Hockeystick

hockeystick

Author:
(1) Sima Mehri, Farzanegan High School. Table of Links Abstract and 1 Introduction and Description of Results 2. Proof of Results and References 2. Proof of Results In the proof of both theorems, we use induction. using properties of Pascal triangle, we get The statement for k + 1 is also true, and the proof is completed. using properties of the trinomial coefficients, we get The statement for k + 1 is also true, and the proof is completed. The hockey stick theorem in the trinomial triangles has been proved. This theorem can be translated in Pascal pyramid as follows : Other similar theorems might be obtained for Pascal’s four dimensional and even n-dimensional pyramid. References 1] G. Andrews, Euler’s ’Exemplum Memorabile Inductionis Fallacis’ and Trinomial Coefficients J. Amer. Math. Soc. 3 (1990), 653-669. [2] P. Hilton and J. Pedersen, Looking into Pascal Triangle, Combinatorics, Arithmetic and Geometry Mathematics Magazine, Vol. 60, No. 5 (Dec., 1987), 305-316. [3] Eric W.Weisstein, Trinomial Coefficient From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/TrinomialTriangle.html [4] Eric W.Weisstein, Trinomial Triangle From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/TrinomialTriangle.html This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Sima Mehri, Farzanegan High School. Author: Author: (1) Sima Mehri, Farzanegan High School. Table of Links Abstract and 1 Introduction and Description of Results Abstract and 1 Introduction and Description of Results 2. Proof of Results and References 2. Proof of Results and References 2. Proof of Results In the proof of both theorems, we use induction. using properties of Pascal triangle, we get The statement for k + 1 is also true, and the proof is completed. using properties of the trinomial coefficients, we get The statement for k + 1 is also true, and the proof is completed. k The hockey stick theorem in the trinomial triangles has been proved. This theorem can be translated in Pascal pyramid as follows : Other similar theorems might be obtained for Pascal’s four dimensional and even n-dimensional pyramid. References 1] G. Andrews, Euler’s ’Exemplum Memorabile Inductionis Fallacis’ and Trinomial Coefficients J. Amer. Math. Soc. 3 (1990), 653-669. [2] P. Hilton and J. Pedersen, Looking into Pascal Triangle, Combinatorics, Arithmetic and Geometry Mathematics Magazine, Vol. 60, No. 5 (Dec., 1987), 305-316. [3] Eric W.Weisstein, Trinomial Coefficient From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/TrinomialTriangle.html [4] Eric W.Weisstein, Trinomial Triangle From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/TrinomialTriangle.html This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Exploring Hockey Stick Theorems: Proof of Results and References

Author:
(1) Sima Mehri, Farzanegan High School. Table of Links Abstract and 1 Introduction and Description of Results 2. Proof of Results and References Abstract There are some theorems in the Pascal’s triangle which their figures resemble to shoot a ball by hockey stick, so they are called hockey stick theorems. P. Hilton and J. Pedersen, in the article ”Looking into Pascal Triangle, Combinatorics, Arithmetic and Geometry”,[2], have stated the little and big hockey stick and puck theorems in the Pascal’s triangle. The big hockey stick theorem is a special case of a general theorem which our goal is to introduce it. We state a hockey stick theorem in the trinomial triangle too. 1 Introduction and Description of Results The big hockey stick and puck theorem, stated in [2] is: Theorem 1.1. [2] (The Big Hockey Stick and Puck Theorem) In [2], this theorem is also demonstrated by Figure 1. We have found the general form of above theorem in Pascal triangle as below. Theorem 1.2. (The Hockey Stick Theorem in Pascal Triangle) An example of this theorem is illustrated in Figure 2. Now we wish to state the hockey stick theorem in trinomial triangle. First using [3] and [4], we explain what is the trinomial triangle. Equivalently, the trinomial coefficients are defined by We have proven the following theorem in this triangle: Theorem 1.3. (The Hockey Stick Theorem in The Trinomial Triangle) For Example see Figure 4. This paper is available on arxiv under CC BY 4.0 DEED license. Author: (1) Sima Mehri, Farzanegan High School. Author: Author: (1) Sima Mehri, Farzanegan High School. Table of Links Abstract and 1 Introduction and Description of Results Abstract and 1 Introduction and Description of Results 2. Proof of Results and References 2. Proof of Results and References Abstract There are some theorems in the Pascal’s triangle which their figures resemble to shoot a ball by hockey stick, so they are called hockey stick theorems. P. Hilton and J. Pedersen, in the article ”Looking into Pascal Triangle, Combinatorics, Arithmetic and Geometry”,[2], have stated the little and big hockey stick and puck theorems in the Pascal’s triangle. The big hockey stick theorem is a special case of a general theorem which our goal is to introduce it. We state a hockey stick theorem in the trinomial triangle too. 1 Introduction and Description of Results The big hockey stick and puck theorem, stated in [2] is: Theorem 1.1. [2] (The Big Hockey Stick and Puck Theorem) Theorem 1.1. [2] (The Big Hockey Stick and Puck Theorem) In [2], this theorem is also demonstrated by Figure 1. We have found the general form of above theorem in Pascal triangle as below. Theorem 1.2. (The Hockey Stick Theorem in Pascal Triangle) Theorem 1.2. (The Hockey Stick Theorem in Pascal Triangle) An example of this theorem is illustrated in Figure 2. Now we wish to state the hockey stick theorem in trinomial triangle. First using [3] and [4], we explain what is the trinomial triangle. Equivalently, the trinomial coefficients are defined by We have proven the following theorem in this triangle: Theorem 1.3. (The Hockey Stick Theorem in The Trinomial Triangle) Theorem 1.3. (The Hockey Stick Theorem in The Trinomial Triangle) For Example see Figure 4. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Exploring Hockey Stick Theorems: Abstract, Introduction and Description of Results

