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Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using … – Nature.com

February 6, 2024 | Author: admin

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Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using ... - Nature.com

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Machine Learning Market Expected to Hit $208 Billion by 2028 – Analytics Insight

February 6, 2024 | Author: admin

Machine Learning Market Prediction: Machine learning, a subset of artificial intelligence, empowers computers to acquire knowledge from data and algorithms without the need for direct programming. Its applications span diverse industries, including healthcare, retail, finance, manufacturing, and media. The Machine Learning market size was valued at US$41.03 billion in revenue in 2023 and is anticipated to reach US$208.16 Billion by 2028, with a CAGR of 38.38% over the forecast period. This remarkable growth fueled by various factors is reshaping industries and driving the adoption of ML technologies.

The abundance of data, coupled with advancements in data quality, is a cornerstone for the growth of the ML market. Access to diverse and high-quality datasets empowers ML models to glean valuable insights, resulting in more accurate and effective outcomes. Industries across the spectrum are leveraging this wealth of information to make informed decisions and enhance their operations.

Industries grappling with challenges such as rising costs, inefficiencies, and inequalities are turning to ML for bespoke solutions. The adaptability of ML models allows them to be tailored to specific needs, offering innovative solutions to longstanding problems. As businesses increasingly seek efficiency gains and competitive advantages, ML becomes a critical tool in their arsenal.

The surge in ML adoption is closely linked to the widespread adoption of cloud and edge computing. These technologies provide the necessary infrastructure and scalability for deploying and running ML models. Cloud and edge computing enable businesses to harness the power of ML without the need for extensive on-premises hardware, facilitating seamless integration and operation.

Ongoing research and development in ML technology, particularly in areas such as natural language processing, deep learning, and speech synthesis, are enhancing the performance and capabilities of ML models. These advancements are driving the development of more sophisticated and versatile applications, expanding the potential use cases for ML across various domains.

The exponential growth in data usage and ML applications raises concerns about privacy and security. The potential exposure of sensitive and personal data to hackers and malicious actors poses a significant threat. Striking a balance between the benefits of ML and safeguarding user and business data is a crucial challenge that the industry must address to ensure sustained growth.

The success of ML applications hinges on user and stakeholder trust. Lack of transparency in ML algorithms can lead to skepticism and hinder widespread acceptance, especially in critical sectors like healthcare and finance. Establishing clear guidelines and fostering transparency is paramount to overcoming this challenge and ensuring the responsible deployment of ML technologies.

The shortage of skilled professionals proficient in designing, developing, and maintaining ML systems and applications is a bottleneck for the industry. As the demand for ML expertise skyrockets, addressing this skills gap becomes crucial for sustained growth. Educational initiatives, upskilling programs, and industry collaborations are essential to cultivating a robust talent pool.

The ethical use of ML is an ongoing concern, with issues such as bias, discrimination, and accountability coming to the forefront. Striking a balance between innovation and responsible deployment is essential to mitigate these ethical challenges. Establishing ethical frameworks and guidelines can help guide the development and implementation of ML technologies in a socially responsible manner.

The machine learning market forecast is indicative of its transformative impact on industries worldwide. The convergence of factors such as data availability, demand for innovation, cloud and edge computing, and R&D advancements propels the industry forward. However, addressing challenges like privacy concerns, building trust, bridging the skills gap, and navigating ethical dilemmas is crucial for sustained and responsible growth. As the machine learning landscape continues to evolve, stakeholders must work collaboratively to harness its potential while ensuring ethical and responsible deployment.

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Cutting-Edge Technology Safeguards Apple Quality: Hyperspectral Imaging and Machine Learning to Combat Codling … – Spectroscopy Online

February 6, 2024 | Author: admin

In a new technology effort to tackle postharvest losses caused by invasive pests, researchers at the University of Kentucky, led by Alfadhl Y. Khaled, Nader Ekramirad, Kevin D. Donohue, et al., have unveiled a research study utilizing non-destructive hyperspectral imaging and machine learning to predict and manage the physicochemical quality attributes of apples during storage, specifically addressing the impact of codling moth infestation. The study, titled "Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth," was published in the journal Agriculture (Volume 13, Issue 5) (1).

As the demand for high-quality apples persists globally, challenges arise in preserving fruit quality during long-term storage, especially in the face of invasive pests such as the codling moth (CM). This study focused on Gala apples, evaluating their firmness, pH, moisture content (MC), and soluble solids content (SSC) under different storage conditions.

