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Advancing Causal Reasoning in Machine Learning

The ability to reason about causality is one property that sets human intelligence apart from Artificial Intelligence (AI). Modern AI algorithms perform well on clearly defined pattern recognition tasks but fall short of generalising in the ways that human intelligence can.

This often leads to unsatisfactory results on tasks that require extrapolation from training such as recognising events or objects in contexts that are different from the training set. To address this problem, U.S. researchers have built a high-fidelity simulation environment that is designed for developing algorithms that improve causal discovery and counterfactual reasoning of AI.

The researchers illustrated a problem by making an analogy of AI in a different context. If a self-driving car were confined to the streets of a neighbourhood in Arizona with few pedestrians, wide, flat roads and street signs with English writing, and then they deployed the car on the narrow, busy streets of Delhi, where street signs are written in Hindi, pattern recognition would be insufficient to operate safely.

The pattern in their training set would be very different from the deployment context. Yet, somehow humans can adapt so quickly to situations that they have not previously observed that someone with an Arizona state-issued driving license is allowed to drive a car in India.

The recent paper took a closer look at this problem and the researchers proposed a new high-fidelity simulation environment. They designed a high-fidelity simulation with the ability to control causal structure. A more robust AI model does more than simply learning patterns. It captures the causal relationships between events.

Humans do this very well, which enables them to reason about the world and adapt more quickly and generally with fewer examples. Humans often do so by making a specific action–an intervention–in the environment, observing the result, building a mental model and then repeating this process to refine the model.

Using interventions is one way to learn about systems, such as the behaviour of traffic in a city and their underlying causal structure. The presence of confounders–factors that impact both the intervention and the outcomes–can complicate the task of causal learning. Imagine driving in a city and noticing an ambulance. In this context, the behaviour of other drivers would be a confounder that might impact the path of both the ambulance and a possible follower vehicle.

Machine learning researchers are increasingly developing models that involve causal reasoning to increase robustness and generalisability. Computer graphics simulations have proven helpful in investigating problems involving causal and counterfactual reasoning as they provide a way to model complex systems and test interventions safely.

The parameters of synthetic environments can be systematically controlled, thereby enabling causal relationships to be established and confounders to be introduced. However, much of the prior work has approached this via a relatively simplistic set of entities and environments.

This leaves little room to explore, and control for, different causal relationships among entities. One challenge involved in creating more realistic systems is the complexity involved in dictating every state and action of every agent at every timestep. To help address this problem, the researchers proposed giving agency to each entity to create simulation environments that reflect the nature and complexity of these types of temporal real-world reasoning tasks.

This includes scenarios where each entity makes decisions on its own while interacting with each other, like pedestrians in a crowded street and cars on a busy road. Agency provides the ability to define scenarios at a higher level, rather than specifying every single low-level action. They can now more easily model scenarios such as the car following the ambulance described above.

The environment that the researchers have developed reflects the real-world, safety-critical scenario of driving. They seek to build a simulation environment that enables controllable scenario generation that can be used for temporal and causal reasoning. This environment allows them to create complex scenarios including different types of confounders with relatively little effort.

AI has been adopted for a variety of functions, such as predicting human behaviour from videos. As reported by OpenGov Asia, U.S. engineering researchers unveil a computer vision technique for giving machines a more intuitive sense of what will happen next by leveraging higher-level associations between people, animals, and objects.

The algorithm is a step toward machines being able to make better predictions about human behaviour, and thus better coordinate their actions with humans. The results of the research open several possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.

PARTNER

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.

PARTNER

CTC Global Singapore, a premier end-to-end IT solutions provider, is a fully owned subsidiary of ITOCHU Techno-Solutions Corporation (CTC) and ITOCHU Corporation.

Since 1972, CTC has established itself as one of the country’s top IT solutions providers. With 50 years of experience, headed by an experienced management team and staffed by over 200 qualified IT professionals, we support organizations with integrated IT solutions expertise in Autonomous IT, Cyber Security, Digital Transformation, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

Well-known for our strengths in system integration and consultation, CTC Global proves to be the preferred IT outsourcing destination for organizations all over Singapore today.

PARTNER

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.

SUPPORTING ORGANISATION

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.

PARTNER

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. 

PARTNER

IBM is a leading global hybrid cloud and AI, and business services provider. We help 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 3,000 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.

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