We Analyzed 20.2 Million CI Jobs In Trunk Flaky Tests: Here's What We Learned - Part 1

A lot of questions are swirling about the state of generative AI right now. How far along are companies with their bespoke GenAI efforts? Are organizations actually building AI applications using their own proprietary data in ways that move the needle? What kind of architecture is required? These questions aren’t surprising — there’s a huge range of opinions about AI out there, from unabashed optimism to jaded cynicism, mixed with a lot of hype. I’ve found that it helps to clarify the state of affairs in the GenAI marketplace across three areas: what the market wants to see, fears about what might happen, and what will happen (and how) in 2024. What Does Everyone Want — and What Are They Afraid of? GenAI exploded on the scene in late 2022 when OpenAI released ChatGPT and showed how powerful and accessible this kind of technology could be. The excitement about the potential upside of AI was everywhere. In short order, GenAI was going to be infused into every application at every enterprise. Investors envisioned a hockey-stick-like growth curve for companies that provide the infrastructure to support GenAI. The naysayers, on the other hand, envisioned a dystopian AI future that’s a cross between “Westworld” and “Black Mirror.” Others warn of an AI bubble. From an investment perspective, some say it’s like crypto all over again — lots of excitement and hype, and then a smoking crater. I think both of these fears are unfounded. Sure, with every new technology wave, there’ll be bad actors using GenAI for the wrong reasons. And the excitement about the possibilities of GenAI is everywhere; it does have a “bubbly” feel to it, and might even be more strident than the crypto buzz. But the big difference between GenAI and crypto is the fact that there are many, many real use cases for the former, across organizations and across industries. In crypto, there was one strong use case: financial transactions between untrusted parties (aka money laundering). That’s something that the mainstream isn’t quite as interested in. Right now, the state of affairs for GenAI applications reminds me of e-commerce in the late 1990s, when companies were trying to figure out how to make it safe to use credit cards over the Internet. It took a little while for organizations to figure out how to do it securely, but once they did, suddenly everyone had an e-commerce site. The parallel I see in GenAI right now: how to ensure that language models don’t return inaccurate responses by hallucinating. The good news? That’s been figured out (thanks to retrieval-augmented generation, or RAG; more on that below). Where Are We Now, and Where Are We Going? A lot of what we saw last year were proof-of-concept GenAI projects: apps to demonstrate to a company’s leadership what’s possible. But very few companies have moved beyond that to build applications that are in full production. By “production,” I mean that an organization has an AI application that is being used by customers or employees in a non-prototype way. In other words, it’s available as a routine part of activities within some segments of business operations. It might be the front office, it might be what’s behind a call to customer service, but it’s somewhere near the mainstream part of business. Walmart is a good early example of this. The retailer announced in January that it has added GenAI-powered search to its shopping app. Apple is reportedly testing a GenAI tool to help its employees provide speedier technical support. Until we start seeing more examples like this, GenAI is going to linger a little longer in the early stage of what Gartner calls the “hype cycle.” Volkswagen just announced an in-house lab to develop GenAI apps for navigation and infotainment applications for its automobiles. That said, we’re not as far from reaching the “plateau of productivity” as some might think. As I mentioned earlier, trusting models’ output has been a hurdle for organizations that are still grappling with how to produce relevant and accurate large language model (LLM) responses by reducing hallucinations. RAG, which provides models with additional data or context in real-time from other sources — most often, a database that can store vectors — is being employed now to help solve this problem. This technology advancement is a key to developing domain-specific, bespoke GenAI applications built on organizations’ most valuable asset: their own data. While RAG has emerged as the de facto method for getting enterprise context into GenAI applications, fine-tuning — when a pre-trained model is trained further on a subset of data — is often mentioned as well. There are times when this method can be useful, but RAG is the right choice if there’s any concern for privacy, security, or speed. Regardless of how context is added to the application, the big question I get often from investors I’ve been speaking with is when will companies start to make money from GenAI apps? My response: Most of the enterprises you track are consumption-based businesses. Many are now supporting the experiments, proofs of concept (POCs), and niche apps that their customers have built; those don’t do much in the way of consumption. But this is starting to change as major AI applications start to go from POC into true production. I predict this is going to happen in a significant way by the end of 2024. It will come to fruition in the second half of 2024 starting in two places. First, it’s taking hold in retail (see the Walmart example mentioned earlier). You’ll also see widespread adoption in what I call the “AI intranet” area: Chat with PDF, knowledge bases, and internal call centers. With the consumption that these kinds of apps drive, companies like Microsoft, Google, and even Oracle are starting to report results from AI. Outside of the realm of hyperscalers, other AI infrastructure companies will likely start to highlight lifts in the earnings reports they release in January, February, and March of next year. The Path to Production for GenAI Applications The groundwork has already been laid for consumption-based AI infrastructure companies. We’ve already seen strong, commercial proof points that show what’s possible for a large base of domain-specific, bespoke applications. From the creative AI apps — Midjourney, Adobe Firefly, and other image generators, for example — to knowledge apps