Many recent research efforts seek to construct neural networks capable of executing algorithmic computation, primarily to endow them with reasoning capabilities – which neural networks typically lack. Critically, every one of these papers generates its own dataset, which makes it hard to track progress, and raises the barrier of entry into the field. The CLRS benchmark, with its readily exposed dataset generators, and publicly available code, seeks to improve on these challenges. We've already seen a great level of enthusiasm from the community, and we hope to channel it even further during ICML. The main dream of our research on algorithmic reasoning is to capture the computation of classical algorithms inside high-dimensional neural executors.

The current series of articles on MLOps started with an analogy with DevOps. To explain how software updates happen, I am introducing the Known Unknown matrix. The so-called "known unknown" matrix is popularized by Donald Rumsfeld and divides our knowledge into four quadrants: Applied to software development, let us consider what the knowns and the unknowns for the feature team would be. As an example, let us assume that the software is an OCR program that recognizes car license plate numbers based on heuristic algorithms. In Software 1.0 (the traditional software), bugs and feature requests are two cases when software updates are needed.

How AI Might Help Revive the Virtuous Cycle of Moore's Law In the wake of the global chip shortage, TSMC has reportedly raised chip prices and delayed the 3nm process. Whether or not it is accurate or indicative of a long-term trend, this kind of news should alert us to the worsening impact of the decline of Moore's Law and compel a rethinking of AI hardware. Would AI hardware be subject to this decline or help reverse it? Suppose we want to revive the virtuous cycle of Moore's Law, in which software and hardware propelled one another, making a modern smartphone more capable than a past-decade warehouse-occupying supercomputer. A popularly accepted post-Moore virtuous cycle, in which bigger data leads to larger models requiring more powerful machines, is not sustainable. We can no longer count on transistor shrinking to build wider and wider parallel processors unless we redefine parallelism. Nor can we rely on Domain-Specific Architecture (DSA) unless it facilitates and adapts to software advancement.

In a paper published in the peer-reviewed scientific journal Nature last week, scientists at Google Brain introduced a deep reinforcement learning technique for floorplanning, the process of arranging the placement of different components of computer chips. The researchers managed to use the reinforcement learning technique to design the next generation of Tensor Processing Units, Google's specialized artificial intelligence processors. The use of software in chip design is not new. But according to the Google researchers, the new reinforcement learning model "automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area." And it does it in a fraction of the time it would take a human to do so. The AI's superiority to human performance has drawn a lot of attention.

Mobility systems have been integrated for the past decade and continue to shape the user experiences in cars. Experiences Per Mile (EPM) is a movement centered on addressing the mobility industry's transformation to an experience-driven vision, which puts the consumer first. The pandemic accelerated digital transformation and most everything else about how technology drives business forward. We look at the top trends for the coming year. The Experiences Per Mile Advisory Council was formed to encourage collaboration among an exclusive group of automotive executives, analysts, and industry insiders regarding the changing value chains in automotive being driven by the connected movement.

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

It's difficult to make predictions, especially about the future. But one fairly safe prediction is that data will continue eating the world in 2020 and the coming decade. The most important tech trend since the 1990s will no doubt accentuate its presence in our lives, for better or for worse. At the beginning of the last decade, IDC estimated that 1.2 zettabytes (1.2 trillion gigabytes) of new data were created in 2010, up from 0.8 zettabytes the year before. The amount of the newly created data in 2020 was predicted to grow 44X to reach 35 zettabytes (35 trillion gigabytes).

In 2015, world leaders at the United Nations adopted 17 Sustainable Development Goals (SDGs) with the ambition by 2030 to end poverty, promote prosperity and well-being for all, and protect the planet. Technologies have long been seen as effective vehicles of growth, but the impact of recent forms like digitization, now morphing into artificial intelligence (AI), have been subject to debate. Tech-optimists believe that AI is the catalyst to a unique era of opportunity for many; skeptics argue that this optimistic view is far from warranted. Although digitization has created some prosperity, many digital divides remain. In the case of AI, many fear that it is a major substitute for work, replacing jobs that are often the only source of income for many citizens.

Perhaps the single most important algorithmic distinction between "born digital" enterprises and legacy companies is not their people, data sets, or computational resources, but a clear real-time commitment to delivering accurate, actionable customer recommendations. Recommendation engines (or recommenders) force organizations to fundamentally rethink how to get greater value from their data while creating greater value for their customers. "Build real recommendation engines fast" is my mission-critical recommendation to companies aspiring -- or struggling -- to creatively cross the digital divide. Use recommenders to make it easier to gain better insight into customers while they're getting better information about you. Recommenders' true genius comes from their opportunity to build virtuous business cycles: The more people use them, the more valuable they become; the more valuable they become, the more people use them.

Dr. Pradeep Dubey is an Intel Fellow and Director of Parallel Computing Lab (PCL), part of Intel Labs. Traditionally, there has been a division of labor between computers and humans where all forms of number crunching and bit manipulation are left to computers, whereas intelligent decision-making is left to us humans. We are now at the cusp of a major transformation that can disrupt this balance. This disruption is triggered by an unprecedented convergence of massive compute with massive data, and some recent algorithmic advances. This confluence has the potential to spur a virtuous cycle of compute.