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@Techmeme - 46d
OpenAI's new o3 model has achieved a significant breakthrough on the ARC-AGI benchmark, demonstrating advanced reasoning capabilities through a 'private chain of thought' mechanism. This approach involves the model searching over natural language programs to solve tasks, with a substantial increase in compute leading to a vastly improved score of 75.7% on the Semi-Private Evaluation set within a $10k compute limit, and 87.5% in a high-compute configuration. The o3 model uses deep learning to guide program search, moving beyond basic next-token prediction. Its ability to recombine knowledge at test time through program execution marks a major step toward more general AI capabilities.
The o3 model's architecture and performance represents a form of deep learning-guided program search, where it explores many paths through program space. This process, which can involve tens of millions of tokens and cost thousands of dollars for a single task, is guided by a base LLM. While o3 appears to be more than just next-token prediction, it’s still being speculated what the core mechanisms of this process are. This breakthrough highlights how increases in compute can drastically improve performance and marks a substantial leap in AI capabilities, moving far beyond previous GPT model performance. The model's development and testing also revealed that it cost around $6,677 to run o3 in "high efficiency" mode against the 400 public ARC-AGI puzzles for a score of 82.8%. Recommended read:
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Theron Mohamed (tmohamed@insider.com)@All Content from Business Insider - 42d
Elon Musk’s AI startup, xAI, has successfully raised $6 billion in its latest Series C funding round. This significant investment will help xAI in its mission to develop Artificial General Intelligence (AGI) with a focus on truth-seeking and the elimination of ideological biases. xAI has not revealed what they will be doing with the massive amount of new capital they have just raised. This also shows that the investors believe in the company and the direction they are heading.
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@the-decoder.com - 14d
OpenAI's o3 model is facing scrutiny after achieving record-breaking results on the FrontierMath benchmark, an AI math test developed by Epoch AI. It has emerged that OpenAI quietly funded the development of FrontierMath, and had prior access to the benchmark's datasets. The company's involvement was not disclosed until the announcement of o3's unprecedented performance, where it achieved a 25.2% accuracy rate, a significant jump from the 2% scores of previous models. This lack of transparency has drawn comparisons to the Theranos scandal, raising concerns about potential data manipulation and biased results. Epoch AI's associate director has admitted the lack of transparency was a mistake.
The controversy has sparked debate within the AI community, with questions being raised about the legitimacy of o3's performance. While OpenAI claims the data wasn't used for model training, concerns linger as six mathematicians who contributed to the benchmark said that they were not aware of OpenAI's involvement or the company having exclusive access. They also indicated that had they known, they might not have contributed to the project. Epoch AI has said that an "unseen-by-OpenAI hold-out set" was used to verify the model's capabilities. Now, Epoch AI is working on developing new hold-out questions to retest the o3 model's performance, ensuring OpenAI does not have prior access. Recommended read:
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@www6b3.wolframalpha.com - 33d
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Recent Questions - Mathematics
, Recent Questions - MathOverflo
, www6b3.wolframalpha.com ...
Recent research is exploring the distribution of prime numbers, employing techniques like the Sieve of Eratosthenes. This ancient method helps identify primes by systematically eliminating multiples of smaller primes, and its principles are being used in efforts to understand the elusive twin prime conjecture. This involves estimating the number of primes and twin primes using ergodic principles, which suggests a novel intersection between number theory and statistical mechanics. These principles suggest an underlying structure to the distribution of primes, which may be linked to fundamental mathematical structures.
Furthermore, the study of prime numbers extends to applications in cryptography. RSA public key cryptography relies on the generation of large prime numbers. Efficient generation involves testing randomly generated numbers, with optimisations like setting the last and top bits to avoid even numbers or very small numbers. Probabilistic tests are favored over deterministic ones in practice. These techniques show the importance of number theory in real world application and the constant push to further our understanding. Recommended read:
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@www.marktechpost.com - 26d
AMD researchers, in collaboration with Johns Hopkins University, have unveiled Agent Laboratory, an innovative autonomous framework powered by large language models (LLMs). This tool is designed to automate the entire scientific research process, significantly reducing the time and costs associated with traditional methods. Agent Laboratory handles tasks such as literature review, experimentation, and report writing, with the option for human feedback at each stage. The framework uses specialized agents, such as "PhD" agents for literature reviews, "ML Engineer" agents for experimentation, and "Professor" agents for compiling research reports.
