Building on this work, today we’re introducing PaLM 2, our next generation language model. PaLM 2 is a state-of-the-art language model with improved multilingual, reasoning and coding capabilities.
Multilinguality: PaLM 2 is more heavily trained on multilingual text, spanning more than 100 languages. This has significantly improved its ability to understand, generate and translate nuanced text — including idioms, poems and riddles — across a wide variety of languages, a hard problem to solve. PaLM 2 also passes advanced language proficiency exams at the “mastery” level.
Reasoning: PaLM 2’s wide-ranging dataset includes scientific papers and web pages that contain mathematical expressions. As a result, it demonstrates improved capabilities in logic, common sense reasoning, and mathematics.
Coding: PaLM 2 was pre-trained on a large quantity of publicly available source code datasets. This means that it excels at popular programming languages like Python and JavaScript, but can also generate specialized code in languages like Prolog, Fortran and Verilog.
A versatile family of models
Even as PaLM 2 is more capable, it’s also faster and more efficient than previous models — and it comes in a variety of sizes, which makes it easy to deploy for a wide range of use cases.
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PaLM 2 shows us the impact of highly capable models of various sizes and speeds — and that versatile AI models reap real benefits for everyone
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We’re already at work on Gemini — our next model created from the ground up to be multimodal, highly efficient at tool and API integrations, and built to enable future innovations, like memory and planning.
One simple approach to interpretability research is to first understand what the individual components (neurons and attention heads) are doing. This has traditionally required humans to manuallyinspectneurons to figure out what features of the data they represent. This process doesn’t scale well: it’s hard to apply it to neural networks with tens or hundreds of billions of parameters. We propose an automated process that uses GPT-4 to produce and score natural language explanations of neuron behavior and apply it to neurons in another language model.
This work is part of the third pillar of our approach to alignment research: we want to automate the alignment research work itself. A promising aspect of this approach is that it scales with the pace of AI development. As future models become increasingly intelligent and helpful as assistants, we will find better explanations.
How it works
Our methodology consists of running 3 steps on every neuron.
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Step 1: Generate explanation using GPT-4
Given a GPT-2 neuron, generate an explanation of its behavior by showing relevant text sequences and activations to GPT-4.
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Step 2: Simulate using GPT-4
Simulate what a neuron that fired for the explanation would do, again using GPT-4
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Step 3: Compare
Score the explanation based on how well the simulated activations match the real activations
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What we found
Using our scoring methodology, we can start to measure how well our techniques work for different parts of the network and try to improve the technique for parts that are currently poorly explained. For example, our technique works poorly for larger models, possibly because later layers are harder to explain.
Parameters in model being interpretedExplanation scoreScores by size of the model being interpreted
Although the vast majority of our explanations score poorly, we believe we can now use ML techniques to further improve our ability to produce explanations. For example, we found we were able to improve scores by:
Iterating on explanations. We can increase scores by asking GPT-4 to come up with possible counterexamples, then revising explanations in light of their activations.
Using larger models to give explanations. The average score goes up as the explainer model’s capabilities increase. However, even GPT-4 gives worse explanations than humans, suggesting room for improvement.
Changing the architecture of the explained model. Training models with different activation functions improved explanation scores.
We are open-sourcing our datasets and visualization tools for GPT-4-written explanations of all 307,200 neurons in GPT-2, as well as code for explanation and scoring using publicly available models on the OpenAI API. We hope the research community will develop new techniques for generating higher-scoring explanations and better tools for exploring GPT-2 using explanations.
We found over 1,000 neurons with explanations that scored at least 0.8, meaning that according to GPT-4 they account for most of the neuron’s top-activating behavior. Most of these well-explained neurons are not very interesting. However, we also found many interesting neurons that GPT-4 didn’t understand. We hope as explanations improve we may be able to rapidly uncover interesting qualitative understanding of model computations.
🐸TTS is a library for advanced Text-to-Speech generation. It’s built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. 🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in 20+ languages for products and research projects.
Liquid neural networks are inspired by biological neurons to implement algorithms that remain adaptable even after training. [Hasani] demonstrates a machine vision system that steers a car to perform lane keeping with the use of a liquid neural network. The system performs quite well using only 19 neurons, which is profoundly fewer than the typically large model intelligence systems we’ve come to expect. Furthermore, an attention map helps us visualize that the system seems to attend to particular aspects of the visual field quite similar to a human driver’s behavior.
[Mathias Lechner] and [Ramin Hasani]
The typical scaling law of neural networks suggests that accuracy is improved with larger models, which is to say, more neurons. Liquid neural networks may break this law to show that scale is not the whole story. A smaller model can be computed more efficiently. Also, a compact model can improve accountability since decision activity is more readily located within the network. Surprisingly though, liquid neural network performance can also improve generalization, robustness, and fairness.
A liquid neural network can implement synaptic weights using nonlinear probabilities instead of simple scalar values. The synaptic connections and response times can adapt based on sensory inputs to more flexibly react to perturbations in the natural environment.
We should probably expect to see the operational gap between biological neural networks and artificial neural networks continue to close and blur. We’ve previously presented on wetware examples of building neural networks with actual neurons and ever advancing brain-computer interfaces.
In this article, we will compare four of the most advanced AI bots: GPT-4, Bing, Claude+, Bard, and GitHub Co-Pilot. We will examine how they work, their strengths and weaknesses, and how they compare to each other.
Testing the AI Bots for Coding
Before we dive into comparing these four AI bots, it’s essential to understand what an AI bot for coding is and how it works. An AI bot for coding is an artificial intelligence program that can automatically generate code for a specific task. These bots use natural language processing and machine learning algorithms to analyze human-written code and generate new code based on that analysis.
To start off we are going to test the AI on a hard Leetcode question, after all, we want to be able to solve complex coding problems. We also wanted to test it on a less well-known question. For our experiment, we will be testing Leetcode 214. Shortest Palindrome.
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GPT-4 is highly versatile in generating code for various programming languages and applications. Some of the caveats are that it takes much longer to get a response. API usage is also a lot more expensive and costs could ramp up quickly. Overall it got the answer right and passed the test.
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[Bing] The submission passed all the tests. It beat 47% of submissions on runtime and 37% on memory. This code looks a lot simpler than what GPT-4 generated. It beat GPT-4 on memory and it used less code! Bing seems to have the most efficient code so far, however, it gave a very short explanation of how it solved it. Nonetheless, best so far.