like GitHub Copilot (over 1 million developers use it), Glean, and others, these applications have enjoyed great adoption and have driven significant productivity gains. Progress on bespoke apps is most advanced in industries and use cases that need to facilitate delivering knowledge to the point of interaction. The knowledge will come from their own data, using off-the-shelf models (either open source or proprietary), RAG, and the cloud provider of their choice. Three elements are required for enterprises to build bespoke GenAI apps that are ready for the rigors of functioning at the production scale: smart context, relevance, and scalability. Smart Context Let’s take a quick look at how proprietary data is used to generate useful, relevant, and accurate responses in GenAI applications. Applications take user input in the shape of all kinds of data and feed it all into an embedding engine, which essentially derives meaning from the data, retrieves information from a vector database using RAG, and builds the “smart context” that the LLM can use to generate a contextualized, hallucination-free response that’s presented to the user in real-time. Relevance This isn’t a topic you hear much about at operational database companies. But in the field of AI and vector databases, relevance is a mix of recall and precision that’s critical to producing useful, accurate, non-hallucinatory responses. Unlike traditional database operations, vector databases enable semantic or similarity search, which is non-deterministic in nature. Because of this, the results returned for the same query can be different depending on the context and how the search process is executed. This is where accuracy and relevance play a key role in how the vector database operates in real-world applications. Natural interaction requires that the results returned on a similarity search are accurate AND relevant to the requested query. Scalability GenAI apps that go beyond POCs and into production require high throughput. Throughput essentially is the amount of data that can be stored, accessed, or retrieved in a given amount of time. High throughput is critical to delivering real-time, interactive, data-intensive features at scale; writes often involve billions of vectors from multiple sources, and GenAI applications can generate massive amounts of requests per second. Wrapping Up As with earlier waves of technology innovation, GenAI is following an established pattern, and all signs point to it moving even faster than previous tech revolutions. If you cut through all the negative and positive hype about it, it’s clear that promising progress is being made by companies working to move their POC GenAI apps to production. And companies like my employer DataStax that provide the scalable, easy-to-build-on foundations for these apps will start seeing the benefits of their customers’ consumption sooner than some might think. By Ed Anuff, DataStax Learn more about how DataStax enables customers to get their GenAI apps to production. A lot of questions are swirling about the state of generative AI right now. How far along are companies with their bespoke GenAI efforts? Are organizations actually building AI applications using their own proprietary data in ways that move the needle? What kind of architecture is required? These questions aren’t surprising — there’s a huge range of opinions about AI out there, from unabashed optimism to jaded cynicism, mixed with a lot of hype. I’ve found that it helps to clarify the state of affairs in the GenAI marketplace across three areas: what the market wants to see, fears about what might happen, and what will happen (and how) in 2024. What Does Everyone Want — and What Are They Afraid of? GenAI exploded on the scene in late 2022 when OpenAI released ChatGPT and showed how powerful and accessible this kind of technology could be. The excitement about the potential upside of AI was everywhere. In short order, GenAI was going to be infused into every application at every enterprise. Investors envisioned a hockey-stick-like growth curve for companies that provide the infrastructure to support GenAI. The naysayers, on the other hand, envisioned a dystopian AI future that’s a cross between “Westworld” and “Black Mirror.” Others warn of an AI bubble. From an investment perspective, some say it’s like crypto all over again — lots of excitement and hype, and then a smoking crater. warn I think both of these fears are unfounded. Sure, with every new technology wave, there’ll be bad actors using GenAI for the wrong reasons. And the excitement about the possibilities of GenAI is everywhere; it does have a “bubbly” feel to it, and might even be more strident than the crypto buzz. But the big difference between GenAI and crypto is the fact that there are many, many real use cases for the former, across organizations and across industries. In crypto, there was one strong use case: financial transactions between untrusted parties (aka money laundering). That’s something that the mainstream isn’t quite as interested in. Right now, the state of affairs for GenAI applications reminds me of e-commerce in the late 1990s , when companies were trying to figure out how to make it safe to use credit cards over the Internet. It took a little while for organizations to figure out how to do it securely, but once they did, suddenly everyone had an e-commerce site. GenAI applications reminds me of e-commerce in the late 1990s The parallel I see in GenAI right now: how to ensure that language models don’t return inaccurate responses by hallucinating . The good news? That’s been figured out (thanks to retrieval-augmented generation , or RAG; more on that below). hallucinating retrieval-augmented generation Where Are We Now, and Where Are We Going? A lot of what we saw last year were proof-of-concept GenAI projects: apps to demonstrate to a company’s leadership what’s possible. But very few companies have moved beyond that to build applications that are in full production. By “production,” I mean that an organization has an AI application that is being used by customers or employees in a non-prototype way. In other words, it’s available as a routine part of activities within some segments of business operations. It might be the front office, it might be what’s behind a call to customer service, but it’s somewhere near the mainstream part of business. Walmart is a good early example of this. The