The Agent Laboratory's workflow is structured around three main components: Literature Review, Experimentation, and Report Writing. The system retrieves and curates research papers, generates and tests machine learning code, and compiles findings into comprehensive reports. AMD has reported that using the o1-preview LLM within the framework produces the most optimal research results, which can assist researchers by allowing them to focus on creative and conceptual aspects of their work while automating more repetitive tasks. The tool aims to streamline research, reduce costs, and improve the quality of scientific outcomes, with a reported 84% reduction in research expenses compared to previous autonomous models. Recommended read:
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@the-decoder.com - 40d
DeepSeek has unveiled its v3 large language model (LLM), a significant advancement in AI. This new model was trained on an impressive 14.8 trillion tokens using 2,788,000 H800 GPU hours at a cost of approximately $5.576 million, a figure remarkably lower than other models of similar capability. DeepSeek v3's training involved both supervised fine-tuning and reinforcement learning, enabling it to achieve performance benchmarks comparable to Claude 3.5 Sonnet, showcasing its strong capabilities. The model is a Mixture-of-Experts (MoE) model with 671 billion parameters, with 37 billion activated for each token.
The release of DeepSeek v3 also includes API access, with highly competitive pricing compared to others in the market. Input is priced at $0.27 per million tokens (or $0.07 with cache hits), and output at $1.10 per million tokens. For comparison, Claude 3.5 Sonnet charges $3 per million tokens for input and $15 for output. These prices, along with its strong performance, indicate DeepSeek v3 is set to disrupt the market in terms of model quality and affordability. The model was also released as fully open-source with all associated papers and training frameworks provided to the research community. Recommended read:
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@thequantuminsider.com - 40d
Recent breakthroughs in quantum research are showing rapid advancements, particularly in quantum teleportation and material simulation. Researchers have successfully demonstrated quantum teleportation through existing fiber optic networks, marking a significant leap from theoretical concepts to practical application. This allows information to be transferred instantly and securely by using quantum entanglement between particles without any physical movement of those particles. This achievement has been considered as a breakthrough and has been considered impossible prior to these findings.
The field of material simulation also shows huge improvements with a new quantum computing method that reduces computational resource requirements. This approach uses “pseudopotentials” to simplify interactions within atomic cores of materials, making simulations more practical and efficient. Quantum simulations were applied to study catalytic reactions, identifying over 3000 unique molecular configurations in the process. These advances demonstrate the growing importance of quantum mechanics in various areas of science, ranging from communication to material design, and also shows the potential for quantum advancements in many practical applications. Recommended read:
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Brianna Wessling@The Robot Report - 46d
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www.techmeme.com
, The Robot Report
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Waymo's autonomous vehicles have demonstrated a significant safety advantage over human-driven cars, according to a recent study conducted in collaboration with Swiss Re, a major insurance company. The research analyzed 25.3 million fully autonomous miles driven by Waymo vehicles across four cities: Phoenix, San Francisco, Los Angeles, and Austin. The study compared Waymo's collision data with human driver baselines, which were based on Swiss Re's database of over 500,000 claims and over 200 billion miles traveled.
