AI/ML news, top picks, and generated innovation digests.
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Ahead of ATx Enterprise 2024, IBM announced the signing of a memorandum of understanding (MOU) with AI Singapore (AISG) on 28 May 2024 to collaborate on the testing of...
Writing up results from a project from Spring 2023
I understand some recent chess engines (like AlphaZero or MuZero) are based on neural networks. This question is not specific to chess, any other game (e.g. go) would do, but I keep chess for concreteness. I am not interested in how these engines are trained but in what they "look like" after training. (For concreteness, it suffices for me that they give a "score" for a given board position.) Somewhat similar questions have been asked here but I can't find what I need. Is it correct to view them as a neural network? Or is there some other major component like search trees or other type of algorithm. I'm assuming that answer here is "yes" in an appropriate sense. Note: The AlphaZero paper talks about various forms of tree search. It is not clear to me how they are used to play, and from the appendix apparently some other engines use very limited search, like depth 2? That would be already interesting to me. What is the depth of the network? Any reference to precise parameters? What is the input and output format of these networks? Is the input just an array with pieces positions (excluding three-fold repetition rules and similar secondary issues). What is the output? Some info is here but I can't quite understand how it works specifically e.g. for chess. What activation functions are used?
I'm considering $F[J,n-k]= \displaystyle \frac{(e_{*}^{'}e_{*}-e'e) \backslash J}{e'e\backslash (n-k)}$ , where $J$ stands for number of restrictions. I want to prove $W=(Rb-q)'(Rs^2(X'X)^{-1}R')^{-1}(Rb-q)=JF$ , where $s^2=\displaystyle{\frac{e'e}{n-k}}$ . So it is enough to show $(e_{*}^{'}e_{*}-e'e)$ is $(Rb-q)'(R(X'X)^{-1}R')^{-1}(Rb-q)$ , $R$ stands for the restrictions. In Greene's textbook, he states this, but I don't know how to derive it. In a particular, probably simpler example, consider a partitioned linear regression model $Y=X_1\beta_1+X_2\beta_2+\epsilon$ , and $H_0$ : $\beta_2=0$ , so $Rb-q=b_2$ which is an OLS estimator of $\beta_2$ . $\beta_1$ is $k_1\times 1$ and $\beta_2$ is $k_2\times 1$ so $J$ should be $k_2$ .We want $k_2F=W$ . But I don't know how to derive it even in this example. Could you please give a proof in example and then maybe in general case?
Let's say I have 3 time series variables, $(X_t)$ , $(Y_{t})$ , $(Z_{t})$ and I estimate the following model with OLS estimator (I believe this form is called ARDL) : $$X_{t} \quad = \quad \alpha_1 X_{t-1} \quad + \quad \alpha_2 Y_{t} \quad + \quad \alpha_3 Z_{t-1} \quad + \alpha_4Z_{t-2} \quad + \quad \varepsilon_t$$ where $\varepsilon_t$ is the error term. What properties should be verified for this regression to hold ? Here is what I thought should be checked : $(X_t)$ , $(Y_{t})$ and $(Z_{t})$ are stationary time series no multicolinearity between $(X_t)$ , $(Y_{t})$ and $(Z_{t})$ i.e. correlations between variables are sufficiently weak. the error $\varepsilon_t$ is homoskedastic : $var(\varepsilon_t) = \sigma$ where $\sigma$ does not depend on $t$ the error $\varepsilon_t$ has no autocorrelation : $cov(\varepsilon_t,\varepsilon_{t-k})=0 \quad \forall (t,k) \in \Bbb{N}^2 $ the error $\varepsilon_t$ is centered : $\mathbb{E}(\varepsilon_t) = 0 $ the error $\varepsilon_t$ is stationary (but is that not already implied by what's above ?) the error $\varepsilon_t$ is normaly distributed (optional) the AR(1) and AR(2) processes of $(X_t)$ and $(Z_t)$ have coefficients smaller than 1 in absolute value in order to be stationary : $ |\alpha_1| \in \left]0;1\right[ $ $ |\alpha_3| \in \left]0;1\right[ $ $ |\alpha_4| \in \left]0;1\right[ $ Is there something I missed ? Thanks a lot !