retailer announced in January that it has added GenAI-powered search to its shopping app. Apple is reportedly testing a GenAI tool to help its employees provide speedier technical support. Until we start seeing more examples like this, GenAI is going to linger a little longer in the early stage of what Gartner calls the “ hype cycle .” announced in January reportedly hype cycle Volkswagen just announced an in-house lab to develop GenAI apps for navigation and infotainment applications for its automobiles. That said, we’re not as far from reaching the “plateau of productivity” as some might think. As I mentioned earlier, trusting models’ output has been a hurdle for organizations that are still grappling with how to produce relevant and accurate large language model (LLM) responses by reducing hallucinations. large language model RAG, which provides models with additional data or context in real-time from other sources — most often, a database that can store vectors — is being employed now to help solve this problem. This technology advancement is a key to developing domain-specific, bespoke GenAI applications built on organizations’ most valuable asset: their own data. While RAG has emerged as the de facto method for getting enterprise context into GenAI applications, fine-tuning — when a pre-trained model is trained further on a subset of data — is often mentioned as well. There are times when this method can be useful , but RAG is the right choice if there’s any concern for privacy, security, or speed. There are times when this method can be useful Regardless of how context is added to the application, the big question I get often from investors I’ve been speaking with is when will companies start to make money from GenAI apps? My response: Most of the enterprises you track are consumption-based businesses. Many are now supporting the experiments, proofs of concept (POCs), and niche apps that their customers have built; those don’t do much in the way of consumption. But this is starting to change as major AI applications start to go from POC into true production. I predict this is going to happen in a significant way by the end of 2024. It will come to fruition in the second half of 2024 starting in two places. First, it’s taking hold in retail (see the Walmart example mentioned earlier). You’ll also see widespread adoption in what I call the “AI intranet” area: Chat with PDF, knowledge bases, and internal call centers. With the consumption that these kinds of apps drive, companies like Microsoft , Google , and even Oracle are starting to report results from AI. Outside of the realm of hyperscalers, other AI infrastructure companies will likely start to highlight lifts in the earnings reports they release in January, February, and March of next year. Microsoft Google Oracle report results The Path to Production for GenAI Applications The groundwork has already been laid for consumption-based AI infrastructure companies. We’ve already seen strong, commercial proof points that show what’s possible for a large base of domain-specific, bespoke applications. From the creative AI apps — Midjourney, Adobe Firefly, and other image generators, for example — to knowledge apps like GitHub Copilot ( over 1 million developers use it ), Glean, and others, these applications have enjoyed great adoption and have driven significant productivity gains. over 1 million developers use it Progress on bespoke apps is most advanced in industries and use cases that need to facilitate delivering knowledge to the point of interaction. The knowledge will come from their own data, using off-the-shelf models (either open source or proprietary), RAG, and the cloud provider of their choice. Three elements are required for enterprises to build bespoke GenAI apps that are ready for the rigors of functioning at the production scale: smart context, relevance, and scalability. Smart Context Let’s take a quick look at how proprietary data is used to generate useful, relevant, and accurate responses in GenAI applications. Applications take user input in the shape of all kinds of data and feed it all into an embedding engine, which essentially derives meaning from the data, retrieves information from a vector database using RAG, and builds the “smart context” that the LLM can use to generate a contextualized, hallucination-free response that’s presented to the user in real-time. Relevance This isn’t a topic you hear much about at operational database companies. But in the field of AI and vector databases, relevance is a mix of recall and precision that’s critical to producing useful, accurate, non-hallucinatory responses. Unlike traditional database operations, vector databases enable semantic or similarity search, which is non-deterministic in nature. Because of this, the results returned for the same query can be different depending on the context and how the search process is executed. This is where accuracy and relevance play a key role in how the vector database operates in real-world applications. Natural interaction requires that the results returned on a similarity search are accurate AND relevant to the requested query. Scalability GenAI apps that go beyond POCs and into production require high throughput. Throughput essentially is the amount of data that can be stored, accessed, or retrieved in a given amount of time. High throughput is critical to delivering real-time, interactive, data-intensive features at scale; writes often involve billions of vectors from multiple sources, and GenAI applications can generate massive amounts of requests per second. Wrapping Up As with earlier waves of technology innovation, GenAI is following an established pattern, and all signs point to it moving even faster than previous tech revolutions. If you cut through all the negative and positive hype about it, it’s clear that promising progress is being made by companies working to move their POC GenAI apps to production. And companies like my employer DataStax that provide the scalable, easy-to-build-on foundations for these apps will start seeing the benefits of their customers’ consumption sooner than some might think. By Ed Anuff, DataStax By Ed Anuff, DataStax Learn more about how DataStax enables customers to get their GenAI apps to production. Learn more about how DataStax enables customers to get their GenAI apps to production . DataStax enables customers to get their GenAI apps to production

