Portland, OR , April 20, 2023 (GLOBE NEWSWIRE) -- According to the report published by Allied Market Research, the global machine learning in pharmaceutical industry market garnered $1.2 billion in 2021, and is estimated to generate $26.2 billion by 2031, manifesting a CAGR of 37.9% from 2022 to 2031. The report provides an extensive analysis of changing market dynamics, major segments, value chain, competitive scenario, and regional landscape. This research offers a valuable guidance to leading players, investors, shareholders, and startups in devising strategies for the sustainable growth and gaining competitive edge in the market.

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The research provides detailed segmentation of the global machine learning in pharmaceutical industry market based on component, enterprise size, deployment, and region. The report discusses segments and their sub-segments in detail with the help of tables and figures. Market players and investors can strategize according to the highest revenue-generating and fastest-growing segments mentioned in the report.

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Based on component, the solution segment held the highest share in 2021, accounting for more than two-thirds of the global machine learning in pharmaceutical industry market and is expected to continue its leadership status during the forecast period. However, the services segment is expected to register the highest CAGR of 39.5% from 2022 to 2031.

On the basis of enterprise size, the large enterprises segment accounted for the highest share in 2021, contributing to around three-fourths of the global machine learning in pharmaceutical industry market, and is expected to maintain its lead in terms of revenue during the forecast period. Moreover, the SMEs segment is expected to manifest the highest CAGR of 40.1% from 2022 to 2031.

Based on deployment, the cloud segment accounted for the highest share in 2021, holding more than two-thirds of the global machine learning in pharmaceutical industry market, and is expected to continue its leadership status during the forecast period. This segment is estimated to grow at the highest CAGR of 40.0% during the forecast period. The report also discusses on-premise segment.

Based on region, North America held the largest share in 2021, contributing to nearly half of the global machine learning in pharmaceutical industry market share, and is projected to maintain its dominant share in terms of revenue in 2031. In addition, the Asia-Pacific region is expected to manifest the fastest CAGR of 42.4% during the forecast period. The report also analyzes the markets in Europe and LAMEA regions.

Leading market players of the global machine learning in pharmaceutical industry market analyzed in the research include BioSymetrics Inc., Deep Genomics, Atomwise Inc., NVIDIA Corporation, International Business Machines Corporation, Microsoft Corporation, IBM, cyclica inc., Cloud Pharmaceuticals, Inc., and Alphabet Inc.

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The report provides a detailed analysis of these key players of the global machine learning in pharmaceutical industry market. These players have adopted different strategies such as new product launches, collaborations, expansion, joint ventures, agreements, and others to increase their market share and maintain dominant shares in different regions. The report is valuable in highlighting business performance, operating segments, product portfolio, and strategic moves of market players to showcase the competitive scenario.

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Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of "Market Research Reports" and "Business Intelligence Solutions." AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.

We are in professional corporate relations with various companies and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Allied Market Research CEO Pawan Kumar is instrumental in inspiring and encouraging everyone associated with the company to maintain high quality of data and help clients in every way possible to achieve success. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.

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Machine Learning in Pharmaceutical Industry Market Is Expected to ... - GlobeNewswire

Artificial intelligence (AI) is becoming ever more prevalent in our lives. Its no longer confined to certain industries or research institutions; AI is now for everyone.

Its hard to dodge the deluge of AI content being produced, and harder yet to make sense of the many terms being thrown around. But we cant have conversations about AI without understanding the concepts behind it.

Weve compiled a glossary of terms we think everyone should know, if they want to keep up.

An algorithm is a set of instructions given to a computer to solve a problem or to perform calculations that transform data into useful information.

The alignment problem refers to the discrepancy between our intended objectives for an AI system and the output it produces. A misaligned system can be advanced in performance, yet behave in a way thats against human values. We saw an example of this in 2015 when an image-recognition algorithm used by Google Photos was found auto-tagging pictures of black people as gorillas.

Artificial general intelligence refers to a hypothetical point in the future where AI is expected to match (or surpass) the cognitive capabilities of humans. Most AI experts agree this will happen, but disagree on specific details such as when it will happen, and whether or not it will result in AI systems that are fully autonomous.

Artificial neural networks are computer algorithms used within a branch of AI called deep learning. Theyre made up of layers of interconnected nodes in a way that mimics the neural circuitry of the human brain.

Big data refers to datasets that are much more massive and complex than traditional data. These datasets, which greatly exceed the storage capacity of household computers, have helped current AI models perform with high levels of accuracy.

Big data can be characterised by four Vs: volume refers to the overall amount of data, velocity refers to how quickly the data grow, veracity refers to how complex the data are, and variety refers to the different formats the data come in.

The Chinese Room thought experiment was first proposed by American philosopher John Searle in 1980. It argues a computer program, no matter how seemingly intelligent in its design, will never be conscious and will remain unable to truly understand its behaviour as a human does.

This concept often comes up in conversations about AI tools such as ChatGPT, which seem to exhibit the traits of a self-aware entity but are actually just presenting outputs based on predictions made by the underlying model.

Deep learning is a category within the machine-learning branch of AI. Deep-learning systems use advanced neural networks and can process large amounts of complex data to achieve higher accuracy.

These systems perform well on relatively complex tasks and can even exhibit human-like intelligent behaviour.

A diffusion model is an AI model that learns by adding random noise to a set of training data before removing it, and then assessing the differences. The objective is to learn about the underlying patterns or relationships in data that are not immediately obvious.

These models are designed to self-correct as they encounter new data and are therefore particularly useful in situations where there is uncertainty, or if the problem is very complex.

Explainable AI is an emerging, interdisciplinary field concerned with creating methods that will increase users trust in the processes of AI systems.

Due to the inherent complexity of certain AI models, their internal workings are often opaque, and we cant say with certainty why they produce the outputs they do. Explainable AI aims to make these black box systems more transparent.

These are AI systems that generate new content including text, image, audio and video content in response to prompts. Popular examples include ChatGPT, DALL-E 2 and Midjourney.

Data labelling is the process through which data points are categorised to help an AI model make sense of the data. This involves identifying data structures (such as image, text, audio or video) and adding labels (such as tags and classes) to the data.

Humans do the labelling before machine learning begins. The labelled data are split into distinct datasets for training, validation and testing.

The training set is fed to the system for learning. The validation set is used to verify whether the model is performing as expected and when parameter tuning and training can stop. The testing set is used to evaluate the finished models performance.

Large language models (LLM) are trained on massive quantities of unlabelled text. They analyse data, learn the patterns between words and can produce human-like responses. Some examples of AI systems that use large language models are OpenAIs GPT series and Googles BERT and LaMDA series.

Machine learning is a branch of AI that involves training AI systems to be able to analyse data, learn patterns and make predictions without specific human instruction.

While large language models are a specific type of AI model used for language-related tasks, natural language processing is the broader AI field that focuses on machines ability to learn, understand and produce human language.

Parameters are the settings used to tune machine-learning models. You can think of them as the programmed weights and biases a model uses when making a prediction or performing a task.