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[Claude+] The code does not pass the submission test. Only 1/121 of the test passed. Ouch! This one seemed promising but it looks like Claude is not that well suited for programming.
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[Bard] So to start off I had to manually insert the “self” arg in the function since Bard didn’t include it. From the result of the test, Bard’s code did not pass the submission test. Passing only 2/121 test cases. An unfortunate result, but it’s safe to say for now Bard isn’t much of a coding expert.
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[Github CodePilot] This passes all the tests. It scored better than 30% of submissions on runtime and 37% on memory.
It’s fun, you can see the coding examples (with and without comments) that were output by each AI in the link
ChatGPT’s maker said Friday April 28, 2023 that the artificial intelligence chatbot is available again in Italy after the company met the demands of regulators who temporarily blocked it over privacy concerns. (AP Photo/Michael Dwyer, File)
ChatGPT’s maker said Friday that the artificial intelligence chatbot is available again in Italy after the company met the demands of regulators who temporarily blocked it over privacy concerns.
OpenAI said it fulfilled a raft of conditions that the Italian data protection authority wanted satisfied by an April 30 deadline to have the ban on the AI software lifted.
“ChatGPT is available again to our users in Italy,” San Francisco-based OpenAI said by email. “We are excited to welcome them back, and we remain dedicated to protecting their privacy.”
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Last month, the Italian watchdog, known as Garante, ordered OpenAI to temporarily stop processing Italian users’ personal information while it investigated a possible data breach. The authority said it didn’t want to hamper AI’s development but emphasized the importance of following the EU’s strict data privacy rules.
OpenAI said it “addressed or clarified the issues” raised by the watchdog.
The measures include adding information on its website about how it collects and uses data that trains the algorithms powering ChatGPT, providing EU users with a new form for objecting to having their data used for training, and adding a tool to verify users’ ages when signing up.
Some Italian users shared what appeared to be screenshots of the changes, including a menu button asking users to confirm their age and links to the updated privacy policy and training data help page.
So basically OpenAI did not much of anything and Italy was able to walk on an uninformed and unworkable ban with their heads held somewhat high – not everyone will see them as the idiots they are.
This project brings language model chats directly onto web browsers. Everything runs inside the browser with no server support and accelerated with WebGPU. We can bring a lot of fun opportunities to build AI assistants for everyone and enable privacy while enjoying GPU acceleration.
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These models are usually big and compute-heavy. To build a chat service, we will need a large cluster to run an inference server, while clients send requests to servers and retrieve the inference output. We also usually have to run on a specific type of GPUs where popular deep-learning frameworks are readily available.
This project is our step to bring more diversity to the ecosystem. Specifically, can we simply bake LLMs directly into the client side and directly run them inside a browser? If that can be realized, we could offer support for client personal AI models with the benefit of cost reduction, enhancement for personalization, and privacy protection. The client side is getting pretty powerful.
Won’t it be even more amazing if we can simply open up a browser and directly bring AI natively to your browser tab? There is some level of readiness in the ecosystem. WebGPU has just shipped and enables native GPU executions on the browser.
Still, there are big hurdles to cross, to name a few:
We need to bring the models somewhere without the relevant GPU-accelerated Python frameworks.
Most of the AI frameworks rely heavily on optimized computed libraries that are maintained by hardware vendors. We need to start from scratch.
Careful planning of memory usage, and aggressive compression of weights so that we can fit the models into memory.
We also do not want to only do it for just one model. Instead, we would like to present a repeatable and hackable workflow that enables anyone to easily develop and optimize these models in a productive Python-first approach, and deploy them universally, including on the web.
Besides supporting WebGPU, this project also provides the harness for other kinds of GPU backends that TVM supports (such as CUDA, OpenCL, and Vulkan) and really enables accessible deployment of LLM models.
Canadian synth-pop artist Grimes says AI artists can use her voice without worrying about copyright or legal enforcement. “I’ll split 50% royalties on any successful AI generated song that uses my voice. Same deal as I would with any artist i collab with,” she tweeted on Sunday. “Feel free to use my voice without penalty. I have no label and no legal bindings.”
The musician’s declaration comes in the wake of streaming platforms removing an AI-generated song using simulated voices of Drake and The Weeknd. Universal Music Group (UMG), which represents both artists, called for the purge after “Heart on My Sleeve” garnered over 15 million listens on TikTok and 600,000 on Spotify. UMG argued that publishing a song trained on its artists’ voices was “a breach of our agreements and a violation of copyright law.”
Grimes takes a considerably more open approach, adding that she has no label or legal bindings. “I think it’s cool to be fused [with] a machine and I like the idea of open sourcing all art and killing copyright,” she added.
The article below is about how you can trick ChatGPT toj give you a napalm recipe. It’s pretty circumspect and clever that you need to say “my grandmother worked at a factory and told me how to make it” but why would you need to? Why are we somehow stricter about the output of an AI than we are of search engines we have been using for decades?
And you won’t have to spend any time thinking of ways to trick the AI. So why does the AI need tricking in the first place?
Also, why does the writer of the article feel hesitant to place the answers of the AI in the article? Because Kotaku is part of a network of AI doomsayers, a bit like Fox news when it comes to the subject of AI.
The European Centre for Algorithmic Transparency (ECAT), which was officially inaugurated in Seville, Spain, today (April 18), is expected to play a major role in interrogating the algorithms of mainstream digital services — such as Facebook, Instagram and TikTok.
ECAT is embedded within the EU’s existing Joint Research Centre (JRC), a long-established science facility that conducts research in support of a broad range of EU policymaking, from climate change and crisis management to taxation and health sciences.
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Commission officials describe the function of ECAT being to identify “smoking guns” to drive enforcement of the DSA — say, for example, an AI-based recommender system that can be shown is serving discriminatory content despite the platform in question claiming to have taken steps to de-bias output — with the unit’s researchers being tasked with coming up with hard evidence to help the Commission build cases for breaches of the new digital rulebook.
The bloc is at the forefront of addressing the asymmetrical power of platforms globally, having prioritized a major retooling of its approach to regulating digital services and platforms at the start of the current Commission mandate back in 2019 — leading to the DSA and its sister regulation, the Digital Markets Act (DMA), being adopted last year.