When Will Infrastructure Companies See Gains from Generative AI?

কখন অবকাঠামো কোম্পানিগুলি জেনারেটিভ এআই থেকে লাভ দেখতে পাবে?

Wann werden Infrastrukturunternehmen von generativer KI profitieren?

¿Cuándo verán las empresas de infraestructura los beneficios de la IA generativa?

Quand les entreprises d’infrastructure bénéficieront-elles de l’IA générative ?

बुनियादी ढांचा कम्पनियों को जनरेटिव एआई से लाभ कब मिलेगा?

インフラ企業はいつジェネレーティブ AI から利益を得られるのでしょうか?

인프라 회사는 언제 Generative AI로부터 이익을 얻을 수 있습니까?

Quando as empresas de infraestrutura verão os ganhos da IA generativa?

Когда инфраструктурные компании увидят выгоду от генеративного искусственного интеллекта?

Altyapı Şirketleri Üretken Yapay Zekadan Kazançları Ne Zaman Görecek?

Khi nào các công ty cơ sở hạ tầng sẽ thu được lợi nhuận từ AI sáng tạo?

基础设施公司何时能从生成式人工智能中获益？

Get ready for an AI earthquake! A team of UCLA researchers ( n, , , ) have dropped some major keys to AGI. It's not only the code to seriously human-sounding AI, but they've also gone and open-sourced the whole thing. @zxche @Yihe__Deng @HuizhuoY @Kaixuan_Ji_19, @QuanquanGu Now you can develop better LLMs without needing to feed it tons of new, human-annotated data. First, let's focus on the game-changer here: a self-teaching language model. This method lets a language model becoming better and better without massive amounts of new, externally curated data. teach itself, Introducing SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models I went full deep-dive mode – read their paper (" "),  scoured the insights on forums like , , and with Google Gemini Ultra and GPT-4 Turbo – and the core concept of SPIN knocked my tech-loving metaphorical socks off: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models HackerNews X Reddit The 'Conversation Partner' Trick Imagine starting with a language model that has mastered basic skills (let's say conversational etiquette). With SPIN, the model building a dataset from what it already knows. generates internal 'conversations,' Instant knowledge expansion! Step two involves unleashing a new model and giving it one task: spot the difference between and genuine human communication. This forces the original model to up its game, getting more and more with every response to avoid detection. machine-generated chats human-like Here's where things get interesting. They started with (already fine-tuned with ). SPIN unleashed an iterative training system with this base model, improving it exponentially without relying on tons of new externally created data. zephyr-7b-sft-full UltraChat corpus SPIN vs. Traditional AI Training (DPO): A New Champion? We usually think machine learning, particularly for these huge language models, requires boatloads of carefully curated and labeled data. methods involve humans painstakingly rating AI responses against each other for training. Not only is this labor-intensive, but it also balloons costs as a dataset grows. Direct Preference Optimization (DPO) Direct Preference Optimization (DTO) is a training method where a model is fine-tuned using a dataset of preferences, often involving human judgments that decide which of the model-generated responses are preferred. This method requires collecting new data where each piece is labeled based on these preferences, which can be resource-intensive. In contrast, SPIN utilizes , significantly reducing the need for new data. iterative self-play By the first iteration, , highlighting its efficiency and effectiveness in leveraging existing data to enhance model performance. SPIN's performance already exceeds that of DPO in most cases SPIN showcases its strength by achieving . The process of , methodically enhances the model's performance across multiple iterations, , especially on challenging benchmarks like TruthfulQA and GSM8k. on-par performance with models trained on more extensive datasets iterative training showcasing substantial improvements So, SPIN outperforms conventional training methods, including DPO, by efficiently leveraging synthetic datasets generated through self-play, without the need for additional human-annotated data. What are SPIN's Strengths and Costs? SPIN throws a curveball with its self-play dynamic. Think of it like a language model sparring with itself in a linguistic boxing ring, with each round teaching it new tricks. SPIN's data efficiency bypasses the need for new human-annotated datasets. But more importantly, it , . accelerates the improvement loop making the model increasingly adept at generating human-like text Not only does SPIN seem to match models trained on larger external datasets, but its iterative power means consistent gains as it essentially studies its own output. Mindblowing, right? Okay, Let's Talk the Elephant in the Room – COST Nous Research co-founder has a point. These big ol' language models don't get smarter for free. Iteratively re-training with SPIN involves the expensive process of Supervised Fine-Tuning (SFT) each time. @Teknium1 However, he also mentions that "I think its worth it!". Also, the long-term benefits of quicker evolution and potentially less dependency on human-annotated data outweigh the initial investment? That's the exciting question! BOOM! It's Open-Source AI Time Just yesterday, , associate professor of computer science at UCLA and director of AI research at ByteDance, announced that . This doesn't just mean code and datasets, but pre-trained models to kickstart your own AI journeys. Quanquan Gu anyone can now use the SPIN model and dataset SPIN mirrors human thought processes. By generating text that feels human, SPIN hints at the foundational elements of reasoning that future AI could do. You know how some LLM outputs feel robotic right? Well, SPIN is different. It actually mirrors the way humans think. The way it writes feels so natural, it's like a peek into how future AI might be able to reason for themselves. This isn't just about making chatbots sound nicer. It's about creating a kind of digital thinking that works like ours. That kind of AI would be so much more flexible and capable of real understanding. While SPIN is a big leap forward in making language models sound more natural, . it's easy to get excited and overestimate what it means The text it produces is impressive (you can take a look to the database), but it's important to remember that AI doesn't yet have the capacity for true independent reasoning. While SPIN isn't true , the way it mimics human-like writing demonstrates impressive advances in how AI could process and use language in the future. AGI Even so, it does suggest amazing possibilities for how AI and language might develop in the future (if you remember that we are at the beginning of the hockey stick, the future is not far from today...) The ripple effects will be huge and here's your access pass: Code: Available on : GitHub https://github.com/uclaml/SPIN Data: Hosted on , the dataset is readily accessible for those eager to apply SPIN methodologies: Hugging Face https://huggingface.co/collections/UCLA-AGI/datasets-spin-65c3624e98d4b589bbc76f3a… Models: Pre-trained models are also available, offering a head start for experimenting with SPIN-enhanced language models: https://huggingface.co/collections/UCLA-AGI/zephyr-7b-sft-full-spin-65c361dfca65637272a02c40… Project Page: For comprehensive insights and further information, the project page is an invaluable resource: https://uclaml.github.io/SPIN/ To sum up, its iterative, self-improving methodology is a significant advancement towards creating LLM that can engage in genuinely human-like communication. Originally shared on my . X account

AGI Blueprint? UCLA Researchers Open-Source SPIN—A Self-Improving Language Model

এজিআই ব্লুপ্রিন্ট? UCLA গবেষকরা ওপেন-সোর্স স্পিন—একটি স্ব-উন্নতিশীল ভাষা মডেল

AGI-Blaupause? UCLA-Forscher Open-Source-SPIN – ein sich selbst verbesserndes Sprachmodell

¿Plan AGI? SPIN de código abierto de investigadores de UCLA: un modelo de lenguaje que se mejora a sí mismo

Plan AGI ? SPIN open source des chercheurs de l'UCLA : un modèle linguistique auto-améliorable

एजीआई ब्लूप्रिंट? यूसीएलए शोधकर्ता ओपन-सोर्स स्पिन-एक स्व-सुधार भाषा मॉडल

AGIブループリント? UCLA の研究者がオープンソースの SPIN — 自己改善型言語モデルを公開

AGI 청사진? UCLA 연구원 오픈 소스 SPIN - 자체 개선 언어 모델

Projeto AGI? SPIN de código aberto para pesquisadores da UCLA - um modelo de linguagem que se aprimora automaticamente

Проект AGI? Исследователи Калифорнийского университета в Лос-Анджелесе SPIN с открытым исходным кодом — самоусовершенствующаяся языковая модель

AGI Planı? UCLA Araştırmacıları Açık Kaynak SPIN—Kendini Geliştiren Bir Dil Modeli

Bản thiết kế AGI? Các nhà nghiên cứu của UCLA SPIN mã nguồn mở—Mô hình ngôn ngữ tự cải thiện

