Samsung SDS - Dec2021 by BizClik Media

Saving lives with AI DIGITAL REPORT 2021

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SAMSUNG SDS

SAVING LIVES WITH AI www.samsungsds.com

SAMSUNG SDS

Using the power of AI and AutoLabel, the IT services giant is looking to alleviate pressure on physicians worldwide

amsung SDS stands for Samsung data services, and it is one of approximately 20 companies that make up the Samsung Group: well-known for manufacturing mobile phones, TV sets, laptops, chips, hard-drives, home electronics like fridges and dishwashers and even operating in heavy industries building ships. One of the core offerings of SDS is the operation of data centres, in which they also run the Brightics AI Accelerator automatedmachine learning (AutoML) platform; enabling users to create AI models much more efficiently, accurately, and quicker than ever before. Dr. Patrick Bangert, Vice President of Artificial Intelligence at Samsung SDSA, directs the AI engineering and AI sciences teams. The AI engineering team develops the Brightics AI Accelerator, providing distributed training and automated machine learning to speed up the creation of an AI model. The AI sciences team makes models, provides expert consulting services, and develops AutoLabel to automatically annotate datasets in preparation for AI modelling. They supply the full spectrum of AI model development, backed by state-of-

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SAMSUNG SDS

the-art technology and human expertise. The AI team sources projects inside and outside of Samsung Group, maintaining their own computing infrastructure and covering a global footprint. The human benefits of automating the imaging process A big focus for Samsung SDS has been the healthcare sector, where AI has been automating imaging processes in areas such as radiology. As Bangert states in no uncertain terms: “The medical system worldwide is overloaded. There are not enough doctors around to fulfill the demand and they are heavily impacted by workflows, bureaucracies, and billing processes. The amount of time that any physician can spend with the real human patient is really a minority of their time. A doctor’s opinion might be wrong about 30% of the time, and so complex diseases – diagnosed Ricky Datta, the director from complex medical of AI engineering at images – are often Samsung SDS, observes; either missed or mis“AI models typically diagnosed. Getting achieve an accuracy a second opinion is in the high 90% range, already a billion-dollar sometimes up to 99.9% market today.” accuracy in certain cases, Second opinions depending on how much typically come from data is available and how other humans. Artificial good quality that data Intelligence provides is. By automating these an objective third party processes, we simply and very neutral second make everything faster. PATRICK BANGERT or third opinion to any The benefit of this would VP OF AI, diagnostic procedure. significantly lower the SAMSUNG SDS Using AI removes the costs from both the variability of any one physician having a providers and the patient’s care costs, while certain amount of experience, because increasing accuracy of diagnosis and speeding the AI model can process vastly more up the overall time from the initial pain point cases and doesn’t forget things. to the start of the treatment cycle.”

“The medical system worldwide is overloaded. There are not enough doctors around to fulfill the demand ”

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This applies to all areas of medical imaging from MRI and CT scans, over X-ray and ultrasound images to regular photos of skin. Zakia Rahman, clinical professor of dermatology at Stanford University School of Medicine, says “dermatology represents two great frontiers for new business models powered by artificial intelligence. Patients

can take mobile phone pictures of their skin and obtain instant reliable diagnoses of any condition, and people may use pictures of their healthy faces and bodies to compare them against society’s beauty standards for precision cosmetics to look better while not looking abnormal. The biggest obstacle to both is having an expertly annotated large www.samsungsds.com

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SAMSUNG SDS

“Annotation or labeling is the principal obstacle in making AI models, which can be reduced 90% by AI” PATRICK BANGERT

PATRICK BANGERT

VP OF AI, SAMSUNG SDS

TITLE: VP OF AI LOCATION: SAN JOSE, CALIFORNIA

dataset of images. We want to create such datasets and models in a partnership with Samsung SDS. The skin, in addition to being the largest organ, is also the most visible. This accessibility has resulted in an exponential increase in the number of images. The skin is, and will likely continue to be, the most imaged organ. While there is potential for democratising diagnosis for the general public, the impact to mental health through image distortion cannot be overstated.”

