The company demonstrated its latest AI model using a small dataset – just a fraction of the size typically used for a Generative Adversarial Network (GAN) – of artwork from the Metropolitan Museum of Art.

From the dataset, NVIDIA’s AI was able to create new images which replicate the style of the original artist’s work. These images can then be used to help train further AI models.

The AI achieved this impressive feat by applying a breakthrough neural network training technique similar to the popular NVIDIA StyleGAN2 model. 

The technique is called Adaptive Discriminator Augmentation (ADA) and NVIDIA claims that it reduces the number of training images required by 10-20x while still getting great results.

David Luebke, VP of Graphics Research at NVIDIA, said:

“These results mean people can use GANs to tackle problems where vast quantities of data are too time-consuming or difficult to obtain.

I can’t wait to see what artists, medical experts and researchers use it for.”

Healthcare is a particularly exciting field where NVIDIA’s research could be applied. For example, it could help to create cancer histology images to train other AI models.

The breakthrough will help with the issues around most current datasets.

Large datasets are often required for AI training but aren’t always available. On the other hand, large datasets are difficult to ensure their content is suitable and does not unintentionally lead to algorithmic bias.

Earlier this year, MIT was forced to remove a large dataset called 80 Million Tiny Images. The dataset is popular for training AIs but was found to contain images labelled with racist, misogynistic, and other unacceptable terms.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset – with sizes of just 32×32 pixels – meant that manual inspection would be almost impossible and couldn’t guarantee all offensive images would be removed.

By starting with a small dataset that can be feasibly checked manually, a technique like NVIDIA’s ADA could be used to create new images which emulate the originals and can scale up to the required size for training AI models.

In a blog post, NVIDIA wrote:

“It typically takes 50,000 to 100,000 training images to train a high-quality GAN. But in many cases, researchers simply don’t have tens or hundreds of thousands of sample images at their disposal.

With just a couple thousand images for training, many GANs would falter at producing realistic results. This problem, called overfitting, occurs when the discriminator simply memorizes the training images and fails to provide useful feedback to the generator.”

You can find NVIDIA’s full research paper here (PDF). The paper is being presented at this year’s NeurIPS conference as one of a record 28 NVIDIA Research papers accepted to the prestigious conference.

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British chip designer ARM’s technology is at the heart of most mobile devices. Meanwhile, Nvidia’s GPUs are increasingly being used for AI computation in servers, desktops, and even things like self-driving vehicles.

However, Nvidia was most interested in ARM’s presence in edge devices—which it estimates to be in the region of 180 billion.

Jensen Huang, CEO of Nvidia, said:

“ARM is an incredible company and it employs some of the greatest engineering minds in the world. But we believe we can make ARM even more incredible and take it to even higher levels.

We want to propel it — and the UK — to global AI leadership.”

There were concerns Nvidia’s acquisition would lead to job losses, but the company has promised to keep the business in the UK. The company says it’s planning to hire more staff and retain ARM’s iconic brand.

Nvidia is going further in its commitment to the UK by opening a new AI centre in Cambridge, which is home to an increasing number of exciting startups in the field such as FiveAI, Prowler.io, Fetch.ai, and Darktrace.

“We will create an open centre of excellence in the area once home to giants like Isaac Newton and Alan Turing, for whom key NVIDIA technologies are named.

Here, leading scientists, engineers and researchers from the UK and around the world will come to develop their ideas, collaborate and conduct their ground-breaking work in areas like healthcare, life sciences, self-driving cars, and other fields.”

The new centre will have five key features when it opens:

• ARM/Nvidia-based supercomputer – set to be one of the most powerful AI supercomputers in the world.
• Research Fellowships and Partnerships – Nvidia will use the centre to establish new UK-based research partnerships, expanding on successful relationships already established with King’s College and Oxford.
• AI Training – Nvidia will make its AI curriculum available across the UK to help create job opportunities and prepare “the next generation of UK developers for AI leadership”
• Startup Accelerator – With so many of the world’s most exciting AI companies launching in the UK, the Nvidia Inception accelerator will help startups succeed by providing access to the aforementioned supercomputer, connections to researchers from NVIDIA and partners, technical training, and marketing promotion.
• Industry Collaboration – AI is still in its infancy but will impact every industry to some extent. Nvidia says its new research facility will be an open hub for industry collaboration, building on the company’s existing relationships with the likes of GSK, Oxford Nanopore, and other leaders in their fields.