In the paper Attention is all you need , here is Table 1, can someone explain what architecture is referred to in the "Convolution" row and hence describe the other 3 columns in it? The other ones are pretty clear, for example Recurrent, takes $O(d^2)$ operations in one time-step to multiply the hidden state with the weight matrix, and there are $n$ such time-steps, which need to be done sequentially ( $O(n)$ ), also making the information from the first and last tokens in the sentence to travel $O(n)$ steps. For Self-attention, every word in the sentence, attends to every other, so $O(n^2)$ pairs, dot product attention taking $O(d)$ operations. Clearly, the path length is $O(1)$ and there are no sequential operation, all these $O(n^2)$ pairs can be computed independently. The "Convolution" row is not clear to me!
Recently, I have seen papers about large datasets for robotics such as DROID( https://droid-dataset.github.io/ ) or Open X-Embodiment( https://robotics-transformer-x.github.io/ ). As I see, the datasets are specific to some types of robots(although X-Embodiment allows one robot to learn from another robot's data) and environments. If one wants to add another robot into the dataset, they have to do all data sampling again, which is quite expensive. Some environments might be difficult to reproduce, especially as they collected data from all the labs in the world. I am wondering: why don't they instead set up data collection procedure on simulation? it will make the data collection way cheaper. When they want to add a new robot and collect data with the same tasks and environments like other robots, they can do it easily. It is also easy to add a new task and collect data from all robots/environments. Then, why they collect data in real world while giving up on such reproducibility/extensibility? Is Sim2Real that bad, even if it can collect way more samples easily?
The AI revolution drove frenzied investment in both private and public companies and captured the public’s imagination in 2023. Transformational consumer products like ChatGPT are powered by Large Language Models (LLMs) that excel at modeling sequences of tokens that represent words or parts of words [2]. Amazingly, structural
My founder friends constantly think about growth. They think about how to measure their business growth and how to get to the next order of magnitude scale. If they’re making $1M ARR today, they think about how to get to $10M ARR. If they have 1,000 users today, they think about how to get to 10,000 users. This made me wonder if/how people are measuring personal growth. I don’t want to use metrics like net worth or the number of followers, because that’s not what I live for. After talking with a lot of friends, I found three interesting metrics: rate of change, time to solve problems, and number of future options. Some friends told me they find this blog post mildly sociopathic. Why do I have to measure everything? Life is to be lived, not to be measured. As someone lowkey fascinated by numbers, I don’t see why measuring and living have to be mutually exclusive – measuring often helps me live better – but I see where they come from. This post is more of a thought exercise than a rigorous experiment. Rate of change I have this theory that life has a circadian rhythm. Every 3-6 years, you become a different person. You work on different problems. Your lifestyle changes. The people you hang out with are different. If you haven’t caught up with a friend in 5 years, you might no longer have anything in common. It’s not a coincidence that schools are structured into chunks of 3-6 years. Looking back, I realized that every 3-6 years, my life completely changed. From grade 3 to grad…
Trained T5 on the Pile
LangChain and Qdrant are collaborating on the launch of Qdrant Hybrid Cloud , which is designed to empower engineers and scientists globally to easily and securely develop and scale their GenAI applications. Harnessing LangChain’s robust framework, users can unlock the full potential of vector search, enabling the creation of stable and effective AI products. Qdrant Hybrid Cloud extends the same powerful functionality of Qdrant onto a Kubernetes-based architecture, enhancing LangChain’s capability to cater to users across any environment.
Diffusion models have demonstrated strong results on image synthesis in past years. Now the research community has started working on a harder task—using it for video generation. The task itself is a superset of the image case, since an image is a video of 1 frame, and it is much more challenging because: It has extra requirements on temporal consistency across frames in time, which naturally demands more world knowledge to be encoded into the model. In comparison to text or images, it is more difficult to collect large amounts of high-quality, high-dimensional video data, let along text-video pairs. 🥑 Required Pre-read: Please make sure you have read the previous blog on “What are Diffusion Models?” for image generation before continue here.
We’re excited about our collaboration with Red Hat to bring the Qdrant vector database to Red Hat OpenShift customers! With the release of Qdrant Hybrid Cloud , developers can now deploy and run the Qdrant vector database directly in their Red Hat OpenShift environment. This collaboration enables developers to scale more seamlessly, operate more consistently across hybrid cloud environments, and maintain complete control over their vector data. This is a big step forward in simplifying AI infrastructure and empowering data-driven projects, like retrieval augmented generation (RAG) use cases, advanced search scenarios, or recommendations systems.