AGI 蓝图？加州大学洛杉矶分校研究人员开源 SPIN——一种自我改进的语言模型

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意大利面

The AI startup landscape has been dynamic, with numerous emerging players and innovative solutions in various industries, including healthcare, finance, transportation, e-commerce, and more, making it highly attractive for investors. From autonomous vehicles to virtual assistants, and the seemingly godlike ChatGPT, AI is transforming industries and disrupting traditional business models. Fueling the AI fire and investor interest is the sheer size and growth the sector has and will continue to enjoy. According to Precedence Research, the global artificial intelligence (AI) market size was estimated at $119.78 billion in 2022 and is . That's a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030. expected to hit almost $1.6 trillion by 2030 However, not all AI startups are created equal, and investors need to exercise caution when deciding where to allocate their capital. To mitigate risk and make informed investment decisions, it's crucial to ask the right questions and thoroughly evaluate the potential of an AI startup. The fast-paced nature of AI development, regulatory uncertainties, ethical concerns, and the competitive landscape can impact the success and viability of AI startups. Therefore, it becomes crucial for investors to thoroughly assess and evaluate the risks and opportunities associated with investing in AI startups. I spoke with Daniil Kirikov, managing partner at , about some of the not-so-common questions, investors should be asking before they commit. Orbita VC Expand your understanding of the age-old "Is there a problem" qualifier One of the first questions all investors consider is whether any startup, AI or otherwise, is addressing a real problem that people are willing to pay for. "AI hype can sometimes overshadow the actual value proposition of a startup," Kirikov said. "Investors need to expand their understanding of whether there's a problem with better questions to critically assess if the startup is solving an old issue that people were already paying to solve, but to someone else. A good litmus test for this is to ask, 'Who were people paying to solve this problem 10 years ago?' If the answer is unclear or non-existent, it could be a sign that the problem being addressed is not significant or sustainable in the long run." Assuming the startup is addressing a genuine problem, the next question to ask is whether AI enables a significant increase in efficiency in terms of cost, speed, or quality. "AI should provide a clear competitive advantage over traditional methods, and the startup should have a compelling value proposition in this regard," Kirikov said. "If the startup's solution merely provides marginal improvements or does not result in a substantial increase in efficiency, it may not be a compelling investment opportunity. Investors should seek startups that have a clear edge over the competition and can deliver a step-change improvement through the use of AI." Problem defined, but does this need solving, and why now? Another critical factor to consider is the timing of the solution and whether it could have already been solved before the hockey-stick growth and interest in AI. After all, it's 2023 - at this point in history, it is nigh on impossible to come up with an idea that hasn't already been solved 700 times or find a gap in the market. "Investors should assess whether the same solution could have been implemented two, three, or five years ago," Kirikov said. "If the answer is yes, it may indicate that the startup is not leveraging new technologies or ideas and that others may have already attempted similar approaches without success. Learning from the failures of others is crucial in making investment decisions. If a solution has been attempted before without significant success, the likelihood of multiple increases in efficiency may be low. Investors should look for startups that are genuinely innovative and are leveraging cutting-edge technologies or ideas that are not readily available in the market." Dig deep on security risk, not just financials Security is also a vital consideration in the AI startup space. As AI technologies become more prevalent and powerful, the potential risks and ethical concerns associated with them increase. "Governments and regulatory bodies are paying close attention to the development and deployment of AI, and startups that do not prioritize security and ethical considerations may face regulatory hurdles and legal challenges," Kirikov said. "Investors should carefully evaluate the startup's approach to security and compliance with relevant regulations to mitigate potential risks and liabilities." Asking deeper and more nuanced questions is now critical given the sheer volume of AI startups being created daily. It is now not enough to understand at a surface level the problem the startup solves, its business model, traction and growth potential, team expertise, financials, risk mitigation, exit strategy, competitive advantage, funding requirements, and the timeline for achieving milestones when evaluating where to commit funds. Kirikov's approach and level of questioning in the assessment phase are now crucial for the industry and can help all investors make more informed decisions.

Assessing the Risk of Funding AI Startups: Deeper Questions Every Investor Should Ask