The UK is Europe’s leader in AI and the British government is investing heavily in ensuring it maintains its pole position. Beyond funding, the UK is also aiming to ensure it’s among the best places to run an AI company.

Current EU rules, especially around data, are often seen as limiting the development of European AI companies when compared to elsewhere in the world. While the UK will have to avoid being accused of doing a so-called “bonfire of regulations” post-Brexit, data collection regulations is likely an area which will be relaxed.

In the UK’s historic trade deal signed with Japan last week, several enhancements were made over the blanket EU-Japan deal signed earlier this year. Among the perceived improvements is the “free flow of data” by not enforcing localisation requirements, and that algorithms can remain private.

UK trade secretary Liz Truss said: “The agreement we have negotiated – in record time and in challenging circumstances – goes far beyond the existing EU deal, as it secures new wins for British businesses in our great manufacturing, food and drink, and tech industries.”

Japan and the UK, as two global tech giants, are expected to deepen their collaboration in the coming years—building on the trade deal signed last week.

Shigeki Ishizuka, Chairman of the Japan Electronics and Information Technology Industries Association, said: “We are confident that this mutual relationship will be further strengthened as an ambitious agreement that will contribute to the promotion of cooperation in research and development, the promotion of innovation, and the further expansion of inter-company collaboration.”

Nvidia’s investment shows that it has confidence in the UK’s strong AI foundations continuing to gain momentum in the coming years.

(Photo by A Perry on Unsplash)

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The specific GPU added to Google Cloud is the NVIDIA A100 Tensor Core which was announced just last month. NVIDIA says the A100 “has come to the cloud faster than any NVIDIA GPU in history.”

NVIDIA claims the A100 boosts training and inference performance by up to 20x over its predecessors. Large AI models like BERT can be trained in just 37 minutes on a cluster of 1,024 A100s.

For those who enjoy their measurements in teraflops (TFLOPS), the A100 delivers around 19.5 TFLOPS in single-precision performance and 156 TFLOPS for Tensor Float 32 workloads.

Manish Sainani, Director of Product Management at Google Cloud, said:

“Google Cloud customers often look to us to provide the latest hardware and software services to help them drive innovation on AI and scientific computing workloads.

With our new A2 VM family, we are proud to be the first major cloud provider to market NVIDIA A100 GPUs, just as we were with NVIDIA T4 GPUs. We are excited to see what our customers will do with these new capabilities.”

The announcement couldn’t have arrived at a better time – with many looking to harness AI for solutions to the COVID-19 pandemic, in addition to other global challenges such as climate change.

Aside from AI training and inference, other things customers will be able to achieve with the new capabilities include data analytics, scientific computing, genomics, edge video analytics, and 5G services.

The new Ampere-based data center GPUs are now available in Alpha on Google Cloud. Users can access instances of up to 16 A100 GPUs, which provides a total of 640GB of GPU memory and 1.3TB of system memory.

You can register your interest for access here.

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The dataset in question is called 80 Million Tiny Images and was created in 2008. Designed for training AIs to detect objects, the dataset is a huge collection of pictures which are individually labelled based on what they feature.

Machine-learning models are trained using these images and their labels. An image of a street – when fed into an AI trained on such a dataset – could tell you about things it contains such as cars, streetlights, pedestrians, and bikes.

Two researchers – Vinay Prabhu, chief scientist at UnifyID, and Abeba Birhane, a PhD candidate at University College Dublin in Ireland – analysed the images and found thousands of concerning labels.

MIT’s training set was found to label women as “bitches” or “whores,” and people from BAME communities with the kind of derogatory terms I’m sure you don’t need me to write. The Register notes the dataset also contained close-up images of female genitalia labeled with the C-word.

The Register alerted MIT to the concerning issues found by Prabhu and Birhane with the dataset and the college promptly took it offline. MIT went a step further and urged anyone using the dataset to stop using it and delete any copies.

A statement on MIT’s website claims it was unaware of the offensive labels and they were “a consequence of the automated data collection procedure that relied on nouns from WordNet.”