Since parameters determine how the model will process and analyse data, they also determine how it will perform. An example of a parameter is the number of neurons in a given layer of the neural network. Increasing the number of neurons will allow the neural network to tackle more complex tasks but the trade-off will be higher computation time and costs.

The responsible AI movement advocates for developing and deploying AI systems in a human-centred way.

One aspect of this is to embed AI systems with rules that will have them adhere to ethical principles. This would (ideally) prevent them from producing outputs that are biased, discriminatory or could otherwise lead to harmful outcomes.

Sentiment analysis is a technique in natural language processing used to identify and interpret the emotions behind a text. It captures implicit information such as, for example, the authors tone and the extent of positive or negative expression.

Supervised learning is a machine-learning approach in which labelled data are used to train an algorithm to make predictions. The algorithm learns to match the labelled input data to the correct output. After learning from a large number of examples, it can continue to make predictions when presented with new data.

Training data are the (usually labelled) data used to teach AI systems how to make predictions. The accuracy and representativeness of training data have a major impact on a models effectiveness.

A transformer is a type of deep-learning model used primarily in natural language processing tasks.

The transformer is designed to process sequential data, such as natural language text, and figure out how the different parts relate to one another. This can be compared to how a person reading a sentence pays attention to the order of the words to understand the meaning of the sentence as a whole.

One example is the generative pre-trained transformer (GPT), which the ChatGPT chatbot runs on. The GPT model uses a transformer to learn from a large corpus of unlabelled text.

The Turing test is a machine intelligence concept first introduced by computer scientist Alan Turing in 1950.

Its framed as a way to determine whether a computer can exhibit human intelligence. In the test, computer and human outputs are compared by a human evaluator. If the outputs are deemed indistinguishable, the computer has passed the test.

Googles LaMDA and OpenAIs ChatGPT have been reported to have passed the Turing test although critics say the results reveal the limitations of using the test to compare computer and human intelligence.

Unsupervised learning is a machine-learning approach in which algorithms are trained on unlabelled data. Without human intervention, the system explores patterns in the data, with the goal of discovering unidentified patterns that could be used for further analysis.

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AI to Z: all the terms you need to know to keep up in the AI hype age - The Conversation

This is a post hoc exploratory analysis of the COVID STEROID 2 trial7. It was conducted according to a statistical analysis plan, which was written after the pre-planned analyses of the trial were reported, but before any of the analyses reported in this manuscript were conducted (https://osf.io/2mdqn/). This manuscript was presented according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist12, with Bayesian analyses reported according to the Reporting of Bayes Used in clinical STudies (ROBUST) guideline13.

HTE implies that some individuals respond differently, i.e., better or worse, than others who receive the same therapy due to differences between individuals. Most trials are designed to evaluate the average treatment effect, which is the summary of all individual effects in the trial sample (see supplementary appendix for additional technical details). Traditional HTE methods examine patient characteristics one at a time, looking to identify treatment effect differences according to individual variables. This approach is well known to be limited as it is underpowered (due to adjustment for multiple testing) and does not account for the fact that many characteristics under examination are correlated and may have synergistic effects. As a result, more complex relationships between variables that better define individuals, and thus may better inform understanding about the variations in treatment response, may be missed using conventional HTE approaches. Thus, identifying true and clinically meaningful HTE requires addressing these data and statistical modeling challenges. BART is inherently an attractive method for this task, as the algorithm automates the detection of nonlinear relationships and interactions hierarchically based on the strength of the relationships, thereby reducing researchers discretion when analyzing experimental data. This approach also avoids any model misspecification or bias inherent in traditional interaction test procedures. BART can also be deployed, as we do herein, within the counterfactual framework to study HTE, i.e., to estimate conditional average treatment effects given the set of covariates or potential effect modifiers11,14,15, and has shown superior performance to competing methods in extensive simulation studies16,17. These features make BART an appealing tool for trialists to explore HTE to inform future confirmatory HTE analyses in trials and hypothesis generation more broadly. Thus, this analysis used BART to evaluate the presence of multivariable HTE and estimate conditional average treatment effects among meaningful subgroups in the COVID STEROID 2 trial.

The COVID STEROID 2 trial7 was an investigator-initiated, international, parallel-group, stratified, blinded, randomized clinical trial conducted at 31 sites in 26 hospitals in Denmark, India, Sweden, and Switzerland between 27 August 2020 and 20 May 20217,18. The trial was approved by the regulatory authorities and ethics committees in all participating countries.

The trial enrolled 1000 adult patients hospitalized with COVID-19 and severe hypoxemia (10 L oxygen/min, use of non-invasive ventilation (NIV), continuous use of continuous positive airway pressure (cCPAP), or invasive mechanical ventilation (IMV)). Patients were primarily excluded due to previous use of systemic corticosteroids for COVID-19 for 5 or more days, unobtainable consent, and use of higher-dose corticosteroids for other indications than COVID-194,17. Patients were randomized 1:1 to dexamethasone 12mg/d or 6mg/d intravenously once daily for up to 10days. Additional details are provided in the primary protocol and trial report7,18.

The trial protocol was approved by the Danish Medicines Agency, the ethics committee of the Capital Region of Denmark, and institutionally at each trial site. The trial was overseen by the Collaboration for Research in Intensive Care and the George Institute for Global Health. A data and safety monitoring committee oversaw the safety of the trial participants and conducted 1 planned interim analysis. Informed consent was obtained from the patients or their legal surrogates according to national regulations.

We examined two outcomes: (1) DAWOLS at day 90 (i.e., the observed number of days without the use of IMV, circulatory support, and kidney replacement therapy without assigning dead patients the worst possible value), and (2) 90-day mortality. Binary mortality outcomes were used to match the primary trial analysis; time-to-event outcomes also generally tend to be less robust for ICU trials19. We selected DAWOLS at day 90 in lieu of the primary outcome of the trial (DAWOLS at day 28) and to align with other analyses of the trial which sought to examine outcomes in a longer term. Both outcomes were assessed in the complete intention-to-treat (ITT) population, which was 982 after the exclusion of patients without consent for the use of their data7. As the sample size is fixed, there was no formal sample size calculation for this study.

While BART is a data-driven approach that can scan for interdependent relationships among any number of factors, we only examined heterogeneity across a pre-selected set of factors deemed to be clinically relevant by the authors and members of the COVID STEROID 2 trial Management Committee. The pre-selected variables that were included in this analysis are listed below with the scale used in parentheses. Continuous covariates were standardized to have a mean of 0 and a standard deviation of 1 prior to analysis. Detailed variable definitions are available in the study protocol18.

participant age (continuous),

limitations in care (yes, no),

level of respiratory support (open system versus NIV/cCPAP versus IMV)

interleukin-6 (IL-6) receptor inhibitors (yes, no),

use of dexamethasone for up to 2days versus use for 3 to 4days prior to randomization,

participant weight (continuous),

diabetes mellitus (yes, no),

ischemic heart disease or heart failure (yes, no),

chronic obstructive pulmonary disease (yes, no), and,

immunosuppression within 3months prior to randomization (yes, no).