The research employed near-infrared hyperspectral imaging (HSI) and machine learning models, utilizing partial least squares regression (PLSR) and support vector regression (SVR) methods. Data preprocessing involved SavitzkyGolay smoothing filters and standard normal variate (SNV), followed by outlier removal using the Monte Carlo sampling method. The study revealed significant effects of CM infestation on near-infrared (NIR) spectra, showcasing the potential impact of pests on apple quality.

Results indicated highly accurate predictive models for apple quality attributes during storage at different temperatures (0 C, 4 C, and 10 C), with maximum correlation coefficients of prediction (Rp) reaching 0.97 for pH, 0.95 for firmness, 0.92 for SSC, and 0.91 for MC. Additionally, the study employed the competitive adaptive reweighted sampling (CARS) method to extract effective wavelengths, enhancing real-time prediction capabilities (1).

The multispectral models derived from this approach demonstrated superior performance compared to full-wavelength HSI models, showcasing the potential for fast, real-time prediction of apple quality characteristics (1).

This new study opens avenues for the development of non-destructive monitoring and evaluation systems, offering valuable insights for the apple industry to combat postharvest losses and ensure the delivery of high-quality produce to consumers.

(1) Khaled, A. Y.; Ekramirad, N.; Donohue, K. D., et al. Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth. Agriculture 2023, 13 (5), 1086. DOI: 10.3390/agriculture13051086

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Transfer learning: Everything you need to know about the ML process – Android Police

February 6, 2024 | Author: admin

Artificial intelligence has begun to mirror a fundamental human skill: transfer learning. This approach is inspired by our cognitive abilities and leverages knowledge acquired in one task to advance in other domains. Just as humans use language to share and build upon their knowledge, artificial intelligence follows a similar path by applying insights from one dataset or problem to another. This article looks at what transfer learning is, how it works, why and when it should be used, and its benefits.

Transfer learning is a powerful technique in machine learning (ML) where a model, initially trained for a specific task, is repurposed for a new, yet related, task. This approach capitalizes on the knowledge and patterns the model acquired. Transfer learning applies insights from a task with abundant data to a new task where data is scarce.

For example, someone who speaks Spanish, a Romance language, generally finds it easier to learn other languages in the same family, like Italian or French. This ease comes from the shared vocabulary, grammar, and structure. Similarly, in AI, a neural network trained to recognize faces in photos can be modified for tasks like recognizing emotions. The network's fundamental understanding of facial features helps it notice small changes in expressions.

Source:Robotic Automation Expert (RAX)

Transfer learning is a valuable technique in machine learning. It's beneficial in scenarios such as data scarcity, time constraints, computational limitations, domain similarity, enhanced generalization, and rapid prototyping. When data is scarce, using a pre-trained model avoids overfitting, often accompanying models trained from scratch. This approach uses the knowledge acquired by these models, improving accuracy.

Transfer learning is also a practical and efficient solution when time and computational resources are limited. It reduces the extensive training periods and computational power as it builds upon pre-existing knowledge bases. By transferring relevant knowledge and patterns between the source and target tasks, this method allows for better generalization to new, unknown data. Furthermore, transfer learning facilitates rapid prototyping, allowing quicker development and deployment of models.

For example, consider a language model like GPT (Generative Pre-trained Transformer), which has been trained on large amounts of text data from the internet. Suppose you want to create a chatbot specializing in medical advice despite the general nature of the GPT's training. In that case, fine-tune this model on a smaller, specialized dataset of medical dialogues and literature.

By doing this, you transfer the general language understanding capabilities of the GPT model and adapt it to the specific context of medical communication. You can leverage the extensive learning of the base model by adjusting the base model to your needs with a relatively small amount of specialized data.

Transfer learning involves essential steps, including finding pre-trained models, freezing layers, training new layers, and fine-tuning the model. Let's explore each of these steps in detail.

The first step is to find a pre-trained model. Organizations might source these models from their collections or open source repositories like PyTorch Hub or TensorFlow Hub. These platforms offer a range of pre-trained models suitable for tasks like image classification, text embeddings, and more.

Deep neural networks are organized in a hierarchical layer structure, each layer serving a distinct role in data processing. The inner layers detect basic features like edges and colors, fundamental in tasks like animal shape recognition. Middle layers increase in complexity, combining these simple patterns to form intricate structures, such as identifying animal fur patterns.

The latter layers are where the network's complex learning occurs, focusing on high-level, task-specific features like distinguishing between animal species. This layered architecture is crucial in transfer learning, where inner and middle layers often retain their learned features for general applicability. In contrast, the latter layers are retrained for specific new tasks.