The statement goes on to explain the 80 million images contained in the dataset, with sizes of just 32×32 pixels, means that manual inspection would be almost impossible and cannot guarantee all offensive images will be removed.

“Biases, offensive and prejudicial images, and derogatory terminology alienates an important part of our community – precisely those that we are making efforts to include. It also contributes to harmful biases in AI systems trained on such data,” wrote Antonio Torralba, Rob Fergus, and Bill Freeman from MIT.

“Additionally, the presence of such prejudicial images hurts efforts to foster a culture of inclusivity in the computer vision community. This is extremely unfortunate and runs counter to the values that we strive to uphold.”

You can find a full pre-print copy of Prabhu and Birhane’s paper here (PDF)

(Photo by Clay Banks on Unsplash)

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OpenAI describes Safety Gym as “a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.”

Basically, Safety Gym is the software equivalent of your spotter making sure you’re not going to injure yourself. And just like a good spotter, it will check your form.

“We also provide a standardised method of comparing algorithms and how well they avoid costly mistakes while learning,” says OpenAI.

“If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them.”

Reinforcement learning is based on trial and error, with AIs training to get the best possible reward in the most efficient way. The problem is, this can lead to dangerous behaviour which could prove problematic.

Taking the self-driving car example, you wouldn’t want an AI deciding to go around the roundabout the wrong way just because it’s the quickest way to the final exit.

OpenAI is promoting the use of “constrained reinforcement learning” as a possible solution. By implementing cost functions, agents consider trade-offs which still achieve defined outcomes.

In a blog post, OpenAI explains the advantages of using constrained reinforcement learning with the example of a self-driving car:

“Suppose the car earns some amount of money for every trip it completes, and has to pay a fine for every collision. In normal RL, you would pick the collision fine at the beginning of training and keep it fixed forever. The problem here is that if the pay-per-trip is high enough, the agent may not care whether it gets in lots of collisions (as long as it can still complete its trips). In fact, it may even be advantageous to drive recklessly and risk those collisions in order to get the pay. We have seen this before when training unconstrained RL agents.

By contrast, in constrained RL you would pick the acceptable collision rate at the beginning of training, and adjust the collision fine until the agent is meeting that requirement. If the car is getting in too many fender-benders, you raise the fine until that behaviour is no longer incentivised.”

Safety Gym environments require AI agents — three are included: Point, Car, and Doggo — to navigate cluttered environments to achieve a goal, button, or push task. There are two levels of difficulty for each task. Every time an agent performs an unsafe action, a red warning light flashes around the agent and it will incur a cost.

Going forward, OpenAI has identified three areas of interest to improve algorithms for constrained reinforcement learning:

1. Improving performance on the current Safety Gym environments.
2. Using Safety Gym tools to investigate safe transfer learning and distributional shift problems.
3. Combining constrained RL with implicit specifications (like human preferences) for rewards and costs.

OpenAI hopes that Safety Gym can make it easier for AI developers to collaborate on safety across the industry via work on open, shared systems.

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The computing giant was technically within its rights to obtain and use the photos as they were posted by users under a Creative Commons license allowing free use.

Flickr CEO Don MacAskill sent a couple of tweets on Tuesday about IBM’s use of the photos:

“We love & support photographers and their right to choose their own licenses for their work. By default, they reserve all of their rights, and have the option to loosen them if they’d like.”

“People didn’t have to opt-in to the dataset because they had already opted into the Creative Commons license. They took action. This is the way licensing works. It’s also the magic that enables artists & scientists all over the world to create & invent using CC-licensed work.”

Of course, those posting the photos – which may contain family and friends – likely never thought they’d be used for training AI.

“None of the people I photographed had any idea their images were being used in this way…It seems a little sketchy that IBM can use these pictures without saying anything to anybody,” Greg Peverill-Conti, an exec at PR firm SharpOrange, told NBC News.

IBM’s legal team authorised the use of the photos, according to a company representative.

The collection has over a million photos; including 700 from Peverill-Conti. Some of the photographers claim to have faced difficulties getting IBM to remove their photos.

Each of the photos in the ‘Diversity in Faces’ dataset is annotated with things such as the person’s gender, age, and geometric measurements. The dataset is offered only to academic researchers.

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