Unite.AI

DECEMBER 20, 2023

Reproducibility, integral to reliable research, ensures consistent outcomes through experiment replication. In the domain of Artificial Intelligence (AI) , where algorithms and models play a significant role, reproducibility becomes paramount. Multiple factors contribute to the reproducibility crisis in AI research.

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Marktechpost

DECEMBER 29, 2024

To address these challenges, researchers from MIT, Sakana AI, OpenAI, and The Swiss AI Lab IDSIA have developed the Automated Search for Artificial Life (ASAL). This innovative algorithm leverages vision-language foundation models (FMs) to automate the discovery of artificial lifeforms.

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Marktechpost

DECEMBER 9, 2023

The study introduces a Markov-chain Monte Carlo expectation-maximization algorithm, drawing inspiration from various related methods. Also, don’t forget to join our 33k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more.

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Marktechpost

DECEMBER 30, 2024

Researchers from NEJM AI, a division of the Massachusetts Medical Society, developed and validated the Sepsis ImmunoScore, the first FDA-authorized AI-based tool for identifying patients at risk of sepsis. All credit for this research goes to the researchers of this project.

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Unite.AI

MAY 10, 2024

Researchers and students often find themselves inundated with lengthy research papers, making it challenging to quickly grasp the core ideas and insights. AI-powered research paper summarizers have emerged as powerful tools, leveraging advanced algorithms to condense lengthy documents into concise and readable summaries.

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Marktechpost

JANUARY 15, 2024

The benchmark challenges existing unlearning algorithms, highlighting their limitations and the need for more effective solutions. Don’t Forget to join our Telegram Channel The post CMU AI Researchers Unveil TOFU: A Groundbreaking Machine Learning Benchmark for Data Unlearning in Large Language Models appeared first on MarkTechPost.

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Unite.AI

JUNE 15, 2024

Graduate student Diego Aldarondo collaborated with DeepMind researchers to train an artificial neural network (ANN) , which serves as the virtual brain, using the powerful machine learning technique deep reinforcement learning. This could provide valuable insights into how real brains learn and adapt to new challenges.

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Flipboard

JUNE 7, 2023

Google’s artificial intelligence (AI) research lab DeepMind has achieved a remarkable feat in computer science through its latest AI system, AlphaDev. This specialized version of AlphaZero has made a significant breakthrough by uncovering faster sorting and hashing algorithms, which are essential …

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Unite.AI

FEBRUARY 15, 2024

Ramprakash Ramamoorthy, is the Head of AI Research at ManageEngine , the enterprise IT management division of Zoho Corp. How did you initially get interested in computer science and machine learning ? As the director of AI Research at Zoho & ManageEngine, what does your average workday look like?

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Marktechpost

MARCH 4, 2024

Efficiency of Large Language Models (LLMs) is a focal point for researchers in AI. A groundbreaking study by Qualcomm AI Research introduces a method known as GPTVQ, which leverages vector quantization (VQ) to enhance the size-accuracy trade-off in neural network quantization significantly.

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Towards AI

JANUARY 29, 2025

Music Generation: AI models like OpenAIs Jukebox can compose original music in various styles. Video Generation: AI can generate realistic video content, including deepfakes and animations. Why Become a Generative AI Engineer in 2025? Generative AI Techniques: Text Generation (e.g., GPT, BERT) Image Generation (e.g.,

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Marktechpost

DECEMBER 7, 2023

Still, there are only a handful of examples with such a dramatic speedup, such as Shor’s factoring algorithm and quantum simulation. The researchers demonstrated that any problem efficiently solvable by a quantum algorithm can be transformed into a situation involving a coupled oscillator network.

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Marktechpost

DECEMBER 15, 2023

To address this issue, a team of researchers from Apple has introduced DeepPCR, a unique algorithm that seeks to speed up neural network training and inference. The team has employed the Parallel Cyclic Reduction (PCR) algorithm to retrieve this solution. If you like our work, you will love our newsletter.

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AI Weekly

SEPTEMBER 26, 2024

for robotics simulation tech One of the most fundamental breakthroughs at Nvidia has been building processors that power and integrate with highly detailed, compute-intensive graphical simulations, which can be used in a wide range of applications, from games and industrial developments through to AI training.

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Marktechpost

SEPTEMBER 16, 2023

The researchers have suggested PagedAttention, an attention algorithm inspired by the traditional virtual memory and paging techniques in operating systems, as a solution to this problem. To further reduce memory utilization, the researchers have also deployed vLLM. Check out the Paper , Github , and Reference Article.

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Marktechpost

DECEMBER 13, 2023

Differential privacy (DP) is a well-known technique in machine learning that aims to safeguard the privacy of individuals whose data is used to train models. Google Researchers introduce an auditing scheme for differentially private machine learning systems focusing on a single training run.

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Marktechpost

SEPTEMBER 14, 2023

The industry has responded by developing advanced battery management systems and employing machine learning techniques to improve prediction accuracy and optimize performance. Leveraging Machine Learning for Battery Optimization BatteryML employs machine learning algorithms to improve various facets of battery performance.

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AI Weekly

SEPTEMBER 21, 2023

pitneybowes.com In The News How Google taught AI to doubt itself Today let’s talk about an advance in Bard, Google’s answer to ChatGPT, and how it addresses one of the most pressing problems with today’s chatbots: their tendency to make things up. [Get your FREE eBook.] Get your FREE eBook.] You can also subscribe via email.

