai model#AI models#ai platform#AlphaFold#Analysis#anthropic#APIs#applications#approach#artificial#Artificial Intelligence#automation#Behavior#benchmark#benchmarks#billion#Biology#Business
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Version 598
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I had a great week fixing bugs and cleaning code.
full changelog
fixing some mistakes
First off, I apologise to those who were hit by the 'serialisation' problems where certain importers were not saving correctly. I screwed up my import folder deduplication code last week; I had a test to make sure the deduplication transformation worked, but the test missed that some importers were not saving correctly afterwards. If you were hit by an import folder, subscription, or downloader page that would not save, this is now completely fixed. Nothing was damaged (it just could not save new work), and you do not have to do anything, so please just unpause anything that was paused and you should return to normal.
I hate having these errors, which are basically just a typo, so I have rejigged my testing regime to explicitly check for this with all my weekly changes. I hope it will not happen again, or at least not so stupidly. Let me know if you have any more trouble!
Relatedly, I went on a code-cleaning binge this week and hammered out a couple hundred 'linting' (code-checking) warnings, and found a handful of small true-positive problems in the mess. I've cleared out a whole haystack here, and I am determined to keep it clean, so future needles should stick out.
other stuff
I moved around a bunch of the checkboxes in the options dialog. Stuff that was in the options->tags and options->search pages is separated into file search, tag editing, and tag autocomplete tabs. The drag and drop options are also overhauled and moved to a new options->exporting page.
I rewrote the main 'ListBook' widget that the options dialog uses (where you have a list on the left that chooses panels on the right). If you have many tag services and they do not fit with the normal tabbed notebook, then under the new options->tag editing, you can now set to convert all tag service dialogs to use a ListBook instead. Everything works the same, it is just a different shape of widget.
A page that has no files selected now only uses the first n files (default 4096) to compute its 'selection tags' list when there are no files selected. This saves a bunch of update CPU time on big pages, particularly if you are looking at a big importer page that is continuously adding new files. You can change the n, including removing it entirely, under options->tag presentation.
If you are an advanced downloader maker, 'subsidiary page parsers' are now import/export/duplicate-able under the parsing UI.
job listing
I was recently contacted by a recruiter at Spellbrush, which is a research firm training AI models to produce anime characters, and now looking to get into games. I cannot apply for IRL reasons, and I am happy working on hydrus, but I talked with the guy and he was sensible and professional and understood the culture. There are several anime-fluent programmers in the hydrus community, so I offered to put the listings up on my weekly post today. If you have some experience and are interested in getting paid to do this, please check it out:
Spellbrush design and train the diffusion models powering both nijijourney and midjourney -- some of the largest-parameter count diffusion models in the world, with a unique focus on anime-style aesthetics. Our team is one of the strongest in the world, many of whom graduated from top universities like MIT and Harvard, worked on AI research at companies like Tencent, Google Deepmind, and Meta, and we have two international math olympiad medalists on our team.
We're looking for a generalist engineer to help us with various projects from architecting and building out our GPU orchestrator, to managing our data pipelines. We have one of the largest GPU inference clusters in the world outside of FAANG, spanning multiple physical datacenters. There's no shortage of interesting distributed systems and data challenges to solve when generating anime images at our scale.
Please note that this is not a remote role. We will sponsor work visas to Tokyo or San Francisco if necessary!
Software Engineer
AI Infra Engineer
next week
I did not find time for much duplicates auto-resolution work this week, so back to that.
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AI's Brain Boost: Small Biz Gets Smarter
OpenAI's new o1 model just dropped jaws and raised eyebrows.
Why it matters:
This AI upgrade isn't just for tech giants. Small business owners can now tap into advanced problem-solving power that rivals human experts, potentially revolutionizing decision-making and operational efficiency.
The big picture:
OpenAI's o1 model scored a whopping 83% on the International Mathematics Olympiad qualifying exam, compared to its predecessor's 13%.
That's like your calculator suddenly becoming a math prodigy.
Overheard at the water cooler:
"Dude, my AI just solved a problem I've been stuck on for weeks. Should I be worried about job security or excited about my new super-smart sidekick?"
By the numbers:
83% score on math olympiad exam (up from 13%)
PhD-level accuracy on science problems
128K context window for deeper understanding
The bottom line:
AI isn't just getting smarter – it's thinking more like us.
