Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance. While supervised methods are effective, they require extensive labeled data for training. Recently, self-supervised foundation models have been introduced to these tasks showing comparable results to supervised methods. However, a significant flaw in these models is evident: they struggle to locate a desired instance when other instances within the same class are presented. In this paper, we explore text-to-image diffusion models for these tasks. Specifically, we propose a novel approach called PDM for Personalized Diffusion Features Matching, that leverages intermediate features of pre-trained text-to-image models for personalization tasks without any additional training.

Over the past decade, calls for better measures to protect sensitive, personally identifiable information have blossomed into what politicians like to call a "hot-button issue." Certainly, privacy violations have become rampant and people have grown keenly aware of just how vulnerable they are. When it comes to potential remedies, however, proposals have varied widely, leading to bitter, politically charged arguments. To date, what has chiefly come of that have been bureaucratic policies that satisfy almost no one--and infuriate many. Now, into this muddled picture comes differential privacy. First formalized in 2006, it's an approach based on a mathematically rigorous definition of privacy that allows formalization and proof of the guarantees against re-identification offered by a system. While differential privacy has been accepted by theorists for some time, its implementation has turned out to be subtle and tricky, with practical applications only now starting to become available. To date, differential privacy has been adopted by the U.S. Census Bureau, along with a number of technology companies, but what this means and how these organizations have implemented their systems remains a mystery to many. It's also unlikely that the emergence of differential privacy signals an end to all the difficult decisions and trade-offs, but it does signify that there now are measures of privacy that can be quantified and reasoned about--and then used to apply suitable privacy protections. A milestone in the effort to make this capability generally available came in September 2019 when Google released an open source version of the differential privacy library that the company has used with many of its core products. In the exchange that follows, two of the people at Google who were central to the effort to release the library as open source--Damien Desfontaines, privacy software engineer; and Miguel Guevara, who leads Google's differential privacy product development effort--reflect on the engineering challenges that lie ahead, as well as what remains to be done to achieve their ultimate goal of providing privacy protection by default.

This multi-part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published) neural networks, computer vision, natural language processing, algorithms, artificial general intelligence, and the difference between video game AI and real AI. As legend has it, a reporter once asked Mahatma Ghandi what he thought of Western Civilization. His response was "I think it would be a good idea." The same sentiment could be applied to artificial intelligence if you compare it directly to human intelligence.

To answer the question, "Where's Waldo?" readers need to look for a number of distinguishing features. Several characters may be spotted with a striped scarf, striped hat, round-rimmed glasses, or a cane, but only Waldo will have all of these features. As described July 22, 2019, in Nature Microbiology, a team led by scientists at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI), a DOE Office of Science User Facility, developed an algorithm that a computer could use to conduct a similar type of search in microbial and metagenomic databases. In this case, the machine "learned" to identify a certain type of bacterial viruses or phages called inoviruses, which are filamentous viruses with small, single-stranded DNA genomes and a unique chronic infection cycle. "We're not sure why we systematically manage to miss them; maybe it's due to the way we currently isolate and extract viruses," said the study's lead author Simon Roux, a JGI research scientist in the Environmental Genomics group.

This breakthrough work has been recognized as a finalist for a 2018 R&D 100 award. R&D 100 awards, called the "Oscars of Innovation," are given out by R&D Magazine to the most significant innovations developed in a given year. Neural networks -- so named because they operate in an interconnected fashion similar to our brains -- offer chemists a major opportunity for faster and more rigorous science because they provide one way in which machines are able to learn and even make determinations about data. To be effective, though, they have to be carefully taught. That is why this area of research is called machine learning.

If Waldo is identified the arm will move to that position and point the little creepy hand at Waldo on the page,

Many people remember spending hours poring over a Where's Wally? However, a robot is now capable of ruining this much-loved childhood classic. Developers have created an AI that uses facial recognition technology to locate the elusive character Wally, known as Waldo in America, in less than four seconds. The AI robot was designed by Matt Reed, 41, from the Nashville-based creative agency Redpepper. It has a camera which takes a photo of the page.

Creative agency Redpepper made a AI-powered robot that can pinpoint Waldo in 4.45 seconds ("better than most five year olds," according to Redpepper). The robot is complete with a rubber hand that points to Waldo on the page. The agency used Google's AutoML Vision service to train AI on photos of Waldo. The drag-and-drop tool allows users to train AI tools without previous coding knowledge, and has been used to categorize anything from ramen based on the shops they came from to the types of attire carried in an online retailer. Matt Reed, a Creative Technologist at Redpepper who led the project, got 62 Waldo heads and 45 full-body Waldos from Google image search, then fed the data Google's AutoML Vision.

Roundup Hello, here are a few bits of AI news for the weekend. You don't always need a ton of cash to buy a wad of GPUs to train your models super quickly. You can do it pretty cheaply on cloud platforms. There's also a robot that can play Where's Waldo (Wally in the UK), and Microsoft's computer that is trying to tell if you've found a joke funny. Public code for training ImageNet super quickly: A group of engineers have managed to train ImageNet to 93 per cent accuracy using hardware rented on public cloud platforms for just $40.

Let me start by stating that here in the UK, we call Waldo Wally. And as I'm writing this post at my desk at Pi Towers, Cambridge, I have taken the decision to refer to the red and white-clad fellow as Wally moving forward. There's Waldo is a robot built to find Waldo and point at him. The robot arm is controlled by a Raspberry Pi using the PYARM Python library for the UARM Metal. Once initialized the arm is instructed to extend and take a photo of the canvas below.