We evaluated HTE on the absolute scale (i.e., mean difference in days for the number of DAWOLS at day 90 and the risk difference for 90-day mortality). The analysis was separated into two stages14,20,21,22. In the first stage, conditional average treatment effects were estimated according to each participants covariates using BART models. The DAWOLS outcome was treated as a continuous variable and analyzed using standard BART, while the binary mortality outcome was analyzed using logit BART. In the second stage, a fit-the-fit approach was used, where the estimated conditional average treatment effects were used as dependent variables in models to identify covariate-defined subgroups differential treatment effects. This second stage used classification and regression trees models23, where the maximum depth was set to 3 as a post hoc decision to aid interpretability. As the fit-the-fit reflects estimates from the BART model, the resulting overall treatment effects (e.g., risk difference) vary slightly from the raw trial data.

BART models are often fit using a sum of 200 trees and specifying a base prior of 0.95 and a power prior of 2, which penalize substantial branch growth within each tree15. Although these default hyperparameters tend to work well in practice, it was possible they were not optimal for this data. Thus, the hyperparameters were evaluated using tenfold cross-validation, comparing predictive performance of the model under 27 pre-specified possibilities, namely every combination of power priors equal to 1, 2, or 3, base priors equal to 0.25, 0.5, or 0.95, and number of trees equal to 50, 200, or 400. The priors corresponding to the lowest cross-validation error were used in the final models. Each model used a Markov chain Monte Carlo procedure consisting of 4 chains that each had 100 burn-in iterations and a total length of 1100 iterations. Posterior convergence for each model was assessed using the diagnostic procedures described in Sparapani et al.24. Model diagnostics were good for all models. All parameters seemed to converge within the burn-in period and the z-scores for Gewekes convergence diagnostic25 were approximately standard normal. All BART models were fit using R statistical computing software v. 4.1.226 with the BART package v. 2.924, and all CART models were fit using the rpart package v. 4.1.1627.

The analysis was performed under the ITT paradigm; compliance issues were considered minimal. As in the primary analyses of the trial, the small amount of missing outcome data was ignored in the primary analyses. Sensitivity analyses were performed under best/worst- and worst/best-case imputation. For best/worst-case imputation, the entire estimation procedure was repeated after setting all missing mortality outcome data in the 12mg/d group to alive at 90days and all missing mortality outcome data in the 6mg/d group to dead at 90days. Then, all days with missing life support data were set to alive without life support for the 12mg/d group and the opposite for the 6mg/d group. Under worst/best-case imputation, the estimation procedure was repeated under the opposite conditions, e.g., setting all missing mortality outcome data in the 12mg/d group to dead at 90days and all missing mortality outcome data in the 6mg/d group to alive at 90days.

The resulting decision trees from each fit-the-fit analysis described above (one for the 90-day mortality outcome, and one for the 90-day DAWOLS outcome) were outputted (with continuous variables de-standardized, i.e., back-translated to the original scales). Likewise, the resulting decision trees for each outcome after best- and worst-case imputation were outputted for comparison with the complete records analyses. All statistical code is made available at https://github.com/harhay-lab/Covid-Steroid-HTE.

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Causal Bayesian machine learning to assess treatment effect ... - Nature.com

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The race to build generative AI is revving up, marked by both the promise of these technologies capabilities and the concern about the dangers they could pose if left unchecked.

We are at the beginning of an exponential growth phase for AI. ChatGPT, one of the most popular generative AI applications, has revolutionized how humans interact with machines. This was made possible thanks to reinforcement learning with human feedback (RLHF).

In fact, ChatGPTs breakthrough was only possible because the model has been taught to align with human values. An aligned model delivers responses that are helpful (the question is answered in an appropriate manner), honest (the answer can be trusted), and harmless (the answer is not biased nor toxic).

This has been possible because OpenAI incorporated a large volume of human feedback into AI models to reinforce good behaviors. Even with human feedback becoming more apparent as a critical part of the AI training process, these models remain far from perfect and concerns about the speed and scale in which generative AI is being taken to market continue to make headlines.

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Lessons learned from the early era of the AI arms race should serve as a guide for AI practitioners working on generative AI projects everywhere. As more companies develop chatbots and other products powered by generative AI, a human-in-the-loop approach is more vital than ever to ensure alignment and maintain brand integrity by minimizing biases and hallucinations.

Without human feedback by AI training specialists, these models can cause more harm to humanity than good. That leaves AI leaders with a fundamental question: How can we reap the rewards of these breakthrough generative AI applications while ensuring that they are helpful, honest and harmless?

The answer to this question lies in RLHF especially ongoing, effective human feedback loops to identify misalignment in generative AI models. Before understanding the specific impact that reinforcement learning with human feedback can have on generative AI models, lets dive into what it actually means.

To understand reinforcement learning, you need to first understand the difference between supervised and unsupervised learning. Supervised learning requires labeled data which the model is trained on to learn how to behave when it comes across similar data in real life. In unsupervised learning, the model learns all by itself. It is fed data and can infer rules and behaviors without labeled data.

Models that make generative AI possible use unsupervised learning. They learn how to combine words based on patterns, but it is not enough to produce answers that align with human values. We need to teach these models human needs and expectations. This is where we use RLHF.

Reinforcement learning is a powerful approach to machine learning (ML) where models are trained to solve problems through the process of trial and error. Behaviors that optimize outputs are rewarded, and those that dont are punished and put back into the training cycle to be further refined.

Think about how you train a puppy a treat for good behavior and a time out for bad behavior. RLHF involves large and diverse sets of people providing feedback to the models, which can help reduce factual errors and customize AI models to fit business needs. With humans added to the feedback loop, human expertise and empathy can now guide the learning process for generative AI models, significantly improving overall performance.

Reinforcement learning with human feedback is critical to not only ensuring the models alignment, its crucial to the long-term success and sustainability of generative AI as a whole. Lets be very clear on one thing: Without humans taking note and reinforcing what good AI is, generative AI will only dredge up more controversy and consequences.

Lets use an example: When interacting with an AI chatbot, how would you react if your conversation went awry? What if the chatbot began hallucinating, responding to your questions with answers that were off-topic or irrelevant? Sure, youd be disappointed, but more importantly, youd likely not feel the need to come back and interact with that chatbot again.

AI practitioners need to remove the risk of bad experiences with generative AI to avoid degraded user experience. With RLHF comes a greater chance that AI will meet users expectations moving forward. Chatbots, for example, benefit greatly from this type of training because humans can teach the models to recognize patterns and understand emotional signals and requests so businesses can execute exceptional customer service with robust answers.

Beyond training and fine-tuning chatbots, RLHF can be used in several other ways across the generative AI landscape, such as in improving AI-generated images and text captions, making financial trading decisions, powering personal shopping assistants and even helping train models to better diagnose medical conditions.

Recently, the duality of ChatGPT has been on display in the educational world. While fears of plagiarism have risen, some professors are using the technology as a teaching aid, helping their students with personalized education and instant feedback that empowers them to become more inquisitive and exploratory in their studies.

RLHF enables the transformation of customer interactions from transactions to experiences, automation of repetitive tasks and improvement in productivity. However, its most profound effect will be the ethical impact of AI. This, again, is where human feedback is most vital to ensuring the success of generative AI projects.

AI does not understand the ethical implications of its actions. Therefore, as humans, it is our responsibility to identify ethical gaps in generative AI as proactively and effectively as possible, and from there implement feedback loops that train AI to become more inclusive and bias-free.

With effective human-in-the-loop oversight, reinforcement learning will help generative AI grow more responsibly during a period of rapid growth and development for all industries. There is a moral obligation to keep AI as a force for good in the world, and meeting that moral obligation starts with reinforcing good behaviors and iterating on bad ones to mitigate risk and improve efficiencies moving forward.

We are at a point of both great excitement and great concern in the AI industry. Building generative AI can make us smarter, bridge communication gaps and build next-gen experiences. However, if we dont build these models responsibly, we face a great moral and ethical crisis in the future.

AI is at crossroads, and we must make AIs most lofty goals a priority and a reality. RLHF will strengthen the AI training process and ensure that businesses are building ethical generative AI models.

Sujatha Sagiraju is chief product officer at Appen.

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How reinforcement learning with human feedback is unlocking the power of generative AI - VentureBeat

World Chess Championship 2023 Game 11 Highlights (Ding Liren vs Ian Nepomniachtchi):Game 11 of the World Chess Championship between Russias Ian Nepomniachtchi and Chinas Ding Liren has ended in a draw. Both players decided to call it quits after 39 moves, just an hour and 40 minutes into the game. Nepo is still a point ahead, with just three more games to go. Scroll down to catch all the action.

Though the last four games ended in a draw, it has generally been an exciting, seesaw contest. While Nepo won Games 2,5 and 7, Ding was victorious in Games 4 and 6.

Grandmaster Pravin Thipsay has been analysing games of the World Chess Championship for The Indian Express. You can read his analysis of Game 10, Game 9,Game 8,Game 7,Game 6,Game 5, Game 4, Game 3, Game 2, and Game 1.

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Keep scrolling for highlights from Game 11.

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World Chess Championship. (Photo: FIDE/Stev Bonhage)

Such an entertaining World Chess Championships, which has seen both players attack, win and defend whenever they have to, could be decided by a single point. Russian Ian Nepomniachtchi, who leads Ding Liren 5.5-4.5 after 10 games, is probably banking on it. He has his task cut out: he just cant afford to lose a game.

Game 10 on Sunday at the St Regis in Astana, Kazakhstan, was a clear indication that Nepo is not going to go on the offensive when playing with Black pieces. More importantly, hes prepared to not give Ding any chance of getting that equalising win. This means that Ding will have to come up with something truly extraordinary in order to win. With only four games to go, it seems a Herculean task. [Read More]

First published on: 24-04-2023 at 13:36 IST

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World Chess Championship 2023 Game 11 As It Happened: Ian Nepomniachtchi stays one point ahead of Ding Liren after another draw - The Indian Express

Steve Cuozzo

Business

realty check

By Steve Cuozzo

April 23, 2023 | 12:57pm

Sources say that UBS and Paul Weiss are considering moves that would involve three first-line office towers.Google Maps

Heres a story of a kind we havent written in a while: a large-scale chess game involving potential moves by two huge companies and three first-line office towers.

Its refreshing to learn of large deals that might or might not even happen at a time when the high-end commercial market is mostly at an eerie standstill, dominated by renewals and small new leases.

Market sources told us that Paul Weiss, a tenant at RXR Realty and China Lifes 1285 Sixth Avenue for nearly 30 years, is considering a move into slightly less spaceat Fisher Brothers 1345 Sixth Ave. when its lease at 1285 expires at the end of 2026.

The powerful law firm has more than 500,000 square feet at 1285 Sixth.

With an eye on landing Paul Weiss, Fisher has supposedly put a hold on several negotiations with prospective tenants at 1345 Sixth to take parts of the former Bernstein floors, which would be used by Paul Weiss. AllianceBernstein moved the bulk of its operations to Nashville, Tenn., last year.

Meanwhile, in a separate but related situation, the buzz is that UBS an even larger tenant at 1285 Sixth than Paul Weiss, with more than 800,000 square feet is toying with moving large units of Credit Suisse, which UBS recently acquired, from SL Greens 11 Madison Avenue to 1285 Sixth.

Well-placed sources said that UBSs intentions have thrown a monkey wrench into the situation at 1285 Sixth, where its lease runs beyond 2030.

Theyre looking to consolidate the Credit Suisse space. If theyre able to do it at 1285 Sixth, it could motivate Paul Weissto find a new home because they were looking to expand. But there arent many blocks [at other buildings] available of the size it needs, one insider said.

A different source said that Paul Weiss actually preferred to stay at 1285 Sixth if it could growthere, but, The Credit Suisse thing complicated things.

An SL Green source said that UBS/CS is a long wayfrom developing a real estate strategy relative to 1285 Sixth and 11 Madison. They may not yet have clarity as to which CS business lines are to be retained or sold and what the final head count will be. Only after that will they focus on where people will be located.

RXR chief executive Scott Rechler declined to comment, as did CBREs Peter Turchin, the agent for 1345 Sixth.

Representatives for Paul Weiss didnt respond to a request for comment. Neither did anyone from Newmark, where a brokerage team represents the law firm. Reps for UBS could not immediately be reached.

Whether or not any parts of the scenario come to pass,the dramaplays out against the backdrop of the Sixth Avenue/Rockefeller Center subdistrict, which is healthier by most metrics than all but one other corridor.

According to CBREs 1Q data, Sixth Avenue availability of 12.5% was bettered only by Park Avenue at 11.5% and miles better than 23.7% in East Midtown, 21.3% in the Times Square area, 23.2% in the Penn District and 18.5% in Midtown overall.

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Big office tenants and towers playing high-stakes chess game in Manhattan - New York Post

Born in Stockholm on 23 April 1963, the same year and month as Garry Kasparov, the Swedish legend started playing chess at the age of ten (before that she played football as a hobby) and hasn't stopped since, still playing chess at the highest level. Pia has become a role model and inspiration for thousands of girls taking up chess.

Cramling has been one of the strongest players in the world since the early 1980s, quickly becoming the clear no 1 on the women's ranking list. She overtook world champion Maia Chiburdanidze in the mid-1980s, and after the arrival of the Polgar sisters Susan (then Zsuzsa) and Judit, Pia remained in the top five/ten for decades.

Today, Pia Cramling turns 60. She has been Sweden's top female player for more than 40 years and is regarded as one of the strongest female chess players in history. Cramling is always looking for new ideas, and is especially good in unusual positions. She is also known for her ability to make practical decisions at the board, and all that makes her an immensely creative yet very consistent athlete. She is still competing at the highest female level, a tireless and tremendous fighter. What a legend!

Young Pia Cramling in 1977 at her first individual tournament abroad in Wijk aan Zee in an amateur group. | Photo: noord-hollandsarchief.nl

When she began to compete, she often signed her name simply as P. Cramling. She did not want to reveal anything until it was obvious that this young chess player was a girl. Pia joined a chess club in Stockholm when she was ten and played in her first tournament at the age of 12. Just three years later she was part of the Swedish team at the Women's Olympiad.

She played in her first Olympiad in 1978, and since then has represented Sweden successfully in both the Open and Women's Chess Olympiads. She won her first individual gold medal in 1984, again in 1988, and her last individual gold medal in 2022, 38 years after her first gold!

Four times between 1990 and 2000 Pia Cramling made it into the Swedish team in the open section of the Chess Olympiad, in 1996 she played on board two (with a respectable 5.5/9), sitting next to the legendary Ulf Andersson on board one. So far Cramling has played 13 Olympiads (nine women's and four men's Olympiads, called the open section) for her home country, plus two online Olympiads (with mixed teams) in 2020 and 2021 following the outbreak of the worldwide Covid pandemic.

Pia Cramling with Ulf Andersson, pictured in 2021. | Photo: Twitter Pia Cramling

Pia was indeed born into a chess family, and her father often played correspondence chess. Brother Dan, IM since 1982, born in 1959, that is four years before Pia, became her motivator and early training partner. In an interview with ChessBase in 2018 (link below) Pia said:

"My older brother Dan was my big hero. I did most of the things he did, like playing football. I even played for a team. So, of course, when I took up chess and became stronger, he influenced me and I tried to follow in his footsteps."

In 1981, at the age of 18, Pia Cramling made her debut in the Swedish National Championship, which her brother Dan won outright to become Swedish National Champion. During the 1980s, Dan and Pia participated several times in the same international tournaments, e.g. in the Rilton Cup, the Gausdal Troll Masters (where Pia beat Dan), the Lugano Open or an invitational tournament in Barcelona.

Dan in play against his sister at the Swedish Championship in 1981, organised in Ystad in a swiss system of 13 rounds with 32 participants, the Cramlings met each other in round 6, a draw. | Photo: Krister Berg via allas.se

Pia Cramling made the Swedish national team at the Olympiad in Buenos Aires 1978 at the age of 15.5. She played as a reserve and won an individual silver medal with an excellent score of +8=2-1. The USSR women won team gold, but not the men's team: Hungary triumphed.

In the women's event Maia Chiburdanidze won the individual gold medal on board one, and in the open it was Viktor Korchnoi who won gold for the best performance on board one. He came straight from the World Championship match against Karpov in Baguio City, which he narrowly lost. Karpov decided not to play in Buenos Aires, but Korchnoi did not seem to be exhausted at all after the long and grueling match.

Korchnoi, who played for Switzerland, came in after missing the first three rounds, but then played all the remaining eleven rounds and did not lose a single game. He scored 81.8%, exactly the same percentage as Maia Chiburdanidze, who played first board for the USSR in the women's section. Both, Korchnoi and Chiburdanidze finished with a score of +7=4-0.

Worth mentioning: Elena Akhmilovskaya (ten years later: Donaldson-Akhmilovskaya, after a whirlwind marriage during the 1988 Olympics to non-playing US team captain IM John Donaldson) won gold on the reserve board with a clean score of 10/10.

From Kingpin no.31, Autumn 1999, "A questionnaire with Mrs Cramling":

Question: "What is your most memorable game?"

Cramling: "I guess it is my first game against Korchnoi at Lloyds Bank Masters 1982 where I made my first IM norm. Korchnoi had been one of the players I had admired most because of both his enormous fighting spirit and the problems he had had in the Soviet Union. It was like a dream for me to play him. He surprised everybody by taking more than an hour over his 5th move! So it was not so strange that we both (this is my bad habit) got short of time. Korchnoi launched an attack with his queen, rook and knight - the only pieces on the board - but left his own king exposed, which gave me a dangerous counterattack. When Viktor Korchnoi offered me his knight I gladly took it, but then found that I could not escape perpetual check. A simple queen move, threatening mate in one, would have given me the full point! The fact that I, a 19-year-old-girl, had made a draw with World Championship challenger Korchnoi caused a sensation. After the game a huge crowd of players came over to analyse. To sit there opposite Korchnoi with all these famous grandmasters analysing my game was unbelievable."

Things went even better for her: In 1984 Cramling beat Korchnoi, still the reigning Vice-World Champion, in the Invitational Tournament in Biel/Bienne (won by Hbner and Hort together, ahead of Korchnoi, who finished clear third). Pia Cramling, the only woman in the field, finished in the middle of the pack with 5/11).

Cramling vs. Korchnoi 1984. | Photo cartoon: Youtube Anna Cramling

That same year, a few weeks after her victory over Korchnoi, Pia Cramling met her future husband, Spanish GM Juan Manuel Bellon Lopez in Zrich at the SGZ Jubilee Open, celebrating 175 years of the Schachgesellschaft Zrich (SGZ).

It was a cosy, familiar and charming 9 round Swiss, with 22 invited players, among them six players from the world's top 30, namely Korchnoi, Spassky, Hort, Nunn, Seirawan and Sosonko, other renowned GMs like Gheorghiu, Forintos or Bellon Lopez, and some prominent names from neighbouring countries, IM Tatai from Italy, IM Dckstein from Austria, Kindermann, at that time still an IM, from Germany; in the line-up were also some native Swiss players and local heroes like IM Dr. Dieter Keller (who had beaten Fischer, Larsen, Geller in his adult career), a working amateur on holiday, or unknown amateur Hans Karl from the city of Zurich, plus two women players, namely Tatjana Lematschko and Pia Cramling.

Dr John Nunn of England, now the reigning World Senior Chess Champion 65+, won the tournament outright, half a point ahead of a group of players that included former World Champion Spassky, and Korchnoi, as well as Juan Manuel Bellon.

It was during this tournament that Pia met Juan and Juan met Pia. Both were in a good mood, having just beaten the great Viktor (Cramling at Biel GMT 1984 in July/August, and now Bellon at Zurich SGZ Jubilee Open 1984 in September, both upsets coming in the first round).

Viktor Korchnoi sometimes jokingly referred to these two events in Switzerland within two months of each other, saying that "just after the two of them had beaten me, they fell in love!"

Cramling and Bellon had coincidentally taken part in the Wijk aan Zee festival in 1977 (not in the same group at that time), but they really met in Zurich in 1984.

As a professional and sentimental couple they travelled together to Havana, Cuba, where Bellon assisted Cramling at the 1985 FIDE Women's Interzonal (which was won by Alexandria, but Cramling also advanced to the Women's Candidates Tournament, which was won by Akhmilovskaya, who thus earned the right to challenge the reigning World Chess Champion Chiburdanidze in 1986. However, Chiburdanidze defended her title with a comfortable margin).

175 Years SG Zrich Jubilee Open 1984 with Cramling & Bellon. | Photo: europe-checs

Cramling: "In 1984 the Schachgesellschaft Zrich celebrated its 175 Anniversary by organizing a high-quality chess tournament. Alois Nagler invited me among the 22 players who participated.

The tournament became a turning-point in my life and that is way Zrich always will be close to my heart. I was not successful in the tournament but I was lucky in life. During the tournament I met the Spanish Grandmaster Juan Bellon my partner in life."

Quotation from CREDIT SUISSE MASTERS HORGEN 1995, official tournament book by Andr Behr, Edition Olms, 1996, introduction by GM Pia Cramling, page 9 (she played in the B-group there, won by Almasi ahead of Hodgson. Ivanchuk and Kramnik co-won the A-group, ahead of 3./4. Ehlvest, Short, eleven players, including Kasparov at only 50%, senior Korchnoi, Vaganian, Gulko, Jussupow, Lautier, and Timman who finished last)

WGM in 1982 at the age of 19, and already the following year she won the Chess Oscar for Women 1983 (the other three players who received this trophy, which was awarded to women only from 1982 to 1988, at least once are Nona Gaprindashvili, Maia Chiburdanidze and Judit Polgar; later the Chess Oscar voting procedure was reintroduced for a certain period, but then abolished again).

After becoming a WGM Cramling had to fight hard to become a Grandmaster. It was in Bern, the capital of Switzerland, that she achieved her final GM norm. There, after Nona Gaprindashvili, Maia Chiburdanidze, Susan (then known as Zsuzsa) and Judit Polgar, WGM & IM Pia Cramling became the fifth woman in the world to be awarded the GM title by FIDE.

The auspicious tournament was the "Swiss Volksbank SVB Open" in February 1992, among the 266 players in the main group were many strong grandmasters like former Candidate's super-finalist Andrei Sokolov, the eventual winner, and Tukmakov, Gavrikov, Sveshnikov, Rozentalis, also Gulko or Hort, Csom, Gheorghiu, a number of young Brits such as Glenn Flear with his wife Christine, Joe Gallagher, Daniel King, the globetrotter Mark Hebden, the Argentine Daniel Campora, Margeir Petursson from Iceland and Pia's husband Juan Manuel Bellon from Spain, among others.

The road to success wasn't easy, and her round 3 game against Joe Gallagher turned out to be especially nerve-wracking. In the notorious endgame king, knight and bishop against king, Pia had to mate her opponent in no more than 50 moves. After mistakes from both sides, she found the right way and mated Gallagher after making 47 moves in the pawnless ending - three more moves, and the game would have been declared a draw. The entire game lasted 124 moves.

In round 4 Cramling faced and beat another top woman, Georgian Ketevan Arakhamia (later Arakhamia-Grant), also a player with a charming and dignified manner, but a determined fighter on the board. At that time she was also a WGM and was trying to become a GM, which she later managed.

After four rounds, four players shared the lead with 4/4: Cramling, A. Sokolov, Flear and Rozentalis. In round 5 Cramling drew against Flear and in round 6 she had to play Istvan Csom, a long-standing icon of Hungarian chess, team gold medallist at the 1978 Olympiad and individual gold medallist at the 1980 Olympiad, his heyday in the 1970s and 1980s.

Cramling won with black against Csom and now faced Andrei Sokolov (who had beaten both Kasparov and Karpov in 1988) in round 7. Drawing this game and the two remaining rounds secured Cramling shared second place with 7.0/9, half a point behind tournament winner Andrei Sokolov. More importantly, her performance was sufficient for her third and final GM norm. At that time Cramling had an Elo rating of 2530, more than the required +2500 and thus she became a GM. Big party for Pia!

By the way: Already one year earlier, in 1991, Cramling had tried to make a GM norm in Bern, then in a cosy little invitational tournament with GM Viktor Gavrikov, the Elo favourite and runner-up, some talented youngsters like GM Klinger from Austria, who won, GM Gallagher from England, then IM Miralles from France, mixed with promising Swiss players like IM Beat Zger. Cramling, the only woman in the field, finished with 50%, which, of course, was not enough for a GM norm.

As of March 2023, there are 40 female chess players (all living) who have achieved the title of Grandmaster, the highest title awarded by the International Chess Federation (FIDE), which is not to be confused with the separate gendered title WGM for Woman Grandmaster, which is easier to obtain.

The Cramling family at the Olympiad in Dresden 2008 | Photo: Twitter Pia Cramling

Pia Cramling and Juan Manuel Bellon Lopez (GM since 1974 and five-time Spanish National Champion) are the first married couple ever, in which both partners are Grandmasters. Hats off!

It was in February 1988 when Pia (WGM, but not yet GM) packed a suitcase and went to Spain, married Juan, and since then they have been travelling a lot together, playing chess. In the ChessBase interview mentioned above, Pia Cramling said: "Without Juan (Bellon), I would have done something else and chess would have become a hobby."

They lived for a long time in Fuengirola, a town and municipality on the Costa del Sol in the province of Malaga in the autonomous community of Andalusia in southern Spain.

Daughter Anna, born on 30 April 2002, is a Spanish-Swedish chess player, Twitch live-streamer and YouTuber who holds the FIDE Master title. Anna represented Sweden at the Baku 2016 Chess Olympiad and recently again at the Chennai 2022 Chess Olympiad. Anna is best known as a successful Twitch streamer.

Anna started playing chess at the age of three while living in Spain, later moving with her family to Sweden at the age of eleven, thus switching federations early on from Spain to Sweden. Throughout her childhood both her parents were active in chess competitions, and Anna usually accompanied her parents to these chess tournaments even as a baby.

Anna started streaming in early 2020, focusing on chess content. Sometimes, her positionally playing mother and her tactically skilled father are guests on her channel, too.

Anna Cramling at Twitch: AnnaCramling - Twitch

In 2013 the Cramlings returned to Sweden after many great years in Spain. Juan, Pia and Anna now all play for the Swedish Chess Federation.

A beautiful and memorable moment: The Cramling family at Baku Olympiad 2016. Juan as captain, Pia and Anna in the womens team. | Photo: David Llada via Swedish Chess Federation

The "Veterans vs. Women" team match in Prague in 1995, sponsored by Joop van Oosterom and called the "Polka Tournament", was by far her greatest moment in this series of dance themes between legends and ladies. Pia Cramling, together with Judit Polgar, was the best scorer with 6.5/10.

Pia played 2-0 against Lajos Portisch, 1.5-0.5 against Vasily Smyslov, 1.5-0.5 against Viktor Korchnoi, 1-1 against Boris Spassky, and only lost her mini-match against Vlastimil Hort 0.5-1.5.

Within ten days, Pia had beaten Smyslov, Spassky, Korchnoi, and Portisch in classical time control!

In this lively dance of Bohemian origin, the ladies performed better; in order not to discriminate between the sexes, it should be pointed out that after ten annual dance theme tournaments between 1992 and 2001, it was the sprightly gentlemen who won by a narrow margin of 299 to 289 points.

Among the prominent Veterans were Smyslov (he participated in all ten tournaments, and despite his advanced age, he still finished three times as individual best or shared best of all contestants), Spassky, Korchnoi (five entries, three times clear individual best), Larsen, Geller, Polugaevsky, Taimanov, Portisch, Hort, Ivkov, Olafsson, Panno, Uhlmann, Dckstein.

Among the prominent ladies were Pia Cramling (six entries, 1992, 1995 individual shared best, 1996, 1997, 1998, 1999), Maia Chiburdanidze, Xie Jun, Zhu Chen, Nana Ioseliani, Alisa Galliamova, Ketevan Arakhamia-Grant, and the three Polgar sisters Zsusza (Susan), Zsofia (Sofia), and Judit.

Pia Cramling is double European Champion. She won the 2003 & 2010 Womens European Championship, that means: Gold at the 4th European Women's Chess Championship in Silivri (Turkey) 2003, and again Gold at the 11th European Women's Chess Championship in Rijeka (Croatia) 2010.

In 2006, Pia Cramling won the Accentus Ladies Tournament in Biel unbeaten with impressive 7/10, one and a half point ahead of Monika Socko at 5.5/10 as sole second, Yelena Dembo took bronze, Anna Muzychuk finished fourth, and Ekaterina Atalik and Almira Stripchenko shared fifth/sixth places at 4/10. This double round robin event had been held during the traditional Biel Festival where Alexander Morozevich won the GMT, in which a young Magnus Carlsen, Andrei Volokitin, and Teimour Radjabov, amongst others, also played. Bartosz Socko from Poland, husband of Monika, won then the Biel MTO (Open) on tie-break.

In 2007, Pia Cramling won the MonRoi invitation tournament (women) in Montreal, ahead of Lela Javakhishvili and Jovanka Houska, who shared second and third place. Ketevan Arakhamia-Grant and Iweta Rajlich shared fourth and fifth place. Irina Krush took also part but did not finish at the top.

Pia Cramling co-won the traditional Rilton Cup in Stockholm in 2007/08, the Open saw a 9-way tie on 6.5/9 points, Radoslaw Wojtaszek from Poland had the best tie-break, Pia the second best, in the leading group also Agrest, Kotronias, Nybck, Kulaots. At the Swedish Championships in 2000, Cramling was close to winning, but in the end finished second behind Tom Wedberg in the tie-break. In 1987 she was runner-up to seven-time national champion Axel Ornstein.

In classical chess Pia Cramling has victories over Smyslov, Spassky, Korchnoi, Geller, Taimanov, Portisch, Csom, Hort, Ftacnik, Uhlmann, Lobron, Miles, Gulko, Alburt, Browne, Benjamin, Rogers, Spraggett, Granda Zuniga, Ponomariov, I. Sokolov or Bologan (see the official Gibraltar stamp below) to name prominent men she has beaten, not including rapid, blitz or online games.

When she was younger, she always played with the boys and was not particularly interested in women's events. Throughout her career Cramling has 'simultaneously' played in closed invitation tournaments and open tournaments, in individual and team events, in men's and women's competitions, in national and international competitions, official FIDE championships and exhibition tournaments such as Ladies versus Veterans. Of course, Cramling also gives chess lectures and lessons, or works as a commentator, but playing on the board is what she loves most.

Sweden, Spain and Switzerland are important countries in Pia Cramling's chess life, but can you guess how many times Pia has played at the famous Gibraltar Festival?

Remember, daughter Anna was born in 2002, the famous Gibraltar series started in 2003: Pia has participated in every Gibraltar Masters since the series started, that is 18 years in a row between 2003 and 2020, in fact Pia has played in all 18 festivals (!) and she has won the prestigious women's first prize at the Gibraltar Open a record three times.

Due to the Covid pandemic there was no Gibraltar Open in 2021. In 2022, instead of the traditional January Masters in Gibraltar, there was a team event in the Scheveningen system called "Battles of the Sexes" (Ladies vs. Men). Of course, Cramling was invited as well, making a total of 19 appearances up to and including 2022, unfortunately there was no event at all in January 2023.

Note: In addition, a FIDE GP was held in Gibraltar in the summer of 2021 (without Pia Cramling, who participated in that cycle, but played in Skolkovo, Monaco and Lausanne).

Gibraltar also honoured GM Pia Cramling with a stamp in a collection of four stamps to celebrate the 10th anniversary of their chess festival.

Stamp collection from the Gibraltar Masters 10th Anniversary of the Festival, released in 2012. | Photo: Royal Gibraltar Post Office

Replay her win (featured on the stamp) against Viorel Bologan at Gibraltar Masters in 2006:

In January 1984 Cramling was ranked the clear number one woman in the FIDE Elo rating list, ahead of the three Georgian chess ladies Maia Chiburdanidze (then World Champion), Nana Alexandria and Nona Gaprindashvili (ex-World Champion); plus joint number one in July 1984 (together with Zsuzsa Polgar, Hungary). Pia Cramling remained in the women's top five throughout the 1980s and 1990s, and in the top ten for decades.

Highest rating: 2550 Elo in October 2008 as clear no. 5 in the world, behind Judit Polgar at the top, Humpy Koneru from India, Hou Yifan and Xie Jun, both from China, ahead of Antoaneta Stefanova from Bulgaria and Marie Sebag from France. Remember, to date there are only three women born in Western Europe who hold the grandmaster title in chess: Cramling, the aforementioned Marie Sebag, and most recently Elisabeth Phtz from Germany.

As of April 2023, Cramling, who plays frequently, is ranked no. 26 in the FIDE list with an Elo rating of 2443, ahead of prominent players (in no particular order) such as Stefanova, Socko, Sebag, Krush, Danielian, Girya, Sachdev, Skripchenko or Pogonina, all of whom are much younger than her, not to mention those who are inactive (remember that Judit Polgar, born in 1976, certainly the most successful and strongest female chess player in the history of the game, retired from competitive chess in 2014 at the age of 38).

An incredible achievement: At the Chennai Olympiad in 2022, Pia Cramling won another, her third, individual women's board one gold medal, 38 years after her first individual board one gold medal at the 1984 Chess Olympiad. Cramling also won individual gold in 1988.

Swedish legend Pia Cramling, in great form at the age of 59, won gold on board 1 with a TPR of 2532, 11 games played, undefeated, ahead of Dutchwoman Eline Roebers, 16, with the same TPR but had played "only" 10 games, which turned out to be the crucial tie-break criteria. Roebers also lost the direct encounter against Cramling, but the Dutch prodigy won silver for her performance on board 1.

The first and the latest Gold medal

Pia Cramling at the Chess Olympiad in Thessaloniki 1984 | Foto: Gerhard F. Hund...

...and at the Chess Olympiad in Chennai 2022 | Photo: Lennart Ootes, FIDE

An interview with Pia Cramling

Trivia (don't take it too seriously, but it is technically correct): Scandinavian compatriot Magnus Carlsen suffered six Olympic "double failures" (not winning individual or team medals), until he finally won a bronze medal in his seventh appearance at a chess Olympiad as the third-best individual on board one in Chennai 2022.

