Algorithm Archives - Liwaiwai

AI Could Make More Work For Us, Instead Of Simplifying Our Lives

There’s a common perception that artificial intelligence (AI) will help streamline our work. There are even fears that it could wipe out the need for some jobs altogether. But in a study of science laboratories I carried out with three colleagues at the University of Manchester, the introduction of automated processes that aim to simplify work — and free people’s time — can also make that work more complex, generating new tasks that many workers might perceive as mundane. In the study, published in Research Policy, we looked at the work of scientists in a field called synthetic biology, or…

Artificial Intelligence

The Future Of Analog AI Hardware

New research from IBM presented at this year’s IEDM conference shows that a future where we can train and run AI systems on energy-efficient analog hardware is on the horizon. New research from IBM presented at this year’s IEDM conference shows that a future where we can train and run AI systems on energy-efficient analog hardware is on the horizon. Since the dawn of the computer era, the world has been working on precise computer chips. The digital world, as we know it, is the result of endless 1s and 0s, constant sums processed on chips with definite answers. But…

What Are Decision Trees In Machine Learning?

A decision Tree is a supervised machine learning algorithm that works on the basis of recursively answering some questions (if-else conditions). The algorithm is used both for regression and classification. However mostly for classification problems. The questions in boxes are called the internal nodes where the answers to the questions split it into branches. The nodes which do not split anymore are called leaf, which represent the decision/output of the model. This tree of course is much bigger and more complex for bigger datasets when compared to our simple example above. The tree grows and forms according to the data…

Machine Learning

Breaking The Scaling Limits Of Analog Computing

As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up. An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy. However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors. In an…

Self-Taught AI May Have a Lot in Common With the Human Brain

Neural networks can use self-supervised learning to figure out what matters. This process might be what helps humans do the same. FOR A DECADE now, many of the most impressive artificial intelligence systems have been taught using a huge inventory of labeled data. An image might be labeled “tabby cat” or “tiger cat,” for example, to “train” an artificial neural network to correctly distinguish a tabby from a tiger. The strategy has been both spectacularly successful and woefully deficient. Such “supervised” training requires data laboriously labeled by humans, and the neural networks often take shortcuts, learning to associate the labels with…

BlenderBot 3: A 175B Parameter, Publicly Available Chatbot That Improves Its Skills And Safety Over Time

Today, we’re announcing that Meta AI has built and released BlenderBot 3, the first 175B-parameter, publicly available chatbot complete with model weights, code, datasets, and model cards. We’ve deployed it in a live interactive conversational AI demo here. BlenderBot 3 is capable of searching the internet to chat about virtually any topic, and it’s designed to learn how to improve its skills and safety through natural conversations and feedback from people “in the wild.” Most previous publicly available datasets are typically collected through research studies with annotators that can’t reflect the diversity of the real world. We combined two recently developed machine learning techniques, SeeKeR and Director, to build conversational models that learn…

Researchers Discover Major Roadblock In Alleviating Network Congestion

When users want to send data over the internet faster than the network can handle, congestion can occur — the same way traffic congestion snarls the morning commute into a big city. Computers and devices that transmit data over the internet break the data down into smaller packets and use a special algorithm to decide how fast to send those packets. These congestion control algorithms seek to fully discover and utilize available network capacity while sharing it fairly with other users who may be sharing the same network. These algorithms try to minimize delay caused by data waiting in queues…

Give This AI A Few Words Of Description And It Produces A Stunning Image – But Is It Art?

A picture may be worth a thousand words, but thanks to an artificial intelligence program called DALL-E 2, you can have a professional-looking image with far fewer. DALL-E 2 is a new neural network algorithm that creates a picture from a short phrase or sentence that you provide. The program, which was announced by the artificial intelligence research laboratory OpenAI in April 2022, hasn’t been released to the public. But a small and growing number of people – myself included – have been given access to experiment with it. As a researcher studying the nexus of technology and art, I…

Machine Learning Is Important, But Some AIs Just Want To Have Fun

This video by the French computer programmer Damien Henry is a clever visual demonstration of machine learning – a term coined by the US computer scientist Arthur Samuel (1901-1990) to describe an algorithm that gives computers the ‘ability to learn without being explicitly programmed’. Using several videos recorded from windows during train rides, Henry trained an algorithm to predict what the next frame of a train ride should look like. Then, starting with a single frame chosen by Henry, the algorithm generated, to the best of its ability, scenes from an hour-long train ride, improving itself roughly every 20 seconds. The resulting…

AI Reveals Unsuspected Math Underlying Search For Exoplanets

Artist’s concept of a sun-like star (left) and a rocky planet about 60% larger than Earth in orbit in the star’s habitable zone. Gravitational microlensing has the ability to detect such planetary systems and determine the masses and orbital distances, even though the planet itself is too dim to be seen. (Image credit: NASA Ames/JPL-Caltech/T. Pyle) Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in…

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