The results of the analysis revealed a substantial 92% reduction in bodily injury claims and an 88% reduction in property damage claims for Waymo's self-driving cars compared to human-driven vehicles. Specifically, over 25.3 million miles, Waymo vehicles were involved in only nine property damage claims and two bodily injury claims, whereas the average human driver would be expected to have 78 property damage claims and 26 bodily injury claims for the same distance. The study also found that Waymo's vehicles performed better than even new cars equipped with the latest safety technology such as automatic emergency braking and lane-keep assist, showcasing the potential of self-driving technology to enhance road safety. Recommended read:
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@bhaveshshrivastav.medium.com - 17d
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medium.com
, medium.com
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Quantum computing and cryptography are rapidly advancing fields, prompting both exciting new possibilities and serious security concerns. Research is focused on developing quantum-resistant cryptography, new algorithms designed to withstand attacks from both classical and quantum computers. This is because current encryption methods rely on mathematical problems that quantum computers could potentially solve exponentially faster, making sensitive data vulnerable. Quantum-resistant algorithms like CRYSTALS-Kyber and CRYSTALS-Dilithium are being actively tested in various scenarios, such as secure government communications and data centers. The race is on to secure digital information before quantum computers become powerful enough to break existing encryption.
Developments in quantum computing are also driving progress in quantum cryptography, which uses the principles of quantum mechanics to secure communication. This offers a level of security that is theoretically impossible to breach using classical methods. Simultaneously, traditional cryptographic techniques such as Elliptic Curve Cryptography (ECC) and Advanced Encryption Standard (AES) are being combined to build secure data encryption tools, ensuring files remain protected in the digital world. Companies like Pasqal and Riverlane have partnered to accelerate the development of fault-tolerant quantum systems, which aim to overcome the reliability issues in current quantum systems and enable more reliable quantum computations. Recommended read:
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@medium.com - 29d
The intersection of mathematics and technology is proving to be a hot topic, with articles exploring how mathematical concepts underpin many aspects of data science and programming. Key areas of focus include the essential math needed for programming, highlighting the importance of Boolean algebra, number systems, and linear algebra for creating efficient and complex code. Linear algebra, specifically the application of matrices, was noted as vital for data transformations, computer vision algorithms, and machine learning, enabling tasks such as vector operations, matrix transformations, and understanding data representation.
The relationship between data science and mathematics is described as complex but crucial, with mathematical tools being the foundation of data-driven decisions. Probability and statistics are also essential, acting as lenses to understand uncertainty and derive insights, covering descriptive statistics like mean, median, mode and the application of statistical models. Computer vision also relies on math concepts, with specific applications like optical character recognition using techniques like pattern recognition and deep learning. Optimization of computer vision models is also discussed, with a focus on making models smaller and faster using techniques like pruning and quantization. Recommended read:
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@quantumcomputingreport.com - 34d
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medium.com
, medium.com
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Quantum computing is rapidly advancing with significant implications for various fields, particularly in the areas of randomness and security. Researchers are exploring the use of quantum computing to redefine randomness and enhance machine learning through technologies such as Quantum Support Vector Machines. These advancements highlight the technology's potential to revolutionize data analysis and processing. Simultaneously, there is a growing focus on developing quantum-resistant encryption methods to protect internet security from future quantum computer attacks. This is vital, as traditional encryption methods could become vulnerable to the power of quantum computing.
The pursuit of robust quantum encryption is evident in recent developments, including the work of cryptographers designing encryption methods that are invulnerable to quantum computers. Additionally, Russia has unveiled a 50-qubit quantum computer prototype, signaling a major step in their quantum computing roadmap and a move towards increased quantum capabilities. Furthermore, institutions like IonQ and Oak Ridge National Laboratory are working on noise-tolerant quantum computing techniques, advancing the technology towards practical applications and commercial availability. These advances all underscore quantum computing's increasing importance as a pivotal technology for the future. Recommended read:
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Math Attack@Recent Questions - MathOverflow - 38d
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sci-hub.usualwant.com
Recent discussions in the mathematical community have centered around complex problems in topology and analysis. One such area involves a deep dive into the proof of Cayley's Theorem, specifically within the context of Topological Groups. This research explores the fundamental structures of groups with the additional layer of topological properties, blending abstract algebra with the study of continuity and limits. Additionally, there is an ongoing discussion around the analytic continuation of a particular function which contains a sinc function as well as the polylogarithm and digamma functions, showing the intersection of real and complex analysis.
