Some of the most well-known examples of the use of machine learning technics in science applications are the detection and classification of gravitational-waves signals from LIGO and Virgo in astrophysics [1], the recent DeepMind Alpha-Fold2 capabilities outperforming classical methods in protein folding [2] or the winning team of the Gordon Bell 2020 with the Deep Potential Molecular Dynamics [3] which is opening new breakthroughs in the drug design process and could speed up future pandemic response efforts.

Beyond these key examples, the convergence between HPC and AI is natural where DL-based surrogate modelling is more and more widely applied in research and recent advances in physics-informed neural networks such as HNN [4] bring physical properties and constraints to neural networks loss functions opening a great path towards a new generation of simulation.

Atos Centers of Excellence – combining data sciences and industry expertise

In Atos, we built a dedicated approach to support the scientific community and Industries by bringing data science and HPC expertise through the Atos Centers of Excellence. Each center is oriented towards a specific domain where our experts and our customers can jointly bring innovations and technologies with the support of some of our partners.

Some of the first Atos Centers of Excellence are dedicated to weather forecast & climate changes [5] and life sciences [6]. In such advanced domains, applying innovation means creating a strong AI research support, defined through specific programs. Those programs aim to enhance the state of the art of machine learning model for science applications as well as exploring coupling capabilities between simulation and AI model inference including the orchestration of AI augmented HPC workflows to brings such these development in production at scale. Some of the use cases we are addressing are about surrogating modelling and dimensionality reduction on CFD applications or data assimilation and deep learning for chaotic systems.

AI for science applications – more options to come!

In addition to this approach, AI into science applications also means adapting technical architectures of such converged systems. Today most of the HPC applications benefit from SIMD acceleration through GPU technologies, acceleration that AI benefit as well. GPU architectures evolved over the time by increasing the part of their silicon dedicated to AI processing to boost this convergence [7] but the maturity of dedicated AI chips such as the Graphcore IPU or Intel Habana is a new potential keystone for science thanks to the performance gap these technologies could provide.

Nevertheless, the current adoption limitation of dedicated AI technologies is due to the low level of maturity of code hybridization between AI and HPC application which is not sufficient enough to embed dedicated AI acceleration. Hardware acceleration for HPC platforms implies support both HPC applications and AI capacities which is today a key limiting factor for the wide adoption of dedicated AI chips due to the associated software stack not able to naturally support legacy codes with associated programming model (MPI/OpenMP) as AI chipmakers are building their software environment on AI frameworks with dedicated back-end compilers and runtimes.  Despite this challenge, promising examples of using AI chips for HPC applications have  been recently published, for example on Graphcore IPU but at the cost of an algorithm reformulation or a kernel focus which naturally exposed a tensor representation in the code to be accelerated by dedicated hardware [8].

Large-scale AI infrastructure brings competitive advantage for HPC-AI convergence 

HPC-AI convergence is also often seen via the use of HPC knowledge to strengthen AI. As deep learning model complexity continue to growth over years, especially related to natural language processing where the computational costs of transformers models such as GPT-3 gathers 15B parameters network to be trained and a training computing cost around ~10²³Flops [9-10], dedicated large scale AI infrastructures becomes crucial for enterprise competitiveness. Architecture design differs from HPC-AI converged platforms by a different tradeoff between general purpose processing and AI hardware acceleration oriented towards more AI acceleration with an increased interconnect capabilities per node.

Turnkey solution – putting AI solution at your fingertips!

In Atos, we bring decades of experience in large scale computing to make available to any industries scalable AI solutions to support AI business growth and reach a new level of performance with AI turnkey solutions integrating AI technologies at scale, management software, data science platform, AI and HPC knowledge in addition of ML-based software to enhance HPC operations addressing data movement, energy consumption, resources allocation and predictive maintenance to globally enhance HPC operations excellence and total cost of ownership by learning behaviors from each production system.

To conclude, artificial intelligence applied to science application is a promising field of research that will bring great scientific breakthroughs in the future and the Atos HPC-AI team is proud to support this leading-edge research topic.

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Over the last few years, deep learning has seen a huge uptake in popularity in businesses and scientific applications as well. It is defined as a subset of artificial intelligence that leverages computer algorithms to generate autonomous learning from data and information. Deep learning is prevalent across many scientific disciplines, from high-energy particle physics and weather and climate modeling to precision medicine and more. The technology has come a long way, when scientists developed a computer model in the 1940s that was organized in interconnected layers, like neurons in the human brain.

Deep learning signifies substantial progress in the ability of neural networks to automatically create problem‐solving features and capture highly complex data distributions. Deep neural networks are now the state-of-the-art machine learning models across diverse areas, including image analysis and natural language processing, among others, and extensively deployed in academia and industry.

Developments in this technology have a vast potential for scientific applications and medical imaging, medical data analysis, and diagnostics. In scientific settings, data analysis is understanding as recognizing the underlying mechanisms that give rise to patterns in the data. When this is the goal, dimensionality reduction, and clustering are simple and unsupervised but highly effective techniques to divulge concealed properties in the data.

In a report, titled A Survey of Deep Learning for Scientific Discovery, where former Google CEO Eric Schmidt and Google AI researcher Maithra Raghu have put together a comprehensive overview on deep learning techniques and their application to scientific research. According to their guide, deep learning algorithms have been very effective in the processing of visual data. They also describe convolutional neural networks (CNNs) as the most eminent family of neural networks and very constructive in working with any kind of image data.