EXECUTIVE BIO

Patrick heads the AI Division at Samsung SDSA. He is responsible for Brightics AI Accelerator, a distributed ML training and automated ML product, and AutoLabel, an automatic image data annotation and modelling tool primarily targeted at the medical imaging community. Among his other responsibilities is to act as a visionary for the future of AI at Samsung. Before joining Samsung, Patrick spent 15 years as CEO at algorithmica technologies, a machine learning software company serving the chemicals and oil and gas industries. Prior to that, he was assistant professor of applied mathematics at Jacobs University in Germany, as well as a researcher at Los Alamos National Laboratory and NASA’s Jet Propulsion Laboratory. Patrick obtained his machine learning PhD in mathematics and his Masters in theoretical physics from University College London.

Samsung’s ultimate AI toolkit: the Brightics AI Accelerator The jewel in the crown of Samsung’s AI efforts is the AutoLabel facility in the Brightics AI Accelerator platform. Dr. Hankyu Moon, the leader of team behind AutoLabel answers in three parts why this toolkit is so crucial: “Firstly, in the case of imaging, for example, an annotation is typically a manual drawing of an outline around something that's important and assigning the category name to it. We see this in street scenes for autonomous vehicles, where we might draw an outline around people to say ‘okay, this is a person who denotes an obstacle that the car must not hit’.” These annotations, he explains, are made very quickly by the AutoLabel facility, which www.samsungsds.com

SAMSUNG SDS

Samsung AI: Saving lives with AI

“ By automating certain clerical and image analysis tasks, physicians and patients can deal more with the planning of treatment” PATRICK BANGERT VP OF AI, SAMSUNG SDS

effectively sorts the images in the correct order that provide the information. The secret is that only a few, typically 10%, of the images in a dataset contain virtually all the information – but finding them is challenging. 10

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The second part, according to Moon, is related to the Brightics AI Accelerator AutoML platform, which conducts feature engineering, which means pre-processing the data in such a way as to bring out those aspects of the data that are most informative, and selecting the correct model for the situation. In Moon’s opinion, artificial intelligence is really an umbrella term for many distinct methods and model types, including neural networks, support vector machines, random forest, or decision trees. It’s a case of picking the right one. Moon explained that the third part that AI can play in healthcare imaging is hyper parameter tuning: “The algorithm that trains one of these models has parameters of its own. They are set typically by a human being, which then leads to a trial-

and-error process of tuning these things correctly. AutoML tunes them for you automatically.” When it comes down to the AI accelerator, distributed training is another tactic of Samsung’s as Datta explains: “We utilise more than one graphics processing unit (GPU). To help speed up the process, we may use many hundreds of these GPU processors, simultaneously distributed over many computers to execute a single training task.” With the AI accelerator as the go-to toolkit for Samsung SDS, the aim is to work on all the areas of efficiency, labelling, tuning of the models and distributed training in parallel, so they can “execute the artificial intelligence workflow a lot more quickly and efficiently

and actually arrive at a much more accurate model in the end.” said Datta. Challenges in healthcare imaging Nasim Eftekhari, director of applied AI and data science at City of Hope, a worldrenowned cancer research and treatment organisation near Los Angeles, says, “All supervised models that we use today have been trained on labeled data. Regardless of industry, labelling the data for training is always the most expensive part of any AI solution. It is very time-consuming and expensive because doctors and healthcare professionals have to annotate these images, and each image takes hours and hours of time. City of Hope wanted to explore www.samsungsds.com

SAMSUNG SDS

HANKYU MOON

RICKY DATTA

TITLE: DIRECTOR OF AI SCIENCE

TITLE: DIRECTOR OF AI ENGINEERING

COMPANY: SAMSUNG SDS

LOCATION: CALIFORNIA

Dr. Hankyu Moon has been a Director of AI Science as part of the AI Team, Samsung SDS Research America since 2015. He received a B.S. degree in Mathematics Education from Seoul National University and a Ph.D. degree in Electrical and Computer Engineering from University of Maryland College Park. His R&D career in AI started when he joined Center for Automation Research, University of Maryland in 1996 as a graduate student. Prior to joining Samsung, he worked at NEC Research Institute and Hughes Research Laboratories as a research scientist. Throughout his career he has collaborated with worldclass researchers, producing numerous publications and patents in the areas of Computer Vision, Deep Learning, and Data Mining. At Samsung, he and his colleagues have developed Data Efficient AI for several years, resulting in successful launching of the AutoLabel solution that leverages Representation Learning, Active Learning, and Unsupervised Learning.

