Enhancing model interpretability can address spurious correlations by revealing how models draw their predictions. Concept Bottleneck Models (CBMs) can provide a principled way of disclosing and guiding model behaviors through human-understandable concepts, albeit at a high cost of human efforts in data annotation. In this paper, we leverage a synergy of multiple foundation models to construct CBMs with nearly no human effort. We discover undesirable biases in CBMs built on pre-trained models and propose a novel framework designed to exploit pre-trained models while being immune to these biases, thereby reducing vulnerability to spurious correlations. Specifically, our method offers a seamless pipeline that adopts foundation models for assessing potential spurious correlations in datasets, annotating concepts for images, and refining the annotations for improved robustness. We evaluate the proposed method on multiple datasets, and the results demonstrate its effectiveness in reducing model reliance on spurious correlations while preserving its interpretability.

Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this technique, we introduce MemGPT (Memory-GPT), a system that intelligently manages different memory tiers in order to effectively provide extended context within the LLM's limited context window, and utilizes interrupts to manage control flow between itself and the user. We evaluate our OS-inspired design in two domains where the limited context windows of modern LLMs severely handicaps their performance: document analysis, where MemGPT is able to analyze large documents that far exceed the underlying LLM's context window, and multi-session chat, where MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users. We release MemGPT code and data for our experiments at https://memgpt.ai.

From mobile work to security and maintenance, perhaps no company has done more for the advancement of technology in today's society than SkyNet, a promising start up out of Austin, Texas that has made great strides during the COVID-19 pandemic. Last year, their T-400 model of home assistant swept the country, combining at home personal assistants with a walking talking android that actually helped with chores and tasks around the house. We had the opportunity to sit down with Barry Snow, the CEO of the skyrocketing company, about SkyNet's future and some of the backlash to what some have called "unnecessarily violent home assistants." Can I have one of those waters? So, your company was already gaining steam a few years ago, but it really seems that during the pandemic you pulled ahead of a lot of your peers with your home androids.

This article is from Full Stack Economics, a newsletter about the economy, technology, and public policy. My wife wants a can opener for Christmas, so I went on Amazon to find one. I typed "can opener" in the search box and sorted the list by the average customer review. My plan was to get the old-fashioned hand-crank kind. But the first page of results had a bunch of electric can openers with excellent reviews and reasonable prices.

The MyQ Smart Garage Hub is a simple, standalone WiFi hub that sits in your garage and communicates with your existing garage door opener (and an open/close sensor you stick on the door itself). If you have a wireless garage door opener in your car, the MyQ Hub works the same way--you use the Learn button on the main unit to "teach" the MyQ Hub how to communicate with the opener, allowing you to open the door without a hardwired connection. Only instead of a little button in your car, the MyQ hub gets its signal from your phone, using your home's WiFi connection. It supports Amazon Key for in-garage deliveries, as well as Google Assistant, IFTTT, and a few other smart home platforms. Here are the MyQ Smart Garage Hub's specs: Best of all, Amazon currently offers a $30 credit after your first in-garage delivery with Amazon Key--which means the Smart Garage Hub pays for itself after one in-garage delivery, provided you live in a supported area.

So let's create our own virtual assistant. This is the latest virtual assistant module, created by me. It provides the basic functionality of any virtual assistant. The prerequisite is only Python ( 3.6). The functionality is cleared by the methods name.

Here's everything our readers were most obsessed with in 2019. Purchases you make through our links may earn us a commission. As we head into the new year, we think it's fun to look at all the wonderful products that we bought in 2019. This year brought some incredible releases like Disney, Apple AirPods Pro, and the all-new Kindle--and honestly, some of these things were apart of what really make the year great. So we decided to roundup 100 of the most popular products that people bought over and over again. Whether it was a massive sale (looking at you Black Friday) or one of the hottest product people couldn't stop talking about (*cough* weighted blankets *cough*), our readers found something that caught their eyes. From robot vacuums to wireless headphones to streaming services, these are the most popular products that people couldn't stop buying in 2019. Everyone become obsessed with Disney in 2019. Although it was just released in November, the new streaming service Disney became the most popular product of the year. With it came nostalgia for the Disney classics, new original shows and movies, and plenty of Baby Yoda content. Seriously, if you're a fan of Marvel, Disney Princesses, Star Wars, Pixar, and all things Disney, you might want to consider following suit and getting a subscription for yourself. We still love the tried-and-true Instant Pot Duo. It's no surprise here--our readers were all about that Instant Pot life this year. The Duo 6 Quart, a.k.a. the most popular model out there, was far and away the biggest seller this year, and in no small part because of the deals that ran on it during Prime Day and Black Friday, respectively. If you were one of the lucky ducks who nabbed it when it was just $50, good on you. But you can still get it for a pretty good price right now, too. Nobody really wants to vacuum, but they also don't want to spend a fortune on a robot vacuum to do their dirty work. That's why the Eufy 11S was so popular this year. It's the best affordable robot vacuum we've ever tested because it balances great cleaning powering and a reasonable price. Our readers loved scooping it up--especially when it was on sale as it is right now.

Get great prices on popular cooking gadgets and more with these deals. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. There is no better way to start the week than with a good deal. There's just something about getting something you actually want at a great price that makes my heart soar. I mean, if you were going to get it anyway, you might as well save.

Scarcely a day goes by without another headline about neural networks: some new task that deep learning algorithms can excel at, approaching or even surpassing human competence. As the application of this approach to computer vision has continued to improve, with algorithms capable of specialized recognition tasks like those found in medicine, the software is getting closer to widespread commercial use--for example, in self-driving cars. Our ability to recognize patterns is a huge part of human intelligence: if this can be done faster by machines, the consequences will be profound. Yet, as ever with algorithms, there are deep concerns about their reliability, especially when we don't know precisely how they work. State-of-the-art neural networks will confidently--and incorrectly--classify images that look like television static or abstract art as real-world objects like school-buses or armadillos. Specific algorithms could be targeted by "adversarial examples," where adding an imperceptible amount of noise to an image can cause an algorithm to completely mistake one object for another.

How smart is the form of artificial intelligence known as deep learning computer networks, and how closely do these machines mimic the human brain? They have improved greatly in recent years, but still have a long way to go, a team of UCLA cognitive psychologists reports in the journal PLOS Computational Biology. Supporters have expressed enthusiasm for the use of these networks to do many individual tasks, and even jobs, traditionally performed by people. However, results of the five experiments in this study showed that it's easy to fool the networks, and the networks' method of identifying objects using computer vision differs substantially from human vision. "The machines have severe limitations that we need to understand," said Philip Kellman, a UCLA distinguished professor of psychology and a senior author of the study.