Raising money is one of the most challenging aspects of being a startup founder. After all, you're asking people to believe in your vision, invest money into your company and stay with you through ups and downs. Now more than ever, it’s -- which it makes it all the more impressive when it happens. extremely difficult to move from a seed round to a Series A One company that recently made the jump is , an all-in-one SaaS buying and management solution. They announced their Series A led by Prosus Ventures and Accel and comes only ten months after its seed round. Spendflo The company experienced a hockey-stick growth trajectory since its inception and has grown 30% month on month since the start of 2022, multiplying its revenue 15 times, growing its customer count by 5X, and expanding the team size fourfold. Talking to Siddharth Sridharan, CEO and Co-founder, let’d dig in more on their recent accomplishments. SE: Congratulations on your Series A. It’s never been more difficult for startups to move from the seed to Series A stage. What do you think sets you apart during this time? SS: Spendflo is an all-in-one solution that solves challenges at every stage of the SaaS procurement lifecycle including buying, management, and security. Every other alternative in the market remains a point solution focusing on just one of the three major challenges, resulting in three times the cost, effort, and resources involved for a business to tackle one problem. Moreover, juggling between three different systems to solve one problem is counterintuitive to the goal of saving time and money for fast-growing businesses. Spendflo remains the truly complete solution that helps modern businesses buy, manage and secure SaaS on autopilot. Seamless sharing of data between its SaaS buying, management, and security hubs streamlines SaaS buying, automates workflows, accelerates buying cycles, and saves up to 30% of a company’s annual SaaS expenses. As companies grapple with mounting SaaS expenses and try to be more efficient and productive, Spendflo addresses the need of the hour perfectly. : Saas spends are typically one of the top 5 expenses of any company. How did you identify this as an issue and recognize you could make an impact? SE SS: Spendflo was founded in 2021 while I was running business operations at the Californian electric vehicle charging company, Volta Charging. Every quarter, my CFO would complain to me that we were spending too much on SaaS solutions, but I didn’t know who owned the contracts or how they were using them. I got so fed up and realized I couldn’t be the only one with this problem. So I teamed up with co-founders Ajay Vardhan and Rajiv Ramanan to commercialize a solution. Spendflo essentially solves three problems: 1. The lack of visibility 2. The sheer chaos caused by multiple stakeholders involved in the buying, negotiation, and renewal processes 3. Price and usage opacity of their SaaS stack With Spendflo, companies can now track all of their SaaS tools and subscriptions in a single location. The solution provides crucial data on how much the business is spending on each subscription, as well as when contracts are due to be renewed; and more importantly, it provides usage data, so the business can see whether it’s getting the maximum possible ROI. SE: Tell me more about how Spendflo works, especially for audiences who could benefit, like: finance, procurement and IT, and security. SS: Spendflo helps companies save hundreds of hours on negotiating SaaS contracts and offers complete visibility while helping them evaluate, right-size and optimize their stack. This leads to faster approvals, easier onboarding, and more informed decision-making for a company's SaaS needs. For each audience, this translates into direct and impactful advantages, such as: CFOs and their finance teams now gain more visibility, get guaranteed savings (Spendflo provides 3x ROI consistently), and have streamlined approvals for their SaaS stack. Benchmark vendor data also helps them know areas where they can save or cut back. Procurement and IT departments receive benchmarked vendor data, enabling them to identify opportunities for cost savings and reduce procurement cycles. With Spendflo’s granular usage data and SaaS sentiment analysis, procurement and IT teams can make data-backed renewal/replacement decisions. Moreover, Spendflo provides access to skilled negotiation experts who are equipped to handle these tasks with proficiency. Security teams can now expect guaranteed better compliance with vendor questionnaires, streamlined onboarding processes, and seamless license renewals. SE: It looks like you’ve experienced tremendous growth in a relatively short amount of time. Tell me about that and what you think is impacting this. SS: Yes, we’ve experienced incredible growth at Spendflo, and we believe that it's due to a combination of factors. In today's economic climate, businesses are seeking innovative ways to optimize their operations, and Spendflo offers a streamlined, and efficient solution to SaaS buying and management. Additionally, our best-in-class, 24x7 multi-channel support, along with industry-leading service level agreements, have helped us gain the trust of leading global brands across industries. \ All of these efforts have helped us reach a wider audience and happier customers, which explains our growth trajectory. SE: How are you going to use the new funds? What are your most immediate and future plans? SS: The funds raised will be deployed towards strengthening the team, rapidly evolving the product offering, and expanding GTM efforts in the North American region. SE: Any tips, advice or best practices  for other founders and startups currently looking to raise a Series A? SS: These are the three things that I learned from Spendflo’s journey so far: Having loyal customers that truly believe in you, and would recommend you anywhere, anytime, can create a huge impact. Having reliable and proficient sales and marketing leaders within the organization makes the journey to Series A much smoother, and probable. Keeping past and potential investors in the loop about developments in the company and product is crucial—I highly recommend sending monthly investor updates.

The future of SaaS is savings, Spendflo raises Series A

Heyo Growth Marketers - The round 5 results of the by and HackerNoon are now LIVE! Growth Marketing Writing Contest mParticle Here’s a quick reminder on how the contest works - You can win from $12,000 prize pool by writing a story on . Here are a few topic suggestions shared by mParticle: #growth-marketing Product Management Customer Data Platforms (CDP) Growth Marketing Data Engineering Data Management ! Submit your #growth-marketing story today The Growth Marketing Writing Contest: Round 5 Nominations & Winners For selecting the top 10 nominations, we picked all the stories with the #growth-marketingtag on HackerNoon, published in January 2022. Then we chose the top stories using 60:30:10 weightage respectively to: Number of hours read The number of people reached The freshness of the content Here are the top 10 nominations: by 6 More Growth Marketing Strategies to 10x Your Startup in 2023 @mrkrystallis by Press Mentions For Your Brand: A Growth Marketer's How-To @darragh by 10 Reasons Why I Sucked At Growth Marketing @nebojsa.todorovic by How to Use No-Code Machine Learning to Optimize your HackerNoon Articles @giorgiob by How to Unlock Product Market Fit and Rapid Growth for Startups @loringuyen by 7 Tips for Product Managers in Web3 @hunais by Behavioral Economics: Growth Hackers' Secret Weapon @aghafasih by How to Achieve 'Product of the Day' on Product Hunt Using a Trello Board @artkulakov by If Growth Marketing Is So Terrible, Why Don't Statistics Show It? @requiredcrx by Why Web3 and NFT Gaming Can Be the Biggest Trend of 2023 @sergey-baloyan In the first place, we have: https://hackernoon.com/press-mentions-for-your-brand-a-growth-marketers-how-to?embedable=true Quick Tips Building Relationships With Journalists: Don’t be self-promotional about ideas for a story; they hate that. Add-value: Follow them on socials and share and interact with content you like and can add value to. It’ll help you stand out. Engage online: Find out what they need for their story or story ideas; be helpful and add-value. Listen more than you talk. Listen: Congratulations, . You have won $1,000! @darragh In the second place, we have: https://hackernoon.com/6-more-growth-marketing-strategies-to-10x-your-startup-in-2023?embedable=true Lastly, here's an extra Growth Marketing Strategy you should never forget… Never stop testing! Constant testing is the backbone of any successful growth marketing strategy. Just make sure that your analytics are set up right so that you can analyze both your successes & failures equally and it won't be long till you discover a strategy with hockey stick growth potential that's worth doubling down on. Congratulations, . You have won $600!!! @mrkrystallis The story scoring the third position is: https://hackernoon.com/10-reasons-why-i-sucked-at-growth-marketing?embedable=true I used to be a humble and happy writer who one day had the Gordon Gekko “moment." You know what I'm talking about, don't you? I thought it was my revelation, but it turned out it was my damnation. I wanted more, plain and simple. It wasn't enough for me to be just a writer. Oh no, how about a writer-marketer?! More shiny titles, more clients. Sounds like a solid business growth plan. What could possibly go wrong? Well, it didn't take me too long to realize one simple growth marketing truth: Every marketer can be a writer; but not every writer can be a marketer. Marketing takes and asks so much more than just writing. So, I didn't get a dime for my rhyme. Well-deserved ! You have won $400. @nebojsa.todorovic Congratulations again to all the nominations and winners. Visit today to view all the current and upcoming contests! These contests are for everyone! Share your stories and win $$$! contests.hackernoon.com

The Growth Marketing Writing Contest: Round 5 Results Announced!