Ricky has been responsible for the technical direction of Brightics AI Accelerator since day one. He oversees all aspects of design and implementation of AI Accelerator. A seasoned Enterprise Software Leader, Ricky is an expert in Machine Learning, Deep Learning and Big Data and works together with customers to ensure all their specialist technical needs are met. He started his career in small software startups to specialise in Enterprise Software. Ricky has a bachelor’s degree in Computer Science from Indian Institute of Technology, New Delhi.

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EXECUTIVE BIOS

SAMSUNG SDS

ZAKIA RAHMAN

NASIM EFTEKHARI

TITLE: C LINICAL PROF OF DERMATOLOGY

TITLE: DIRECTOR OF APPLIED AI & DATA SCIENCE

COMPANY: STANFORD UNIVERSITY

COMPANY: CITY OF HOPE

LOCATION: CALIFORNIA

LOCATION: LOS ANGELES

Dr. Zakia Rahman, MD, FAAD is a Clinical Professor of Dermatology at Stanford where she is Director of the Resident Laser and Aesthetic Clinic. She is also Assistant Chief and Co-Director of Dermatologic Surgery at the Livermore Division of the PAVAHCS. She serves on the advisory board of multiple medical device companies where her collaborative work as the principal investigator has resulted in FDA clearances for consumer and professional class lasers. She is also a subject matter expert for venture and angel investors in dermatology medical devices. Dr. Rahman is the Chair of the Stanford School of Medicine Faculty Senate. She is passionate about physician professional fulfillment and is a member of the Wellness Directors Council for Stanford Hospital. She sits on the Diversity Taskforce, Electronic Medical Records Taskforce and the Media Expert Panel for the American Academy of Dermatology. Dr. Rahman is also a member of the Federal Affairs Work Group and Research Workgroup for the American Society for Dermatologic Surgery.

Nasim Eftekhari leads the Applied AI and Data Science Department at City of Hope, aworld-renowned research and treatment organisation for cancer, diabetes and other life-threatening diseases located near Los Angeles. She helps supervise teams responsible for applying machine learning in clinical decision support and patient care, research andprecision medicine, as well as operations and finance. Before joining City of Hope in 2017, Nasim co-founded an AI company focused on social media data mining and financial markets and political events prediction. She continues to advise startup companies on successful implementation of AI solutions. Nasim holds a master’s degree in computer science from Southern Illinois University and a bachelor’s degree in computer engineering from Khaje Nasir Toosi University in Tehran, Iran. On a personal level, Nasim enjoys an active lifestyle in sunny Los Angeles and has a passion for fitness and sports. She plays soccer and beach volleyball and goes to the gym every day.

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SAMSUNG SDS

DAN WATERS TITLE: DIRECTOR OF BUSINESS DEVELOPMENT AND PRODUCT

Dan leads Business Development for the AI Team at Samsung SDS America and is responsible for the go-to-market strategy. As a Senior Business Development leader, Dan’s devoted his career to bringing to market disruptive products, building teams to scale rapidly and quickly growing revenue for companies like xnor.ai, Zebra Technologies, Apple, Siri, and Motorola. Previously, the VP of Marketing & Business Development for XNOR. ai, Dan validated the commercial product/market fit for XNOR. AI’s research technology spun out of the Paul Allen Institute for Artificial Intelligence (Ai2), built brand awareness and executed proof-of-concept, commercial and government agreements to close deals, recognise revenue, and grow the sales pipeline. In 2009, Dan introduced Siri to Apple and was retained by Apple following its acquisition of Siri in 2010. Dan is an MBA graduate of the Kellogg Graduate School of Business, has a BSEE from Iowa State University and is conversant in Spanish.