In transfer learning, the inner and middle layers of the pre-trained model are often frozen, meaning it retains the learned features (like recognizing basic shapes in image recognition tasks) from the original training, which are generally applicable to the new task.

After the appropriate layers have been identified and frozen, the next step involves augmenting the pre-trained model with new layers tailored to the task. These added layers bridge the pre-existing knowledge within the frozen layers and the nuances of the new dataset.

Training these new layers involves exposing the model to the new dataset, where it learns to adjust its internal parameters, weights, and biases based on the input data and the desired output. Through iterations and adjustments, the model fine-tunes itself to optimize its performance on the specific task.

Although not always necessary, fine-tuning can enhance model performance. This involves unfreezing some layers and retraining them at a low learning rate on the new dataset. It allows the model to adjust more finely to the specificities of the new task. The aim is to achieve superior performance in the targeted domain.

In practice, the decision on which layers to freeze or train is based on the level of feature similarity between the pre-trained model and the new task.

For example, consider a neural network trained for general object recognition. It can identify cars, trees, animals, and other objects. If we want to adapt this network for a more specific task, like recognizing different types of birds, we can freeze the inner and middle layers. These layers, which have learned to detect edges, colors, and basic shapes, are helpful for any image recognition task, including birds.

The latter layers, which are specialized for recognizing an array of objects, aren't as effective for the specific task of bird classification. Therefore, we would retrain these layers on a bird-specific dataset, allowing the network to develop the high-level understanding necessary for distinguishing different bird species.

Transfer learning is a versatile technology with applications in various industries. Let's explore where it can be used.

Transfer learning is necessary in improving machine learning models for NLP tasks. It empowers models to detect and understand language elements, dialects, phrases, and vocabulary.

In computer vision, transfer learning takes pre-trained models and repurposes them for tasks involving smaller datasets or specific image features. It's handy for tasks such as object detection, where models can leverage the knowledge of identifying common objects or image structures.

Transfer learning has become indispensable in deep learning and neural networks. Training complex neural networks demands substantial computational resources and time. Transfer learning alleviates this burden by transferring useful features from one network to another, making it an efficient approach for model development. These transfer learning techniques find practical application in various industries, such as:

Transfer learning is a shortcut for AI that changes how we teach machines to be more intelligent. It makes AI more effective in understanding human behavior, which means better Health and Fitness apps, self-driving cars, AI-ready smartphones, and shopping experiences. In the words of Mark Van Doren, "The art of teaching is the art of assisting discovery." Now, AI is doing both teaching and discovering for us.

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Measuring CO2 with Machine Learning The Independent – The Indy Online

February 6, 2024 | Author: admin

Artificial intelligence seems almost inescapable in today's increasingly technology driven world.

Deep learning models, such as OpenAIs Chat GPT, have been at the forefront of public amazement and controversy since their mainstream introduction in late 2022.

Today, Fort Lewis College students are discovering new ways that artificial intelligence can be used to reduce the costs of studying the environment.

Lincoln Scheer, a third-year computer engineering student, said he is using machine learning to measure carbon dioxide levels in areas affected by wildfires.

While one goal of this project is to map carbon dioxide levels, the project also seeks to reduce the cost necessary for environmental science, he said.

"It's really important that we lower the costs for these sensors, he said. We need lower cost tools, because a lot of these communities don't have the funding.

So what is the price difference between these tools? Scheer says the $30,000 machines typically used in this study could eventually be replaced by inexpensive alternatives that cost $60.

Scheer said the inexpensive sensors are less accurate than their thousand dollar counterparts, but can be calibrated with AI to match the results of high-end equipment.

Dr. Joanna Casey, assistant professor of physics and engineering, agrees with the necessity for inexpensive alternatives.

According to the World Health Organization, 7 million people die premature deaths due to air pollution, Casey said.

Having low-cost tools to measure air quality and levels of pollution can help people understand and minimize their exposure, and have lower and less health consequences, she said.

And for Durango, an area affected by wildfire smoke, students have a perfect testing ground, Scheer said.

While Scheers project is about a years time from completion, he is currently working to collect wildfire data, such as at the recent Perins Peak fire, he said.

However, this process of machine learning is slightly different from deep learning language models, such as the previously mentioned ChatGPT.

Anders Ladow, a third year computer engineering major and recent AI collaborator with Scheer, said that machine learning models require human intervention.

