I am trying to play with an evolutionary algorithm and a nueral network. It works by creating a network with random weights then tweaking those weights on a population. The mutations and evolution seem to work just fine, but I want to have a possible mutation that adds a new layer to the network. To do this, I basically duplicate a random layer. The problem is that this destroys the fitness of any existing member. Is there a way to set the weights so that the new network is neutral when added? E.g. So that the new layer effectively just passes on the same information as the layer it was copied from (it appears as the next layer)---then that layer could survive and be tweaked by smaller mutations over time. I am not using any kind of gradient decent. I am just experimenting and trying to learn a bit about this stuff, and am wondering how to improve the evolutionary algorithm so that networks can grow in size. I am trying to play with an evolutionary algorithm and a neural network. It works by creating a network with random weights and then tweaking those weights on a population. For some reason I thought setting the weights of the new layer to "1" would do it, but that doesn't work. In retrospect, I see why it would not, but I still can't come up with a solution. submitted by /u/TheGandPTurtle [link] [comments]

Hello guys, I'm currently working on a project where I need to serialize tensors efficiently. Right now, I'm using Python's pickle library, but I'm looking for something that might be faster and more efficient, particularly for large tensors. My main requirement is to serialize tensors directly without any intermediate conversions (like through NumPy arrays). Does anyone have experience with or can recommend a library that is faster than pickle for this purpose? I'm mainly dealing with tensors from libraries like PyTorch. Some points I'm considering: Speed and efficiency of serialization. Compatibility with PyTorch tensors. No intermediate conversions to other formats (like NumPy) before serialization. Any insights, experiences, or recommendations would be greatly appreciated! Thank you! submitted by /u/darkNightCoder [link] [comments]

Interactive demo sample arXiv: https://arxiv.org/abs/2312.03414 GitHub: https://github.com/snu-mllab/Context-Memory Project page: https://janghyun1230.github.io/memory/ Summary: Our approach dynamically creates compressed memory of contexts during LLM interactions. Our approach only requires training a conditional LoRA for compression. We use a fully parallelized training strategy for recurrent compression procedures. We conduct evaluations on diverse applications: conversation, multi-task ICL, and personalization, achieving the performance level of a full context model with 5x smaller context memory space. https://preview.redd.it/5vmwxa7luy4c1.png?width=2162&format=png&auto=webp&s=0f717a1528688b6fd9b47f3c7e43be66b1b5c78b submitted by /u/janghyun1230 [link] [comments]

submitted by /u/Avienir [link] [comments]

Google might have lied in their Demo about Gemini. ​ https://techcrunch.com/2023/12/07/googles-best-gemini-demo-was-faked/ submitted by /u/donfangzahn [link] [comments]

submitted by /u/Tadpole5050 [link] [comments]

Hi all. I am trying to create a straightforward and concise ML curriculum. Any input or contribution is welcomed. The goal is to keep it to the point with the main curriculum while adding additional resources in separate sections. ​ Link: https://github.com/pytholic/Machine-Learning-Curriculum submitted by /u/rajahaseeb147 [link] [comments]

You probably heard about the new LLM from Google DeepMind called Gemini. At the face of it, we finally have a model that outperforms GPT-4 on a bunch of benchmarks, but the results are not that straightforward. We have prepared a detailed report about Gemini. The first in-depth article about the model doesn't merely give into the hype but covers how the model archives multimodal support, how it was trained and how it compares to other LLMs in the field. Gemini's benchmarks are turning heads, but are they truly ahead or is it all smoke and mirrors? We have found several controversies and inaccuracies in Google's report. Check out the full article: https://www.linkedin.com/pulse/gemini-in-depth-analysis-chatgpt-killer-scam-thelionai-igwgf https://preview.redd.it/ehc792pooy4c1.png?width=1920&format=png&auto=webp&s=50bf5d465fd6b97a924e7bea0101d8eb528ae638 submitted by /u/Avienir [link] [comments]

submitted by /u/TheIndulgery [link] [comments]

