63% of consumers now expect personalization as standard, and businesses need to reflect that in their marketing efforts. But delivering a truly personalized experience means collecting and analyzing a ton of data in order to understand your customers’ needs and find the most effective ways to engage them.
That’s where AI comes in.
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Want to identify more of your ideal customers automatically?
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Our approach to aligning AGI is empirical and iterative. We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.
Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent. We take an iterative, empirical approach: by attempting to align highly capable AI systems, we can learn what works and what doesn’t, thus refining our ability to make AI systems safer and more aligned. Using scientific experiments, we study how alignment techniques scale and where they will break.
We tackle alignment problems both in our most capable AI systems as well as alignment problems that we expect to encounter on our path to AGI. Our main goal is to push current alignment ideas as far as possible, and to understand and document precisely how they can succeed or why they will fail. We believe that even without fundamentally new alignment ideas, we can likely build sufficiently aligned AI systems to substantially advance alignment research itself.
Unaligned AGI could pose substantial risks to humanity and solving the AGI alignment problem could be so difficult that it will require all of humanity to work together. Therefore we are committed to openly sharing our alignment research when it’s safe to do so: We want to be transparent about how well our alignment techniques actually work in practice and we want every AGI developer to use the world’s best alignment techniques.
At a high-level, our approach to alignment research focuses on engineering a scalable training signal for very smart AI systems that is aligned with human intent. It has three main pillars:
Aligning AI systems with human values also poses a range of other significant sociotechnical challenges, such as deciding to whom these systems should be aligned. Solving these problems is important to achieving our mission, but we do not discuss them in this post.
RL from human feedback is our main technique for aligning our deployed language models today. We train a class of models called InstructGPT derived from pretrained language models such as GPT-3. These models are trained to follow human intent: both explicit intent given by an instruction as well as implicit intent such as truthfulness, fairness, and safety.
Our results show that there is a lot of low-hanging fruit on alignment-focused fine-tuning right now: InstructGPT is preferred by humans over a 100x larger pretrained model, while its fine-tuning costs <2% of GPT-3’s pretraining compute and about 20,000 hours of human feedback. We hope that our work inspires others in the industry to increase their investment in alignment of large language models and that it raises the bar on users’ expectations about the safety of deployed models.
Our natural language API is a very useful environment for our alignment research: It provides us with a rich feedback loop about how well our alignment techniques actually work in the real world, grounded in a very diverse set of tasks that our customers are willing to pay money for. On average, our customers already prefer to use InstructGPT over our pretrained models.
Yet today’s versions of InstructGPT are quite far from fully aligned: they sometimes fail to follow simple instructions, aren’t always truthful, don’t reliably refuse harmful tasks, and sometimes give biased or toxic responses. Some customers find InstructGPT’s responses significantly less creative than the pretrained models’, something we hadn’t realized from running InstructGPT on publicly available benchmarks. We are also working on developing a more detailed scientific understanding of RL from human feedback and how to improve the quality of human feedback.
Aligning our API is much easier than aligning AGI since most tasks on our API aren’t very hard for humans to supervise and our deployed language models aren’t smarter than humans. We don’t expect RL from human feedback to be sufficient to align AGI, but it is a core building block for the scalable alignment proposals that we’re most excited about, and so it’s valuable to perfect this methodology.
RL from human feedback has a fundamental limitation: it assumes that humans can accurately evaluate the tasks our AI systems are doing. Today humans are pretty good at this, but as models become more capable, they will be able to do tasks that are much harder for humans to evaluate (e.g. finding all the flaws in a large codebase or a scientific paper). Our models might learn to tell our human evaluators what they want to hear instead of telling them the truth. In order to scale alignment, we want to use techniques like recursive reward modeling (RRM), debate, and iterated amplification.
Currently our main direction is based on RRM: we train models that can assist humans at evaluating our models on tasks that are too difficult for humans to evaluate directly. For example:
Our alignment techniques need to work even if our AI systems are proposing very creative solutions (like AlphaGo’s move 37), thus we are especially interested in training models to assist humans to distinguish correct from misleading or deceptive solutions. We believe the best way to learn as much as possible about how to make AI-assisted evaluation work in practice is to build AI assistants.
There is currently no known indefinitely scalable solution to the alignment problem. As AI progress continues, we expect to encounter a number of new alignment problems that we don’t observe yet in current systems. Some of these problems we anticipate now and some of them will be entirely new.
We believe that finding an indefinitely scalable solution is likely very difficult. Instead, we aim for a more pragmatic approach: building and aligning a system that can make faster and better alignment research progress than humans can.
As we make progress on this, our AI systems can take over more and more of our alignment work and ultimately conceive, implement, study, and develop better alignment techniques than we have now. They will work together with humans to ensure that their own successors are more aligned with humans.