You have to define exactly what the machine learning algorithm is doing, he said. What you give to it to analyze has to be really specific, and the algorithm can't make any changes to that data that you're feeding to the model.

The main difference between deep learning models, like ChatGPT, and Scheers machine learning project is that deep learning models can actively change the data sets it has been fed, Ladow said.

Despite these differences, both models are very useful for data extraction, Ladow said.

Additionally, Casey said that air quality sensing systems using machine learning have already entered the market.

We're standing on the shoulders of giants, Casey said. What we're able to do now is move into more complex problems that would be difficult to model or understand without these tools.

Some of these problems that artificial intelligence could assist with are analyzing complex visual data, such as analyzing security footage, Ladow said.

While tangible effects of artificial intelligence are likely a few years away, projects like Scheers highlight the capabilities of machine learning.

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High-Stakes Chess: Alaska and Hawaiian Airlines’ Latest Maneuvers Examined – Beat of Hawaii

February 6, 2024 | Author: admin

Alaska Airlines Ben Minicucci and Hawaiian Airlines Peter Ingram have been on tour in Hawaii to help bolster their proposed merger, which will soon be in the sights of the US Justice Department. Their most recent stop among many was a business meeting at the Hilton Hawaiian Village Waikiki Beach Resort hosted by the Chamber of Commerce Hawaii.

How much does getting local buy-in to their 1.9B plan of merger matter now, with the whole deal contingent on federal regulatory approval? We say it could be important to have the communitys backing.

Alaska thinks so, too. They recently established a Hawaii Community Advisory Board to honor Hawaii and Hawaiian Airlines.

During their discussions, the well-known CEOs pointed to significant benefits that would be realized, including a major route expansion. We predict the fleet will be rebranded Alaska-Hawaiian, and Alaska will strategically deploy the widebody fleets, originally from Hawaiian, globally.

It would not surprise us to see Seattle to Sydney, for example. Currently, that route does not exist. What Hawaiian gives Alaska is the chance to be an international widebody carrier, especially across the Asia Pacific region. That would not be possible otherwise, given Alaskas all Boeing 737 fleet.

Honolulu would become the second-largest hub in the Alaska network. We would expect to see the current Hawaiian Airlines lounges in Honolulu significantly upgraded. Alaska has further said it will continue serving POG and be competitive and robust with interisland service.

Hawaiian was enthusiastic about the strengthened loyalty program that would ensue, saying, Now you can use those miles on a larger network. Now youve chosen what miles on oneworld, and really the strength of that combined loyalty program is going to be really powerful for our guests from here in Hawaii.

Exactly how a combined loyalty program would work remains elusive and will be determined with other significant issues later in the process.

We cant help but recall the expression on Peter Ingrams face when they announced the merger, and it appeared to us to be anything but enthusiastic (image above). But that was two months ago, and now the situation is different. By the way, this is more like Peter normally looks.

Alaskas affable Minicucci emphasized something inaccessible to Hawaiian Airlines passengers. That was the recognition that the most elite passengers would receive access to all of Alaskas lounges and those of oneworld Alliance.

Currently, Hawaiian Miles are relatively useless beyond the scope of Hawaiians limited flight network. That compared with Alaskas miles, which BOH editors have used for travel on other global airlines nimbly.

As a global alliance comprising more than a dozen airlines, the CEOs believe the merger will enhance competitiveness against the Big Four airlines. However, they acknowledged that the merger is still in the planning stages, requiring careful consideration of factors such as combining reservation systems and staff.

Alaska said it plans to grow the combined airlines presence in Honolulu and Hawaii. Minicucci said, Our idea, just to be clear, is to grow this pie, not to keep it the same. We see a big presence here.

That posturing aims to assure Hawaii stakeholders that the 7,000 Hawaiian Air employees, primarily based in Hawaii, will still be needed. Ben continued, Most of the operations personnel, of course, well need. The question is what the back-office support will be. Obviously, theres a duplication in both companies. Were going to work through that whole process, and were going to be extremely communicative in terms of what our progress is.

There is no further information since the merger was first announced. At that time, questions from those in the audience included how the two envision combining staff that have both union and non-union personnel.

Also this week, Alaska announced its annual incentive payout, which paid employees $200 million last year. Based on company performance, employees earn an added bonus. Alaska said that equated to more than 6 percent of most employees annual pay last year.

