They might be attracted to here because of the standard of quality and name-recognition of the large network sites though (not sure what either of those things has to do directly with community). Or they might be here for magic internet points and/or to alleviate an inferiority complex. Regardless of what is central to the future of AI, what is really central to Stack Exchange (which is what I care about) is fact-based Q&A. To say community is central to Stack Exchange (not that anyone has said that here) could be misleading, since we’re not a discussion platform and we don’t do chit-chat (except in chat subdomain sites). “The unified GPU back-end support gives deep learning developers more hardware vendor choices with minimal migration costs,” Meta said in its blog post.
Meta touted the model’s potential for assisting scientists, from summarizing academic papers to solving math problems, writing scientific code and more. Hours after release, it became clear that Galactica could be easily exploited to generate biased, misleading yet authoritative-sounding content. Scientists began sharing examples of concerns and ethics criticisms online, and within 48 hours Meta had paused the demo. While the model’s developers describe running a “toxicity and bias” evaluation pre-release, the analysis was clearly insufficient to prevent potential harms.
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A very large amount of work and effort has been put into finding and removing AI-content on SO, and the rest of the network (when it is banned on a site). I cannot speak for others, but I certainly do not welcome that, nor am I excited for that in any way. Harsh, but that feels like you are flat-out oblivious to the fact that (overall) the community does not want ChatGPT answers (per the vote-counts on the ChatGPT ban announcement on MSO a.k.a. the absolute highest scoring post on MSO).
This includes our first custom silicon chip for running AI models, a new AI-optimized data center design and the second phase of our 16,000 GPU supercomputer for AI research. These efforts — and additional projects still underway — will enable us to develop larger, more sophisticated AI models and then deploy them efficiently at scale. AI is already at the core of our products, enabling better personalization, safer and fairer products, and richer experiences while also helping businesses reach the audiences they care about most. We introduce an automated method for evaluating the quality of AI-generated text by repurposing prior human evaluation data.
These things that seem to sprout like flowers in other people’s IT gardens just don’t happen to me. Every question I ask the AI comes with errors at best, that’s when the AI decides to be creative and starts spewing a bunch of information taken from I don’t know where. Public interest is high, the news both tech and non-tech are excitedly reporting about it left and right and the average Joe is lapping that up.
If they want an actual human, often expert, to answer their questions, they will log into Stack Overflow. Quite simply, if people want to use some kind of AI assistance to answer their questions quickly, then they can use those. I was honestly hoping for a bit more information about how Stack Exchange as a company (rather than the individual sites) plans to handle the deluge of unverified AI answers that all too often turn out to be hallucinations. I haven’t looked, but what specifically does “popular code repositories” actually mean? And how is this (un-named) source determining how much of the code is AI-assisted? Also, when exactly do you consider the latest wave of AI to have started?
Introducing ATOM: Borealis AI’s research focus on Asynchronous Temporal Models
Senior members of the company staff will be reviewing everything here and will respond to questions, though it may be 24 hours or so before answers are ready, just because of the amount of interest that this is likely to get. Please https://www.metadialog.com/blog/ leave any questions that you have below as individual answers. The new design would be 31% cheaper and could be built twice as quickly as the company’s current data centers, an employee said in a video explaining the changes.
- Meta tags influence how your blog post appears on the search engine result page.
- I was honestly hoping for a bit more information about how Stack Exchange as a company (rather than the individual sites) plans to handle the deluge of unverified AI answers that all too often turn out to be hallucinations.
- These platforms will not only offer the necessary guidance to refine AI algorithms and models, but also serve as a space for healthy debate and exchange of ideas, fostering the spirit of innovation and pushing the boundaries of what AI can accomplish.
- Is this something that is genuinely in the best interests of the company and the network and would add real value without that hype?
- I would love to see SO release it as an LLM model compatible with Dolly 2.0 or whatever the open format ends up being.
- Scientists began sharing examples of concerns and ethics criticisms online, and within 48 hours Meta had paused the demo.
In contrast to many other advanced models, with Stable Diffusion you don’t need any sophisticated infrastructure or thousands of GPUs to run. It works beautifully even on a simple mid-range laptop or PC. The key takeaway here is, again – the bigger you can get your model to be, the better it will perform. In this case, Whisper does remarkably well even when dealing with noisy recordings, such as people talking over the phone or at home. Minerva was trained on 1.2M papers submitted to arXiv (58GB) and numerous web pages containing maths (TeX, AsciiMath, MathML) (60GB), which is quite a massive dataset.
Converse Task-Oriented Dialogue System Simplifies Chatbot Building, Handles Complex Tasks
The first feature lets brands generate different variations of the same copy for different audiences while trying to keep the core message of the ad similar. With this feature, companies can adapt their messaging to specific demographics or locations. The background generation feature makes it easier to create different assets for a campaign. Finally, the image cropping feature helps companies create visuals in different aspect ratios for various mediums, such as social posts, stories, or short videos like Reels. Over the next decade, we’ll see increased specialization and customization in chip design, purpose-built and workload-specific AI infrastructure, new systems and tooling for deployment at scale, and improved efficiency in product and design support.