Developers are constantly seeking new ways to enhance their AI applications with new customer experiences. At the core of this are vector databases, as they enable the efficient handling of complex, unstructured data, making it possible to power applications with semantic search, personalized recommendation systems, and intelligent Q&A platforms. However, when deploying such new AI applications, especially those handling sensitive or personal user data, privacy becomes important. DigitalOcean and Qdrant are actively addressing this with an integration that lets developers deploy a managed vector database in their existing DigitalOcean environments. With the recent launch of Qdrant Hybrid Cloud , developers can seamlessly deploy Qdrant on DigitalOcean Kubernetes (DOKS) clusters, making it easier for developers to handle vector databases without getting bogged down in the complexity of managing the underlying infrastructure.
Aleph Alpha and Qdrant are on a joint mission to empower the world’s best companies in their AI journey. The launch of Qdrant Hybrid Cloud furthers this effort by ensuring complete data sovereignty and hosting security. This latest collaboration is all about giving enterprise customers complete transparency and sovereignty to make use of AI in their own environment. By using a hybrid cloud vector database, those looking to leverage vector search for the AI applications can now ensure their proprietary and customer data is completely secure.
We’re excited to share that Qdrant and Vultr are partnering to provide seamless scalability and performance for vector search workloads. With Vultr’s global footprint and customizable platform, deploying vector search workloads becomes incredibly flexible. Qdrant’s new Qdrant Hybrid Cloud offering and its Kubernetes-native design, coupled with Vultr’s straightforward virtual machine provisioning, allows for simple setup when prototyping and building next-gen AI apps. Adapting to Diverse AI Development Needs with Customization and Deployment Flexibility In the fast-paced world of AI and ML, businesses are eagerly integrating AI and generative AI to enhance their products with new features like AI assistants, develop new innovative solutions, and streamline internal workflows with AI-driven processes. Given the diverse needs of these applications, it’s clear that a one-size-fits-all approach doesn’t apply to AI development. This variability in requirements underscores the need for adaptable and customizable development environments.
Qdrant and STACKIT are thrilled to announce that developers are now able to deploy a fully managed vector database to their STACKIT environment with the introduction of Qdrant Hybrid Cloud . This is a great step forward for the German AI ecosystem as it enables developers and businesses to build cutting edge AI applications that run on German data centers with full control over their data. Vector databases are an essential component of the modern AI stack. They enable rapid and accurate retrieval of high-dimensional data, crucial for powering search, recommendation systems, and augmenting machine learning models. In the rising field of GenAI, vector databases power retrieval-augmented-generation (RAG) scenarios as they are able to enhance the output of large language models (LLMs) by injecting relevant contextual information. However, this contextual information is often rooted in confidential internal or customer-related information, which is why enterprises are in pursuit of solutions that allow them to make this data available for their AI applications without compromising data privacy, losing data control, or letting data exit the company’s secure environment.
In a move to empower the next wave of AI innovation, Qdrant and Scaleway collaborate to introduce Qdrant Hybrid Cloud , a fully managed vector database that can be deployed on existing Scaleway environments. This collaboration is set to democratize access to advanced AI capabilities, enabling developers to easily deploy and scale vector search technologies within Scaleway’s robust and developer-friendly cloud infrastructure. By focusing on the unique needs of startups and the developer community, Qdrant and Scaleway are providing access to intuitive and easy to use tools, making cutting-edge AI more accessible than ever before.
With the official release of Qdrant Hybrid Cloud , businesses running their data infrastructure on OVHcloud are now able to deploy a fully managed vector database in their existing OVHcloud environment. We are excited about this partnership, which has been established through the OVHcloud Open Trusted Cloud program, as it is based on our shared understanding of the importance of trust, control, and data privacy in the context of the emerging landscape of enterprise-grade AI applications. As part of this collaboration, we are also providing a detailed use case tutorial on building a recommendation system that demonstrates the benefits of running Qdrant Hybrid Cloud on OVHcloud.
We’re happy to announce the collaboration between LlamaIndex and Qdrant’s new Hybrid Cloud launch , aimed at empowering engineers and scientists worldwide to swiftly and securely develop and scale their GenAI applications. By leveraging LlamaIndex’s robust framework, users can maximize the potential of vector search and create stable and effective AI products. Qdrant Hybrid Cloud offers the same Qdrant functionality on a Kubernetes-based architecture, which further expands the ability of LlamaIndex to support any user on any environment.
