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Unravelling the Deep Learning Black Box Mystery

Stefanie Jegelka, an associate professor in MIT’s Department of Electrical Engineering and Computer Science, investigated the prospect of unravelling the “black boxes” enigma left by the deep learning process. The so-called black box nomenclature developed because of the complexity of deep learning nodes, and even the scientists who built them do not understand everything underneath the shell.

Researchers still need to learn everything that happens inside a deep-learning model or how it might impact how a model learns and behaves. But Jegelka is excited to continue researching these things since she isn’t happy with the “black box” prompt.

“With machine learning, you can achieve much, but only if you have the correct model and data. So building an understanding relevant to practice will help us design better models and help us understand what is going on inside them so we know when we can deploy a model and when we can’t. It is not a black-box device that you throw at data, and it works,” said the woman, who is also a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS) (IDSS).

Deep learning models frequently outperform people in the real world, such as detecting financial crime from credit card activity or identifying cancer in medical pictures. But what are these deep learning models learning?

These strong machine-learning models are often built on artificial neural networks with millions of nodes processing data to create predictions. She then delved into deep learning to understand what these models can learn, how they behave, and how to incorporate specific prior knowledge into these models.

Building an understanding that is relevant in deep learning practice, according to Jegelka, will help researchers develop a better model and comprehend what is happening. The more she studied machine learning, the more she was drawn to the challenges of understanding how models behave and how to manipulate this behaviour.

Graph models

Jegelka is specifically interested in optimising machine-learning models with graph input information. Graph data presents unique issues because it contains information about individual nodes and edges and the structure — what is related to what. Furthermore, graphs feature mathematical symmetries that must be honoured by the machine-learning model such that, for example, the same chart always yields the same forecast.

However, incorporating such symmetries into a machine-learning model is typically tricky. Take, for example, molecules. Molecules can be represented as graphs, with vertices representing atoms and edges representing chemical bonds between them. As a result, pharmaceutical companies may use deep learning to rapidly anticipate the properties of numerous compounds, reducing the number of molecules that must be physically tested in the lab.

Jegelka investigates approaches for developing mathematical machine-learning models that can effectively take graph data as input and produce something else, in this case, a prediction of the chemical properties of a molecule. This is especially difficult because the qualities of a molecule are influenced not only by the atoms within it but also by the connections between them. Traffic routing, chip design, and recommender systems are some applications of machine learning on graphs.

Deep learning consistency

What motivates Jegelka is her interest in the principles of machine learning, particularly the issue of robustness. Frequently, a model performs well on training data but degrades when deployed on slightly different data.

For instance, the model may have been trained on small molecular graphs or traffic networks, but the charts it encounters once deployed are much larger or more complex. Building past knowledge into a model can increase its reliability but recognising what information the model requires and how to incorporate it is more complicated.

She approaches this challenge by fusing her interest in algorithms and discrete mathematics with her enthusiasm for machine learning. She feels the model will be unable to learn everything due to various difficulties in computer science. However, how you build up the model determines what you can and cannot comprehend.

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Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

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As a Titanium Black Partner of Dell Technologies, CTC Global Singapore boasts unparalleled access to resources.

Established in 1972, we bring 52 years of experience to the table, solidifying our position as a leading IT solutions provider in Singapore. With over 300 qualified IT professionals, we are dedicated to delivering integrated solutions that empower your organization in key areas such as Automation & AI, Cyber Security, App Modernization & Data Analytics, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and consulting services provider, helping clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,800 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity, and service. For more information, visit www.ibm.com