Just as tractors made farmers more productive, we believe these new generative AI tools are something all developers will need to use if they want to remain competitive. Given that, we want to help democratize knowledge about these new AI technologies, ensuring that they are accessible to all, so that no developers are left behind. We are working closely with community members throughout this process to experiment with GenAI to build solutions to solve for historical pain points with the site. Leveraging GenAI to give newer community members real-time feedback on asking questions that are appropriate on Stack Overflow might reduce the load on community members. Yes, the CEO has some sort of vision about wedding SE with AI in some sort of warped matrimony.
If the input data is poor quality, a model-generated result will not be good as well. That is why, the Meta team tried metadialog.com to select high-quality images to train their model. The team has created a data engine to filter the raw image dataset.
As things stand there has been an immediate failure in the company’s commitment to the community. I will absolutely not consent to having my data processed in pursuit of any AI integration, and I’ll be looking at what my options are to prevent that in the coming weeks, should you move forward with this approach. You’re free to experiment with it, but don’t pollute Stack Overflow with it. We’re not interested in fact-checking AI content; we are interested in generating the content ourselves.
Any good executive always has the long-term interests of the company as priority. I think the CEO needs to explain how allowing a bulge of untested, far-too-often incorrect, information on this site will benefit the community or the corporation in the long term or even the short term. SE’s biggest failures have been when they’ve just steamrolled ahead with a plan without seeking input from the community, or when they get input from the community and proceed to ignore it. But some of their best moments are when they ask for our feedback, acknowledge our feedback, and respond to it by, at the very minimum, factoring it into the decision-making. I agree with the general consensus that ChatGPT in its current state can be dangerous when used incorrectly (which is especially likely with inexperienced coders).
- These are of course only as good as the training data, but that training data is by definition as reliable as the answers given by humans.
- These would a have direct impact on quite a few requests from the community, as well as the company’s ability to support the community.
- Apparently, even before this initiative picked up steam, the result of this has been, and to use the exact term I used before – “a large negative impact to staffing and resources”.
- I’ve seen the project about generating the website – I agree, it’s pretty interesting.
- For knowledge work, as the cost of an action diminishes, we often do more of it.
- Abstracting away repetitive or tedious tasks frees
technologists up to make new discoveries or progress innovation.
In addition to detailing its chip work, Meta provided an update on plans to redesign its data centers around more modern AI-oriented networking and cooling systems, saying it would break ground on its first such facility this year. Meta has been engaged in a massive project to upgrade its AI infrastructure this past year, after executives realized it lacked the hardware and software needed to support demand from product teams building AI-powered features. The first MTIA chip was focused exclusively on an AI process called inference, in which algorithms trained on huge amounts of data make judgments about whether to show, say, a dance video or a cat meme as the next post in a user’s feed, the posts said. We’re even reimagining how we code by deploying CodeCompose, a generative AI-based coding assistant we developed to make our developers more productive throughout the software development lifecycle.
How I Turned My Company’s Docs into a Searchable Database with OpenAI
The first thing to realize is that Cicero is a very complex system. Its high-level structure is considerably more complex than systems like AlphaZero, which mastered Go and chess, or GPT-3 which focuses purely on sequences of words. Essentially the thesis here – that LLM/AI tools have had an effect on software development, with significant amounts of code written by AI assistance. There’s also chatbots that can translate various human inputs to code and answer questions. Since there’s a few complaints over the writing style of the blog post – so I made an attempt to ‘translate’ this to something a little more accessible.
A deeper audit informed by the application’s use context could have generated a clearer picture of the limitations before public release. Cicero is perhaps a reminder that there is indeed a lot more to natural language processing than deep learning. While many of the current communities were proposed and supported by Stack Overflow users from their conception to their launch, each community has developed their own culture and workings. This observation is relevant as most communities are being affected by ChatGPT and have reviewed the possibility of banning generated text content. Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Meta stated that these features are currently available to select advertisers only.
The Segment Anything Data Engine created a 1 Billion masks dataset (SA-1B) on 11 Million diverse, high resolution (3300×4900 pixels on average) and licensed images. It is worth mentioning that 99.1% of masks were generated automatically, however the quality is so high because they are carefully selected. The main goal for Meta AI team was to create a promptable image segmentation model that would work with user input prompt as it is working with ChatGPT. Therefore, they came up with the solution to integrate user input with the image to produce segmentation masks. Segmentation prompt can be any information indicating what to segment in an image.
- Minerva was trained on 1.2M papers submitted to arXiv (58GB) and numerous web pages containing maths (TeX, AsciiMath, MathML) (60GB), which is quite a massive dataset.
- German customers of popular fast food chain Kentucky Fried Chicken were shocked by the insensitive nature of an automated push notification in the KFC mobile app.
- I feel rather than a literal interpretation between the ‘business’/’management’ writing style – annotations make more sense.
- By rethinking how we innovate across our infrastructure, we’re creating a scalable foundation to power emerging opportunities in areas like generative AI and the metaverse.
- For example, we can easily collocate GPUs, CPUs, network and storage if it will better support our workloads.
- And, needless to say, “the bigger, the better” remains very much true when it comes to language models.