We’re thrilled to announce the collaboration between Qdrant and Jina AI for the launch of Qdrant Hybrid Cloud , empowering users worldwide to rapidly and securely develop and scale their AI applications. By leveraging Jina AI’s top-tier large language models (LLMs), engineers and scientists can optimize their vector search efforts. Qdrant’s latest Hybrid Cloud solution, designed natively with Kubernetes, seamlessly integrates with Jina AI’s robust embedding models and APIs. This synergy streamlines both prototyping and deployment processes for AI solutions.
We’re excited to share that Qdrant and Haystack are continuing to expand their seamless integration to the new Qdrant Hybrid Cloud offering, allowing developers to deploy a managed vector database in their own environment of choice. Earlier this year, both Qdrant and Haystack, started to address their user’s growing need for production-ready retrieval-augmented-generation (RAG) deployments. The ability to build and deploy AI apps anywhere now allows for complete data sovereignty and control. This gives large enterprise customers the peace of mind they need before they expand AI functionalities throughout their operations.
In their mission to support large-scale AI innovation, Airbyte and Qdrant are collaborating on the launch of Qdrant’s new offering - Qdrant Hybrid Cloud . This collaboration allows users to leverage the synergistic capabilities of both Airbyte and Qdrant within a private infrastructure. Qdrant’s new offering represents the first managed vector database that can be deployed in any environment. Businesses optimizing their data infrastructure with Airbyte are now able to host a vector database either on premise, or on a public cloud of their choice - while still reaping the benefits of a managed database product.
I am doing sequence analysis using Traminer and I have employment sequences from age 51 to age 75. However, because some participants enter the survey after age 51, some of the sequences start later (left missing values). I also have some right missing, and some internal missing gaps. I would like to treat internal missing gaps as an additional state, and left and right gaps as voids. However, if I set the option "DEL" for left missing values, the sequences get shifted to the left and the voids appear to the right. Do you have any advice on how to treat left missing gaps so that they are set as void without shifting the sequences? I am afraid shifting sequences would distort timing too much when using the indel costs. Here is my code: States_Wide.seq
A brief overview and discussion on gender bias in AI
I have a data in which I have to apply a competing risk. 4 variables: Temps_Competing_Descompensacio: the time to event. Competing_Descompensacio: factor variable to identifie the event, censored, event or competing event. Grup_IQ: stratified analisis (2 groups). IPTW: the weights of the observation from a previous propensity score phase. My problem is to apply a method for competing risks with the propensity score IPTW weights. I haven't found a way to do it. The analysis without the weights was correct. Already tested and compared with a SAS sintax. Here my code from the for the crr function from the cmprsk package fit.crr $Temps_Competing_Descompensacio, fstatus = Competing_dataset$ Competing_Descompensacio, cov1 = Competing_dataset$Grup_IQ, failcode = 1, cencode = 0) The issue comes when I try to add the weights, as I do not see or find an argument to ponderate the results. I considered multiplying the time variables for the weights, but does not seem correct from methodolgy perspective, and I haven't found a solution from other libraries.
Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.
Using /proc to find fileless malware
[ Hacker News discussion , LinkedIn discussion , Twitter thread ] Update (Feb 2026) : The full list of open source AI repos is hosted at Good AI List , updated daily. It’s balooned to 15K repos, and you can submit missing repos. You can also find some of them on my cool-llm-repos list on GitHub. Four years ago, I did an analysis of the open source ML ecosystem . Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. Data I searched GitHub using the keywords gpt , llm , and generative ai . If AI feels so overwhelming right now, it’s because it is. There are 118K results for gpt alone. To make my life easier, I limited my search to the repos with at least 500 stars. There were 590 results for llm , 531 for gpt , and 38 for generative ai . I also occasionally checked GitHub trending and social media for new repos. After MANY hours, I found 896 repos. Of these, 51 are tutorials (e.g. dair-ai/Prompt-Engineering-Guide ) and aggregated lists (e.g. f/awesome-chatgpt-prompts ). While these tutorials and lists are helpful, I’m more interested in software. I still include them in the final list, but the analysis is done with the 845 software repositories. It was a painful but rewarding process. It gave me a much better understanding of what people are working on, how incredibly collaborative the open source community is, and just how much China’s open source ecosystem diverges from the Western one. The Ne…
Despite the challenges faced by the media industry, the Haitian Times –a print and digital newspaper catering to Haitian immigrants in the United States– has managed to not only survive but thrive by adapting to the changing needs of its audience. Through a combination of smart financial decisions, leveraging technology like AI, and deeply understanding […] The post The Haitian Times thrives by understanding its audience, making smart financial decisions and embracing AI appeared first on LatAm Journalism Review by the Knight Center .