For small businesses, this means access to genius-level problem-solving at the click of a button. Time to befriend the bots and level up your business game.
#artificial intelligence#automation#machine learning#business#digital marketing#professional services#marketing#web development#web design#social media#tech#technology
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AI achieves silver-medal standard solving International Mathematical Olympiad problems
See on Scoop.it - Education 2.0 & 3.0
Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics
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#artificialintelligence#Ai#news#business#technology#datascience#data scientist#africa#middleeast#saudiarbia#education#zindi
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OpenAI o1-preview, o1-mini: Advanced Reasoning Models
OpenAI o1-preview, OpenAI o1-mini, A new collection of models for reasoning that address challenging issues.
OpenAI o1-preview
OpenAI has created a new line of AI models that are meant to deliberate longer before reacting. Compared to earlier versions, they can reason their way through challenging tasks and tackle more challenging math, science, and coding challenges.
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The first installment of this series is now available through ChatGPT and its API. OpenAI anticipates frequent upgrades and enhancements as this is only a preview. OpenAI is also including evaluations for the upcoming upgrade, which is presently being developed, with this release.
How it functions
These models were trained to think through situations more thoroughly before responding, much like a human would. They learn to try various tactics, improve their thought processes, and own up to their mistakes through training.
In OpenAI experiments, the upcoming model upgrade outperforms PhD students on hard benchmark tasks in biology, chemistry, and physics. It also performs exceptionally well in coding and math. GPT-4o accurately answered only 13% of the questions in an exam used to qualify for the International Mathematics Olympiad (IMO), compared to 83% for the reasoning model. Their coding skills were tested in competitions, and in Codeforces tournaments, they scored in the 89th percentile.
Many of the functions that make ChatGPT valuable are still missing from this early model, such as posting files and photographs and searching the web for information. In the near future, GPT-4o will be more capable in many typical instances.
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However, this marks a new level of AI power and a substantial advancement for complicated thinking tasks. In light of this, OpenAI is calling this series OpenAI o1-preview and resetting the counter to 1.
Security
In the process of creating these new models, OpenAI is also developed a novel method for safety training that uses the models’ capacity for reasoning to force compliance with safety and alignment requirements. It can implement their safety regulations more successfully by reasoning about them in the context of the situation.
Testing how effectively their model adheres to its safety guidelines in the event that a user attempts to circumvent a process known as “jailbreaking” is one method they gauge safety. GPT-4o received a score of 22 (out of 100) on one of OpenAI’s most difficult jailbreaking tests, but OpenAI o1-preview model received an 84. Further information about this can be found in their study post and the system card.
OpenAI has strengthened its safety work, internal governance, and federal government coordination to match the enhanced capabilities of these models. This includes board-level review procedures, such as those conducted by its Safety & Security Committee, best-in-class red teaming, and thorough testing and evaluations utilizing its Preparedness Framework.
OpenAI recently finalized collaborations with the AI Safety Institutes in the United States and the United Kingdom to further its commitment to AI safety. OpenAI has initiated the process of putting these agreements into practice by providing the institutes with preliminary access to a research version of this model. This was a crucial initial step in its collaboration, assisting in the development of a procedure for future model research, assessment, and testing both before and after their public release.
For whom it is intended
These improved thinking skills could come in handy while solving challenging puzzles in math, science, computing, and related subjects. For instance, physicists can use OpenAI o1-preview to create complex mathematical formulas required for quantum optics, healthcare researchers can use it to annotate cell sequencing data, and developers across all domains can use it to create and implement multi-step workflows.
OpenAI O1-mini
The o1 series is excellent at producing and debugging complex code with accuracy. OpenAI is also launching OpenAI o1-mini, a quicker, less expensive reasoning model that excels at coding, to provide developers with an even more effective option. For applications requiring reasoning but not extensive domain knowledge, o1-mini is a powerful and economical model because it is smaller and costs 80% less than o1-preview.
How OpenAI o1 is used
Users of ChatGPT Plus and Team will have access to o1 models as of right now. The model selector allows you to manually choose between o1-preview and o1-mini. The weekly rate limits at launch will be 30 messages for o1-preview and 50 for o1-mini. The goal is to raise those rates and make ChatGPT capable of selecting the appropriate model on its own for each request.