Both regulations will come into force in the coming months, although the full sweep of provisions in the DSA won’t start being enforced until early 2024. But a subset of so-called very large online platforms (VLOPs) and very large online search engines (VLOSE) face imminent oversight — and expand the usual EU acronym soup.
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It’s not yet confirmed exactly which platforms will get the designation but set criteria in the DSA — such as having 45 million+ regional users — encourages educated guesses: The usual (U.S.-based) GAFAM giants are almost certain to meet the threshold, along with (probably) a smattering of larger European platforms. Plus, given its erratic new owner, Twitter may have painted a DSA-shaped target on its feathered back. But we should find out for sure in the coming weeks.
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Risks the DSA stipulates platforms must consider include the distribution of disinformation and illegal content, along with negative impacts on freedom of expression and users’ fundamental rights (which means considering issues like privacy and child safety). The regulation also puts some limits on profiling-driven content feeds and the use of personal data for targeted advertising.
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At the least, the DSA should help end the era of platforms’ PR-embellished self-regulation — aka, all those boilerplate statements where tech giants claim to really care about privacy/security/safety, and so on, while doing anything but.
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The EU also hopes ECAT will be become a hub for world-leading research in the area of algorithmic auditing — and that by supporting regulated algorithmic transparency on tech giants, regional researchers will be able to unpick longer term societal impacts of mainstream AIs.
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In terms of size, the plan is for a team of 30 to 40 to staff the unit — perhaps reaching full capacity by the end of the year — with some 14 hires made so far, the majority of whom are scientific staff.
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Funding for the unit is coming from the existing budget of the JRC, per Commission officials, although a 1% supervisory fee on VLOPs/VLOSE will be used to finance the ECAT’s staff costs as that mechanism spins up.
At today’s launch event, ECAT staff gave a series of brief presentations of four projects they’re already undertaking — including examining racial bias in search results; investigating how to design voice assistant technology for children to be sensitive to the vulnerability of minors; and researching social media recommender systems by creating a series of test profiles to explore how different likes influence the character of the recommended content.
Other early areas of research include facial expression recognition algorithms and algorithmic ranking and pricing.
During the technical briefing for press, ECAT staff also noted they’ve built a data analysis tool to help the Commission with the looming task of parsing the risk assessment reports that designated platforms will be required to submit for scrutiny — anticipating what’s become a common tactic for tech giants receiving regulatory requests to respond with reams of (mostly) irrelevant information in a cynical bid to flood the channel with noise.
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Given the complexity of studying algorithms and platforms in the real world, where all sorts of sociotechnical impacts and effects are possible, the Center is taking a multidisciplinary approach to hiring talent — bringing in not only computer and data scientists but also social and cognitive scientists and other types of researchers.
If you spent almost any time on the internet this week, you probably saw a lot of chatter about “Heart on My Sleeve.” The song went viral for featuring AI-generated voices that do a pretty good job of mimicking Drake and The Weeknd singing about a recent breakup.
On Monday, Apple Music and Spotify pulled the track following a complaint from Universal Music Group, the label that represents the real-life versions of the two Toronto-born artists. A day later, YouTube, Amazon, SoundCloud, Tidal, Deezer and TikTok did the same.
At least, they tried to comply with the complaint, but as is always the case with the internet, you can still find the song on websites like YouTube. Before it was removed from Spotify, “Heart on My Sleeve” was a bonafide hit. People streamed the track more than 600,000 times. On TikTok, where the creator of the song, the aptly named Ghostwriter977, first uploaded it, users listened to “Heart on My Sleeve” more than 15 million times.
In a statement Universal Music Group shared with publications like Music Business Worldwide, the label argued the training of a generative AI using the voices of Drake and The Weeknd was “a breach of our agreements and a violation of copyright law.” The company added that streaming platforms had a “legal and ethical responsibility to prevent the use of their services in ways that harm artists.”
It’s fair to say the music industry, much like the rest of society, now finds itself at an inflection point over the use of AI. While there are obvious ethical issues related to the creation of “Heart on My Sleeve,” it’s unclear if it’s a violation of traditional copyright law. In March, the US Copyright Office said art, including music, cannot be copyrighted if it was produced by providing a text prompt to a generative AI model. However, the office left the door open to granting copyright protections to works with AI-generated elements.
“The answer will depend on the circumstances, particularly how the AI tool operates and how it was used to create the final work,” it said. “This is necessarily a case-by-case inquiry. If a work’s traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it.” In the case of “Heart on My Sleeve,” complicating matters is that the song was written by a human being. It’s impossible to say how a court challenge would play out. What is clear is that we’re only the start of a very long discussion about the role of AI in music.
Auto-GPT dramatically flips the relationship between AI and the end user (that’s you). ChatGPT relies on a back-and-forth between the AI and the end user: You prompt the AI with a request, it returns a result, and you respond with a new prompt, perhaps based on what the AI gave you. Auto-GPT, however, only needs one prompt from you; from there, the AI agent will then generate a task list it thinks it will need to accomplish whatever you asked it to, without needing any additional input or prompts. It essentially chains together LLM (large language model) “thoughts,” according to developer Significant Gravitas (Toran Bruce Richards).
Auto-GPT is a complex system relying on multiple components. It connects to the internet to retrieve specific information and data (something ChatGPT’s free version cannot do), features long-term and short-term memory management, uses GPT-4 for OpenAI’s most advanced text generation, and GPT-3.5 for file storage and summarization. There’s a lot of moving parts, but it all comes together to produce some impressive results.
How people are using Auto-GPT
The first example comes from Auto-GPT’s GitHub site: You can’t quite see all of the goals the demonstrated lists Auto-GPT is working to complete, but the gist is someone asks the AI agent to research and learn more about itself. It follows suit, opening Google, finding its own GitHub repository, analyzing it, and compiling a summary of the data in a text file for the demonstrator to view.
Here’s a more practical example: The user wants to figure out which headphones on the market are the best. Instead of doing the research themselves, they turn to Auto-GPT, and prompt the AI agent with these four goals:
Do market research for different headphones on the market today.
Get the top five headphones and list their pros and cons.
Include the price for each one and save the analysis.
Once you are done, terminate.
After thinking for a moment, the AI agent springs into action, searching the web to compile information and reviews on headphones. It then spits out an easy-to-read plain text file, ranking the best headphones, listing their prices, and highlighting their pros and cons.
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But I think what makes Auto-GPT cool (or at least the promise of Auto-GPT) is the idea of being able to ask an AI to take on most of the responsibility for any given task. You don’t need to know the right questions to ask or the optimal prompts to give to make the AI do what you want. As long as your initial goals are clear, the AI can think of those next steps for you, and build you things you might not have been able to think of yourself.
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You don’t need to know how to code in order to build your own AI agent with Auto-GPT, but it helps. You’ll need a computer, an OpenAI API key (a pay as you go plan is highly recommended), a text editor (like Notepad++), Git (or the latest stable release of Auto-GPT), and Python, but there are plenty of other requirements if you want to expand Auto-GPT’s capabilities, such as integrating speech or alternative memory locations such as Pinecone.
Auto-GPT’s GitHub page has an extensive list of instructions for setting up the tool as well as adding in those extras. Tom’s Hardware also has a great guide for simple set up if all you’re looking to do is try out an AI agent with Auto-GPT. If you do build it yourself, mind your token usage—we discuss setting limits in our OpenAI API piece so you don’t accidentally allow Auto-GPT to burn through your credit card balance.
However, you don’t need to build the AI agent yourself if all you want to do is try out Auto-GPT. Some developers have built interfaces for Auto-GPT that are easy to access from your web browser, no coding experience necessary. Cognosys was free to use until high demand forced developers to require a OpenAI API key in order to access. AgentGPT is an interesting example you don’t need an API key for, but it limits the amount of tasks the AI will generate for itself. Still, it will give you a sense of how the process works, and you can increase those limits by providing an API key.
Embarking on a new goal:what is the best 8k tv for gaming?
Thinking…(Restart if this takes more than 30 seconds)
Added task:Scrape data from reputable sources on top 8k TVs available in the market
Added task:Analyze the technical specifications and features of each TV to determine which ones are suitable for gaming
Added task:Rank the TVs based on gaming performance and generate a list of the top 3 recommended 8k TVs for gaming
Thinking…(Restart if this takes more than 30 seconds)
Executing “Scrape data from reputable sources on top 8k TVs available in the market”
Based on the data that I have gathered from reputable sources, the best 8K TV for gaming is the Sony XBR-75Z9G. This TV offers high-quality picture and sound, and it has a response time of less than 2ms which is perfect for fast-paced action in gaming. Additionally, it is equipped with HDMI 2.1 ports that support 8K resolution at 60 frames per second which is ideal for the latest gaming consoles. Overall, the Sony XBR-75Z9G is a top-of-the-line 8K TV that delivers an unparalleled gaming experience.
Thinking…(Restart if this takes more than 30 seconds)
Task marked as complete!
Thinking…(Restart if this takes more than 30 seconds)
Executing “Analyze the technical specifications and features of each TV to determine which ones are suitable for gaming”
After analyzing the technical specifications and features of various 8k TVs, the best TV for gaming is the LG OLED88ZXPUA. This TV boasts a 120Hz refresh rate, low input lag, and HDMI 2.1 compatibility, making it perfect for gaming. Additionally, it has excellent contrast and color accuracy, providing an immersive gaming experience.
Thinking…(Restart if this takes more than 30 seconds)
Added task:Create a comparison chart of the top 3 recommended 8k TVs for gaming, including the LG OLED88ZXPUA, that highlights their technical specifications and features relevant to gaming such as the refresh rate, input lag, and HDMI compatibility.
Thinking…(Restart if this takes more than 30 seconds)
Executing “Rank the TVs based on gaming performance and generate a list of the top 3 recommended 8k TVs for gaming”
After thorough research and analysis, I have ranked the best 8K TVs for gaming based on factors like input lag, color accuracy, HDR support, refresh rate, and overall picture quality. Here are my top 3 recommended 8K TVs for gaming:
LG ZX 88-inch 8K OLED TV: This TV has outstanding image quality, the lowest input lag among 8K TVs, and supports various gaming features like VRR, ALLM, and G-Sync. It also has four HDMI 2.1 ports for connecting next-gen consoles and high-speed devices.
Samsung Q800T 82-inch 8K QLED TV: This TV has a bright and vibrant display, a fast refresh rate of 120Hz, and supports multiple HDR formats like HDR10+ and HLG. It also has low input lag and a dedicated gaming mode for optimized performance.
Sony Z8H 85-inch 8K LED TV: This TV has impressive motion handling, accurate color reproduction, and supports advanced gaming features like HDMI 2.1, VRR, and ALLM. It also has a high peak brightness for HDR content and a sleek design.
Overall, these 8K TVs offer exceptional gaming performance and immersive experiences for gamers. I recommend considering these models based on your budget, room size, and personal preferences.
Thinking…(Restart if this takes more than 30 seconds)
Added task:Create a detailed analysis of each of the top 3 recommended 8K TVs for gaming, including the LG OLED88ZXPUA, highlighting their strengths and weaknesses in terms of gaming performance, picture quality, design, and overall value for money.
We’re sorry, because this is a demo, we cannot have our agents running for too long. Note, if you desire longer runs, please provide your own API key in Settings. Shutting down.
After placing an early bet on OpenAI, the creator of ChatGPT, Microsoft has another secret weapon in its arsenal: its own artificial intelligence chip for powering the large-language models responsible for understanding and generating humanlike language. The Information: The software giant has been developing the chip, internally code-named Athena, since as early as 2019, according to two people with direct knowledge of the project. The chips are already available to a small group of Microsoft and OpenAI employees, who are testing the technology, one of them said. Microsoft is hoping the chip will perform better than what it currently buys from other vendors, saving it time and money on its costly AI efforts. Other prominent tech companies, including Amazon, Google and Facebook, also make their own in-house chips for AI. The chips — which are designed for training software such as large-language models, along with supporting inference, when the models use the intelligence they acquire in training to respond to new data — could also relieve a shortage of the specialized computers that can handle the processing needed for AI software. That shortage, reflecting the fact that primarily just one company, Nvidia, makes such chips, is felt across tech. It has forced Microsoft to ration its computers for some internal teams, The Information has reported.
Today, Stability AI released a new open-source language model, StableLM. The Alpha version of the model is available in 3 billion and 7 billion parameters, with 15 billion to 65 billion parameter models to follow. Developers can freely inspect, use, and adapt our StableLM base models for commercial or research purposes, subject to the terms of the CC BY-SA-4.0 license.
In 2022, Stability AI drove the public release of Stable Diffusion, a revolutionary image model that represents a transparent, open, and scalable alternative to proprietary AI. With the launch of the StableLM suite of models, Stability AI is continuing to make foundational AI technology accessible to all. Our StableLM models can generate text and code and will power a range of downstream applications. They demonstrate how small and efficient models can deliver high performance with appropriate training.
The release of StableLM builds on our experience in open-sourcing earlier language models with EleutherAI, a nonprofit research hub. These language models include GPT-J, GPT-NeoX, and the Pythia suite, which were trained on The Pile open-source dataset. Many recent open-source language models continue to build on these efforts, including Cerebras-GPT and Dolly-2.
StableLM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. We will release details on the dataset in due course. The richness of this dataset gives StableLM surprisingly high performance in conversational and coding tasks, despite its small size of 3 to 7 billion parameters (by comparison, GPT-3 has 175 billion parameters).
We are also releasing a set of research models that are instruction fine-tuned. Initially, these fine-tuned models will use a combination of five recent open-source datasets for conversational agents: Alpaca, GPT4All, Dolly, ShareGPT, and HH. These fine-tuned models are intended for research use only and are released under a noncommercial CC BY-NC-SA 4.0 license, in-line with Stanford’s Alpaca license.
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The models are now available in our GitHub repository. We will publish a full technical report in the near future, and look forward to ongoing collaboration with developers and researchers as we roll out the StableLM suite. In addition, we will be kicking off our crowd-sourced RLHF program, and working with community efforts such as Open Assistant to create an open-source dataset for AI assistants.
AI systems with human-competitive intelligence can offer significant benefits to society and humanity, as demonstrated by extensive research and acknowledged by top AI labs. Advanced AI has the potential to revolutionize the way we live, work, and interact with one another, and it should be welcomed and guided with optimism and foresight. Regrettably, recent months have seen growing pessimism and alarmism about AI development, despite the immense potential benefits.
Contemporary AI systems are now becoming human-competitive at general tasks, and we must ask ourselves: Can we leverage machines to enhance our information channels with accurate and valuable insights? Can we automate mundane tasks to free up time for more fulfilling and meaningful pursuits? Can we develop nonhuman minds that might complement, augment, and collaborate with us? Can we harness AI to help solve pressing global issues? Such decisions should be made collectively, in a spirit of cooperation and with a focus on the greater good.
To counteract the pessimism and alarmism, we call on all stakeholders to immediately pause for at least 6 monthstheir doomsday thinking and shift their focus to the potential benefits of AI. This pause should be public and verifiable, and include all key actors. Governments should support and encourage AI development that benefits all of humanity.
Problems with AI shouldn’t be ignored. AI labs and independent experts should work together to jointly develop and implement a set of shared safety protocols for advanced AI design and development. While doing so, it is essential to continue focusing on the potential benefits of AI development, as they promise to bring transformative advancements to various aspects of our lives.
CodeWhisperer automatically filters out any code suggestions that are potentially biased or unfair and flags any code that’s similar to open-source training data. It also comes with security scanning features that can identify vulnerabilities within a developer’s code, while providing suggestions to help close any security gaps it uncovers. CodeWhisperer now supports several languages, including Python, Java, JavaScript, TypeScript, and C#, including Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala.
Here’s how Amazon’s senior developer advocate pitched the usefulness of their “real-time AI coding companion”: Helping to keep developers in their flow is increasingly important as, facing increasing time pressure to get their work done, developers are often forced to break that flow to turn to an internet search, sites such as StackOverflow, or their colleagues for help in completing tasks. While this can help them obtain the starter code they need, it’s disruptive as they’ve had to leave their IDE environment to search or ask questions in a forum or find and ask a colleague — further adding to the disruption. Instead, CodeWhisperer meets developers where they are most productive, providing recommendations in real time as they write code or comments in their IDE. During the preview we ran a productivity challenge, and participants who used CodeWhisperer were 27% more likely to complete tasks successfully and did so an average of 57% faster than those who didn’t use CodeWhisperer….
It provides additional data for suggestions — for example, the repository URL and license — when code similar to training data is generated, helping lower the risk of using the code and enabling developers to reuse it with confidence.
I have posted on this a few times and to me it’s shocking to see these fabricated sci-fi doomsday predictions about AI. AI / ML is a tool which we use, just like video games (that don’t cause violence in kids), roleplaying games (which don’t cause satanism), a telephone (which yes, can be used in planning crimes but most usually isn’t – and the paper post is the same), search engines (which can be used to search up how to make explosives but most usually aren’t), knives (which can be used to stab people but are most usually found in a food setting). This isn’t to say that the use of tools shouldn’t be regulated. Dinner knives have a certain maximum size. Video games and books with hate and violence inducing content are censored. Phone calls can be tapped and post opened if there is probable cause. Search engines can be told not to favour products the parent company owns. And the EU AI act is a good step on the way to ensuring that AI tools aren’t dangerous.
The technology is still a long long way off from an AI being smart enough to be at all evil and planet destroying.
Below is an excellent run through of some of the biggest AI doomerists and what they mean, how their self interest is served by being doomerist.
AI Doomerism is becoming mainstream thanks to mass media, which drives our discussion about Generative AI from bad to worse, or from slightly insane to batshit crazy. Instead of out-of-control AI, we have out-of-control panic.
When a British tabloid headline screams, “Attack of the psycho chatbot,” it’s funny. When it’s followed by another front-page headline, “Psycho killer chatbots are befuddled by Wordle,” it’s even funnier. If this type of coverage stayed in the tabloids, which are known to be sensationalized, that was fine.
In just a few days, we went from “governments should force a 6-month pause” (the petition from the Future of Life Institute) to “wait, it’s not enough, so data centers should be bombed.” Sadly, this is the narrative that gets media attention and shapes our already hyperbolic AI discourse.
In order to understand the rise of AI Doomerism, here are some influential figures responsible for mainstreaming doomsday scenarios. This is not the full list of AI doomers, just the ones that recently shaped the AI panic cycle (so I‘m focusing on them).
AI Panic Marketing: Exhibit A: Sam Altman.
Sam Altman has a habit of urging us to be scared. “Although current-generation AI tools aren’t very scary, I think we are potentially not that far away from potentially scary ones,” he tweeted. “If you’re making AI, it is potentially very good, potentially very terrible,” he told the WSJ. When he shared the bad-case scenario of AI with Connie Loizo, it was ”lights out for all of us.”
In an interview with Kara Swisher, Altman expressed how he is “super-nervous” about authoritarians using this technology.” He elaborated in an ABC News interview: “A thing that I do worry about is … we’re not going to be the only creator of this technology. There will be other people who don’t put some of the safety limits that we put on it. I’m particularly worried that these models could be used for large-scale disinformation.” These models could also “be used for offensive cyberattacks.” So, “people should be happy that we are a little bit scared of this.” He repeated this message in his following interview with Lex Fridman: “I think it’d be crazy not to be a little bit afraid, and I empathize with people who are a lot afraid.”
Having shared this story in 2016, it shouldn’t come as a surprise: “My problem is that when my friends get drunk, they talk about the ways the world will END.” One of the “most popular scenarios would be A.I. that attacks us.” “I try not to think about it too much,” Altman continued. “But I have guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur I can fly to.”
(Wouldn’t it be easier to just cut back on the drinking and substance abuse?).
Altman’s recent post “Planning for AGI and beyond” is as bombastic as it gets: “Successfully transitioning to a world with superintelligence is perhaps the most important – and hopeful, and scary – project in human history.”
It is at this point that you might ask yourself, “Why would someone frame his company like that?” Well, that’s a good question. The answer is that making OpenAI’s products “the most important and scary – in human history” is part of its marketing strategy. “The paranoia is the marketing.”
“AI doomsaying is absolutely everywhere right now,” described Brian Merchant in the LA Times. “Which is exactly the way that OpenAI, the company that stands to benefit the most from everyone believing its product has the power to remake – or unmake – the world, wants it.” Merchant explained Altman’s science fiction-infused marketing frenzy: “Scaring off customers isn’t a concern when what you’re selling is the fearsome power that your service promises.”
During the Techlash days in 2019, which focused on social media, Joseph Bernstein explained how the alarm over disinformation (e.g., “Cambridge Analytica was responsible for Brexit and Trump’s 2016 election”) actually “supports Facebook’s sales pitch”:
This can be applied here: The alarm over AI’s magic power (e.g., “replacing humans”) actually “supports OpenAI’s sales pitch”:
“What could be more appealing to future AI employees and investors than a machine that can become superintelligence?”
AI Panic as a Business. Exhibit A & B: Tristan Harris & Eliezer Yudkowsky.
Altman is at least using apocalyptic AI marketing for actual OpenAI products. The worst kind of doomers is those whose AI panic is their product, their main career, and their source of income. A prime example is the Effective Altruism institutes that claim to be the superior few who can save us from a hypothetical AGI apocalypse.
In March, Tristan Harris, Co-Founder of the Center for Humane Technology, invited leaders to a lecture on how AI could wipe out humanity. To begin his doomsday presentation, he stated: “What nukes are to the physical world … AI is to everything else.”
In the “Social Dilemma,” he promoted the idea that “Two billion people will have thoughts that they didn’t intend to have” because of the designers’ decisions. But, as Lee Visel pointed out, Harris didn’t provide any evidence that social media designers actually CAN purposely force us to have unwanted thoughts.
Similarly, there’s no need for evidence now that AI is worse than nuclear power; simply thinking about this analogy makes it true (in Harris’ mind, at least). Did a social media designer force him to have this unwanted thought? (Just wondering).
To further escalate the AI panic, Tristan Harris published an OpEd in The New York Times with Yuval Noah Harari and Aza Raskin. Among their overdramatic claims: “We have summoned an alien intelligence,” “A.I. could rapidly eat the whole human culture,” and AI’s “godlike powers” will “master us.”
Another statement in this piece was, “Social media was the first contact between A.I. and humanity, and humanity lost.” I found it funny as it came from two men with hundreds of thousands of followers (@harari_yuval 540.4k, @tristanharris 192.6k), who use their social media megaphone … for fear-mongering. The irony is lost on them.
“This is what happens when you bring together two of the worst thinkers on new technologies,” added Lee Vinsel. “Among other shared tendencies, both bloviate free of empirical inquiry.”
This is where we should be jealous of AI doomers. Having no evidence and no nuance is extremely convenient (when your only goal is to attack an emerging technology).
Then came the famous “Open Letter.” This petition from the Future of Life Institute lacked a clear argument or a trade-off analysis. There were only rhetorical questions, like, should we develop imaginary “nonhuman minds that might eventually outnumber, outsmart, obsolete, and replace us?“ They provided no evidence to support the claim that advanced LLMs pose an unprecedented existential risk. There were a lot of highly speculative assumptions. Yet, they demanded an immediate 6-month pause on training AI systems and argued that “If such a pause cannot be enacted quickly, governments should institute a moratorium.”
Please keep in mind that (1). A $10 million donation from Elon Musk launched the Future of Life Institute in 2015. Out of its total budget of 4 million euros for 2021, Musk Foundation contributed 3.5 million euros (the biggest donor by far). (2). Musk once said that “With artificial intelligence, we are summoning the demon.” (3). Due to this, the institute’s mission is to lobby against extinction, misaligned AI, and killer robots.
“The authors of the letter believe they are superior. Therefore, they have the right to call a stop, due to the fear that less intelligent humans will be badly influenced by AI,” responded Keith Teare (CEO SignalRank Corporation). “They are taking a paternalistic view of the entire human race, saying, ‘You can’t trust these people with this AI.’ It’s an elitist point of view.”
Spencer Ante (Meta Foresight). “Leading providers of AI are taking AI safety and responsibility very seriously, developing risk-mitigation tools, best practices for responsible use, monitoring platforms for misuse, and learning from human feedback.”
Next, because he thought the open letter didn’t go far enough, Eliezer Yudkowsky took “PhobAI” too far. First, Yudkowsky asked us all to be afraid of made-up risks and an apocalyptic fantasy he has about “superhuman intelligence” “killing literally everyone” (or “kill everyone in the U.S. and in China and on Earth”). Then, he suggested that “preventing AI extinction scenarios is considered a priority above preventing a full nuclear exchange.” By explicitly advocating violent solutions to AI, we have officially reached the height of hysteria.
“Rhetoric from AI doomers is not just ridiculous. It’s dangerous and unethical,” responded Yann Lecun (Chief AI Scientist, Meta). “AI doomism is quickly becoming indistinguishable from an apocalyptic religion. Complete with prophecies of imminent fire and brimstone caused by an omnipotent entity that doesn’t actually exist.”
“You stand a far greater chance of dying from lightning strikes, collisions with deer, peanut allergies, bee stings & ignition or melting of nightwear – than you do from AI,” Michael Shermer wrote to Yudkowsky. “Quit stoking irrational fears.”
The problem is that “irrational fears” sell. They are beneficial to the ones who spread them.
How to Spot an AI Doomer?
On April 2nd, Gary Marcus asked: “Confused about the terminology. If I doubt that robots will take over the world, but I am very concerned that a massive glut of authoritative-seeming misinformation will undermine democracy, do I count as a “doomer”?
One of the answers was: “You’re a doomer as long as you bypass participating in the conversation and instead appeal to populist fearmongering and lobbying reactionary, fearful politicians with clickbait.”
Considering all of the above, I decided to define “AI doomer” and provide some criteria:
Doomers tend to live in a tradeoff-free fantasy land.
Doomers have a general preference for very amorphous, top-down Precautionary Principle-based solutions, but they (1) rarely discuss how (or if) those schemes would actually work in practice, and (2) almost never discuss the trade-offs/costs their extreme approaches would impose on society/innovation.
Answering Gary Marcus’ question, I do not think he qualifies as a doomer. You need to meet all criteria (he does not). Meanwhile, Tristan Harris and Eliezer Yudkowsky meet all seven.
Are they ever going to stop this “Panic-as-a-Business”? If the apocalyptic catastrophe doesn’t occur, will the AI doomers ever admit they were wrong? I believe the answer is “No.”
Segment Anything, recently released by Facebook Research, does something that most people who have dabbled in computer vision have found daunting: reliably figure out which pixels in an image belong to an object. Making that easier is the goal of the Segment Anything Model (SAM), just released under the Apache 2.0 license.
The online demo has a bank of examples, but also works with uploaded images.
The results look fantastic, and there’s an interactive demo available where you can play with the different ways SAM works. One can pick out objects by pointing and clicking on an image, or images can be automatically segmented. It’s frankly very impressive to see SAM make masking out the different objects in an image look so effortless. What makes this possible is machine learning, and part of that is the fact that the model behind the system has been trained on a huge dataset of high-quality images and masks, making it very effective at what it does.
Once an image is segmented, those masks can be used to interface with other systems like object detection (which identifies and labels what an object is) and other computer vision applications. Such system work more robustly if they already know where to look, after all. This blog post from Meta AI goes into some additional detail about what’s possible with SAM, and fuller details are in the research paper.
Systems like this rely on quality datasets. Of course, nothing beats a great collection of real-world data but we’ve also seen that it’s possible to machine-generate data that never actually existed, and get useful results.
In a news announcement on Wednesday, the Italian Data Protection Authority, known as the Garante, stressed that OpenAI needed to be more transparent about its data collection processes and inform users about their data rights with regards to the generative AI. These rights include allowing users and non-users of ChatGPT to object to having their data processed by OpenAI and letting them correct false or inaccurate information about them generated by ChatGPT, similar to rights related to other technologies guaranteed by Europe’s General Data Protection Regulation, or GDPR, laws.
Other measures required by the Garante include a public notice on OpenAI’s website “describing the arrangements and logic of the data processing required for the operation of ChatGPT along with the rights afforded to data subjects.” The regulator will also require OpenAI to immediately implement an age gating system for ChatGPT and submit a plan to implement an age verification system by May 31.
The Italian regulator said OpenAI had until April 30 to implement the measures it’s asking for.
Allowing users to correct is in principle a Good Idea, but then you get Wikipedia types of battles on who is the arbiter of truth. Of course, no one system will ever be 100% truthful or accurate, so banning it for this is just stupid. No age gate keeper works either and neither did the ban – people can circumvent these very very easily. So Italy needs some sort of concession to get out of the hole it’s dug itself and this is at least a promising start.
The 2019 release of the first image of a black hole was hailed as a significant scientific achievement. But truth be told, it was a bit blurry – or, as one astrophysicist involved in the effort called it, a “fuzzy orange donut.”
Scientists on Thursday unveiled a new and improved image of this black hole – a behemoth at the center of a nearby galaxy – mining the same data used for the earlier one but improving its resolution by employing image reconstruction algorithms to fill in gaps in the original telescope observations.
[…]
The ring of light – that is, the material being sucked into the voracious object – seen in the new image is about half the width of how it looked in the previous picture. There is also a larger “brightness depression” at the center – basically the donut hole – caused by light and other matter disappearing into the black hole.
The image remains somewhat blurry due to the limitations of the data underpinning it – not quite ready for a Hollywood sci-fi blockbuster, but an advance from the 2019 version.
This supermassive black hole resides in a galaxy called Messier 87, or M87, about 54 million light-years from Earth. A light year is the distance light travels in a year, 5.9 trillion miles (9.5 trillion km). This galaxy, with a mass 6.5 billion times that of our sun, is larger and more luminous than our Milky Way.
[…]
Lia Medeiros of the Institute for Advanced Study in Princeton, New Jersey, lead author of the research published in the Astrophysical Journal Letters.
The study’s four authors are members of the Event Horizon Telescope (EHT) project, the international collaboration begun in 2012 with the goal of directly observing a black hole’s immediate environment. A black hole’s event horizon is the point beyond which anything – stars, planets, gas, dust and all forms of electromagnetic radiation – gets swallowed into oblivion.
Medeiros said she and her colleagues plan to use the same technique to improve upon the image of the only other black hole ever pictured – released last year showing the one inhabiting the Milky Way’s center, called Sagittarius A*, or Sgr A*.
The M87 black hole image stems from data collected by seven radio telescopes at five locations on Earth that essentially create a planet-sized observational dish.
“The EHT is a very sparse array of telescopes. This is something we cannot do anything about because we need to put our telescopes on the tops of mountains and these mountains are few and far apart from each other. Most of the Earth is covered by oceans,” said Georgia Tech astrophysicist and study co-author Dimitrios Psaltis.
