Sign up for our newsletter and get the latest HPC news and analysis.
Send me information from insideHPC:


IBM Publishes Compendium of Ai Research Papers

COO and VP of AI and Quantum, IBM Research

Today IBM Research released a 2018 retrospective and blog essay by Dr. Dario Gil, COO of IBM Research, that provides a sneak-peek into the future of AI.

Today we released a 2018 retrospective that provides a sneak-peek into the future of AI. We have curated a collection of one hundred IBM Research AI papers we have published this year, authored by talented researchers and scientists from our twelve global Labs. These scientific advancements are core to our mission to invent the next set of fundamental AI technologies that will take us from today’s “narrow” AI to a new era of “broad” AI, where the potential of the technology can be unlocked across AI developers, enterprise adopters and end-users. Broad AI will be characterized by the ability to learn and reason more broadly across tasks, to integrate information from multiple modalities and domains, all while being more explainable, secure, fair, auditable and scalable.

Specifically, the company is following these three trends:

  1. Human intuition that helps us understand cause/effect presents an enormous obstacle to machines – we’ll see improvements in “causal modeling” techniques that will push AI from asking “what happened” to asking “what would happen,” based on possible actions.
  2. Trust and transparency will continue to drive the AI-conversation – with companies applying new anti-bias techniques, in combination with guidance from in-house and industry ethics advisory groups, to make their products and platforms fairer.
  3. We’ll see increased research into how quantum computing may help scale AI models – a key area of exploration as we continue to drive toward real-world use cases for quantum.

In recent years, machines have met and surpassed human performance in many cognitive tasks. Long-standing challenges in Artificial Intelligence have been conquered. But are machines truly intelligent? Can AI reach or surpass human capabilities? How can AI augment and scale human expertise and aid us in solving real-world challenges? Much of the recent progress in AI has relied on data-driven techniques like deep learning and artificial neural networks. Given sufficiently large labeled training data sets and enough computation, these approaches are achieving unprecedented results. As a result, there has been a rapid gain on “narrow AI” – tasks in areas such as computer vision, speech recognition, and language translation. However, a broader set of AI capabilities is needed to progress AI towards solving real-world challenges. In practice, AI systems need to learn effectively and efficiently without large amounts of data. They need to be robust, fair and explainable. They need to integrate knowledge and reasoning together with learning to improve performance and enable more sophisticated capabilities.

These predictions are well-informed by major advances made in 2018, as featured in this curated collection of 100 IBM Research AI papers published this year, authored by talented researchers and scientists from our 12 global labs. The papers discuss scientific advancements that will take the world from today’s “narrow” AI to a new era of “broad” AI, where the potential of the technology can be unlocked across AI developers, enterprise adopters and end-users. Broad AI will be characterized by the ability to learn and reason more broadly across tasks, to integrate information from multiple modalities and domains, all while being more explainable, secure, fair, auditable and scalable.

So where is this all going? Quantum could give AI an assist.

In 2019 we’ll see accelerated traction in quantum experimentation and research, and new research on how quantum computing can potentially play a role in training and running AI models. A core element of quantum algorithms is the exploitation of exponentially large quantum state spaces through controllable entanglement and interference. As the complexity of AI problems grows, quantum computing—which thousands of organizations are already accessing via IBM’s cloud quantum computing services—could potentially change how we approach AI computational tasks.

Sign up for our insideHPC Newsletter

Leave a Comment

*

Resource Links: