Podcast appearances and mentions of sameer singh

In this episode, we'll talk to Sameer Singh, an angel investor known for his excellent blog on network effects, Breadcrumb.vc, former Atomico Angel, and current venture partner at SpeedInvest.We'll discuss how Sameer evaluates marketplace businesses:Evaluating Network Effects: Sameer walks through his four-step evaluation process for marketplace investments, focusing on unique multiplayer interactions and defensibility.Challenges in Early-Stage Marketplaces: He discusses common mistakes made by aspiring marketplace founders, such as picking overly narrow markets or trying to tackle too many problems at once.Future Trends in Marketplaces: Sameer emphasizes the lasting power of network effects and we explore some opportunities driven by behavioral shifts, especially with Gen Z and AI technology.A terrific episode, packed with valuable tips and sights. If you're building a marketplace or network effects business, you're going to love this.

In the 84th episode of Product&Growth, we talked to Sameer Singh, Venture Partner at Speedinvest. Sameer's blog: https://breadcrumb.vc/ What we talked about: - Definition of network effects and companies who have them - How to define metrics for network effects products - FOMO in VC - Defensibility and economies of scale in AI - How to evaluate and measure network effects - How to scale network effects - Why investors should leave problem solving to founders Sameer's recommendations: 1. Josh Breinlinger - https://acrowdedspace.com/ 2. Bill Gurley - https://abovethecrowd.com/ 3. Jeff Jordan - https://a16z.com/author/jeff-jordan/ 4. Lenny - https://www.lennyrachitsky.com/ 5. James Currier's NFX Manual - https://www.nfx.com/post/network-effects-manual

We end season two of the Talking about Platforms Podcast by bringing on a true expert on applied network effects. Sameer Singh looks back on a long and successful track record investing in network effects-based startups. In this episode, we discuss how Sameer differentiates between networked products and platforms, how to measure network effects, and why network effects-based businesses must also start with solving a real customer's problem.

Today we continue our AI Trends 2023 series joined by Sameer Singh, an associate professor in the department of computer science at UC Irvine and fellow at the Allen Institute for Artificial Intelligence (AI2). In our conversation with Sameer, we focus on the latest and greatest advancements and developments in the field of NLP, starting out with one that took the internet by storm just a few short weeks ago, ChatGPT. We also explore top themes like decomposed reasoning, causal modeling in NLP, and the need for “clean” data. We also discuss projects like HuggingFace's BLOOM, the debacle that was the Galactica demo, the impending intersection of LLMs and search, use cases like Copilot, and of course, we get Sameer's predictions for what will happen this year in the field. The complete show notes for this episode can be found at twimlai.com/go/613.

FNB's Wayne McCurrie on markets as they fall, despite US jobs data coming out strong. Old Mutual's Sameer Singh unpacks the investment case for Mondi as the share price continues to struggle.Gabriel Theron from Cilo Cybin on the company's proposed listing as a SPAC on the JSE.

Proceeds of the sale of the Russian assets could contribute to a special dividend, says research analyst Sameer Singh.

Startup advisor and early-stage investor focused on network effects. "Creator" of Breadcrumb.vc and Applied Network Effects (one of the highest rated courses on Maven) — both are globally renowned resources to learn about network effects. I'm also part of the Atomico Angel Program, where I invest in early-stage startups with network effects. Please direct all pitches and consulting/speaking requests to sameer@breadcrumb.vc. 14 years of experience in the technology ecosystem — distributed between technology startups and investing. For much of this time, I studied technology business models and network effects in a professional capacity and via independent projects. Previously, spent 5 years at App Annie, a Sequoia-funded, late-stage startup during its hypergrowth phase. There, I led a global, cross-functional team, working across product, marketing, and sales. Advised leading tech companies like Spotify, Shpock and Trainline. Before that, I was an early-stage investor focused on commerce businesses. I have been quoted or mentioned in leading publications, including Reuters, Sifted, Business Insider, The Guardian, Sifted, and Techcrunch. My prior independent work has also been referenced in Philip Tetlock's book, Superforecasting.

