With LLMs taking the world, the prediction “What comes after serverless? Conversational Programming!” becomes a reality. It is interesting that even in 1967 Marvin Minsky understood the possibility of a 2-way conversation between programmer and computer, where the program is written in collaboration:
Today Yann LeCun wrote: “Auto-regressive LLM have very limited reasoning and planning abilities. I do not believe we can get anywhere close to human-level AI (even cat-level AI) without (1) learning world models from sensory inputs like video, (2) an architecture that can reason and plan (not just auto-regress). Now, if we have architectures that can plan, they will be objective driven: their planning will work by optimizing a set of objectives at inference time (not just training time). These objectives can include guardrails that will make those system safe and subservient even if they end up having much better world models that humans. Then, the problem becomes to design (or train) good objectives functions that will guarantee safety and efficiency. It’s a hard engineering problem, but not as hard as some have made it to be.” Link
A project lead of Google Bard just announced that “Bard is improving at mathematical tasks, coding questions and string manipulation through a new technique called implicit code execution. Plus, it has a new export action to Google Sheets… With this latest update, we’ve combined the capabilities of both LLMs (System 1) and traditional code (System 2) to help improve accuracy in Bard’s responses.” Link
Continue readingMarc Andreessen wrote: “What AI is: The application of mathematics and software code to teach computers how to understand, synthesize, and generate knowledge in ways similar to how people do it. AI is a computer program like any other – it runs, takes input, processes, and generates output. AI’s output is useful across a wide range of fields, ranging from coding to medicine to law to the creative arts. It is owned by people and controlled by people, like any other technology.
What AI isn’t: Killer software and robots that will spring to life and decide to murder the human race or otherwise ruin everything, like you see in the movies.
What AI could be: A way to make everything we care about better.” Link
This problem used to be one of the popular benchmarks for rule engines 20 years ago. It is interesting to see how modern decision engines can represent and solve this problem today. So, Miss Manners is throwing a party, and being a good host, she wants to arrange good seating. She wants to seat the guests in a boy-girl-boy-girl arrangement so that each guest will have someone on the left or right that has a common hobby. Help Miss Manners to do it for parties with 16, 32, 64, and 128 guests described in the Excel file that you may download from here. Please submit your solutions using your favorite decision modeling tool. Link
Yann LeCun tweeted:
“Imagine a future in which your daily interaction with the world of information is mediated by an AI assistant.
This AI assistant would be like an active repository of all human knowledge.
It will become your best rampart *against* misinformation.” Link
Geoffrey De Smet: OptaPlanner, an Open Source project for planning optimization, has entered a new chapter. “The project I worked on for seventeen years, has matured under Red Hat’s wings for the past ten years. Last year, when Red Hat’s strategy changed, it became apparent that the project needed a new, sustainable future. Therefore, we founded a company around it. A company that lives and breathes planning optimization, to further nurture the Open Source project to its fullest potential, under a new name. Timefold is the continuation of OptaPlanner.” Link
Frequently Asked Questions about IBM Process Automation Manager Open Edition and IBM Decision Manager Open Edition, formerly known as Red Hat Process Automation Manager and Red Hat Decision Manager – latest update Link
Do Language Models Know When They’re Hallucinating References? Current state-of-the-art language models (LMs) are notorious for generating text with “hallucinations,” a primary example being book and paper references that lack any solid basis in their training data. However, we find that many of these fabrications can be identified using the same LM, using only black-box queries without consulting any external resources. Consistency checks done with direct queries about whether the generated reference title is real are compared to consistency checks with indirect queries which ask for ancillary details such as the authors of the work. This suggests that the hallucination may be more a result of generation techniques than the underlying representation. Link
Kurt Cagle’s “When the Jobs Don’t Come Back“: “The hopes that AI will result in significant staff reductions may be premature at best. What it will do is make it easier for smaller companies to become capable of producing products and services that the larger companies currently compete in. Put it another way, because investors reacted so quickly to reduce headcount at a time when human specialized skills are needed more than ever, they are putting themselves at a significant business disadvantage at a critical juncture in time.” Link
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