Highlights

The core of RAG is a general-purpose fine-tuning approach where both the retriever and the generator are trained jointly and end-to-end on downstream NLP tasks. This means that theparameters of the retriever (specifically the query encoder) and the generator are adjusted based on the task-specific data

“One age cannot be completely understood if all the others are not understood. The song of history can only be sung as a whole”

Professional developers agree the issue is not user error: twice as many professionals cite lack of trust or understanding the codebase as the top challenge of AI tools compared to proper training.

Similar to last year, developers remain split on whether they trust AI output: 43% feel good about AI accuracy and 31% are skeptical. Developers learning to code are trusting AI accuracy more than their professional counterparts (49% vs. 42%).

“Amazon Lex (what’s inside Alexa), Amazon Polly, and Amazon Rekognition remove the heavy lifting from natural language understanding, speech generation, and image analysis. They can be accessed with simple API calls – no machine learning expertise required.”

Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf. No customer ever asked Amazon to create the Prime membership program, but it sure turns out they wanted it, and I could give you many such examples. ~Jeff Bezos' 2016 Letter to Amazon Shareholders

“I believe that if you can focus obsessively enough on customer experience,selection , ease of use , low prices , and more information to make purchase decisions with, you can give customers all that plus great customer service. I think you have a good chance. And that's what we're trying to do."

“It does not matter to me if we are a pure internet play, what matters to me is that we provide the best customer services”

"Rulebased systems can be used successfully, but they can be hard to maintain and can become brittle over time. In many cases, advanced machine learning techniques provide more accurate classification and can self-heal to adapt to changing conditions."

"Some of this work is highly visible: our autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to  eliminate checkout lines ; and Alexa, our cloud-based AI assistant. "

Asystem that tackles AI tasks using multiple interacting components , including multiple calls to models , retrievers , orexternal tools. In contrast, an AI Model is simply a statistical model, e.g., a Transformer that predicts the next token in text.

In intelligence and artificial intelligence, an intelligent agent (IA) is an agent acting in anintelligent manner. It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state), or a biome.[[1]](https://en.wikipedia.org/wiki/Intelligentagent#citenote-FOOTNOTERussellNorvig2003chpt.2-1)

While compound AI systems can offer clear benefits, the art of designing, optimizing, and operating them is still emerging. On the surface, an AI system is a combination of traditional software and AI models, but there are many interesting design questions. For example, should the overall “control logic” be written in traditional code (e.g., Python code that calls an LLM), or should it be driven by an AI model (e.g. LLM agents that call external tools)? Likewise, in a compound system, where should a developer invest resources—for example, in a RAG pipeline, is it better to spend more FLOPS on the retriever or the LLM, or even to call an LLM multiple times?

Note : brand plays a role here as well. Sometimes, nonconsumption can be by design via positioning/targeting a specific slice of consumers. This usually happens via brand design, that said, even then companies with great brands are at risk of disruption. We probably will talk about this in more detail in upcoming posts.

ℹ️ The spike in the graph does not reflect the demand curve but rather content generated, which is a rather poor proxy to adoption, but it’s all that we have in that timeline beyond the narratives described in the previous post.

With the benefit of hindsight, the field of artificial intelligence stems from the research originally done on Hopfield nets, Boltzmann machines, the backprop algorithm, and reinforcement learning. However, the evolution of backprop networks into deep learning networks had to wait for three related developments:1) much faster computers, 2) massively bigger training data sets, and, 3) incremental improvements in learning algorithms ~ from A Very Short History of Artificial Neural Networks | by James V Stone

Control Alt delete: Exploring OpenAI's corporate structure, after Sam Altman’s shock dismissal

“A feedback loop reinforced correct choices by increasing the probability that the computer would make them again—a more complicated version of Shannon’s method, and a level closer to how our minds really work. Eventually, the rat learned the maze.”

“It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture.” “How can a set of (hypothetical) neurons be arranged so as to form concepts? Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained, but the problem needs more theoretical work.”

Note: As I was writing this, I wrote way more sections than I should have, so I decided to break it down into two parts (it will not likely fit an E-mail anyway). This here is part I, in part II, we will compare and contrast history and present, look at AI and innovations in general from the business and economic perspective, and brainstorm on how to build better products catering to a larger group of consumers from the lessons learned in part I.

The Dartmouth Summer Research Project of 1956 is often taken as the event that initiated AI as a research discipline ~Artificial Intelligence (AI) Coined at Dartmouth

“The major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have.”

We evaluated GPT-4 in a public online Turing test. The best-performing GPT-4 prompt passed in 49.7% of games, outperforming ELIZA (22%) and GPT-3.5 (20%), but falling short of the baseline set by human participants (66%) from[[2310.20216] Does GPT-4 pass the Turing test?](https://arxiv.org/abs/2310.20216)

We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire.The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it ~ from A proposal for the Dartmouth Summer Research Project on Artificial Intelligence

A comparison of training throughput (tokens per second) for the 7B model with a context length of 512 on a p4de.24xlarge node. The lower memory footprint of LoRA allows for substantially larger batch sizes, resulting in an approximate 30% boost in throughput. ~Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama 2

Gas is the fee required to successfully conduct a transaction or execute a contract on the Ethereum blockchain platform. ~ How Gas Fees Work on the Ethereum Blockchain.

Pre-training refers to the process of initializing a model with pre-existing knowledge before fine-tuning it on specific tasks or datasets. In the context of AI, pre-training involves leveraging large-scale datasets to train a model on general tasks, enabling it to capture essential features and patterns across various domains. ~Lark

Google+ is a prime example of our complete failure to understand platforms from the very highest levels of executive leadership (hi Larry, Sergey, Eric, Vic, howdy howdy) down to the very lowest leaf workers (hey yo). We all don't get it. The Golden Rule of platforms is that you Eat Your Own Dogfood. The Google+ platform is a pathetic afterthought.We had no API at all at launch, and last I checked, we had one measly API call. One of the team members marched in and told me about it when they launched, and I asked: "So is it the Stalker API?" She got all glum and said "Yeah." I mean, I was joking, but no... the only API call we offer is to get someone's stream. So I guess the joke was on me.

The foundation upon which value is being delivered

To reduce the cognitive load of developers, the Platform teamshould cover the entire tech stack: Infrastructure/DevOps/SRE, but also frontend, backend, and security topics. ~ from Platform Engineering, Part 2: WHAT Are The Goals of a Platform Engineering Team? | by Benoit Hediard | Stories by Agorapulse | Medium

"A web application that uses content from more than one source to create a single new service displayed in a single graphical interface [1]"

The Open Container Initiative (OCI) is a lightweight, open governance structure (project), formed under the auspices of the Linux Foundation, for the express purpose of creating open industry standards around container formats and runtime. The OCI was launched on June 22nd 2015 by Docker, CoreOS and other leaders in the container industry

Cilium is open source software for transparently securing the network connectivity between application services deployed using Linux container management platforms like Docker and Kubernetes.