One key aspect of intelligence is the ability to quickly learn how to perform a new task when given a brief instruction. For instance, a child may recognise real animals at the zoo after seeing a few pictures of the animals in a book, despite differences between the two. But for a typical visual model to learn a new task, it must be trained on tens of thousands of examples specifically labelled for that task. If the goal is to count and identify animals in an image, as in “three zebras”, one would have to collect thousands of images and annotate each image with their quantity and species. This process is inefficient, expensive, and resource-intensive, requiring large amounts of annotated data and the need to train a new model each time it’s confronted with a new task. As part of DeepMind’s mission to solve intelligence, we’ve explored whether an alternative model could make this process easier and more efficient, given only limited task-specific information.
Today, in the preprint of our paper, we introduce Flamingo, a single visual language model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended multimodal tasks. This means Flamingo can tackle a number of difficult problems with just a handful of task-specific examples (in a “few shots”), without any additional training required. Flamingo’s simple interface makes this possible, taking as input a prompt consisting of interleaved images, videos, and text and then output associated language. Read More
Monthly Archives: June 2022
The Best Examples Of Digital Twins Everyone Should Know About
The digital twin is an exciting concept and undoubtedly one of the hottest tech trends right now. It fuses ideas including artificial intelligence (AI), the internet of things (IoT), metaverse, and virtual and augmented reality (VR/AR) to create digital models of real-world objects, systems, or processes. These models can then be used to tweak and adjust variables to study the effect on whatever is being twinned – at a fraction of the cost of carrying out experiments in the real world.
Businesses around the globe are looking to deploy Digital Twins across a broad range of applications, ranging from engineering design of complex equipment and 3D immersive environments to precision medicine and digital agriculture. However, to date, applications have been highly customized and only accessible for high value use-cases, such as the operations of jet engines, industrial facilities and power plants. Now leading technology companies like AWS are working hard to lower the costs and simplify the deployment of this technology, with AWS IoT TwinMaker, making it easier and more accessible for all kinds and sizes of companies to build their own Digital Twins. Read More
FLAWED AI MAKES ROBOTS RACIST, SEXIST
A robot operating with a popular Internet-based artificial intelligence system consistently gravitates to men over women, white people over people of color, and jumps to conclusions about peoples’ jobs after a glance at their face.
The work, led by Johns Hopkins University, Georgia Institute of Technology, and University of Washington researchers, is believed to be the first to show that robots loaded with an accepted and widely-used model operate with significant gender and racial biases. The work is set to be presented and published this week at the 2022 Conference on Fairness, Accountability, and Transparency. Read More
Copilot, GitHub’s AI-powered programming assistant, is now generally available
Last June, Microsoft-owned GitHub and OpenAI launched Copilot, a service that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Available as a downloadable extension, Copilot is powered by an AI model called Codex that’s trained on billions of lines of public code to suggest additional lines of code and functions given the context of existing code. Copilot can also surface an approach or solution in response to a description of what a developer wants to accomplish (e.g., “Say hello world”), drawing on its knowledge base and current context.
Copilot was previously only available in technical preview. But after signaling that the tool would reach generally availability this summer, GitHub today announced that Copilot is now available to all developers. As previously detailed, it’ll be free for students as well as “verified” open source contributors — starting with roughly 60,000 developers selected from the community and students in the GitHub Education program. Read More
Object Detection State of the Art 2022
Object detection has been a hot topic ever since the boom of Deep Learning techniques. This article goes over the most recent state of the art object detectors.
First we will start with an introduction to the topic of object detection itself and it’s key metrics.
The evolution of object detectors began with Viola Jones detector which was used for detection in real-time. Traditionally, object detection algorithms used hand-crafted features to capture relevant information from images and a structured classifier to deal with spatial structures. Read More
Transfer Learning for Time Series Forecasting
In this article, we will see how transfer learning can be applied to time series forecasting, and how forecasting models can be trained once on a diverse time series dataset and used later on to obtain forecasts on different datasets without training. We will use the open-source Darts library to do all this with in a few lines of code. A self-contained notebook containing everything needed to reproduce the results is available here.
Time series forecasting has numerous applications in supply chain, energy, agriculture, control, IT operations, finance and other domains. For a long time, the best-performing approaches were relatively sophisticated statistical methods such as Exponential Smoothing or ARIMA. However, since recently, machine learning and deep learning have started to outperform these classical approaches on a number of forecasting tasks and competitions.
One of the distinctive features of machine learning models is that their parameters can be estimated on a potentially large number of series; unlike classical methods, which are usually estimated on a single series at a time. Although machine learning shows great potential, its utilisation still poses a few practical challenges. Read More
Sponge Examples: Energy-Latency Attacks on Neural Networks
The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs. While such devices enable us to train large-scale neural networks in datacenters and deploy them on edge devices, their designers’ focus so far is on average-case performance. In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical. We show how adversaries can exploit carefully-crafted sponge examples, which are inputs designed to maximise energy consumption and latency, to drive machine learning (ML) systems towards their worst-case performance. Sponge examples are, to our knowledge, the first denial-of-service attack against the ML components of such systems. We mount two variants of our sponge attack on a wide range of state-of-the-art neural network models, and find that language models are surprisingly vulnerable. Sponge examples frequently increase both latency and energy consumption of these models by a factor of 30×. Extensive experiments show that our new attack is effective across different hardware platforms (CPU, GPU and an ASIC simulator) on a wide range of different language tasks. On vision tasks, we show that sponge examples can be produced and a latency degradation observed, but the effect is less pronounced. To demonstrate the effectiveness of sponge examples in the real world, we mount an attack against Microsoft Azure’s translator and show an increase of response time from 1ms to 6s (6000×). We conclude by proposing a defense strategy: shifting the analysis of energy consumption in hardware from an average-case to a worst-case perspective. Read More
Are we close to achieving Artificial General Intelligence?
In the summer of 1956, AI pioneers John McCarthy, Marvin Minsky, Nat Rochester, and Claude Shannon wrote: “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. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” They figured this would take 10 people two months.
Fast-forward to 1970 and they went again : “In from three to eight years, we will have a machine with the general intelligence of an average human being. I mean a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight. At that point the machine will begin to educate itself with fantastic speed. In a few months it will be at genius level, and a few months after that, its powers will be incalculable.” Read More
Google suspends engineer who claims its AI is sentient
Google has placed one of its engineers on paid administrative leave for allegedly breaking its confidentiality policies after he grew concerned that an AI chatbot system had achieved sentience, the Washington Post reports. The engineer, Blake Lemoine, works for Google’s Responsible AI organization, and was testing whether its LaMDA model generates discriminatory language or hate speech.
The engineer’s concerns reportedly grew out of convincing responses he saw the AI system generating about its rights and the ethics of robotics. In April he shared a document with executives titled “Is LaMDA Sentient?” containing a transcript of his conversations with the AI (after being placed on leave, Lemoine published the transcript via his Medium account), which he says shows it arguing “that it is sentient because it has feelings, emotions and subjective experience.” Read More
Rick's Cafe AI 