We believe that evaluating alignment research is substantially easier than producing it, especially when provided with evaluation assistance. Therefore human researchers will focus more and more of their effort on reviewing alignment research done by AI systems instead of generating this research by themselves. Our goal is to train models to be so aligned that we can off-load almost all of the cognitive labor required for alignment research.
Importantly, we only need “narrower” AI systems that have human-level capabilities in the relevant domains to do as well as humans on alignment research. We expect these AI systems are easier to align than general-purpose systems or systems much smarter than humans.
Language models are particularly well-suited for automating alignment research because they come “preloaded” with a lot of knowledge and information about human values from reading the internet. Out of the box, they aren’t independent agents and thus don’t pursue their own goals in the world. To do alignment research they don’t need unrestricted access to the internet. Yet a lot of alignment research tasks can be phrased as natural language or coding tasks.
Future versions of WebGPT, InstructGPT, and Codex can provide a foundation as alignment research assistants, but they aren’t sufficiently capable yet. While we don’t know when our models will be capable enough to meaningfully contribute to alignment research, we think it’s important to get started ahead of time. Once we train a model that could be useful, we plan to make it accessible to the external alignment research community.
We’re very excited about this approach towards aligning AGI, but we expect that it needs to be adapted and improved as we learn more about how AI technology develops. Our approach also has a number of important limitations:
We’re looking to hire more talented people for this line of research! If this interests you, we’re hiring Research Engineers and Research Scientists!
source https://openai.com/blog/our-approach-to-alignment-research/
When measuring customer engagement, you often look at pageviews, time on page, bounce rate, pages per session, and more. It’s expected that a small percentage of those engaged will lead to action and conversion. Why have we settled with that small percentage?
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Just as advertising channels have become more fragmented over the past few decades, so have the ways in which advertisers run campaigns. Do you need help with optimization? There’s a tool for that. What about analytics? Technology can do that. But all those tools create multiple data sources and, in some instances, do not integrate with each other.
Essentially, all this technology at our fingertips can create more manual work.
But what if there was a closed loop system that could do it all in one place–where audience building through reporting could all happen seamlessly?
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In 2014, during an event organized by the University of Reading, a chatbot passed the legendary Turing Test after it convinced 33% of the judges it was actually a 13-year-old boy.
That was nearly a decade ago, and machine learning has only evolved further. In the process, it’s become an integral part of the marketing world.
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Are you writing content for your customers, or are you writing for search engines?
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![Top AI and Machine Learning Conferences [2022]](https://www.marketingaiinstitute.com/hubfs/Imported_Blog_Media/headway-F2KRf_QfCqw-unsplash-1.webp)
There’s no question the event world has changed…
However, plenty of top artificial intelligence and machine learning conferences and events are still putting on world-class multi-day experiences, both virtually and in-person.
Below is our most current list of top AI conferences and machine learning conferences to investigate in 2022.
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We are introducing a new-and-improved content moderation tool: The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.
To help developers protect their applications against possible misuse, we are introducing the faster and more accurate Moderation endpoint. This endpoint provides OpenAI API developers with free access to GPT-based classifiers that detect undesired content — an instance of using AI systems to assist with human supervision of these systems. We have also released both a technical paper describing our methodology and the dataset used for evaluation.
When given a text input, the Moderation endpoint assesses whether the content is sexual, hateful, violent, or promotes self-harm — content prohibited by our content policy. The endpoint has been trained to be quick, accurate, and to perform robustly across a range of applications. Importantly, this reduces the chances of products “saying” the wrong thing, even when deployed to users at-scale. As a consequence, AI can unlock benefits in sensitive settings, like education, where it could not otherwise be used with confidence.
Moderation endpoint
The Moderation endpoint helps developers to benefit from our infrastructure investments. Rather than build and maintain their own classifiers—an extensive process, as we document in our paper—they can instead access accurate classifiers through a single API call.
As part of OpenAI’s commitment to making the AI ecosystem safer, we are providing this endpoint to allow free moderation of all OpenAI API-generated content. For instance, Inworld, an OpenAI API customer, uses the Moderation endpoint to help their AI-based virtual characters “stay on-script”. By leveraging OpenAI’s technology, Inworld can focus on their core product – creating memorable characters.
Additionally, we welcome the use of the endpoint to moderate content not generated with the OpenAI API. In one case, the company NGL – an anonymous messaging platform, with a focus on safety – uses the Moderation endpoint to detect hateful language and bullying in their application. NGL finds that these classifiers are capable of generalizing to the latest slang, allowing them to remain more-confident over time. Use of the Moderation endpoint to monitor non-API traffic is in private beta and will be subject to a fee. If you are interested, please reach out to us at support@openai.com.
Get started with the Moderation endpoint by checking out the documentation. More details of the training process and model performance are available in our paper. We have also released an evaluation dataset, featuring Common Crawl data labeled within these categories, which we hope will spur further research in this area.
source https://openai.com/blog/new-and-improved-content-moderation-tooling/
