Excellent progress has been made recently in solving ARC Challenge problems. However, it seems that new techniques may be required to push beyond 60% accuracy. Even commercial Large Language Models (LLMs) struggle to 'understand' many of the problems (when given the input and output grids), which makes discovering solutions by LLM-lead program search somewhat futile. In this work, LLM 'understanding' is attempted from a stronger starting position : An LLM is given complete solutions to tasks in code, and then asked to explain how the task is being solved at various levels of abstraction. Specifically, the LLM was given code solutions implemented in arc-dsl-llm (an LLM-legible version of Hodel's arc-dsl to obtain: (a) commented code; (b) code refactored into reusable functional chunks; (c) problem solution steps; and (d) high-level problem-solving tactics. We demonstrate that 'Sparks of Abstraction' can be extracted from the LLM output - in a form that could be used in downstream tasks with Local LLMs eligible to enter the ARC Prize. Both the arc-dsl-llm DSL framework (with the re-engineered solutions) and the Gemini LLM-generated data (along with the generation code) are made Open Source.

The last major Windows 11 23H2 feature update was released in Fall 2023. Contrary to previous fears, Windows 10 users are not completely excluded from further development. Microsoft is also planning new functions for the older operating system. Given the popularity of Windows 10, there is even speculation as to whether Microsoft could extend support beyond the planned end in October 2025. There's also speculation about Windows 11 24H2, which could be called Windows 12 . There have been no official announcements to date. The only thing that seems certain is that the high hardware requirements will remain.

Microsoft is significantly expanding the functions of Paint in Windows 11. The app is also getting a new version. The outdated program is to become a modern image editor that also contains AI functions. In the future, you will be able to use the OpenAI-LLM Dall-E directly in Windows 11 and in Paint. The new functions are also available after installing the Microsoft Paint app from the App Store.

We establish results on the number and nature of the fixed points of these functions, including a generalisation of a classic result of Singer. Lead paragraph The set of functions with negative Schwarzian derivative is closed under composition. As a result, it is a popular tool for studying the dynamics of functions in dimension one. A comprehensive treatment involving one-dimensional dynamics, with many results involving negative Schwarzian derivatives, is given in the book of de Melo and van Strien [1993].

Writing working code can be a challenge. Even relatively easy languages like HTML require the coder to understand the specific syntax and available tools. Writing code to control robots is even more involved and often has multiple steps: There's code to detect objects, code to trigger the actuators that move the robot's limbs, code to specify when the task is complete, and so on. Something as simple as programming a robot to pick up a yellow block instead of a red one is impossible if you don't know the coding language the robot runs on. But Google's robotics researchers are exploring a way to fix that.

A common approach used to control robots is to program them with code to detect objects, sequencing commands to move actuators, and feedback loops to specify how the robot should perform a task. While these programs can be expressive, re-programming policies for each new task can be time consuming, and requires domain expertise. What if when given instructions from people, robots could autonomously write their own code to interact with the world? It turns out that the latest generation of language models, such as PaLM, are capable of complex reasoning and have also been trained on millions of lines of code. Given natural language instructions, current language models are highly proficient at writing not only generic code but, as we've discovered, code that can control robot actions as well.

The story of a Google engineer (and Christian mystic) who saw signs of personhood in Google's latest artificially intelligent chatbot software and was later fired has reignited public debate over whether any of today's AI systems are sentient. The consensus among experts is that no, they are not: see this, this, this, and this, for example. We reached the same conclusion via a different path, using a little mathematical formalism to burn off the fog of confusion. A chatbot is a function. But functions can be powerful.

Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don't have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device's internal chip -- like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could keep devices up to date while reducing our electronic waste. Now MIT engineers have taken a step toward that modular vision with a LEGO-like design for a stackable, reconfigurable artificial intelligence chip. The design comprises alternating layers of sensing and processing elements, along with light-emitting diodes (LED) that allow for the chip's layers to communicate optically.

With the applications of artificial intelligence and deep learning (DL) on the rise, organisations seek easy and faster solutions to the problems presented by AI and deep learning. The challenge has always been about how to imitate the human brain and be able to deploy its logic artificially. Result: Neural Networks that are essentially designed on the human brain wiring. Neural Networks can be described as a set of algorithms that are loosely modelled on human brain. They are designed to recognise patterns.

Linear mixed-effects models (LMM) offer a consistent way of performing regression and analysis of variance tests which allows accounting for non-independence in the data. Over the past decades, LMMs have subsumed most of the General Linear Model, with a stead increase in popularity (Meteyard & Davies, 2020). Since their conception, LMMs have presented the challenge of model convergence. In essence, the issue of convergence boils down to the widespread tension between parsimony and completeness in data analysis. That is, on the one hand, a good model must allow the accurate, parsimonious analysis of each predictor.