Hawaiian has confirmed that it has obtained FAA approval to deploy its free Starlink WiFi on the companys troubled Airbus A321neo, which is currently being installed on that fleet. It will be the first major airline to put this technology on board, and it is expected to be the fastest, most capable inflight connectivity available worldwide, offered free to every guest.

In addition to regulatory approval, the merger awaits concurrence from shareholders, including those at Hawaiian, who will vote in just two weeks. A Hawaiian Airlines shareholder lawsuit has also been filed to block the merger. The entire process may take up to 18 months.

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Honorary Grandmaster Title Posthumously Awarded To Sultan Khan – Chess.com

February 6, 2024 | Author: admin

On Friday, February 2, 2024, the legendary chess player Mian Sultan Khan (1903-1966) was posthumously awarded the honorary grandmaster title. FIDE President Arkady Dvorkovich presented the award to caretaker Prime Minister of Pakistan Anwaar-ul-Haq Kakar at a ceremony in Islamabad.

Khan was one of the most remarkable chess talents the chess world has ever seen. As a British subject from Punjab (he became a Pakistani citizen in 1947), he visited Europe between 1929 and 1933, when he proved himself to be at the level of some of the world's best chess players.

In a period of just four years, before returning to India, Khan scored a number of great successes on the chessboard:

Below is Khan's win against Capablanca, played on December 31, 1930. "The fact that even under such conditions he succeeded in becoming a champion reveals a genius for chess which is nothing short of extraordinary," wrote Capablanca years later.

Sadly for the chess world, Khan's career at the board was short-lived. In late 1933 he returned to the Khushab district of the Punjab (present-day Pakistan), where he owned land and where he lived until his death in 1966.

Acknowledging his world-class results, some chess fans had been pushing for Khan to get the grandmaster title for a while. This finally happened at the ceremony on Friday in Islamabad.

GM Daniel King, who published a book about Khan in 2020, commented: "In 1950, FIDE made the grandmaster title official, recognizing several leading players of the day, as well as players from the past who were beyond their peak, but still living. Sultan Khan was at least as strong as several of those players, and that was a sad omission. It is fitting that this great player and great man has been posthumously awarded the grandmaster title."

Khan might not be the only one to receive this treatment. "We are considering a couple more such successful players," Dvorkovich told Chess.com.

"We appreciate and welcome this belated recognition by FIDE," Khan's granddaughter Atiyab Sultan commented to Chess.com. Sheadded that the correct name of her grandfather is Mian Sultan Khan, saying: "Mir was added erroneously by western writers."

As part of their efforts to give him his due, Sultan and her father Ather Sultan have authored an authentic and comprehensive biography of him for which they are currently seeking an international publisher.

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Honorary Grandmaster Title Posthumously Awarded To Sultan Khan - Chess.com

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This is What Happens When You Train Chess Every Day for a Month! – ChessBase

February 6, 2024 | Author: admin

2/1/2024 ChessBase media manager Arne Kaehler comes up with amusing ideas daily. One of these has now been brought to life: The challenge is to study chess using all ChessBase products for at least two hours daily over a month, and to win a tournament as the group's lowest-rated player. Sounds crazy? Perhaps. But what could possibly go wrong when chess is such a delightful activity? In the forthcoming video series, Arne invites you to join his adventure, looking into nearly everything ChessBase offers, learning from chess experts, experiencing ups and downs, and, most importantly, addressing the ultimate question: Will he actually succeed? | Photo: (right) Sandra Schmidt

ChessBase 17 & Fritz 19

The perfect combination: the professional database program ChessBase 17 plus the brand new world champion program Fritz19. This Christmas bundle includes ChessBase 17 as a single program and the full version of the new chess program Fritz19.

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This is What Happens When You Train Chess Every Day for a Month! - ChessBase

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S Mari Arul clinches 20th International Chess Puzzle Solving Contest 2024 – ChessBase India

February 6, 2024 | Author: admin

FIDE Master and former Indian youth Under-25 champion S Mari Arul of Southern Railways took the first place in the World Chess Federation of Composition's 20th International Chess Solving contest 2024 which concluded recently at Aladi Aruna Public School, Chennai. The contest was organised by Aladi Aruna Foundation under the aegis FIDE in association with Mount Chess Academy & Magnus Chess Academy on 21st January 2024.

Champion and Runner-up - SMari Arul and S Raghuraman

The second place was taken by another renowned solver S Raghuraman of Chesslang Group while the third spot went to veteran solver Seetharaman Kalyan.