The bottleneck when it comes to multimodal models is glaringly obvious, a 5-year-old who understands the technology could spot it. There is always one Encoder and one Decoder for the Tokenizer. It is always shared, you always need a 'Controller', a 'man in the middle'. How do you get around this problem? I do not have a flipping clue, M-FAB is the best I could come up with. It appears from all speculation, that Google doesn't know how to solve this problem either. The Theranos story is always prevalent in my mind as I research and navigate these waters. How hard could it actually be to develop a digital blood test? Let's just make promises, break some stuff, and beg for forgiveness later, after we have actually cracked the code! This is the Silicon Valley way. It does not work for everything. I think AI is the kryptonite for this mentality. What happens when you lock two AI models together in the same 'room'? What happens when you lock two people together in the same room? They either compete, or they cooperate in some way in which one operates like the instructor and the other acts like the student. This is literally the same thing that happens when you lock two AI models into a 'room'. You can choose to have them 'compete' vs each other, or you can have them cooperate with each other. If they cooperate, one will take on the role of Instructor, while the other takes on the role of Assistant. You still have the bottleneck of the singular encoder and decoder for both models. The Train Station of Data: A Tale of Bottlenecks and Multimodal Harmony Imagine a bustling train station teeming with activity. Trains from all corners of the world arrive and depart, each carrying unique cargo and heading towards distinct destinations. Just like these trains, data streams in the digital world travel across diverse channels, carrying information in various formats – text, images, audio, and more. This vast network of data transport is analogous to a complex multimodal ecosystem, where each mode of data represents a different train on the tracks. However, managing this network presents significant challenges. Unlike the physical world, where railways are carefully designed to avoid collisions, the digital realm lacks such safeguards. Multiple data streams can attempt to occupy the same metaphorical "track" simultaneously, resulting in congestion and bottlenecks. Imagine two trains arriving at the same station at the same time, competing for the same platform and causing delays for everyone else. This bottleneck effect is a major obstacle in the world of multimodality. Each data stream, like a train, requires its own processing and communication protocols. They don't always understand each other directly, often leading to misinterpretations and inefficiencies. The lack of a standardized "language" for data interchange creates a communication barrier, similar to trains speaking different languages and needing translators to understand each other's signals. Furthermore, the sheer volume of data streams can overwhelm the system, leading to information overload and delays. Imagine a train station flooded with trains arriving simultaneously, causing chaos and slowing down the entire operation. Similarly, the influx of diverse data streams can overload processing systems, hindering their ability to analyze and utilize information effectively. The way this gets solved with LLM models is actually strikingly similar to this process with trains. You solve it with a conductor. In the instance of multimodality, the LLM model is the conductor. You can have a Diffusion model train, a CNN train, and an RNN train, for all of your video, image, and audio processing needs. All of these inputs and outputs must always pass through the conductor though. The individual trains do not talk to each other, and they do not speak the same language. Only the conductor is capable of translating for all of the trains. If each individual modality is a train, the complexity of communication involved with scaling the number of trains on the tracks scales exponentially. It is more like a Richter scale. Three trains on the track is 10x as hard as two trains, and four trains on the track is 100x as hard as two trains. When I began seeing people's hype for Gemini, I knew it upfront that it was overblown in this particular area. I had serious doubts that Google actually solved all of this. I do not think it is a money problem. It is not a brute force problem. That is a larger aspect of all of this that most people do not get. This technology is not like other technology. What is AI ultimately comprised of? It is comprised of mathematical equations and fairy dust. That is the 'form' of AI. Why am I able to pluck a mathematical equation from the pure ether, and bring it into some sort of physical form? Why does a certain emergent reaction occur if I combine a whole bunch of these mathematical equations together with a whole bunch of computing power? I do not know. Neither does Google, or OpenAI, or Microsoft, or even ChatGPT. I am not even fully convinced at the moment that Transformers are all you need. I think there is a fair argument to be made that Transformers are not actually needed in the equation. If there is any validity at all to that thought, then throwing a trillion dollars at Transformers to improve the technology further will simply result in the waste of a trillion dollars in the end. I also fully acknowledge that these ideas could be completely incorrect. Transformers and the Attention Mechanism definitely do something. Since it is all mathematics and pixie dust, there is the possibility that Google has figured out the mathematically correct pixie dust combination for multimodality. I just do not believe any of this to be the case given current evidence. We are ultimately limited by our brains when it comes to these things though. Our brains are very good at classifying and simplifying things. When you have a business problem, you simply throw money at it, and that makes the business problem go away. That is how our brains classify these things. These are not ultimately business problems though, that is where the misclassification comes in. These are mathematical problems. Mathematics doesn't give a rip about your 48 Laws of Power. It doesn't care that this is the way you have always done business related things. You cannot persuade math that 2+2=7. People would be really well served to reframe their expectations and understanding of these things, or we are going to have the second coming of the AI Winter very soon. submitted by /u/Fantastic-Ninja3839 [link] [comments]