It's far too early, but perhaps in twenty years or so the ageless Pia Cramling will be playing in the annual FIDE World Senior Chess Championships.

Recall that Viktor Korchnoi made his first and only senior appearance shortly after celebrating his 75th birthday and won the title in style (in 2006), Vlastimil Jansa became the Senior World Chess Champion in the 65+ category at the age of 76 (in 2018), Nona Gaprindashvili won her last gold medal in the women's 65+ category at the age of 81 and a half (in 2022).

A wonderful spotlight: Pia Cramling and Anna Cramling | Photo: Twitter

Excerpt from:

Ten Highlights in the Life and Career of Chess Grandmaster Pia Cramling - ChessBase

Premium

There were reports that former World Champion Vladimir Kramnik is helping Ian Nepomniachtchi in his fight against Ding Liren at the 2023 World Chess Championship. While we dont know if its actually true, we can see that Nepo has been playing a lot like Kramnik in the last two Games.

Game 11 on Monday was the second consecutive game in which Nepo didnt really seem to be interested in going for a win. Mind you, this time he was playing with White. (GrandmasterPravin Thipsay has been analysing games of the World Chess Championship forThe Indian Express since the start of the event. You can also read his analysis of Game 10,Game 9,Game 8,Game 7,Game 6,Game 5,Game 4,Game 3,Game 2, andGame 1.)

That begs the question: why should he push for a win? He has a comfortable lead of one point (6-5) and there are just three games to go in this title clash. He could really become a world champion this week.

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Not pushing hard for a victory is perhaps Kramniks influence on him. The Soviet school of thought is that its more difficult to win than to draw a game, so why push for a win if you dont need to?

Kramnik wouldve told him to play it safe. Even when Kramnik defeated Garry Kasparov to win the World Championship in 2000, he had more draws than wins. He won just two games in the 16-game match and that was enough for him to triumph 8.5-6.5. The key part here is that he didnt lose a single match. Interestingly, it was the first time since 1921 that a defending champion lost his crown without winning a single game in the Championships.

Thats definitely the leaf Nepo has taken out of Kramniks book, if he has learned anything from him during these couple of weeks. Hes going to continue keeping it simple.

Thats what he did on Monday. He knew that going on the attack often exposes the defense that Ding would surely pounce on. So why should he take a risk and lose with White?

He knows that he just needs to keep it tight and even if he loses one game, the match will go into tie-breakers and I do believe that he will be much better prepared for that scenario than Ding.

It was perhaps with this thought that he chose the tried and tested Ruy Lopez Spanish opening in Game 11. But Ding knows that he has to try something out of the box in order to draw level and give himself a chance at winning. The Chinese replied with the variation he had chosen in the fifth game, showing that he had prepared something better.

Sensing that Ding was ready to play a complex game, Nepo went for a quieter option on move 8, making it clear that he was going to play something similar to Game 5, but at a much slower pace.

Ding went for a less flexible option on move 8, thereby forcing the positional course of the game. Unlike game 5, Ding placed his Queen Bishop on the correct square this time. Nepo tried to make some headway with a novelty (new move) on move 15 but Ding replied with a sharp aggressive move, trying to create initiative on the Queen side at the cost of weakening his position.

Nepo did get a slight positional advantage but on move 19, he decided to simplify matters by opening up the position. The next twenty moves were almost forced but quite simple. The players claimed a draw after a three-fold repetition of the final position.

Nepo made it clear that he is not going to go for double-edged battles as long as he is leading. And were seeing a very matured Nepo here. Hes not going to crumble as he did against five-time World Champion Magnus Carlsen in 2021. Hes prepared, and not making impulsive decisions.

Lets not forget theres a lot riding on Ding too. Being the first Grandmaster from China to compete for the World crown, theres certainly a lot of pressure on him. He has two games with White and thats what I think could be decisive. He has to go all-out in Game 12 on Wednesday if he wants a direct win or will he leave the best for last in the 14th game?

In the Candidates to qualify for this Championship, he won in the last game (finished second behind Nepo). But that was only the Candidates. Theres a lot more at stake here and he definitely cant afford to wait.

(Pravin Thipsay is an Indian Grandmaster and a recipient of the Arjuna Award)

Moves (Game 11)

1.e4 e5 2.Nf3 Nc6 3.Bb5 a6 4.Ba4 Nf6 5.00 Be7 6.d3 b5 7.Bb3 d6 8.a3 Na5

[Less flexible option.] 9.Ba2 c5 10.Nc3 Be6 11.Bg5 00 12.Bxf6 Bxf6 13.Nd5 g6 14.Qd2 Bg7 15.Ng5! [New Move.] 15c4?! 16.Nxe6 fxe6 17.Ne3 Bh6 18.Rad1 Rb8 19.dxc4?! 19Nxc4 20.Bxc4 bxc4 21.Qxd6 Qxd6 22.Rxd6 Bxe3 23.fxe3 Rxf1+ 24.Kxf1 Rxb2 25.Rxe6

Rxc2 26.Rxa6 Ra2 27.Rc6 Rxa3 28.Rxc4 Rxe3 29.Kf2 Ra3 30.Rc5 Ra2+ 31.Kf3 Ra3+ 32.Kg4 Ra2 33.Kh3 Re2 34.Rxe5 Kf7 35.Kg3 Kf6 36.Re8 Kf7 37.Re5 Kf6 38.Re8 Kf7 39.Re5

Game drawn by the rule of threefold repetition.

First published on: 24-04-2023 at 20:57 IST

Originally posted here:

Pravin Thipsay writes: Not pushing hard for win is perhaps Vladimir Kramniks influence on Nepo - The Indian Express

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Chess: Nepo stays ahead of Ding as world title match nears its finish - Financial Times

Home Chess News Events World Chess Championship 2023 Quiet draw in game 11 of the World Chess Championship

World Chess Championship 2023 (11)

Mark Crowther - Monday 24th April 2023

Ian Nepomniachtchi and Ding Liren drew the eleventh game of their WorldChampionships match by repetition in 39 moves and less than two hours of play.

Nepomniachtchi again played 1.e4 and the Ruy Lopez followed, no Berlin andwith 6.d3. The line of the Closed Ruy Lopez that was played is fairly topical.14.Qd2 was rare and 14...Bg7 was new. Commentators wondered if black mightat least be under pressure, 15...c4!? seemed like a bold way of going aboutthings, 18...Rb8 was the best computer continuation. 19.dxc4 *19.Qe2 the way to keep the pressure)quickly led to liquidation of most of the pieces and a drawn Rook and Pawn endgame, Nepomniachtchi thought he might get a symbolic advantage, as it was he didn't get eventhat.

Is the hardest part of the match behind Nepomniachtchi? "You've got to be kidding!" There is a rest day tomorrow, Ding will probably have to come out swinging.

Score Nepomniachtchi 6 Ding 5. Best of 14 games.

Rest day Tuesday 25th April.

Game 12 Ding vs Nepomniachtchi 10am BST Wednesday 26th April.

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Quiet draw in game 11 of the World Chess Championship - The Week in Chess