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Marktechpost

DECEMBER 27, 2024

Machine unlearning is driven by the need for data autonomy, allowing individuals to request the removal of their data’s influence on machine learning models. In conclusion, The work introduces a reconstruction attack capable of recovering deleted data from simple machine-learning models with high accuracy.

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Marktechpost

MARCH 22, 2024

Data scientists and engineers frequently collaborate on machine learning ML tasks, making incremental improvements, iteratively refining ML pipelines, and checking the model’s generalizability and robustness. To minimize the possibility of mistakes, the user must repeat and check each step of the machine-learning workflow.

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Unite.AI

OCTOBER 18, 2023

This combination has allowed GPT-4 to surpass other models, validating the potential of tailored architectures and the symbiotic relationship between human intelligence and machine learning in advancing the field. The direction in which the community leans has profound implications for AI research.

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Marktechpost

JANUARY 12, 2024

.’ To tackle these challenges, the research community has employed a range of methodologies. The backbone of most diarization systems is a combination of voice activity detection, speaker turn detection, and clustering algorithms. These systems typically fall into two categories: modular and end-to-end systems.

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Marktechpost

DECEMBER 9, 2023

To address these limitations, MIT and ETH Zurich researchers have pioneered a data-driven machine-learning technique that promises to revolutionize how we approach and solve complex logistical challenges. Recognizing this, the researchers sought to reinvigorate MILP solvers with a data-driven approach.

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AssemblyAI

MARCH 21, 2023

What are the actual advantages of Graph Machine Learning? This article will recap on some highly impactful applications of GNNs, the first article in a series that will take a deep dive into Graph Machine Learning, giving you everything you need to know to get up to speed on the next big wave in AI.

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Marktechpost

SEPTEMBER 24, 2023

LLVM’s optimizer is incredibly complex, with thousands of rules and algorithms written in over 1 million lines of code in the C++ programming language. In contrast, state-of-the-art machine learning approaches lead to regressions and require thousands of compilations. improvement with 2.5 billion compilations.

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Marktechpost

MAY 19, 2024

Google AI researchers describe their novel approach to addressing the challenge of generating high-quality synthetic datasets that preserve user privacy, which are essential for training predictive models without compromising sensitive information. All credit for this research goes to the researchers of this project.

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Marktechpost

FEBRUARY 16, 2025

Despite these advances, no method has effectively addressed all three key challenges: Long-context generalization Efficient memory management Computational efficiency Researchers from the KAIST, and DeepAuto.ai The model achieves this through a hierarchical token pruning algorithm, which dynamically removes less relevant context tokens.

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Unite.AI

JULY 24, 2024

This is not the distant future; this is now with Apple's groundbreaking AI. Apple has been among the leaders in integrating Artificial Intelligence (AI) into its devices, from Siri to the latest advancements in machine learning and on-device processing. Notable acquisitions include companies like Xnor.a

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IBM Journey to AI blog

AUGUST 13, 2024

One IBM researcher of note, Arthur Samuel, called this process “machine learning,” a term he coined that remains central to AI today. Just a decade later, IBM made another major contribution to the field of AI with the introduction of a “Shoebox” at the 1962 World’s Fair.

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Marktechpost

DECEMBER 23, 2023

DNNs have gained immense prominence in various fields, including computer vision, natural language processing, and pattern recognition, due to their ability to handle large volumes of data and extract high-level features, leading to remarkable advancements in machine learning and AI applications.

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AI Weekly

DECEMBER 21, 2023

About two-thirds of Australian employees report using generative AI for work. theconversation.com Stanford : What to Expect in AI in 2024 This past year marked major advances in generative AI as terms like ChatGPT and Bard become household names. yahoo.com Research The AI–quantum computing mash-up: will it revolutionize science?

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Marktechpost

DECEMBER 18, 2023

Quantum machine learning and variational quantum algorithms were formerly hot topics, but the desert plateau event dampened their initial excitement. Due to the exponential training resources required, variational quantum algorithms are not scalable in such settings. Check out the Paper.

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Marktechpost

SEPTEMBER 3, 2023

Neuromodulators like dopamine, noradrenaline, serotonin, and acetylcholine work at many synapses and come from widely scattered axons of specific neuromodulatory neurons to produce global modulation of synapses during reward-associated learning.

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Marktechpost

JULY 5, 2024

Machine learning models for vision and language, have shown significant improvements recently, thanks to bigger model sizes and a huge amount of high-quality training data. Research shows that more training data improves models predictably, leading to scaling laws that explain the link between error rates and dataset size.

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Marktechpost

DECEMBER 23, 2023

According to recent research, the results of perturbations can be predicted using machine learning models. They use pre-existing Perturb-seq datasets to train their algorithms, forecasting the expression results of unseen perturbations, individual genes, or combinations of genes.

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Marktechpost

FEBRUARY 22, 2025

Also, OctoTools employs a task-specific toolset optimization algorithm that selects the most relevant tools for each task, thereby improving efficiency and accuracy. The research team extensively evaluated 16 benchmarks covering vision, mathematical reasoning, scientific analysis, and medical applications. OctoTools achieved a 20.7%

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Marktechpost

JANUARY 18, 2024

AI systems struggle to adapt to diverse environments outside their training data, which is critical in areas like self-driving cars, where failures can have catastrophic consequences. This issue has prompted dedicated research groups, workshops, and societal considerations.

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