This essay was originally written in December 2022 as I pondered the future of my job. I sat on it because I wasn’t sure of the optics of posting such an essay while employed by Google Brain. But then Google made my decision easier by laying me off in January. My severance check cleared, and last week, Brain and DeepMind merged into one new unit, killing the Brain brand in favor of “Google DeepMind”. As somebody with a unique perspective and the unique freedom to share it, I hope I can shed some light on the question of Brain’s existence. I’ll lay out the many reasons for Brain’s existence and assess their continued validity in today’s economic conditions. Read More
#big7Monthly Archives: April 2023
Why
Navigating the High Cost of AI Compute
The generative AI boom is compute-bound. It has the unique property that adding more compute directly results in a better product. Usually, R&D investment is more directly tied to how valuable a product was, and that relationship is markedly sublinear. But this is not currently so with artificial intelligence and, as a result, a predominant factor driving the industry today is simply the cost of training and inference.
While we don’t know the true numbers, we’ve heard from reputable sources that the supply of compute is so constrained, demand outstrips it by a factor of 10(!) So we think it’s fair to say that, right now, access to compute resources — at the lowest total cost — has become a determining factor for the success of AI companies.
In fact, we’ve seen many companies spend more than 80% of their total capital raised on compute resources!
In this post, we try to break down the cost factors for an AI company. The absolute numbers will of course change over time, but we don’t see immediate relief from AI companies being bound by their access to compute resources. So, hopefully, this is a helpful framework for thinking through the landscape. Read More
ChatGPT Answers Beat Physicians’ on Info, Patient Empathy, Study Finds
— Evaluators gave chatbot the better rating for responses to patient queries by a nearly 4:1 ratio
The artificial intelligence (AI) chatbot ChatGPT outperformed physicians when answering patient questions, based on quality of response and empathy, according to a cross-sectional study.
Of 195 exchanges, evaluators preferred ChatGPT responses to physician responses in 78.6% (95% CI 75.0-81.8) of the 585 evaluations, reported John Ayers, PhD, MA, of the Qualcomm Institute at the University of California San Diego in La Jolla, and co-authors.
The AI chatbot responses were given a significantly higher quality rating than physician responses (t=13.3, P<0.001), with the proportion of responses rated as good or very good quality (≥4) higher for ChatGPT (78.5%) than physicians (22.1%), amounting to a 3.6 times higher prevalence of good or very good quality responses for the chatbot, they noted in JAMA Internal Medicine. — Read More
#chatbots
Architecting the Edge for AI and ML
Believe it or not, the Raspberry Pi came out 11 years ago. In that time, single board computers (SBCs) have gotten unbelievably powerful. During this same decade every major telecom provider started rolling out 5G services. Oh, and by the way, AlexNet, the neural network that completely changed they way we process imagery, landed on the computer vision scene in 2012.
This convolution (ha) of small, powerful computers, fast network access, and practical neural networks created the perfect conditions for edge computing to blossom. We live in the golden age of small, cheap computers capable of running software that didn’t and couldn’t have existed 10 years ago. It’s a great time to be alive! Read More
Addressing the Security Risks of AI
In recent weeks, there have been urgent warnings about the risks of rapid developments in artificial intelligence (AI). The current obsession is with large language models (LLMs) such as GPT-4, the generative AI system that Microsoft has incorporated into its Bing search engine. However, despite all the concerns about LLMs hallucinating and trying to break up marriages (the former quite real, the latter more on the amusing side), little has been written lately about the vulnerability of many AI-based systems to adversarial attack. A new Stanford and Georgetown report offers stark reminders that the security risks for AI-based systems are real. Moreover, the report—which I signed, along with 16 others from policy research, law, industry, and government—recommends immediately achievable actions that developers and policymakers can take to address the issue. Read More
Artificial Intelligence Radio
How to Make ChatGPT Copy Your Writing Style
Key Takeaway: Published writers can ask ChatGPT to emulate their style by referencing existing work; anyone can submit samples of their own writing for emulation, or you can simply describe a style using plain language.
ChatGPT can generate excellent text on virtually any subject, but by default, it has a very bland (and obvious) tone. Instead of editing that text into your own style to use it, you can simply teach ChatGPT your style instead. Read More
What is Visual Prompting?
Landing AI’s Visual Prompting capability is an innovative approach that takes text prompting, used in applications such as ChatGPT, to computer vision. The impressive part? With only a few clicks, you can transform an unlabeled dataset into a deployed model in mere minutes. This results in a significantly simplified, faster, and more user-friendly workflow for applying computer vision.
Traditionally, building a natural language processing (NLP) model was a time-consuming process that required a great deal of data labeling and training before any predictions could be made. However, things have changed radically. Thanks to large pre-trained transformer models like GPT-4, a single API call is all you need to begin using a model. This low-effort setup has removed all the hassle and allowed users to prompt an AI and start getting results in seconds.
Similarly to what has happened in NLP, large pre-trained vision transformers have made it possible for us to implement Visual Prompting. This approach accelerates the building process, as only a few simple visual prompts are required. You can have a working computer vision system deployed and make inferences in seconds or minutes; this will benefit both individual projects and enterprise solutions. Read More
Visual Prompting Livestream With Andrew Ng
Beyond Avatars: How AI is Reshaping Online Identity
In a recent episode, we delved into the first wave of technology that was used to create digital influencers like ‘Lil Miquela back in 2016.
Fast forward to today, we’re presented with a whole new set of tools that enable almost anyone to build their own digital influencer.
In this episode, Sinead Bovell and Danny Postma discuss the transformative impact of artificial intelligence on the world of modeling, online creation, and self-representation.
From the rise of AI-generated photos to the democratization of creativity, they discuss the potential of AI in shaping the future of digital expression. Read More
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