The PhiSat-1 satellite is about the size of a cereal box and was ejected from a rocket’s dispenser alongside 45 other satellites. The rocket launched from Guiana Space Centre on September 2nd.

Intel has integrated its Movidius Myriad 2 Vision Processing Unit (VPU) into PhiSat-1 – enabling large amounts of data to be processed on the device. This helps to prevent useless data being sent back to Earth and consuming precious bandwidth.

“The capability that sensors have to produce data increases by a factor of 100 every generation, while our capabilities to download data are increasing, but only by a factor of three, four, five per generation,” says Gianluca Furano, data systems and onboard computing lead at the ESA.

Around 30 percent data savings are expected by using AI at the edge on the PhiSat-1.

“Space is the ultimate edge,” says Aubrey Dunne, chief technology officer of Ubotica. “The Myriad was absolutely designed from the ground up to have an impressive compute capability but in a very low power envelope, and that really suits space applications.”

PhiSat-1 is currently in a sun-synchronous orbit around 329 miles (530 km) above Earth and travelling at over 17,000mph (27,500kmh).

The satellite’s mission is to assess things like polar ice for monitoring climate change, and soil moisture for the growth of crops. One day it could help to spot wildfires in minutes rather than hours or detect environmental accidents at sea.

A successor, PhiSat-2, is currently planned to test more of these possibilities. PhiSat-2 will also carry another Myriad 2.

Myriad 2 was not originally designed for use in orbit. Specialist chips which are protected against radiation are typically used for space missions and can be “up to two decades behind state-of-the-art commercial technology,” explains Dunne.

Incredibly, the Myriad 2 survived 36 straight hours of being blasted with radiation at CERN in late-2018 without any modifications.

ESA announced the joint team was “happy to reveal the first-ever hardware-accelerated AI inference of Earth observation images on an in-orbit satellite.”

PhiSat-1 and PhiSat-2 will be part of a future network with intersatellite communication systems.

(Image Credit: CERN/M. Brice)

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The Perelman School of Medicine at UPenn is working with Intel Labs to co-develop technology based on federated learning, a machine learning technique which trains an algorithm across various devices without exchanging data samples.

The goal is therefore to preserve privacy. Penn Medicine and Intel Labs have claimed they were first to publish a paper on federated learning in medical imaging, offering accuracy with a trained model to more than 99% of a model trained in a non-private method. Work which will build on this, according to the two companies, will ‘leverage Intel software and hardware to implement federated learning in a manner that provides additional privacy protection to both the model and the data.’

The two companies will be joined by 29 healthcare and research institutions from seven countries.

“AI shows great promise for the early detection of brain tumours, but it will require more data than any single medical centre holds to reach its full potential,” said Jason Martin, principal engineer at Intel Labs in a statement.

Artificial intelligence initiatives in healthcare continue apace. Microsoft recently announced details of a $40 million ‘AI for Health’ project, while last month startup Babylon Health stated its belief that it can appropriately triage patients in 85% of cases.

Read the full Intel announcement here.

Photo by jesse orrico on Unsplash

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With concerns around AI spanning from dangerous indifference to innovation-halting scaremongering; it’s clear there’s a need for a system to achieve a healthy balance.

“AI systems have been developed in ways that are inconsistent with the stated values of those developing them,” the researchers wrote. “This has led to a rise in concern, research, and activism relating to the impacts of AI systems.”

The researchers note that significant work has gone into articulating ethical principles by many players involved with AI development, but the claims are meaningless without some way to verify them.

“People who get on airplanes don’t trust an airline manufacturer because of its PR campaigns about the importance of safety – they trust it because of the accompanying infrastructure of technologies, norms, laws, and institutions for ensuring airline safety.”

Among the core ideas put forward is to pay developers for discovering bias in algorithms. Such a practice is already widespread in cybersecurity; with many companies offering up bounties to find bugs in their software.

“Bias and safety bounties would extend the bug bounty concept to AI and could complement existing efforts to better document data sets and models for their performance limitations and other properties,” the authors wrote.

“We focus here on bounties for discovering bias and safety issues in AI systems as a starting point for analysis and experimentation but note that bounties for other properties (such as security, privacy protection, or interpretability) could also be explored.”

Another potential avenue is so-called “red teaming,” the creation of a dedicated team which adopts the mindset of a possible attacker to find flaws and vulnerabilities in a plan, organisation, or technical system.

“Knowledge that a lab has a red team can potentially improve the trustworthiness of an organization with respect to their safety and security claims.”

A red team alone is unlikely to give too much confidence; but combined with other measures can go a long way. Verification from parties outside an organisation itself will be key to instil trust in that company’s AI developments.

“Third party auditing is a form of auditing conducted by an external and independent auditor, rather than the organization being audited, and can help address concerns about the incentives for accuracy in self-reporting.”

“Provided that they have sufficient information about the activities of an AI system, independent auditors with strong reputational and professional incentives for truthfulness can help verify claims about AI development.”