In scientific contexts, one of the best applications of CNNs is medical imaging analysis. Human experts such as radiologists and physicians have mostly performed the medical image interpretation. However, owing to large variations in pathology and potential fatigue of human experts, researchers now have started capitalizing on computer-assisted interventions. Already, many deep learning algorithms are in use to analyze CT scans and x-rays and assist in the diagnosis of diseases. Recently, in the time of crisis caused by COVID-19, scientists have started using CNNs to find out symptoms of the virus in chest x-rays.

Deep learning algorithms are also effective is natural language processing. It deals with building computational algorithms to automatically assess and represent human language. Today, NLP-based systems have enabled a various number of applications, and are useful to train machines to perform complex natural language-related tasks like machine translation and dialogue generation.

Moreover, deep learning models originally inspired by biological neural networks, which encompasses artificial neurons, or nodes, connected to a web of other nodes through edges, allowing these artificial neurons to collect and send information to each other.

The post DEEP LEARNING: AN OVERVIEW IN SCIENTIFIC APPLICATIONS appeared first on Artificial Intelligence.

Hyderabad: As scientific disciplines go, the field of ‘Deep Learning’ is but an infant. However, it will soon have a disruptive effect on the field of drug design, experts say.

They were speaking at a panel discussion as part of the ongoing international conference on high performance computing in Hyderabad on Wednesday.

Deep learning, a subset of machine learning, functions by imitating the workings of the human brain to process large amounts of data. The artificial neural networks used in these functions have neuron nodes connected together like a web.

This enables deep learning systems to process data in a non-linear manner, unlike traditional programmes which do so in a linear way.

The panel experts on Monday, which included members from both academia and industry, said deep learning has proven instrumental in areas such as modelling protein structures, human genome interpretation for personalised medicine and understanding host-pathogen interactions (HPI).

Devapriya Kumar, associate professor and head of the centre for computational natural sciences and bioinformatics (CCNSB) at IIIT, Hyderabad, said, “For long, if one had to design a drug for a particular purpose, we took existing compounds which might be close to what we want and performed experiments on them. We would eventually arrive at a synthesised compound that has the properties we want. However, using deep learning functions, we can tell the computer what properties we want in a drug and it would arrive at a compound itself.”

Kumar explained that computing models can also help predict how easily these drugs can be synthesised on a large-scale basis. “After all, it isn’t enough for us to have a compound that does what we need it to do. It should be easy to produce for commercial viability,” he added.

Gopalakrishnan Bulusu, principal scientist at TCS Innovation Labs, Hyderabad, said deep learning could be used to understand the human genome better. “We still do not have a clear understanding of how many pathogens infect hosts such as a human body. We need more data and we need to process this data properly as well. Deep learning functions can help in these aspects,” he said.

‘Deep learning’ in drug design is such a new field that around the world, there are only 200 startups working on it. Most pharmaceutical giants have started to work in this field but there too the activity is in the nascent stage. Speaking with Deccan Chronicle, Kumar said, “Three years ago, this industry didn’t exist. In fact, the number of startups, which is 200 today, was only 80 six months ago.”

Kumar admitted there was a long way to go before India is considered a major player in the field. “Most activity in deep learning in India has been in the field of medical diagnostics.” There are a few startups in Hyderabad that do this. Not a lot has been done in drug design. However, I won’t say we have missed the bus or anything. The field is still brand new and there is ample scope for future activity,” he said.

The post ‘Deep Learning’ picking up fast in India: Experts appeared first on Artificial Intelligence.

Artificial intelligence (AI) continues to expand its footprint in the enterprise and the economy. That’s the word from the AI Index, an annual data update from Stanford University’s Human-Centered Artificial Intelligence Institute. The index tracks AI growth across a range of metrics, from papers published to patents granted to employment numbers.

In terms of total employment, while AI-related jobs are but a small fraction, the share is rapidly expanding. In the U.S., the share of jobs in AI-related topics increased from 0.26% of total jobs posted in 2010 to 1.32% in October 2019 — or five-fold growth. The highest share is in machine learning, with 0.51% of total jobs, the report notes. AI labor demand is growing especially in high-tech services and the manufacturing sector. “The traditional services sector, which includes construction, arts, public administration, healthcare and social assistance, demonstrates a relatively lower demand for AI jobs,” the report states.

A majority of large companies, 58%, report adopting AI in at least one function or business unit in 2019, up from 47% in 2018, the report states. At the same time, “only 19% of large companies surveyed say their organizations are taking steps to mitigate risks associated with explainability of their algorithms, the report adds, with 13% are mitigating risks to equity and fairness, such as algorithmic bias and discrimination.”

The index also tracked global private AI investment as a telling metric — which topped $70 billion. Mergers and acquisitions spurred by AI totaled $37 billion, with another $34 billion associated with initial public offerings. Globally, investment in AI startups grew rapidly over the past decade, from a total of $1.3 billion raised in 2010 to more than $40.4 billion. Funding has increased with an average annual growth rate of over 48%.

Among industry sectors, the tech, service sectors and manufacturing saw the greatest rise in demand for AI skills. “Tech service sectors such as information services have the highest proportion of AI jobs posted — 2.3% of the total jobs posted — followed by professional, scientific and technical Services (2%).”

In terms of AI projects, autonomous vehicles received the largest share of global investment over the last year with $7.7 billion, or about 10% of the total. Drug, cancer and therapy received $4.7 billion, or more than six percent.

Other contenders for funding include facial recognition, video content, fraud detection and finance.

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