Despite the challenges faced by the media industry, the Haitian Times –a print and digital newspaper catering to Haitian immigrants in the United States– has managed to not only survive but thrive by adapting to the changing needs of its audience. Through a combination of smart financial decisions, leveraging technology like AI, and deeply understanding […] The post The Haitian Times thrives by understanding its audience, making smart financial decisions and embracing AI appeared first on LatAm Journalism Review by the Knight Center .
Exploring the utility of large language models in autonomous driving: Can they be trusted for self-driving cars, and what are the key challenges?
'Vec2text' can serve as a solution for accurately reverting embeddings back into text, thus highlighting the urgent need for revisiting security protocols around embedded data.
When should one not use normalization between layers like Batch Norm, Layer Norm, Instance Norm, and Group Norm in deep learning while training a DL model?
A challenge of building AI applications is choosing which model to use. What if we don’t have to? What if we can predict the best model for any prompt? Predictive human preference aims to predict which model users might prefer for a specific query. Human preference has emerged to be both the Northstar and a powerful tool for AI model development. Human preference guides post-training techniques including RLHF and DPO . Human preference is also used to rank AI models, as used by LMSYS’s Chatbot Arena . Chatbot Arena aims to determine which model is generally preferred. I wanted to see if it’s possible to predict which model is preferred for each query . One use case of predictive human preference is model routing. For example, if we know in advance that for a prompt, users will prefer Claude Instant’s response over GPT-4, and Claude Instant is cheaper/faster than GPT-4, we can route this prompt to Claude Instant. Model routing has the potential to increase response quality while reducing costs and latency. Another use case of predictive human preference is interpretability. Mapping out a model’s performance on different prompts can help us understand this model’s strengths and weaknesses. See section Experiment results for examples. Here’s what predictive human preference for different model pairs looks like for the prompt “ What’s the best way to cluster text embeddings? ”. The predictions were generated by my toy preference predictor. The bright yellow color for the (GPT-4,…
Investing in reliability and test engineering
I am reading The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman, and I don’t understand the meaning of "factor" in the following paragraph: Qualitative variables are typically represented numerically by codes. The easiest case is when there are only two classes or categories, such as “success” or “failure,” “survived” or “died.” These are often represented by a single binary digit or bit as 0 or 1, or else by −1 and 1. For reasons that will become apparent, such numeric codes are sometimes referred to as targets. When there are more than two categories, several alternatives are available. The most useful and commonly used coding is via dummy variables. Here a K-level qualitative variable is represented by a vector of K binary variables or bits, only one of which is “on” at a time. Although more compact coding schemes are possible, dummy variables are symmetric in the levels of the factor . Also I want to know what mathematics knowledge I should have to understand this book and if it’s deprecated or not.
I am reading The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman and I don´t understand the meaning of "factor" in the following paragraph: Qualitative variables are typically represented numerically by codes. The easiest case is when there are only two classes or categories, such as “suc- cess” or “failure,” “survived” or “died.” These are often represented by a single binary digit or bit as 0 or 1, or else by −1 and 1. For reasons that will become apparent, such numeric codes are sometimes referred to as targets. When there are more than two categories, several alternatives are available. The most useful and commonly used coding is via dummy variables. Here a K-level qualitative variable is represented by a vector of K binary variables or bits, only one of which is “on” at a time. Although more compact coding schemes are possible, dummy variables are symmetric in the levels of the factor . Also I want to know what math´s knowledge I should have to understand this book and if it´s deprecated or not.
Have you ever trained a model you thought was good, but then it failed miserably when applied to real world data? If so, you’re in good company.
