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Microsoft Research Blog

Recent successes in machine intelligence hinge on core computation ability to efficiently search through billions of possibilities in order to make decisions.

Human intelligence, on the other hand, is highly generalizable, adaptive, robust and exhibits characteristics that the current state-of-the-art machine intelligence systems simply are not yet capable of producing.

For example, the earliest work on affect recognition started almost three decades ago, where physiological sensors, cameras, microphones, and so on were used to detect a host of affective responses.

While there is much debate about how consistently and universally people express emotions on their faces and other physiological signals, and whether these really reflect how they feel inside, researchers have successfully built algorithms to identify useful signals in the noisy world of human expressions as well as demonstrated that these signals are consistent with socio-cultural norms.

This architecture challenges the model to generate realistic sounding speech that is faithful to the textual content while maintaining an easily controllable dial for changing the emotion expressed in an independent fashion.

Our model achieves start-of-the-art results across multiple tasks, including style transfer (content and style swapping), emotion modeling, and identity transfer (fitting a new speaker’s voice).

While the recognition, expression and intervention aspects of artificially emotionally intelligent systems have been studied in-depth over the past 20 years, there is a still more compelling form of intelligence—a system that utilizes the affective mechanisms effectively in order to learn better and make choices efficiently.

In the most recent line of work, we hope to explore questions of how to build such affective mechanisms that help our computational processes achieve more than what they accomplish currently.

As a human learns to navigate the world, the body’s (nervous system’s) responses provide constant intrinsic feedback about the potential consequence of action choices, for example, becoming nervous when close to a cliff’s edge or when driving fast around a bend.

The anticipatory response in humans to a threatening situation is for the heart rate to increase, heart rate variability to decrease, and for blood to be diverted from the extremities and for the sweat glands to dilate.

However, rich opportunities exist for building holistic affective computing mechanisms that go a step beyond and to help us build robust, efficient and non-myopic artificial intelligence.

AstraZeneca starts artificial intelligence collaboration to accelerate drug discovery

AstraZeneca and BenevolentAI today began a long-term collaboration to use artificial intelligence (AI) and machine learning for the discovery and development of new treatments for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF).

Scientists from the two organisations will work side-by-side to combine AstraZeneca’s genomics, chemistry and clinical data with BenevolentAI’s target identification platform and biomedical knowledge graph – a network of contextualised scientific data (genes, proteins, diseases and compounds) and the relationship between them.

By combining AstraZeneca’s disease area expertise and large, diverse datasets with BenevolentAI’s leading AI and machine learning capabilities, we can unlock the potential of this wealth of data to improve our understanding of complex disease biology and identify new targets that could treat debilitating diseases.”

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