In our last piece, we've shed light on the demand side of tokenomics. We've looked into the buying point, meta demand, and market analysis to see what influences a token's demand, and hence influences its price. We want to take this a step further now by looking at tokenomics more comprehensively. Supply and demand are often analyzed separately. This tends to overlook the connection between supply and demand, leading to an incomplete picture. We call our more comprehensive approach token flow analysis.Apart from supply and demand, tokenomics can be seen as the design of the flow of tokens e.g. distribution determines how many tokens flow to whom. If too many tokens flow to investors, there might be selling pressure. Utility determines under which condition tokens flow from users to other parts of the ecosystem, which is key to keeping tokens within the ecosystem. Token Flow ModelGenerally, these flows can be represented as follows:The model consists of the following components:Flows: token inflow into the ecosystem, token outflow out of the ecosystem (to the secondary market), and the inside flow of the token within the ecosystem. If too many tokens flow out of the ecosystem, it will probably create selling pressure.Ecosystem is where the product, stakeholders, tokens and other components of a specific project interact with each other. Supply origin is the origin of the token and the start of the token flow. It includes the genesis release (how many tokens will flow at the beginning), periodical release and distribution (how many tokens flow to whom in a given period) etc.Incentivization is attaching a reward to a desired behavior. The reward is the tokens and might come from supply origin or reserve or treasury.Stakeholders include investors, advisors, users, holders and project team members. Different stakeholders tend to behave differently within the ecosystem. For example, investors are more inclined to sell tokens early rather than hold and use them, as ultimately, they need a return on their investment.Hold can be defined as when stakeholders will not sell tokens on the secondary market. This is a favorable action for the ecosystem so it's classified as separate behavior within the ecosystem. People hold in anticipation of price appreciation, and hold is one of the two inside flows that are initiated by stakeholders.Utility of a token like staking, governance, payment etc. is one of the two inside flows that are initiated by stakeholders.Analysis of the modelWe will mainly use the play-to-earn game Axie Infinity's SLP token to conduct the analysis, answering three main questions:How to prevent token outflow?How to generate token inflow?How to keep tokens flowing inside the ecosystem?Token OutflowToken outflow, not met by sufficient token inflow, increases selling pressure and might drop the price. The best way to prevent token outflow is to keep tokens flowing within the ecosystem.How to keep tokens flowing within the ecosystem? The diagram above shows that tokens have three possible destinations: outflow to the secondary market, utility and hold. For SLP, the utility of breeding in-game character Axies encourages the flow within the ecosystem.Token inside flow: Utility & HoldHow tokens flow from stakeholders to the utility can be translated to:Why will people use a token?To answer this question, we look at the essence of utility first: people use a product or service because it brings them value. For example, people use ketchup to make their dishes delicious. A token can help access and capture the value created by a product or service. The value of ketchup is to some degree captured in the share price of the Kraft Heinz Company. The value of the Ethereum network is to some degree captured in the ETH token.What value can people get using a token?Let's look at the SLP token. The utility of SLP is to breed Axies, the in-game character that players play with. In this case, Axies create value as they help players earn income. The SLP token helps players capture value as, without SLP, players cannot breed new Axies. Value is about return on investment so if it takes a long time to profit from investing into SLP, it might be less valuable.A game also creates value for players if it delivers a good in-game experience. The experience of playing Axie Infinity does not seem to be that good and by that might discourage people from using the token. SLP prices fell 93% from all-time highsIf the product can create decent value, people would use the token to capture that value, that's how tokens can flow from stakeholders to utility to keep token circulation inside the ecosystem.Token InflowKeeping tokens flowing in and preventing them from flowing out drives up the price, which influences the willingness to hold.What will drive token inflow?Demand for the token which comes from token utility and the product's unique selling proposition (USP).In our demand analysis, we concluded that a product's competitive advantage is the main driver behind generating token demand.For SLP, the main utility is to breed Axies to compete in battles and earn an income in SLP. People do this to receive a return on their investment and for the fun of the in-game experience. While the SLP income each player receives in the battle mode is set, the ROI is determined by the price of SLP. The price is influenced by the not so great in-game experience, which fails to attract new players, causing the price to drop. While the price drops, it takes longer for people to start making a profit with Axies. In the long-term this could create a negative feedback loop: ROI not attractive -> fewer people buy -> price drops -> ROI decrease -> Fewer people buy…It seems that Axie Infinity does not have a very solid USP to create token inflow, and this will also adversely affect people's willingness to hold. If there is no SLP token inflow, holding and utility are not keeping tokens inside the ecosystem and token outflow increases, the price plummets.IncentivizationWhile the product is the driving force of token's demand, incentives play an important role in fulfilling the product's USP. Incentives attach a reward to a desired behavior that could increase the product's value.Sameer Singh describes, in his article, how properly designed incentives can increase a product's utility. The product can deliver its USP, increase its value, make it more attractive to new users, and ideally generate token inflow.Compound is a decentralized lending protocol whose competitive advantage is the ease for borrowers to access funds with little background checks. Lenders are incentivised by interest and COMP tokens to provide funds into a pool. With the incentive of COMP, more and more lenders provide funds, which increases the value of Compound, attracting yet more borrowers.However, if token incentives cannot align with increasing product utility, the product's value might not increase. It would be hard for the product to attract new users if it can't increase utility.In Axie Infinity, players are incentivized with SLP if they win battles in the game. This mechanism only incentivizes players to play the game, not necessarily incentivising them to add any value to the whole ecosystem i.e. increase its utility. The value of Axie Infinity does not increase and thus it is not attracting enough token inflow.Token flow initiation: Supply OriginThe most important thing about the supply origin is whether it can be met by a matching demand. If not, tokens will flow out of the ecosystem.How many tokens are distributed to whom?Distribution is not just about who gets tokens, it's also about the allocation of value to those who might create value in the future.Let's say a project distributes a large percentage (e.g., 35%) of tokens to investors. Investors' interest is to gain profit as early as possible. They are more inclined to dump tokens earlier than others as they buy tokens at much lower, pre-IDO prices. This means a huge amount of tokens will flow out of the ecosystem if too many tokens are distributed to investors. If instead a lot of tokens are allocated to those who create value for the ecosystem, like Compound's lenders, they are more incentivised to increase product value. As a result, more tokens will flow in. Vesting is another option. If investors need to hold on to tokens for years, they will have skin in the game and contribute to the project.For SLP, its distribution is quite simple: all SLPs are distributed to players via play-to-earn. If players play the game to profit rather than for the gaming experience itself, they will sell their tokens. This simple distribution inevitably causes tokens to flow out of the ecosystem. How many new tokens emit at what time Token emissions and vesting schedules influence token outflow. If in a given period, a lot of tokens emit and can't be matched by demand for using and holding tokens, they will flow out of the ecosystem.SLP doesn't have a predetermined emission schedule and no supply cap. It flows to players at a fixed amount whenever they win battles or trigger other tipping conditions. If there are not many new users joining and playing the game, the supply exceeds token demand. Eventually, the SLP flows out and causes the price to plummet.A better way for SLP would be to introduce a token-burn mechanism and control the emission rate. So the newly issued tokens can match the token demand. ConclusionWe've tried to analyse tokenomics comprehensively, using Axie Infinity's SLP token as an example.Key takeaways are:Supply origin will heavily affect token outflow when not met by sufficient demand.Token inflow is mainly impacted by the competitive advantage of the product and incentive mechanisms.Utility and hold keep tokens within the ecosystem, and they are the direct drivers of token inflowIncentives influence a product's value and therefore, token inflow and outflow  The above diagram is a generic framework with questions about the token flow model that people can use when evaluating tokenomics. Other components could influence token flow like market cap, which we might talk about in our next articles. Get full access to Tokenomics Newsletter at tokenomicsdao.substack.com/subscribe

"Tokens can be an effective way to bootstrap networks that need passive user participation, but they can be counterproductive for those that need active participation" ___________________________________

Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB YT version: https://youtu.be/RzGaI7vXrkk This week we speak with Yasaman Razeghi and Prof. Sameer Singh from UC Urvine. Yasaman recently published a paper called Impact of Pretraining Term Frequencies on Few-Shot Reasoning where she demonstrated comprehensively that large language models only perform well on reasoning tasks because they memorise the dataset. For the first time she showed the accuracy was linearly correlated to the occurance rate in the training corpus, something which OpenAI should have done in the first place! We also speak with Sameer who has been a pioneering force in the area of machine learning interpretability for many years now, he created LIME with Marco Riberio and also had his hands all over the famous Checklist paper and many others. We also get into the metric obsession in the NLP world and whether metrics are one of the principle reasons why we are failing to make any progress in NLU. [00:00:00] Impact of Pretraining Term Frequencies on Few-Shot Reasoning [00:14:59] Metrics [00:18:55] Definition of reasoning [00:25:12] Metrics (again) [00:28:52] On true believers [00:33:04] Sameers work on model explainability / LIME [00:36:58] Computational irreducability [00:41:07] ML DevOps and Checklist [00:45:58] Future of ML devops [00:49:34] Thinking about future Prof. Sameer Singh https://sameersingh.org/ Yasaman Razeghi https://yasamanrazeghi.com/ References; Impact of Pretraining Term Frequencies on Few-Shot Reasoning [Razeghi et al with Singh] https://arxiv.org/pdf/2202.07206.pdf Beyond Accuracy: Behavioral Testing of NLP Models with CheckList [Riberio et al with Singh] https://arxiv.org/pdf/2005.04118.pdf “Why Should I Trust You?” Explaining the Predictions of Any Classifier (LIME) [Riberio et al with Singh] https://arxiv.org/abs/1602.04938 Tim interviewing LIME Creator Marco Ribeiro in 2019 https://www.youtube.com/watch?v=6aUU-Ob4a8I Tim video on LIME/SHAP on his other channel https://www.youtube.com/watch?v=jhopjN08lTM Our interview with Christoph Molar https://www.youtube.com/watch?v=0LIACHcxpHU Interpretable Machine Learning book @ChristophMolnar https://christophm.github.io/interpretable-ml-book/ Machine Teaching: A New Paradigm for Building Machine Learning Systems [Simard] https://arxiv.org/abs/1707.06742 Whimsical notes on machine teaching https://whimsical.com/machine-teaching-Ntke9EHHSR25yHnsypHnth Gopher paper (Deepmind) https://www.deepmind.com/blog/language-modelling-at-scale-gopher-ethical-considerations-and-retrieval https://arxiv.org/pdf/2112.11446.pdf EleutherAI https://www.eleuther.ai/ https://github.com/kingoflolz/mesh-transformer-jax/ https://pile.eleuther.ai/ A Theory of Universal Artificial Intelligence based on Algorithmic Complexity [Hutter] https://arxiv.org/pdf/cs/0004001.pdf

The Agenda

Today we continue the 2020 AI Rewind series, joined by friend of the show Sameer Singh, an Assistant Professor in the Department of Computer Science at UC Irvine.  We last spoke with Sameer at our Natural Language Processing office hours back at TWIMLfest, and was the perfect person to help us break down 2020 in NLP. Sameer tackles the review in 4 main categories, Massive Language Modeling, Fundamental Problems with Language Models, Practical Vulnerabilities with Language Models, and Evaluation.  We also explore the impact of GPT-3 and Transformer models, the intersection of vision and language models, and the injection of causal thinking and modeling into language models, and much more. The complete show notes for this episode can be found at twimlai.com/go/445.

Today we’re joined by Sameer Singh, an assistant professor in the department of computer science at UC Irvine.  Sameer’s work centers on large-scale and interpretable machine learning applied to information extraction and natural language processing. We caught up with Sameer right after he was awarded the best paper award at ACL 2020 for his work on Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. In our conversation, we explore CheckLists, the task-agnostic methodology for testing NLP models introduced in the paper. We also discuss how well we understand the cause of pitfalls or failure modes in deep learning models, Sameer’s thoughts on embodied AI, and his work on the now famous LIME paper, which he co-authored alongside Carlos Guestrin.  The complete show notes for this episode can be found at twimlai.com/go/406.

We interviewed Sameer Singh for this episode, and discussed an overview of recent work in interpreting NLP model predictions, particularly instance-level interpretations. We started out by talking about why it is important to interpret model outputs and why it is a hard problem. We then dove into the details of three kinds of interpretation techniques: attribution based methods, interpretation using influence functions, and generating explanations. Towards the end, we spent some time discussing how explanations of model behavior can be evaluated, and some limitations and potential concerns in evaluation methods. Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. Some of the techniques discussed in this episode have been implemented in the AllenNLP Interpret framework (details and demo here: https://allennlp.org/interpret).

Join the discussion on our Discord server As ML plays a more and more relevant role in many domains of everyday life, it's quite obvious to see more and more attacks to ML systems. In this episode we talk about the most popular attacks against machine learning systems and some mitigations designed by researchers Ambra Demontis and Marco Melis, from the University of Cagliari (Italy). The guests are also the authors of SecML, an open-source Python library for the security evaluation of Machine Learning (ML) algorithms. Both Ambra and Marco are members of research group PRAlab, under the supervision of Prof. Fabio Roli.   SecML Contributors Marco Melis (Ph.D Student, Project Maintainer, https://www.linkedin.com/in/melismarco/) Ambra Demontis (Postdoc, https://pralab.diee.unica.it/it/AmbraDemontis)  Maura Pintor (Ph.D Student, https://it.linkedin.com/in/maura-pintor) Battista Biggio (Assistant Professor, https://pralab.diee.unica.it/it/BattistaBiggio) References SecML: an open-source Python library for the security evaluation of Machine Learning (ML) algorithms https://secml.gitlab.io/. Demontis et al., “Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks,” presented at the 28th USENIX Security Symposium (USENIX Security 19), 2019, pp. 321–338. https://www.usenix.org/conference/usenixsecurity19/presentation/demontis W. Koh and P. Liang, “Understanding Black-box Predictions via Influence Functions,” in International Conference on Machine Learning (ICML), 2017. https://arxiv.org/abs/1703.04730 Melis, A. Demontis, B. Biggio, G. Brown, G. Fumera, and F. Roli, “Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid,” in 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017, pp. 751–759. https://arxiv.org/abs/1708.06939 Biggio and F. Roli, “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning,” Pattern Recognition, vol. 84, pp. 317–331, 2018. https://arxiv.org/abs/1712.03141 Biggio et al., “Evasion attacks against machine learning at test time,” in Machine Learning and Knowledge Discovery in Databases (ECML PKDD), Part III, 2013, vol. 8190, pp. 387–402. https://arxiv.org/abs/1708.06131 Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” in 29th Int'l Conf. on Machine Learning, 2012, pp. 1807–1814. https://arxiv.org/abs/1206.6389 Dalvi, P. Domingos, Mausam, S. Sanghai, and D. Verma, “Adversarial classification,” in Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, 2004, pp. 99–108. https://dl.acm.org/citation.cfm?id=1014066 Sundararajan, Mukund, Ankur Taly, and Qiqi Yan. "Axiomatic attribution for deep networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017. https://arxiv.org/abs/1703.01365  Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016). https://arxiv.org/abs/1606.05386 Guo, Wenbo, et al. "Lemna: Explaining deep learning based security applications." Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2018. https://dl.acm.org/citation.cfm?id=3243792 Bach, Sebastian, et al. "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation." PloS one 10.7 (2015): E0130140. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0130140 

Alison (radiomics skeptic) and Nick (radiomics hopeful) sit down to discuss the benefits, drawbacks, and potential of radiomics. A variety of papers were discussed and can be found below. We also briefly discussed (though we did try not to) deep learning and broader AI applications.Are you a radiomics optimist or pessimist? Tell us at https://www.reddit.com/r/HormesisPodcast/comments/ct6p1q/episode_4_radiomics_how_to_maybe_classify_your/.Listen and subscribe to our podcast at Apple Podcasts, Stitcher, Google Podcasts, or through the RSS Feed.References:[1] Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar, Sara Carvalho, Ruud G.P.M. van Stiphout, Patrick Granton, Catharina M.L. Zegers, Robert Gillies, Ronald Boellard, Andre ́ Dekker, and Hugo J.W.L. Aerts. “Radiomics: Extracting more information from medical images using advanced feature analysis.” European Journal of Cancer, vol. 48: 441-446. [DOI: 10.1016/j.ejca.2011.11.036].[2] Afsaneh Jalalian, Syamsiah Mashohor, Rozi Mahmud, Babak Karasfi, M. Iqbal B. Saripan, and Abdul Rahman B. Ramli. “Foundation and Methodologies in Computer-Aided Diagnosis Systems for Breast Cancer Diagnosis.” EXCLI Journal, vol. 16:113-137. [DOI: 10.17179/excli2016-701].[3] Virendra Kumar, Yuhua Gu, Satrajit Basu, Anders Berglund, Steven A. Eschrich, Matthew B. Schabath, Kenneth Forster, Hugo J.W.L. Aertsf, Andre Dekkerf, David Fenstermacher, Dmitry B. Goldgof, Lawrence O. Hall, Philippe Lambin, Yoganand Balagurunathan, Robert A. Gatenby, and Robert J. Gillies. “Radiomics: the process and the challenges.” Magnetic Resonance Imaging, vol. 30: 1234-1248. [DOI: 10.1016/j.mri.2012.06.010][4] Sunderland and Christian. “Quantitative PET/CT Scanner Performance Characterization Based Upon the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network Oncology Clinical Simulator Phantom.” Journal of Nuclear Medicine, vol. 56: 145-152. [DOI: 10.2967/jnumed.114.148056].[5] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. “Why Should I Trust You?: Explaining the Predictions of Any Classifier.” Association for Computing Machinery. [DOI: 10.1145/2939672.2939778].[6] Brijesh Verma, Peter McLeod, and Alan Klevansky. “Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer.” International Journal of Computer Applications, vol. 37: 3344-3351. [DOI: 10.1016/j.eswa.2009.10.016].[7] David L Raunig, Lisa M McShane, Gene Pennello, Constantine Gatsonis, Paul L Carson, James T Voyvodic, Richard L Wahl, Brenda F Kurland, Adam J Schwarz, Mithat Gönen, Gudrun Zahlmann, Marina Kondratovich, Kevin O'Donnell, Nicholas Petrick, Patricia E Cole, Brian Garra, Daniel C Sullivan and QIBA Technical Performance Working Group. “Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment.” Stat Methods Med Res, vol. 0, 1-41. [DOI: 10.1177/0962280214537344].[8] Christie Lin, Stephanie Harmon, Tyler Bradshaw, Jens Eickhoff, Scott Perlman, Glenn Liu, and Robert Jeraj. “Response-to-repeatability of quantitative imaging features for longitudinal response assessment.” Physics in Medicine & Biology, 64. [DOI: 10.1088/1361-6560/aafa0a].[9] D. Karunanithi, Omar Alheyasat, Divya Thomas, and G. Kavitha. “Attacks on Artificial Intelligence Applications through Adversarial Image.” International Journal of Pure and Applied Mathematics, vol. 118: 4491-4495.

This week David and Dara are joined by a very special guest, ‘Jungleman' Daniel Cates. They also sit down with poker player and pro sports bettor Jan Suchanek. Allen Kessler calls in to chat about the hot topic of Mark-Up. Ian has all the news and Sameer Singh stops by with a hand from his deep Irish Open run.

This week David and Dara are joined by a very special guest, ‘Jungleman' Daniel Cates. They also sit down with poker player and pro sports bettor Jan Suchanek. Allen Kessler calls in to chat about the hot topic of Mark-Up. Ian has all the news and Sameer Singh stops by with a hand from his deep Irish Open run.

This week we are joined by a man who has 4 runner up finishes but not a single bracelet at the World Series of poker, Alan ‘the chainsaw' Kessler. We are joined by twitch phenom Ben ‘Spraggy' Spraggs. David is torn a new one by Dara O'Kearney and guest Sameer Singh for a mistake he made in his Grudge Match with Ian Simpson. Ian brings us the latest from the WPT and Highroller bowl, plus a preview of the now running Unibet online Series. We are also joined by WSOP Czar Kevin Mathers who gives us the lowdown on this year's Series.

This week we are joined by a man who has 4 runner up finishes but not a single bracelet at the World Series of poker, Alan ‘the chainsaw' Kessler. We are joined by twitch phenom Ben ‘Spraggy' Spraggs. David is torn a new one by Dara O'Kearney and guest Sameer Singh for a mistake he made in his Grudge Match with Ian Simpson. Ian brings us the latest from the WPT and Highroller bowl, plus a preview of the now running Unibet online Series. We are also joined by WSOP Czar Kevin Mathers who gives us the lowdown on this year's Series.

Sameer Singh grew up playing chess and teen patti, and he quickly became one of the sharpest rounders at the National Law School of India. This wide-ranging conversation covers everything from cuisine and literature to the Irish Open to overlimping ... Read more...

Dara and David are back from hiatus with what promises to be a great show. Headlining this week is best-selling author and poker pro Maria Konnikova. They are also joined by back to back Irish Open 6th place finisher and recent online triple crown winner Sameer Singh. Last year's WSOP main event final-tablist Jack Sinclair stops by for a strategy segment that looks at David's Jack high call versus him at last month's JP MASTERS in Dublin. Ian brings us the news and results from the recent Unibet UKTOUR Brighton and Unibet DSO Lyon and the lads talk about the dangers of re-entry poker tournaments for the health of the poker eco-system.

Dara and David are back from hiatus with what promises to be a great show. Headlining this week is best-selling author and poker pro Maria Konnikova. They are also joined by back to back Irish Open 6th place finisher and recent online triple crown winner Sameer Singh. Last year's WSOP main event final-tablist Jack Sinclair stops by for a strategy segment that looks at David's Jack high call versus him at last month's JP MASTERS in Dublin. Ian brings us the news and results from the recent Unibet UKTOUR Brighton and Unibet DSO Lyon and the lads talk about the dangers of re-entry poker tournaments for the health of the poker eco-system.

He's the best at what he does, and what he does is campaign to build a Wolverine Statue in Fort McMurray. Justin interviews Sameer Singh about his plan to raise the money needed to build a bronze statue of Canada's X-Man. Sameer wants the statue to be a symbol of Fort McMurray's resilience after devastating forest fires. Justin and Sameer also discuss Tom King and Scott Snyder's very different Batman runs. Originally aired on CJRU 1280 AM in Toronto. Produced by Justin Chandler.

EMNLP 2017 paper by Nitish Gupta, Sameer Singh, and Dan Roth. Nitish comes on to talk to us about his paper, which presents a new entity linking model that both unifies prior sources of information into a single neural model, and trains that model in a domain-agnostic way, so it can be transferred to new domains without much performance degradation. https://www.semanticscholar.org/paper/Entity-Linking-via-Joint-Encoding-of-Types-Descrip-Gupta-Singh/a66b6a3ac0aa9af6c178c1d1a4a97fd14a882353

Sameer Singh from App Annie and our host Bernard Leong offer their five predictions to what will rock the Asia landscape in 2017 and examine side events that hit the road ahead after a turbulent and unpredictable 2016. We discuss: (a) Softbank’s gambit with Sprint & T-Mobile, (b) Asia messaging apps vs Facebook & Whatsapp The post Episode 155: 5 Predictions for Asia in 2017 with Sameer Singh appeared first on Analyse Asia.

Sameer Singh from App Annie and our host Bernard Leong discuss the five major events which rocked the Asia landscape in 2016: (a) Softbank’s acquisition of ARM for US$32B, (b) Pokemon Go, (c) Uber and Didi’s deal in China, (d) the Indian unicorn startups apocalypse & (e) the ecommerce war between Amazon & Alibaba in The post Episode 154: The 5 Major Events in 2016 with Sameer Singh appeared first on Analyse Asia.

Continuing from our first discussion, Sameer Singh from Tech-thoughts.net and our host analysed the changing alliances in both the transportation and gaming industry where allies within the space turn to rivalries in two interesting cases: Google versus Uber in ride-sharing and autonomous vehicles, and Niantic versus Nintendo in the mobile gaming space. We also dissected why The post Episode 136: With Friends Like These, Who needs Enemies Edition with Sameer Singh appeared first on Analyse Asia.

Sameer Singh from Tech-thoughts.net joined us for a two parter conversation on the recent major events in technology & their impact in Asia. In this episode, we discussed the recent announcements which came out of the Apple iPhone 7 event and their implications to the smartphone industry. We also examined whether the new iPhone offered The post Episode 135: The Apple iPhone 7 Event with Sameer Singh appeared first on Analyse Asia.

Continuing our discussed from the last episode, Sameer Singh from Tech-thoughts.net analysed the recent Apple Q1 2016 earning and challenged the notion whether Apple’s Asia (India and China) and their rumoured car strategy will bring them back to growth. Through the lens of the Apple rumoured car, we dived deeper into the conversation on artificial intelligence & The post Episode 114: Will Apple’s Asia & Car strategy work? with Sameer Singh appeared first on Analyse Asia.

Sameer Singh from Tech-thoughts.net joined us in to reflect on the major themes that has been ongoing in the technology space from messaging apps to self driving cars. In the first part, we discussed the recent Facebook F8 announcements on their new chatbots platform and video live streaming. From there, we analysed the implications of The post Episode 113: Facebook vs Asia Messaging Apps with Sameer Singh appeared first on Analyse Asia.

Starting the new year, Sameer Singh, author of Tech-Thoughts.net and our host Bernard Leong discussed the five predictions on Asia technology landscape 2016 in the second part of the two-episodes arc. Building on their earlier conversation on the five major events that rocked 2015, they forecast the events in 2016 which will shape the technology landscape The post Episode 84: The Five Predictions on Asia in 2016 with Sameer Singh appeared first on Analyse Asia.

Sameer Singh, author of Tech-Thoughts.net and senior industry analysis manager of App Annie, joined our host in a two episodes arc discussion on the five major events that shook Asia for the year of 2015 and the predictions for the year of 2016. In the first part, we discuss the five major events in Asia The post Episode 83: The Five Major Events in Asia 2015 with Sameer Singh appeared first on Analyse Asia.

Sameer Singh, our first guest on the podcast, joined us to discuss all things business and technology from Alphabet to Uber. We began instead with an interview our host of the show, Bernard on his reflections on the first year of Analyse Asia to the future plans of the podcast including the upcoming launch of The post Episode 55: From Alphabet to Uber and 1 Year on with Sameer Singh appeared first on Analyse Asia.

Sameer Singh from Tech-thoughts.net, also a long time recurring guest, joined Bernard for an awesome discussion on post Google I/O and Apple WWDC 2015, and how some of the announcements from the two major conferences of the year will impact Asia as a whole. We have a brief and short chat on Softbank’s recent launch The post Episode 40: Is there a bubble in Asia? with Sameer Singh appeared first on Analyse Asia.

Sameer Singh from Tech-Thoughts is back for an interesting discussion with Bernard Leong on the three major topics which dominate the Asian technology and business landscape. Discussing in depth the Apple Watch conundrum, they explained why there are flawed misconceptions from western analysts on the Apple Watch market in Asia. The discussion also revolved around The post Episode 25: The Apple Watch Conundrum in Asia with Sameer Singh appeared first on Analyse Asia.

Sameer Singh (@sameer_singh17) from Tech-Thoughts.net comes online with our host Bernard Leong (@bleongcw) to discuss and debate with an Asian perspective on Benedict Evans’ recent piece “New Questions in Mobile”. We first return to a lost discussion where we discuss Uber and its recent problems in Asia and categorically provide an answer to some US The post Episode 12: New Questions in Mobile, the Asia Edition appeared first on Analyse Asia.