I have read (*) that a common technique to attack a black box AI system based on a neural network is to use it to train a surrogate model to make the same classifications as the black box one. Once this is done, one can look for adversarial examples on the surrogate model (on which the attacker has access to all the weights and can compute gradients). The key property that makes such attacks successful is transferability : an adversarial example on the surrogate model is likely to be an adversarial example on the black box model. Question: Do we know why such transferability properties hold and under which conditions? (*) Although I don't remember where I first read it, this is mentioned in the book Not with a bug but with a sticker although it doesn't get into technical details (this is a book for the general public).
I am having a ML based production application, using flask, deployed on GCP server using gunicorn workers. In each incoming request, a text sentence is received. It is using sentence transformers (All-MiniLM-L6-v2 model), which is loaded globally one time, to create embeddings of the incoming text and then use pre trained kmeans (also loaded globally) to predict/map it to a intent cluster. Basically, goal is to find intent of the sentence. I have ample resources and the requests are also constant in number and texts are also similar, but still each day the CPU load is gradually increasing. Avg response time on 1st day was around 200 ms average, after 10 days now it is 400 ms. I have tried deleting the embedding variable using 'del' command in the code itself, also forcing python garbage collector using 'gc.collect()' in a thread which executes after the main process execution is completed, but still the issue is coming. One thing I have noticed is that if I dont use del and gc.collect(), the RAM starts to go down gradually. With both these, RAM is constant but now CPU usage is gradually going up day by day, hence the load and response time. I have spent weeks on this issue trying to debug it but have got no solution, any help would be appreciated.
I'm studying Variational Autoencoders and a lot of the literature says that the posterior is intractable because the marginal distribution p(x) is intractable since the space of z is so large we cannot possibly integrate over it all. So to avoid this they create a lower bound on the log likelihood, the ELBO, which they then try to maximize. The term for the ELBO is: $$E_q[log~ p(z,x)]-E_q[log~q(z)]$$ What I am trying to understand is how is this now tractable. The expectations in the ELBO are still over the distribution of q. Take the first term for example: $$E_q[log~ p(z,x)]=\int_{}^{}q(z)~log~p(z,x)dz$$ Is this not still an integral over all z values? How did we make this problem any more tractable by finding the ELBO? Additional Question: Also another thing I was confused about is we always say the posterior p(z|x) is not computable because we don't have p(x), but how exactly do we have the numerator, p(x,z). $$p(z|x)=\frac{p(x,z)}{p(x)}$$ Is this because we assume a prior, and then also assume that we can model p(x|z) with a decoder?
Effective developer tools
The best reason I have seen for using variational autoencoders is when dealing with sparse data. The Gaussian noise "splats" out the input distribution (see this StackExchange answer ). However, normalizing flows do the same thing, without the loss of information a VAE incurs. It feels as if VAEs are used only because everyone else is using them, and then a bunch of StackExchange posts reinforce the message that VAEs are the way to go , when they're theoretically suboptimal. I understand that normalizing flows are a little slower (at either training or inference), and more difficult to implement, but is there a theoretical reason that makes VAEs a legitimate choice?
I have seen a few Kaggle notebooks that list without reason that RFE works better when removing correlated variables. I struggle to see the reason why so I conducted some of my own research and would like to verify if my conclusions are correct. From my research with sklearn's CART algorithm, I have taken a good predictive feature (Feature A) and a highly correlated feature with some extra noise (Feature B). It seems that due to high correlation, their mean impurity decrease is very similar and the splits roughly split the feature importance between the two variables. This creates situations where Feature A and Feature B can be ranked highly if Feature A is a good predictor despite Feature B being redundant. Feature B will likely not be removed for several iterations and reduce the model score for these first few iterations, thereby limiting the combination of features that RFE considers. But I presume there are multiple factors that influence the way we identify a "highly correlated variable". At which correlation cutoff point do we determine that the variables will hurt the RFE feature selection process? For example, the number of splits in the algorithm will be one determining factor. If Feature A only has a single split, then Feature B will not benefit from having high correlation with Feature A, and likely be removed by RFE without problems.
Update June 2024: Anyscale Endpoints (Anyscale's LLM API Offering) and Private Endpoints (self-hosted LLMs) are now available as part of the Anyscale Platform. Click [here](https://console.anyscale.com/?utm_source=anyscale&utm_medium=blog&utm_campaign=blog_callout&utm_content=june2024_product_update_subheading) to get started on the Anyscale platform.