The researchers highlight that a current roadblock with third-party auditing is that there’s yet to be any techniques or best practices established specifically for AI. Frameworks, such as Claims-Arguments-Evidence (CAE) and Goal Structuring Notation (GSN), may provide a starting place as they’re already widely-used for safety-critical auditing.

Audit trails, covering all steps of the AI development process, are also recommended to become the norm. The researchers again point to commercial aircraft, as a safety-critical system, and their use of flight data recorders to capture multiple types of data every second and provide a full log.

“Standards setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.”

The final suggestion for software-oriented methods of verifying AI ethics claims is the use of privacy-preserving machine learning (PPML).

Privacy-preserving machine learning aims to protect the privacy of data or models used in machine learning, at training or evaluation time, and during deployment.

Three established types of PPML are covered in the paper: Federated learning, differential privacy, and encrypted computation.

“Where possible, AI developers should contribute to, use, and otherwise support the work of open-source communities working on PPML, such as OpenMined, Microsoft SEAL, tf-encrypted, tf-federated, and nGraph-HE.”

The researchers, representing some of the most renowned institutions in the world, have come up with a comprehensive package of ways any organisation involved with AI development can provide assurance to governance and the wider public to ensure the industry can reach its full potential responsibly.

You can find the full preprint paper on arXiv here (PDF)

(Photo by Alexander Sinn on Unsplash)

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Thermal imaging is often used to protect privacy because it obscures personally identifying details such as eye colour. In some places, like medical facilities, it’s often compulsory to use images which obscure such details.

AI is opening up many new possibilities so Intel’s researchers set out to determine whether thermal imaging still offers a high degree of privacy.

Intel’s team used two sets of data sets:

• The first set, known as SC3000-DB, was created using a Flir ThermaCam SC3000 infrared camera. The data set features 766 images of 40 volunteers (21 women and 19 men) who each sat in front of a camera for two minutes.

• The second set, known as IRIS, was created by the Visual Computing and Image Processing Lab at Oklahoma State University. It features 4,190 images collected by 30 people and differs from the first set in that it contains various head angles and expressions. 

Each image from the data sets were first cropped to only contain each person’s face. 

A machine learning model then sought to numerically label facial features from the images as vectors. Another model, trained on VGGFace2 – a model trained on visible light images – was used to validate whether it could be applied to thermal images.

Here’s the full results for each data set:

The model trained on visible image data performed well in distinguishing among volunteers by extracting their facial features. 99.5 percent accuracy was observed for the SC3000-DB data set and 82.14 percent for IRIS.

Intel’s research shows that thermal imaging may not offer the privacy that many currently believe it to and it’s already possible to distinguish people using it.

“Many promising visual-processing applications, such as non-contact vital signs estimation and smart home monitoring, can involve private and or sensitive data, such as biometric information about a person’s health,” wrote the researchers.

“Thermal imaging, which can provide useful data while also concealing individual identities, is therefore used for many applications.”

You can find Intel’s full research here.

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The new Nervana Neural Network Processor for Inference (NNP-I) processor has a more approachable codename of Spring Hill.

Spring Hill is a modified 10nm Ice Lake processor which sits on a PCB and slots into an M.2 port typically used for storage.

According to Intel, the use of a modified Ice Lake processor allows Spring Hill to handle large workloads and consume minimal power. Two compute cores and the graphics engine have been removed from the standard Ice Lake design to accommodate 12 Inference Compute Engines (ICE).

In a summary, Intel detailed six main benefits it expects from Spring Hill:

1. Best in class perf/power efficiency for major data inference workloads.
2. Scalable performance at wide power range.
3. High degree of programmability w/o compromising perf/power efficiency.
4. Data centre at scale.
5. Spring Hill solution – Silicon and SW stack – sampling with definitional partners/customers on multiple real-life topologies.
6. Next two generations in planning/design.

Intel’s first chip for AI comes after the company invested in several Isreali artificial intelligence startups including Habana Labs and NeuroBlade. The investments formed part of Intel’s strategy called ‘AI Everywhere’ which aims to increase the firm’s presence in the market.

Naveen Rao, Intel vice president and general manager, Artificial Intelligence Products Group, said:

“To get to a future state of ‘AI everywhere,’ we’ll need to address the crush of data being generated and ensure enterprises are empowered to make efficient use of their data, processing it where it’s collected when it makes sense and making smarter use of their upstream resources.

Data centers and the cloud need to have access to performant and scalable general purpose computing and specialized acceleration for complex AI applications. In this future vision of AI everywhere, a holistic approach is needed—from hardware to software to applications.”

Facebook has said it will be using Intel’s new Spring Hill processor. Intel already has two more generations of the NNP-I in development.

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Speaking on stage at the conference in Beijing, Intel corporate vice president Naveen Rao made the announcement.