Users of ChatGPT Edu and Enterprise will have access to both models starting next week.
With a rate limit of 20 RPM, developers that meet the requirements for API usage tier 5(opens in a new window) can begin prototyping with both models in the API right now. Following more testing, OpenAI aims to raise these restrictions. Currently, these models lack support for system messaging, streaming, function calling, and other capabilities in their API. Check out the API documentation to get started.
OpenAI also intends to provide all ChatGPT Free users with access to o1-mini.
Next up
These reasoning models are now available in ChatGPT and the API as an early release. To make them more helpful to everyone, it plans to add browsing, file and image uploading, and other capabilities in addition to model updates.
In addition to the new OpenAI o1 series, OpenAI also wants to keep creating and publishing models in its GPT series.
Read more on govindhtech.com
#OpenAI#o1preview#o1mini#AdvancedReasoningModels#GPT4o#AImodels#OpenAIo1preview#AISafety#APIdocumentation#technology#technews#news#govindhtech
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OpenAI Unveils Advanced ChatGPT with Enhanced Math, Coding, and Science Abilities
OpenAI has released an upgraded version of its popular chatbot, ChatGPT, now equipped with advanced capabilities to handle complex tasks in fields like math, coding, and science. This new iteration is powered by OpenAI o1, a breakthrough AI technology designed to address common issues found in previous models, such as struggling with basic math or generating incomplete code.
The updated ChatGPT, introduced on Thursday, is engineered to "reason" more effectively than its predecessors. Unlike earlier versions that provided immediate responses, the new ChatGPT takes a more deliberate approach to problem-solving. "This model can take its time, think through the problem in English, and break it down to find the best solution," said Jakub Pachocki, OpenAI’s chief scientist.
During a live demonstration, Pachocki and OpenAI technical fellow Szymon Sidor showcased the bot solving an intricate acrostic puzzle, answering a Ph.D.-level chemistry question, and diagnosing a patient based on detailed medical information. These examples highlighted the chatbot’s enhanced reasoning skills and its potential for tackling more sophisticated tasks.
OpenAI o1 technology represents a broader trend in AI development, where companies like Google, Meta, and Microsoft are all pushing to create systems that can reason through problems step by step, mimicking human logic. Microsoft's partnership with OpenAI ensures this technology will soon be integrated into its products, offering a range of practical applications, from helping programmers write code to serving as automated math tutors.
This structured problem-solving ability could also be valuable in fields like physics, where complex mathematical formulas need to be generated, or healthcare, where researchers can use AI to assist in experiments.
Since ChatGPT’s initial release in 2022, it has revolutionized AI interactions by responding to user queries, writing essays, and generating code. However, earlier models were not without their shortcomings—occasionally producing mistakes, buggy code, or repeating misinformation found online.
To overcome these challenges, OpenAI has employed reinforcement learning in the new system. This process allows the AI to learn from trial and error, improving its accuracy by repeatedly working through problems and identifying successful strategies. However, the system remains imperfect and can still make errors. "It’s not going to be perfect," Sidor admitted, "but it’s more likely to provide the right answer by working harder."
The upgraded ChatGPT is now available to ChatGPT Plus and ChatGPT Teams subscribers, as well as developers and businesses seeking to incorporate the technology into their own applications.
In standardized testing, OpenAI reports that its new model outperforms previous versions. On the International Mathematical Olympiad (IMO) qualifying exam, the prior iteration of ChatGPT scored 13%, while OpenAI o1 achieved an impressive 83%. However, experts caution that standardized tests may not fully reflect real-world performance, especially when it comes to tasks like tutoring students.
"There’s a difference between problem-solving and assistance," said Angela Fan, a research scientist at Meta. "New models that reason can solve problems, but that doesn’t necessarily mean they can guide someone through their homework."
Despite its limitations, OpenAI’s latest version of ChatGPT marks a significant leap forward in AI, with the potential to tackle more intricate, real-world challenges across multiple industries.
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AI achieves silver-medal standard solving International Mathematical Olympiad problems - Google DeepMind
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Google AI earns silver medal equivalent at International Mathematical Olympiad http://dlvr.it/TB51B8
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Google DeepMind: AI achieves silver-medal standard solving International Mathematical Olympiad problems
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