Machine Learning Archives - Liwaiwai

Peacock: Tackling ML Challenges By Accelerating Skills

At Peacock, we are acutely aware of global trends and changes in adopting machine learning (ML) techniques, particularly in the field of media and entertainment. We anticipate that, within several years, most software applications will have an element of ML and will require fine tuning of a model, putting increasing demand on ML training infrastructure. As the Director of Analytics Tooling at Peacock, I head up a team of engineers whose primary aim is to build scalability into our tools and processes, enabling us to keep up with the ever-changing field of ML engineering and create a better user experience.…

Coop Reduces Food Waste By Forecasting With Google’s AI And Data Cloud

Although Coop has a rich history spanning nearly 160 years, the machine learning (ML) team supporting its modern operations is quite young. Its story began in 2018 with one simple mission: to leverage ML-powered forecasting to help inform business decisions, such as demand planning based on supply chain seasonality and expected customer demand. The end goal? By having insight into not only current data but also projections of what could happen in the future, the business can optimize operations to keep customers happy, save costs, and support its sustainability goals (more on that later!). Coop’s initial forecasting environment was one…

Gods In The Machine? The Rise Of Artificial Intelligence May Result In New Religions

We are about to witness the birth of a new kind of religion. In the next few years, or perhaps even months, we will see the emergence of sects devoted to the worship of artificial intelligence (AI). The latest generation of AI-powered chatbots, trained on large language models, have left their early users awestruck —and sometimes terrified — by their power. These are the same sublime emotions that lie at the heart of our experience of the divine. People already seek religious meaning from very diverse sources. There are, for instance, multiple religions that worship extra-terrestrials or their teachings. As…

6 ways Google AI Is Helping You Sleep Better

Sleep tracking in consumer hardware products has been around for a while — Fitbit, for instance, has provided it for 15 years. But these tracking and analysis offerings have evolved over time. “We’ve added so much more capability,” says Dr. Conor Heneghan. Conor is a research scientist who works on sleep health at Google across consumer hardware. His team is responsible for developing the helpful new sleep features for Fitbit and Pixel Watch, which uses Fitbit’s health and fitness technology. A lot of this progress is thanks to AI. “The team I work on relies on various machine learning algorithms…

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…

Using ML To Predict The Weather And Climate Risk

Introduction All beings on this planet are affected by atmosphericphenomena we call the weather. Because of this, humans have invented all sorts of measuring tools, and luckily have loads of data from observations. All this data has been on quite a technological journey; from being collected on paper and local servers in basements, to now living on open cloud platforms that normalize all the different sensor data for anyone to research. AI techniques to build improved predictive models have also made way using neural networks, called deep learning. My colleagues in Google Research who study AI Weather & Climate, built…

ChatGPT: How To Prevent It Becoming A Nightmare For Professional Writers

Nearly half of white-collar professionals have tried using ChatGPT to help with their work, according to a recent survey of more than 10,000 people at blue chips such as Google, JP Morgan and McKinsey. That’s staggering, considering the AI chatbot was only released to the public in November. It’s potentially very exciting for the future of work, but it also brings serious risks. ChatGPT and other imminent rivals are part of a long history of technologies geared to reducing the labour of writing. These range from the printing press to the telegram, the typewriter, word processors and personal computing. AI…

Sentiment Analysis With BigQuery ML

Introduction We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications. There are numerous applications of sparse features such as text generation and sentiment analysis. In…

MuAViC: The First Audio-Video Speech Translation Benchmark

In countless everyday situations, background noise — the sound of traffic, music, other people speaking – makes it more difficult to understand what other people are saying. Humans often use information from our other senses, especially vision, to help us communicate (as pointed out by Harry McGurk and John MacDonald in their 1976 study “Hearing Lips and Seeing Voices”). For example, if you’re speaking to a friend at a loud concert, you will likely focus on their face to supplement what you can hear. AI researchers have recently built systems (such as Meta AI’s publicly available AV-HuBERT and RAVen models) that use visual information…

Machine Learning

Large Language Models Are Biased. Can Logic Help Save Them?

Turns out, even language models “think” they’re biased. When prompted in ChatGPT, the response was as follows: “Yes, language models can have biases, because the training data reflects the biases present in society from which that data was collected. For example, gender and racial biases are prevalent in many real-world datasets, and if a language model is trained on that, it can perpetuate and amplify these biases in its predictions.” A well-known but dangerous problem. Humans (typically) can dabble with both logical and stereotypical reasoning when learning. Still, language models mainly mimic the latter, an unfortunate narrative we’ve seen play…

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