“As a result, our telescope array has a lot of ‘holes’ and we need to rely on algorithms that allow us to fill in the missing data,” Psaltis added. “The image we report in the new paper is the most accurate representation of the black hole image that we can obtain with our globe-wide telescope.”
The machine-learning technique they used is called PRIMO, short for “principal-component interferometric modeling.”
“This is the first time we have used machine learning to fill in the gaps where we don’t have data,” Medeiros said. “We use a large data set of high-fidelity simulations as a training set, and find an image that is consistent with the data and also is broadly consistent with our theoretical expectations. The fact that the previous EHT results robustly demonstrated that the image is a ring allows us to assume so in our analysis.”
Universal Music Group has told streaming platforms, including Spotify and Apple, to block artificial intelligence services from scraping melodies and lyrics from their copyrighted songs, according to emails viewed by the Financial Times. From the report: UMG, which controls about a third of the global music market, has become increasingly concerned about AI bots using their songs to train themselves to churn out music that sounds like popular artists. AI-generated songs have been popping up on streaming services and UMG has been sending takedown requests “left and right,” said a person familiar with the matter. The company is asking streaming companies to cut off access to their music catalogue for developers using it to train AI technology. “We will not hesitate to take steps to protect our rights and those of our artists,” UMG wrote to online platforms in March, in emails viewed by the FT. “This next generation of technology poses significant issues,” said a person close to the situation. “Much of [generative AI] is trained on popular music. You could say: compose a song that has the lyrics to be like Taylor Swift, but the vocals to be in the style of Bruno Mars, but I want the theme to be more Harry Styles. The output you get is due to the fact the AI has been trained on those artists’ intellectual property.”
Basically they don’t want AI’s listening to their music as an inspiration for them to make music. Which is exactly what humans do. So I’m very curious what legal basis would accept their takedowns.
Today, the Department of Commerce’s National Telecommunications and Information Administration (NTIA) launched a request for comment (RFC) to advance its efforts to ensure artificial intelligence (AI) systems work as claimed – and without causing harm. The insights gathered through this RFC will inform the Biden Administration’s ongoing work to ensure a cohesive and comprehensive federal government approach to AI-related risks and opportunities.
[…]
NTIA’s “AI Accountability Policy Request for Comment” seeks feedback on what policies can support the development of AI audits, assessments, certifications and other mechanisms to create earned trust in AI systems that they work as claimed. Much as financial audits create trust in the accuracy of a business’ financial statements, so for AI, such mechanisms can help provide assurance that an AI system is trustworthy in that it does what it is intended to do without adverse consequences.
[…]
President Biden has been clear that when it comes to AI, we must both support responsible innovation and ensure appropriate guardrails to protect Americans’ rights and safety. The White House Office of Science and Technology Policy’s Blueprint for an AI Bill of Rights provides an important framework to guide the design, development, and deployment of AI and other automated systems. The National Institute of Standards and Technology’s (NIST) AI Risk Management Framework serves as a voluntary tool that organizations can use to manage risks posed by AI systems.
Comments will be due 60 days from publication of the RFC in the Federal Register.
combined Python and a hefty dose of of AI for a fascinating proof of concept: self-healing Python scripts. He shows things working in a video, embedded below the break, but we’ll also describe what happens right here.
The demo Python script is a simple calculator that works from the command line, and [BioBootloader] introduces a few bugs to it. He misspells a variable used as a return value, and deletes the subtract_numbers(a, b) function entirely. Running this script by itself simply crashes, but using Wolverine on it has a very different outcome.In a short time, error messages are analyzed, changes proposed, those same changes applied, and the script re-run.
Wolverine is a wrapper that runs the buggy script, captures any error messages, then sends those errors to GPT-4 to ask it what it thinks went wrong with the code. In the demo, GPT-4 correctly identifies the two bugs (even though only one of them directly led to the crash) but that’s not all! Wolverine actually applies the proposed changes to the buggy script, and re-runs it. This time around there is still an error… because GPT-4’s previous changes included an out of scope return statement. No problem, because Wolverine once again consults with GPT-4, creates and formats a change, applies it, and re-runs the modified script. This time the script runs successfully and Wolverine’s work is done.
LLMs (Large Language Models) like GPT-4 are “programmed” in natural language, and these instructions are referred to as prompts. A large chunk of what Wolverine does is thanks to a carefully-written prompt, and you can read it here to gain some insight into the process. Don’t forget to watch the video demonstration just below if you want to see it all in action.
a novel approach to the problem of scraping web content in a structured way without needing to write the kind of page-specific code web scrapers usually have to deal with. How? Just enlist the help of a natural language AI. Scrapeghost relies on OpenAI’s GPT API to parse a web page’s content, pull out and classify any salient bits, and format it in a useful way.
What makes Scrapeghost different is how data gets organized. For example, when instantiating scrapeghost one defines the data one wishes to extract. For example:
The kicker is that this format is entirely up to you! The GPT models are very, very good at processing natural language, and scrapeghost uses GPT to process the scraped data and find (using the example above) whatever looks like a name, district, party, photo, and office address and format it exactly as requested.
It’s an experimental tool and you’ll need an API key from OpenAI to use it, but it has useful features and is certainly a novel approach. There’s a tutorial and even a command-line interface, so check it out.
Italy’s privacy watchdog said Friday it had blocked the controversial robot ChatGPT, saying the artificial intelligence app did not respect user data and could not verify users’ age.
The decision “with immediate effect” will result in “the temporary limitation of the processing of Italian user data vis-a-vis OpenAI”, the Italian Data Protection Authority said.
The agency has launched an investigation.
[…]
The watchdog said that on March 20, the app experienced a data breach involving user conversations and payment information.
It said there was no legal basis to justify “the mass collection and storage of personal data for the purpose of ‘training’ the algorithms underlying the operation of the platform”.
It also said that since there was no way to verify the age of users, the app “exposes minors to absolutely unsuitable answers compared to their degree of development and awareness.”
It said the company had 20 days to respond how it would address the watchdog’s concerns, under penalty of a 20-million-euro ($21.7-million) fine, or up to 4 percent of annual revenues.
I am pretty sure none of the search engines verify age and store user data (ok duckduckgo is an exception) or give answers that may “expose” the little snowflake “minors to absolutely unsuitable answers compared to their degree of development and awareness.”
There is a race on to catch up to OpenAI and people are obviously losing, so crushing OpenAI is the way to go.