Second Runner-up - Seetharaman Kalyan

20th International Chess Puzzle Solving Contest 2024 banner

January 4th of every year is celebrated as the International Day of Chess Composition and in that month contests related to Chess Problem Solving, Problem Composition and other activities are held throughout the world.

Winner - S Mari Arul solving a puzzle

On 21st January, there were solving contest for Advanced, Intermediate solvers as well as for newcomers to the world of chess problem solving. Event was inaugurated with an introductory lecture on Chess Problem Solving and Composition by renowned International Master of Chess Composition, CGS Narayanan.

Coordinator IM CGS Narayanan in action

The intermediate and the junior category were won by K S Naveen and Jason Jebezilin respectively.

Winner in Junior category - Jason Jebezilin

Solvers in action

The problem solving contests had total of 56 participants across all categories filling up the venue hall and carried a total prize amount of 10000 and 5000 worth of cups for ten prize winners in the junior category.

Organizer and winner's team

The Chief Guests of the Valedictory function were Women Grand Master Savitha Shri B and Dr. V Balaji, Founder and Correspondent, Aladi Aruna Foundation jointly distributed the prizes to the winners in the presence of Mr. CGS Narayanan, International Master for Chess Composition, Event Coordinators V Ravichandran, FIDE Trainer and Candidate Master and R Muthukumar, International Arbiter.

20th International Solving Contest 2023 organised by Aladi Aruna Foundation

Advanced Category

Intermediate Category

Junior Category - Under-13

V Ravichandran

Fide Trainer and Candidate Master

Local Controller for Chennai-India

International Solving Contest 2024

V Ravichandran is an International Rated Chess player, FIDE Trainer, Candidate Master and International Arbiter. He is also a Research & Development Committee Member, All India Chess Federation. He has officiated as an Arbiter in the 44th Chess Olympiad 2022, officiated as Indian team chess coach at Commonwealth Chess Championship in Sri Lanka 2022. Furthermore, he is a founder member of Mount Chess Academy, Chennai which organised many Nationals and International rated chess tournaments.

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S Mari Arul clinches 20th International Chess Puzzle Solving Contest 2024 - ChessBase India

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Who is Jose Martinez? Chess streamer gets once in a million draw against GMHikaru – Sportskeeda

February 6, 2024 | Author: admin

The first major online Chess tournament of the year, the Champions Chess Tour Chessable Masters 2024, witnessed an unexpected turn. Notably, Peruvian streamer and Grandmaster Jose Martinez, also known as "GMJospemm," secured a draw against fellow streamer and Grandmaster Hikaru "GMHikaru" from what appeared to be a completely losing position; the phenomenon has been described as "once in a million."

GMJospemm is one of the leading Spanish-speaking Twitch streamers with nearly 30K followers. He's renowned for his chess content and holds impressive FIDE ratings of 2610 (Classical), 2641 (Rapid), and 2703 (Blitz).

On Chess.com, the streamer boasts a Rapid rating of 2724 and a Blitz rating of 3067. He has previously won the platform's weekly online blitz tournament called Titled Tuesdays.

Jose Martinez, also known as GMJospemm, had an intriguing day during the Play-in of the 2024 Champions Chess Tour Chessable Masters. The streamer and Grandmaster concluded the day at the top of the table with 7.5 points, 0.5 points ahead of third-place holder, GMHikaru.

Despite delivering a strong performance throughout the day, the Peruvian streamer found himself in a deeply poor position when he faced GMHikaru. However, on move 55, the latter appeared to blunder his knight.

Until that point, the position heavily favored white with a significant advantage of +4.9 (with GMHikaru playing as white). However, a simple oversight ended up costing him the win and his material advantage.

Naturally, a visibly startled GMHikaru was seen with his head in his hand, and this reaction was captured by the Chess.com livestream. Watch the moment he made a blunder:

(Timestamp: 04:10:41)

Jose Martinez wrapped up Day 1 of the Champions Chess Tour Chessable Masters 2024 by topping the table. This which was especially noteworthy as it aligned with his birthday on January 31, making it a particularly enjoyable day for him.

At the end of Chess.com's stream, the 25-year-old was interviewed about his performance. The Peruvian said (Transcript via Chess.com stream):

(Timestamp: 04:54:15)

Meanwhile, GM Hikaru has been one of the most discussed players in recent months. He has been involved in a back-and-forth with fellow Grandmaster Vladimir Kramnik, who has been suggesting that Hikaru's online matches should be scrutinized.

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Who is Jose Martinez? Chess streamer gets once in a million draw against GMHikaru - Sportskeeda

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