Artificial intelligence is no longer a futuristic idea. It’s already here, and it has turned out to be a powerful, disruptive force in healthcare fueling some of the most innovative diagnostic tools of today.
Let’s take a look at 7 examples where AI has started to transform healthcare!
Its Up!
We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.
Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT-3.5 as a first “test run” of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT-4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety.
We are releasing GPT-4’s text input capability via ChatGPT and the API (with a waitlist). To prepare the image input capability for wider availability, we’re collaborating closely with a single partner to start. We’re also open-sourcing OpenAI Evals, our framework for automated evaluation of AI model performance, to allow anyone to report shortcomings in our models to help guide further improvements.
Google will soon offer ways to generate text and images using machine learning in its Workspace products as part of a scramble to catch up with rivals in the new AI race.
Google has announced a suite of upcoming generative AI features for its various Workspace apps, including Google Docs, Gmail, Sheets, and Slides.
Google is pumping its productivity apps full of AI.
OpenAI has released a powerful new image-and text-understanding AI model, GPT-4, that the company calls “the latest milestone in its effort in scaling up deep learning.”
GPT-4 is available today to OpenAI’s paying users via ChatGPT Plus (with a usage cap), and developers can sign up on a waitlist to access the API.
Pricing is $0.03 per 1,000 “prompt” tokens (about 750 words) and $0.06 per 1,000 “completion” tokens (again, about 750 words). Tokens represent raw text; for example, the word “fantastic” would be split into the tokens “fan,” “tas” and “tic.” Prompt tokens are the parts of words fed into GPT-4 while completion tokens are the content generated by GPT-4.
Microsoft laid off an entire team dedicated to guiding AI innovation that leads to ethical, responsible and sustainable outcomes. The cutting of the ethics and society team, as reported by Platformer, is part of a recent spate of layoffs that affected 10,000 employees across the company.
The elimination of the team comes as Microsoft invests billions more dollars into its partnership with OpenAI, the startup behind art-and text-generating AI systems like ChatGPT and DALL-E 2, and revamps its Bing search engine and Edge web browser to be powered by a new, next-generation large language model that is “more powerful than ChatGPT and customized specifically for search.”
The move calls into question Microsoft’s commitment to ensuring its product design and AI principles are closely intertwined at a time when the company is making its controversial AI tools available to the mainstream.
Anthropic, a startup co-founded by ex-OpenAI employees, today launched something of a rival to the viral sensation ChatGPT.
Called Claude, Anthropic’s AI — a chatbot — can be instructed to perform a range of tasks, including searching across documents, summarizing, writing and coding, and answering questions about particular topics. In these ways, it’s similar to OpenAI’s ChatGPT. But Anthropic makes the case that Claude is “much less likely to produce harmful outputs,” “easier to converse with” and “more steerable.”
Organizations can request access. Pricing has yet to be detailed.
AI or bust. Right now, AI is what everyone is talking about, and for good reason. After years of seeing AI doled out to help automate the processes that make businesses run smarter, we’re finally seeing AI that can help the average business employee working in the real world. Generative AI, or the process of using algorithms to produce data often in the form of images or text, has exploded in the last few months. What started with OpenAI’s ChatGPT has bloomed into a rapidly evolving subcategory of technology. And companies from Microsoft to Google to Salesforce and Adobe are hopping on board.
What started with ChatGPT has bloomed into an entire subcategory of technology with Meta, AWS, Salesforce, Google, Microsoft all racing to out innovate and deliver exciting generative AI capabilities to consumers, enterprise, developers, and more. Exploring the rapid progress in the AI space.
Now its building one that even bigger and even more sophisticated.
Nearly five years ago, a little-known company approached Microsoft with a special request to put together computing horsepower to the scale it had never done before. Microsoft then spent millions of dollars in putting together tens of thousands of powerful chips to build a supercomputer. OpenAI used this to train its large language model, GPT, and the rest, as they say, is history.
Microsoft is no stranger to building artificial intelligence (AI) models that help users work more efficiently. The automatic spell checker that has helped millions of users is an example of an AI model trained on language.
Microsoft.
How Microsoft put together a supercomputer for OpenAI.