“The next few years will see an explosion in the complexity of AI models and the need for massive deep learning compute at scale. Intel and Baidu are focusing their decade-long collaboration on building radical new hardware, codesigned with enabling software, that will evolve with this new reality – something we call ‘AI 2.0.’

Intel’s so-called Neural Network Processor for Training is codenamed NNP-T 1000 and designed for training deep learning models at lightning speed. A large amount (32GB) of HBM memory and local SRAM is put closer to where computation happens to enable more storage of model parameters on-die, saving significant power for an increase in performance.

The NNP-T 1000 is set to ship alongside the Neural Network Processor for Inference (NNP-I 1000) chip later this year. As the name suggests, the NNP-I 1000 is designed for AI inferencing and features general-purpose processor cores based on Intel’s Ice Lake architecture.

Baidu and Intel have a history of collaborating in AI. Intel has helped to optimise Baidu’s PaddlePaddle deep learning framework for its Xeon Scalable processors since 2016. More recently, Baidu and Intel developed the BIE-AI-Box – a hardware kit for analysing the frames of footage captured by cockpit cameras.

Intel sees a great deal of its future growth in AI. The company’s AI chips generated $1 billion in revenue last year and Intel expects a growth rate of 30 percent annually up to $10 billion by 2022.

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Speaking during the company’s Innovation Summit in Santa Clara, Intel Executive VP Navin Shenoy revealed a new focus on AI development.

The company’s AI-focused Xeon processors generated $1 billion in revenues during 2017. By 2022, it expects to be generating around $10 billion per year.

AI is set to be implemented in many areas of our lives in the coming years, across a variety of devices.

Shenoy claims recent breakthroughs have increased the company’s AI performance by 200x since 2014. He teases further improvements are on their way in upcoming releases.

The company will be launching its ‘Cascade Lake’ Xeon processor later this year with 11 times better performance for AI image recognition.

Arriving in 2019 will be ‘Cooper Lake’ which uses 14-nanometer manufacturing and will feature even better performance. In 2020, however, the company is targeting ‘Ice Lake’ with 10-nanometer manufacturing technology.

“After 50 years, this is the biggest opportunity for the company,” says Shenoy. “We have 20 percent of this market today.”

The admission it currently has a small share of the market today is bold and shows the company is confident about significantly upping that percentage in the coming years. It faces significant competition from Nvidia in particular.

Intel’s revenues were around a third data-centric five years ago. Now, it’s around half of Intel’s business.

Shenoy’s comments today show how seriously Intel is taking its AI business and the firm’s confidence it will be a major player.

What are your thoughts on Intel’s AI business?

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Brian Krzanich made the comment during an on-stage interview at the WSJD Live conference in Laguna Beach, California. The news Intel is working on its own AI chips is no surprise, but the choice of partner may be.

“This is the first piece of silicon,” Krzanich said. “We have a whole family planned for this, (Facebook) is helping us, along with others, as to where this is going.”

Many consumers are wary of Facebook because, like Google, the company relies on collecting vast amounts of data about users. While it’s unlikely Intel would allow Facebook to perform any data collection of its users; there will doubtless be some concerns.

Facebook was the only named company but Intel is also collaborating with others for its AI chips. The extent of the partnerships, or what benefits the partners receive for providing their resources, is currently unknown. We’ve reached out to Facebook and Intel for clarification.

Intel is aiming to build the first Neural Network Processor (NNP) before the end of this year. The company is calling this ambition Nervana, following the company of the same name Intel acquired in August last year, and it promises to “revolutionise AI computing” across a myriad of industries.

In a blog post, Krzanich provided the following examples:

• Healthcare: AI will allow for earlier diagnosis and greater accuracy, helping make the impossible possible by advancing research on cancer, Parkinson’s disease, and other brain disorders.
• Social media: Providers will be able to deliver a more personalized experience to their customers and offer more targeted reach to their advertisers.
• Automotive: The accelerated learning delivered in this new platform brings us another step closer to putting autonomous vehicles on the road.
• Weather: Consider the immense data required to understand the movement, wind speeds, water temperatures and other factors that decide a hurricane’s path. Having a processor that takes better advantage of data inputs could improve predictions on how subtle climate shifts may increase hurricanes in different geographies.

Krzanich says multiple generations of Nervana products are in the pipeline. Last year, the company set the goal of achieving 100 times greater AI performance by 2020. Intel believes these NNPs will help them achieve this lofty goal.

Nervana, even prior to its acquisition by Intel, has been working on neuromorphic chips for years and even developed its own called ‘Lake Crest’ as it found traditional GPUs to be unsuitable for neural networking. These chips are designed to mimic the human brain to make decisions based on patterns and associations. Intel announced its own ‘Loihi’ chip self-learning neuromorphic chip back in September.

According to Naveen Rao, co-founder of Nervana, the first member of the NNP family will begin shipping “soon”. We’ll keep you informed of all developments.

What are your thoughts on the NNPs being developed by Intel and partners?

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