গ্রোথ মার্কেটিং রাইটিং কনটেস্ট: রাউন্ড 5 এর ফলাফল ঘোষণা করা হয়েছে!

Der Wachstumsmarketing-Schreibwettbewerb: Ergebnisse der fünften Runde bekannt gegeben!

Concurso de redacción sobre marketing de crecimiento: ¡se anuncian los resultados de la ronda 5!

Concours d'écriture sur le marketing de croissance : les résultats de la 5e ronde sont annoncés !

ग्रोथ मार्केटिंग राइटिंग कॉन्टेस्ट: राउंड 5 के परिणाम घोषित!

The Growth Marketing Writing Contest: 第 5 ラウンドの結果が発表されました!

그로스 마케팅 글쓰기 콘테스트: 5차 결과 발표!

O Concurso de Redação de Growth Marketing: Resultados da 5ª Rodada Anunciados!

Büyüme Pazarlaması Yazma Yarışması: 5. Tur Sonuçları Açıklandı!

Cuộc thi Viết về Tiếp thị Tăng trưởng: Đã công bố Kết quả Vòng 5!

增长营销写作比赛：第 5 轮结果公布！

Apple's Tim Cook is one of many metaverse detractors, but it is no surprise that he favors augmented reality (AR) as the next significant technological shift. "I always think it's important that people understand what something is," the Apple CEO told on Friday. "And I'm really not sure the average person can tell you what the metaverse is." Dutch publication Bright Other big names calling out the metaverse and promoting AR all have the same thing in common: their companies are expected to launch AR solutions soon, and they compete with the loudest kid in the metaverse playground, Meta. "In true technical style, there is no single universally accepted definition of the metaverse," Olga Vorobyeva, founder at Vox Consulting, wrote in . "Simply put, the metaverse is a vast network of 3D worlds and simulations rendered in real-time for cooperation and participation. This is not a virtual reality experience or a virtual economy with avatars sticking out, but a way to maintain identity, objects, payments, history, and ownership continuity." VentureBeat You don't have to be a budding Sherlock Holmes to see those speaking publicly about AR versus the metaverse tend to side with whichever horse their company decided to back. And the point made about people understanding a technology seems illogical at best. I don't need to understand Kotlin, Javascript, or AI algorithms to rent an e-scooter, order food, or watch an endless stream of people lip-syncing in front of a ring light. I simply You don't have to be a budding Sherlock Holmes to see those speaking publicly about AR versus the metaverse tend to side with whichever horse their company decided to back. And the point made about people understanding a technology seems illogical at best. I don't need to understand Kotlin, Javascript, or AI algorithms to rent an e-scooter, order food, or watch an endless stream of people lip-syncing in front of a ring light. I simply need to hear about a cool app that makes my life better, download it, and use it. Put a different way, Pokemon Go was the app that made AR a mainstream topic, and I don't need to understand AR to catch 'em all. But not only is AR versus the metaverse yet another example of binary thinking, causing polarization and division, but it's also missing the point. AR and the metaverse are best served together, like macaroni and cheese. While many analysts separate the two when preparing their market size reports and forecasts, Grand View Research understands that when looking at the future of the metaverse, it should include AR, VR, MR, and all associated technologies. , the global metaverse market size was estimated at $38.85 billion in 2021. It is expected to expand at a compound annual growth rate (CAGR) of 39.4% from 2022 to 2030. According to Grand View But all of this is moot unless we, the users, get apps that can make a positive difference in our daily lives. I looked at what the post-Pokemon AR world had to offer. There are some exciting and engaging offerings, including real-time , measuring body size and shape for , , and even . navigation in airports custom clothing designing and engineering buildings detecting and battling wildfires However, one new app combines AR with the metaverse/Web3 and is backed by some heavy firepower. is a street sports app designed for those who love skateboarding, rollerblading, BMX, parkour, snowboarding, and other similar activities. ShredSpots Founded by former pro skater Lev Filimonov and with an executive team that includes former YouTube, Snap, and Google execs, the app consists of features such as a community-driven street sports map and the ability to upload your tricks so others can see your skills in action. ShredSpots differs from other street sports map apps because it is soon to include AR features, and by building the app with Web3 in mind, the future possibilities go way beyond what you could offer by adding AR alone. The upcoming AR addition will allow users to see other users' tricks where they were performed, using your smartphone as the viewing device (and, one would assume, in AR smart glasses at some point). That's cool, but by combining AR and Web3, ShredSpots can offer users a whole raft of additional benefits. Imagine being able to monetize your skills and earn for sick tricks, or being able to immerse yourself in a world of street culture, with original street art and more presented to you as you look at the world through ShredSpots and your smartphone camera. I imagine the team has several ideas that combine all the latest technologies, including NFTs. ShredSpots is an excellent example of a team that understands there is no AR vs. metaverse - the two are complimentary, help both sides to gain mainstream acceptance and adoption, and - most importantly - should not even be highlighted in the first place. The most important thing is not the technology we build these experiences on but the experience itself. So if you want to take advantage of the hockey-stick growth analysts expect in Web3, AR, the metaverse, and other related technologies, be like ShredSpots. Design a fantastic app that works, appeal to a broad audience, and make it easy for them to use and understand. Tim Cook is right about that part. He's just wrong about making this a them vs. us thing.

Why Tim Cook has got it wrong on augmented reality vs. the metaverse

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