The challenges also include the calculation of integrals involving the digamma function. The exploration of this particular function’s integral representation is proving useful in approximations of other functions. There's also a practical approach being explored for finding approximate formula for the nth prime, using integral transformations of a function with the digamma function. The discussion also includes using Sci-Hub to provide greater access to research papers and help facilitate collaboration on these advanced mathematical topics. Recommended read:
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@medium.com - 39d
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medium.com
, ujangriswanto08.medium.com
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Statistical analysis is a key component in understanding data, with visualizations like boxplots commonly used. However, boxplots can be misleading if not interpreted carefully, as they can oversimplify data distributions and hide critical details. Additional visual tools such as stripplots and violinplots should be considered to show the full distribution of data, especially when dealing with datasets where quartiles appear similar but underlying distributions are different. These tools help to reveal gaps and variations that boxplots might obscure, making for a more robust interpretation.
Another crucial aspect of statistical analysis involves addressing missing data, which is a frequent challenge in real-world datasets. The nature of missing data—whether it's completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR)—significantly impacts how it should be handled. Identifying the mechanism behind missing data is critical for choosing the appropriate analytical strategy, preventing bias in the analysis. Additionally, robust regression methods are valuable as they are designed to handle outliers and anomalies that can skew results in traditional regressions. Recommended read:
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@tracyrenee61.medium.com - 33d
Recent discussions have highlighted the importance of several key concepts in probability and statistics, crucial for data science and research. Descriptive measures of association, statistical tools used to quantify the strength and direction of relationships between variables are essential for understanding how changes in one variable impact others. Common measures include Pearson’s correlation coefficient and Chi-squared tests, allowing for the identification of associations between different datasets. This understanding helps in making informed decisions by analyzing the connection between different factors.
Additionally, hypothesis testing, a critical process used to make data-driven decisions, was explored. It determines if observations from data occur by chance or if there is a significant reason. Hypothesis testing involves setting a null hypothesis and an alternative hypothesis then the use of the P-value to measure the evidence for rejecting the null hypothesis. Furthermore, Monte Carlo simulations were presented as a valuable tool for estimating probabilities in scenarios where analytical solutions are complex, such as determining the probability of medians in random number sets. These methods are indispensable for anyone who works with data and needs to make inferences and predictions. Recommended read:
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@docs.google.com - 71d
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A new survey, the College Mathematics Beliefs and Belonging (CMBB) Survey, has been developed to better understand how students' beliefs about mathematics and their sense of belonging within the mathematical community impact their success. The CMBB, detailed in a recent publication in the *International Journal of Research in Undergraduate Mathematics Education*, explores students' perceptions of mathematical practices and reasoning, their beliefs about the subject, and their feelings of inclusion. The survey uses quantitative methods to gain insight into the qualitative experiences of students, particularly those from underrepresented groups, aiming to identify areas for improvement in mathematics education.
This research, spearheaded by a team including mathematicians, mathematics education researchers, and psychologists, acknowledges the pervasive negative reactions associated with mathematics, especially among students from under-resourced backgrounds. David Bressoud, a prominent researcher involved in the project, notes that the way mathematics is often taught—emphasizing rote memorization and procedural fluency—can be uninspiring and discouraging. Societal biases further contribute to low persistence in the field, particularly for underrepresented groups. The CMBB survey aims to address these issues by measuring student beliefs and sense of belonging. The CMBB survey consists of fifteen clusters of statements assessing students' beliefs and sense of belonging. Data from primarily first- and second-year undergraduate students at a large US university were used to develop and validate the survey, with confirmatory factor analysis confirming its fifteen-factor structure. Researchers and instructors can use the CMBB to assess student perceptions in these key areas, ultimately working towards creating more inclusive and supportive learning environments that promote success for all students in college mathematics courses. Recommended read:
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