For context, I work as a mechanical inspector in the automotive industry. I asked both of them to tell me what's going on in the photo I sent them. Neither was able to determine that the spring was broken but I think you'll find the Gemini response to be humourous. submitted by /u/meepydeeps [link] [comments]

Hi everyone, Last year I helped a friend grow her restaurant in Sydney. I noticed she spent far too much time posting on social media. It was exhausting. So I spent the last 18 months coding, and the end result was an app that automates a large part of this process. It is called Amazely. Basically, all you need to do is upload photos of the business. Amazely uses AI to learn about the business, understand what each photo is about. It will then craft contents automatically and propose a month-by-month posting schedule for the business owner to approve. Upon approval, it will post to FB&IG automatically. Obviously, this cannot replace humans. The goal is to take the tedious part away from managing social media so marketers can spend time on other activities. What do you think of the idea ? You can try a demo in 30 seconds here https://r.amazely.co , just select a restaurant or business you know and hit generate. Cheers. submitted by /u/Think_Housing4111 [link] [comments]

I read through the Google Gemini technical report yesterday. It was pretty vague and not that interesting but one section got me wondering. Section 3 "Training Infrastructure" mentioned all of the technical challenges they faced including dealing with "cosmic rays" and other rare failure modes that happen at the scale of thousands of chips. I haven't heard of GPU training having the same issues with cosmic rays and other large AI labs haven't mentioned how challenging training at this scale can be. So this has me wondering:-- Is Google just talking up their achievement and making it sound more difficult and advanced than it really is here?-- Are those challenges unique to Google's infra and TPUs?-- What are the real challenges at training at a large scale (i.e. 300B-1T parameters)? submitted by /u/cowzombi [link] [comments]

Hi guys, I am a web developer who is interested in AI development. Since I am more experienced with front end, my company placed me with tensorflow.js training program. But I also want to deepen my skills with AI so after this program I am planning to learn python so I can go deeper AI tools. Please advice of my plan is good or not submitted by /u/Tantan88112 [link] [comments]

Has anyone else ever had Microsoft's Bing Chat effectively block the remaining number of chats after having had a conversation it clearly didn't like? I've seen screenshots whereupon Bing ends the chat but never of the service banning any further attempts to chat for 24 hours. Just thought it was different really, I normally have these kind of chats with OpenAI's chat and it never has a tantrum like this, if anything it'll argue back at me. This seemed biased from the start and when asked for an explanation it seems the order to cut communication was put into effect, very odd. submitted by /u/Clouds_Compendium [link] [comments]

I ran the NanoGPT of Karparthy replacing Self-Attention with Mamba on his TinyShakespeare Dataset and within 5 minutes it started spitting out the following: ​ https://preview.redd.it/4r96tp6lxx4c1.png?width=836&format=png&auto=webp&s=10f2f61cd4cea96f4f903cb2070835fc5d1df951 ​ https://preview.redd.it/32ler5vnxx4c1.png?width=622&format=png&auto=webp&s=dd00e53f43dd0afa058758a987901ee6789d2258 ​ https://preview.redd.it/sc96i4xoxx4c1.png?width=678&format=png&auto=webp&s=94d2ed279054363d3ed2b6beed65be89468582b0 So much faster than self-attention, and so much smoother, running at 6 epochs per second. I'm honestly gobsmacked. https://colab.research.google.com/drive/1g9qpeVcFa0ca0cnhmqusO4RZtQdh9umY?usp=sharing ​ Some loss graphs: Multihead attention without truncation(x is iterations in 10s, and y is loss) Multihead attention with truncation(x is iterations in 10s, and y is loss) Mamba loss graph(x is iterations in 10s, and y is loss) ​ ​ submitted by /u/ExaminationNo8522 [link] [comments]

submitted by /u/Algoartist [link] [comments]

