In the News
10 startups riding the wave of AI innovation
Organizations are increasingly adopting AI-enabled technologies to address existing and emerging problems within the enterprise ecosystem, meet changing market demands and deliver business outcomes at scale.
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In The News
Nvidia’s Next GPU Shows That Transformers Are Transforming AI
Transformers, the type of neural network behind OpenAI’s GPT-3 and other big natural-language processors, are quickly becoming some of the most important in industry, and they are likely to spread to other—perhaps all—areas of AI.
Does this artificial intelligence think like a human?
A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
Meet DALL-E, the AI that draws anything at your command
At OpenAI, one of the world’s most ambitious artificial intelligence labs, researchers are building technology that lets you create digital images simply by describing what you want to see.
Applied use cases
12 examples of artificial intelligence in everyday life
In the article below, you can check out twelve examples of AI being present in our everyday lives.
How facial recognition is identifying the dead in Ukraine
Last month, Clearview AI, announced it had given its technology to the Ukrainian government.
New Technology, Old Problems: The Missing Voices in Natural Language Processing
Recently, NLP technology facilitated access and synthesis of COVID-19 research with the release of a public, annotated research dataset and the creation of public response resources.
Ethics
No, Machine Learnings can't predict trustworthiness based on faces
Though facial recognition technology is used by police departments, governments, and even online proctoring services, it has little scientific basis and often leads to discriminatory and harmful outcomes.
AI & Society
AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its many forms has become ubiquitous and its benefits to society and the individual have grown, its impacts are varied.
Rethinking AI for Good Governance
This essay examines what AI can do for government, specifically through three generic tools at the heart of governance: detection, prediction, and data-driven decision-making. ¨
Robotics
Designing Expandable-Structure Robots for Human-Robot Interaction
We detail various implementation considerations for researchers seeking to integrate such structures in their own work and describe how expandable structures may lead to novel forms of interaction for a variety of different robots and applications, including structures that enable robots to alter...
Trustworthy robots
I’ll be chatting to three roboticists working on various aspects of trustworthiness in robotics: Anouk van Maris (University of the West of England), Faye McCabe (University of Birmingham), Daniel Omeiza (University of Oxford). Anouk van Maris is a research fellow in responsible robotics. She is a...
Diligent Robotics Raises Over $30 Million in Series B Funding Round to Deploy Collaborative Robots to Healthcare Systems Across the Nation
Led by Tiger Global, funding will accelerate product development to meet demand for healthcare service robot and enhance interoperability, integrations and efficiency in healthcare institutions AUSTIN, Texas,
Research
Equilibrium Aggregation
In this post, we1 will look at a core building block of many graph neural networks: permutation invariant aggregation functions. This misses an entire subfield of deep learning: implicit layers – for instance Deep Equilibrium Models, or Neural ODEs. The following table shows the pairwise potential...
Measuring Goodhart’s Law
For this reason, we train a model to predict these human preferences, known as a reward model, and use the reward model’s predictions as a proxy objective. The naive approach is to use a Monte Carlo estimator: run best-of-n sampling many times, measure the true objective on those samples, and...
An empirical analysis of compute-optimal large language model training
By training 400 language models ranging from 70 million to 10 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the training dataset size should be scaled equally: for every doubling of model size the training dataset size should also be...
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