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EXECUTIVE BIO

LOCATION: SAN JOSE, CALIFORNIA

using Samsung’s active learning in autolabeling images. There is no question about the value that deep learning brings to auto-labelling pathology images. We are really excited to explore the idea of a system that can learn little-by-little from pathologists.”A doctor takes 20 to 30 minutes to draw their annotation on top of a single image. And to train an AI algorithm to recognise anything meaningful, thousands of such annotated images are needed. The process is the most timeconsuming and expensive of the entire AI workflow. It’s the primary obstacle that the AutoLabel facility of the Brightics AI Accelerator is there to overcome, using a technique called active learning to speed that process up by 90%, so it will reduce the amount of human labour from 100%

“ AI has a large role to play in trying to limit the rise of various greenhouse gases. That has a lot to do with how efficiently we can run things” PATRICK BANGERT VP OF AI, SAMSUNG SDS

down to 10% - a very significant gain, usually in the double-digit millions per model. A further challenge for Dan Waters, the director of business development and product at Samsung SDS, is related to his home in the United States, where The Food and Drug Administration (FDA) must approve any physical device used in the medical industry. Waters states that “because governmental agencies are not as advanced in their treatment of artificial intelligence, getting a physical device approved can take a long time. The established processes and personnel are simply not familiar with this new technology, as far as they're concerned, AI is relatively new.” www.samsungsds.com

SAMSUNG SDS

Medical Diagnostics by Artificial Intelligence

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AI applications beyond healthcare “Climate change is the one great challenge of humanity” says Bangert. “AI has a large role to play in trying to limit the rise of various greenhouse gases. That has a lot to do with how efficiently we can run things in factories, vehicles, airplanes, and people's homes,” he continued. Samsung’s consumer electronics and home automation devices can help through their efficient use of energy and in helping to reduce the total carbon footprint of any home or building. Data centres – especially because of AI workloads – are often blamed for exacerbating the climate impact of the modern world but

this is short sighted. “Other carbon emitting factors of the physical world are circumvented due to AI. Take, for example, the primary use of the internet, which is shopping. You go to a website, buy something, and that is delivered by the postal service. That is a lot more efficient from a greenhouse gas perspective than if you take your SUV by yourself and drive 20 miles to the next mall.” AI, ethics and earning trust One of the biggest cultural disconnects with AI is trust. Companies such as Samsung SDS take their ethical responsibilities very seriously: “We make sure that not only have we created the model in an ethical manner, but also that we are using it in an ethical manner,” says Bangert. This leads to explainability, where the AI model should not just deliver the answer, but it should deliver an explanation of why that is the correct - and best - answer. www.samsungsds.com

Elevator Pitch: Brightics AI Accelerator

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SAMSUNG SDS

“AI has the potential to revolutionise healthcare in diagnosis, treatment, and clerical processes creating new jobs and allowing existing staff to be more efficient” PATRICK BANGERT VP OF AI, SAMSUNG SDS

“It must present the information in a way that's understandable for the audience. In healthcare, this is of course the doctor and the patient. So, if you're going to tell people that they have cancer – you better say why.” said Bangert. It’s clear that trust is something that Samsung SDS must generate alongside the global AI community. But beyond just the imaging domain, Samsung is exploring electronic medical records that go beyond the pure image to unify the analysis of natural language processing, which would be relevant to the text that the doctor writes.

“The ‘multimodal’ AI system can take care of image input, text input, numerical input, and unify it to a holistic picture of what might be the case, and what should be done about it.” said Bangert. Bangert focuses on the good that AI can bring: “By automating certain tasks, physicians and patients can deal more with the planning of the treatment, discussing how treatment might impact the patient's life. Doctors can act as doctors rather than expert data processors.” Another criticism of AI-enabled automation is that it’s making a lot of people redundant, but that is not true according to Bangert: “AI, in general, is creating new jobs and allowing people to work in very different areas – in medicine specifically. Because of the lack of personnel, this is extremely welcome. And of course, patients expect doctors to be patient-facing rather than be internal facing.” Discover Brightics AI Accelerator