When working with LLMs, I frequently experience token agony. Error: This model's maximum context length is 4097 but you are trying to push in all of War and Peace, you imbecile Perhaps you've experienced it too! The issue is particularly pronounced with retrieval augmented pipelines, since you have potentially quite a large set of documents which you could perhaps include in the prompt if only you knew how big it could be. I got tired of hacking around this headache, so I wrote flex-prompt to address it. I wish I didn't have to. Perhaps someone can point me to a better solution! But I couldn't find one, so alas, here it is. flex-prompt provides a basic layout and component model to help you describe how you want the pieces of your prompt to grow and shrink and a token-aware renderer which renders your prompt to fit your model's window. Github, Intro to flex prompt colab Quick examples You can just render(Flex(...)), and flex prompt will fit the prompt into the context window, and tell you how many tokens are left over for the response: from flex_prompt import render, Flex, Expect rendered = render( Flex([ "Given the text, answer the question.", "--Text--", WAR_AND_PEACE, "--End Text--", "Question: What's the title of this text?", "Answer:", Expect() ], join='\n'), model='text-davinci-002') # rendered.output is the string to send to the model # rendered.max_response_tokens is how many tokens you can # request in response without exceeding the model's context window print(rendered.output, rendered.max_response_tokens) More typically, you'll want to define a prompt which takes parameters. To do this, you can create a class (probably a dataclass) which derives Flexed: from flex_prompt import Flexed, Expect from dataclasses import dataclass @dataclass class Ask(Flexed): text: str question: str answer: str | Expect = Expect() instruct: str = "Given a text, answer the question." flex_join = '\n' # yielded items will be joined by newlines def content(self, _ctx): if self.instruct: yield 'Given the text, answer the question.' yield '' yield '-- Begin Text --' # note: we're using `Flex` here just to attach a flex_weight # to the text, telling the renderer we'd like more space for the # text than anything else. yield Flex([self.text], flex_weight=2) yield '-- End Text --' yield 'Question: ', self.question yield 'Answer: ', self.answer The renderer works much as you might expect. You can `yield` anything which you can pass to the top-level render function, including other components, creating a whole tree. Note that the component above can be used to render both the actual prompt and examples. Examples simply have an answer. This is useful for experimenting with different ways of structuring a prompt while ensuring that all the examples we present to the LLM are in the same format. LangChain and Haystack Integrations Flex prompt doesn't really care how you execute your prompt. For convenience, render(model=) does accept both LangChain and Haystack models: ask_tolstoy = Ask(text=WAR_AND_PEACE, question="Who wrote this?") # Using LangChain from langchain.llms import OpenAI lc_llm = OpenAI() rendering = render(ask_tolstoy, model=lc_llm) print(lc_llm(rendering.output, max_tokens=rendering.max_response_tokens)) # Using Haystack from haystack.nodes import PromptModel hs_llm = PromptModel(model_name_or_path='text-davinci-002', api_key=os.environ['OPENAI_API_KEY']) rendering = render(ask_tolstoy, model=hs_llm) print(hs_llm.invoke(rendering.output, max_tokens=rendering.max_response_tokens)) Is it worth it? As models grow larger and larger context windows, I've asked myself whether this is worth it. Won't context sizes eventually big enough to put in everything we might want without worry? One response: "everything I might want" is a very, very big set, plausibly bigger than any window size we're going to see soon. Another: being able to do this kind of token accounting is useful even if we don't completely fill context windows. For example, we might be able to augment our prompt with examples, documents, and tips. How much space should we allocate to each? The answer might well be model-dependent. How do we figure it out? Flex prompt's output, a Rendering object, actually holds the entire component tree. You can look through the object to see how many tokens were allocated to each child. This is currently very manual, but it does provide the bedrock infrastructure to e.g. run tests to discover the optimal balance of augmented data for a given prompt and model. Additionally, the right admixture (and for that matter, the right phrasing) may well be model-dependent. Flex prompt currently provides only very limited model-specific rendering (you can look at ctx.target, but it doesn't tell you much), but there's no reason that can't be significantly improved. At the extreme limit is prompt erasure, where we fine-tune a model to require no or minimal instructions/examples for a given set of prompts. Flex prompt can enable transitions like this with no changes to the pipelines themselves: you'd still use the same prompt components, they'd just render differently if the target is a fine-tuned model vs. a generic one. Status & Future Work Flex prompt is very much in early development. I would love to hear if and how people find it useful, and would love input and contributions! Some things I'd like to tackle in the future: Rendering message lists. Flex prompt currently only renders strings, though it's set up to be able to render any type of output. Message histories basically grow without bound, so supporting this seems like a no-brainer. Pagination. If your rendering overflows (as above, where we're trying to stuff the entirety of war and peace into a prompt), flex prompt will clip the offending pieces to fit. But there's currently no way to get "the next page". But the Rendering actually retains enough information to do this! It would be great to be able to call render(...).pages() to get the sequence of prompts as we "scroll" whatever has overflowed. This is medium-hanging fruit—a little tricky because we do have to descend the tree of renderings to find the exact one(s) which overflowed and then update only those. Token accounting. As mentioned above, you can currently grovel around in Rendering and look at the pieces of the prompt. This would be more useful if it were a little easier, e.g. if you could use rendering[Examples] to find all the parts rendered by the Examples component, or rendering['advice'] to find all the parts which are tagged (somehow) as "advice". The use case here is prompt optimization: discovering the optimal number or percentage of tokens to allot to each thing we might want to drop into the prompt. More integrations. Currently, flex prompt only supports OpenAI models. You can register your own target finders, but it would be great to have more support out of the box. This is mostly a matter of digging around and finding the tokenizers and window sizes for common models, and then writing the appropriate target finders. Contributions very welcome! Model tuning. As mentioned above, the rendering context could provide a mechanism for fetching model-specific parameters. The basic idea is that ctx[param] will evaluate param against the context, and then we can define some parameter types which load their model-specific values from gestures vaguely somewhere. Thanks for reading! Flex prompt Github Intro to flex prompt colab My website. shameless plug: I have a lot of engineering experience and a bit of machine learning experience and I am currently looking for a job submitted by /u/queerviolet [link] [comments]

submitted by /u/axdroid [link] [comments]

In the future, how will we solve problems for which 'professionals' are currently our best answer? submitted by /u/amonle [link] [comments]

Hi, Is there a tool that indicates which parts of the input prompt impact the LLM's output the most? I do not care which LLM the tool is for if it exists. I guess it could be backtracked via the weights of each node in the neural network, but you guys are smarter than me so I'll listen to y'all. My use case is I have a prompt that slightly changes variation to variation. The output of the model is "Yes" or "No", so I want to see which parts of the prompt I change impact its response Best, A Reddit User submitted by /u/ToughOpening [link] [comments]

I'm aware that AI detectors have struggled with accuracy, and this was confirmed by most of you. Even so, I posted this for students whose university grades rely on Turnitin AI reports. It's important to note a significant update as of this morning (at least, this is when it became available on my account). Previously, you could often bypass Turnitin's AI detection by using tools like Quillbot or making minor text alterations. Also, texts generated using GPT-4 (NOT GPT-3.5 - which always came with a high AI rate) were less likely to be flagged as AI-generated, or the percentage was very low. However, my Turnitin instructor account highlighted a change in how the system analyzes texts as of this morning. Either the AI detection sensitivity appears to have been heightened, or the detection capability has been drastically improved. Through various text tests, I found that Turnitin now consistently identifies a 100% AI involvement in submissions, including texts that had passed through Turnitin undetected (2-3 days ago). Texts that yesterday had 0% AI now have 100%. Based on my testing, the only effective method to avoid detection is extensive editing of ChatGPT outputs. Changing a few words or rewording the sentences is not enough (even though this worked in the past). It now requires extensive edits in most of them to be able to reduce it to an acceptable rate (by extensive I mean that in most scenarios you will have to actually rewrite each sentence, piece by piece). I wanted to highlight this for students relying on ChatGPT for academic purposes. We will likely see a rise in the number of cases identified as AI-generated compared to before. PS: Also, stay away from the scams or poorly optimized websites that promise to rewrite the text so it is no longer AI detectable (the automatic ones) - I tried most of them (free or premium), and all failed - all were detected as 100% AI. submitted by /u/Fuzzy_Entertainment7 [link] [comments]

submitted by /u/Kalocacola [link] [comments]

Are you aware of any public datasets that have at least 2 trials / samples / rows per subject? Could be in any domain. Preferably with > 100 subjects, and the tests not sampled years apart (but not dealbreakers). For instance, a large cohort of patients who have had ECG scans collected on 2 separate occasions. I am slowly working my way through the PhysioNet databases: https://physionet.org/about/database/ Most of course only have one scan per subject. submitted by /u/ZeApelido [link] [comments]

Really trying to find an unrestricted model in order to help me build part of my project on top of. One element needs to be unrestricted. Anyone away or something like this? Can't seem to find submitted by /u/cobalt1137 [link] [comments]

I asked for different scenarios showing what my food does when I close the door to my fridge. submitted by /u/GreenLionRPG [link] [comments]

Today, MedBright AI (MBAI.c MBAIF) announced that Dermatology Institute & Skin Scare Center, a leader in dermatologic services in Southern California, has chosen to implement MedMatrix to improve its clinic operations. The Center specializes in cosmetic, medical and surgical dermatology in Los Angeles and is led by board-certified dermatologist Paul Yamauchi, MD, PhD, who stated regarding the news: “There is clearly a need for AI to help operations and the efficiency of clinics,” and “I’m excited to see the improvements we can make as a result of using the MedMatrix platform.” MedMatrix The industry-leading AI-powered data analytics platform, MedMatrix, harnesses the power of artificial intelligence to evaluate and analyze key aspects of healthcare facilities, including patient needs and resource matching, revenue cycle management, patient demographics, facility geography, and competition. MedMatrix is set to drastically improve healthcare operations by empowering doctors and operators to enhance clinic revenue by 10%, reduce physician burnout and improve patient satisfaction. Through its advanced AI and data analytics capabilities, MedMatrix works alongside healthcare professionals to optimize each of these critical areas, resulting in more efficient clinic operations and improved patient outcomes. Previous News This news follows the recent election of two US healthcare experts to its Board of Directors, Dr. Jaime Gerber, who will serve as Chairman of the Board, and Dr. Konita Wilks. Specializing in preventative cardiology and complex cardiology cases that involve coronary artery disease, peripheral artery disease and risk factor modification, Dr. Gerber is a Professor of Medicine at Yale Medical School and serves as the co-director and director of a range of programs. With over 25 years of experience as a dynamic, visionary leader in healthcare and the military, Dr. Wilks is the CEO of Olive View-UCLA Medical Center, a 355-bed general acute care hospital that employs nearly 5500 persons and has a $900-million dollar annual operating budget. With the complexity and rise of data in healthcare expected to increase the use of artificial intelligence in the field, MBAI is strongly positioned to capitalize will result in a significant and positive impact in the field. Investor presentation: https://assets-global.website-files.com/654400b037e09f716a28c32b/655f911132d0b133ffc9ca1c_Medbright%20Q4%202023%20Investor%20Presentation.pdf Full News Release: https://www.medbright.ai/blog-posts/medbright-ai-announces-beta-customer-for-medmatrix-the-ai-driven-data-and-analytics-platform Posted on Behalf of MedBright AI Investments Inc. submitted by /u/massettawm [link] [comments]

I’ve got experience in “traditional” ML like building classification and regression models with GB Trees and the like, so I’m curious how, if at all LLMs can be combined with other ML modeling approaches. If my use case entails structured data as well as something like chat history, is there a need to “combine” the modeling approaches? Thanks for any resources or input you might have. submitted by /u/yoquierodata [link] [comments]

submitted by /u/ExtremelyVW [link] [comments]

I already saw someone‘s post which asked for an AI tool to automatically organize unsorted folders full of random files. I get that this is pretty much to ask and difficult to do. How would things change though, if I already have a nice folder structure? Is there a tool out there which can be trained to my personal folders? I‘m tired of sorting scanned (ocr) pdfs into the correct folder and have already tried to automate most of reoccurring documents (e.g monthly internet bill). As soon as a new kind of document comes in though (e.g. new bill of just changed electricity provider), I would like this one to also automatically be sorted to „ElectrivityCompanyB“->„Bills“. Is such a tool realistic? submitted by /u/Abujajuba [link] [comments]