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The AI Index 2019 Report – 16 December, 2019

The AI Index is a starting point for informed conversations about the state of artificial intelligence (AI). The report aggregates a diverse set of metrics, and makes the underlying data easily accessible to the general public.

The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence. Its mission is to provide unbiased, rigorously-vetted data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Expanding annually, the Report endeavors to include data on AI development from communities around the globe. More….

AI and Compute – 16 May, 2018

We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period).[1]

Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.

We’ve updated our analysis with data that span 1959 to 2012. Looking at the data as a whole, we clearly see two distinct eras of training AI systems in terms of compute-usage: (a) a first era, from 1959 to 2012, which is defined by results that roughly track Moore’s law, and (b) the modern era, from 2012 to now, of results using computational power that substantially outpaces macro trends. More….

Global AI Survey – November, 2019

Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers shows the way.

Adoption of artificial intelligence (AI) continues to increase, and the technology is generating returns.  The findings of the latest McKinsey Global Survey on the subject show a nearly 25% year-over-year increase in the use of AI in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business.  A majority of executives whose companies have adopted AI report that it has provided an uptick in revenue in the business areas where it is used, and 44% say AI has reduced costs. More….

How the future of computing can make or break the AI revolution

June 2019: The way forward – The regulatory constraints and the energy and bandwidth costs associated with centralized super-computing are likely to drive data storage and computing to the edge. The imminent end of Moore’s law is likely to drive the emergence of more specialized computing architectures, focused on changing the way processing units and individual circuits are structured, in order to achieve performance gains without further shrinking the size of transistors or processors. More….

How In-Memory Computing Powers Artificial Intelligence – May 2019

AI is designed to process and interpret vast sums of data (aka Big Data), and while humanity has always generated a lot of data, the volumes in the last few years have spiked sharply. Right now we are generating 2.5 quintillion bytes of data, per day, and this number is just a preview. Understanding what is in this non-stop stream of data is impossible as a manual task, and entirely impractical for earlier generations of information systems.

However, the rise of cloud (that is, distributed data architectures), in-memory processing (1000x faster than what preceded it), 5G networks (100X faster) and next-gen chips are suddenly creating a framework where processing quintillions of bytes is not only feasible; it’s already happening. More….

Increase compute power to enable AI – May 2018

The evolution of artificial intelligence (AI) techniques and the increased gathering of data require an increasing ability to perform computations. Without a corresponding increase in the availability of compute power, AI would not be able to take advantage of these. More….

AI development is starting to slow down, Facebook head of AI says

December, 2019 – Some experts believe that AI in its current form is starting to slow down, reaching its maximum capacity — at least for the time being. In an interview with Wired, Facebook’s head of AI, Jerome Pesenti, theorized that the development of artificial intelligence and machine learning is about to “hit the wall.” According to Pesenti, the deep learning mechanisms that currently help power and push the advancement of AI are pushing up against their limitations. Some of that has to do with the lack of necessary computing power to continue improving. He told Wired that deep learning works best when it can be scaled up and given more room to operate. More….

AI is changing the entire nature of compute – June, 2019

Machine learning, especially deep learning, is forcing a re-evaluation of how chips and systems are designed that will change the direction of the industry for decades to come. The world of computing, from chips to software to systems, is going to change dramatically in coming years as a result of the spread of machine learning. In practice they will be different from the way they have been built and used up to now. More….

What a little more computing power can do – September, 2019

Commercial cloud service providers give artificial intelligence computing at MIT a boost. Neural networks have given researchers a powerful tool for looking into the future and making predictions. But one drawback is their insatiable need for data and computing power (“compute”) to process all that information. At MIT, demand for compute is estimated to be five times greater than what the Institute can offer. More…,

The computing power needed to train AI is now rising seven times faster than ever before
November, 2019 – An updated analysis from OpenAI shows how dramatically the need for computational resources has increased to reach each new AI breakthrough. More….
State Of AI And Machine Learning In 2019 – September, 2019

2019 was a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals. More….

Top Trends on the Gartner Hype Cycle for Artificial Intelligence – September, 2019

This Gartner Hype Cycle highlights how AI is reaching organizations in many different ways. Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner’s 2019 CIO Agenda survey. AI is reaching organizations in many different ways compared with a few years ago, when there was no alternative to building your own solutions with machine learning (ML). More….


Background sources:

  • FT: Hardware revolution pushes AI into the mainstream More….
  • FT: Why China’s AI companies are struggling to evolve beyond surveillance More….
  • Intel: Guide to Developing an AI Infrastructure Strategy More…. Whitepaper: More….
  • Hpc: Architecting for AI Workloads More….
  • Red hat: Why Use Containers, Kubernetes, and OpenShift for AI/ML Workloads? More….
  • IBM: Considering IT Infrastructure in the AI Era More….
  • AI Hardware and the Battle for More Computational Power More….
  • McKinsey: The promise and challenge of the age of artificial intelligence More….
  • Forbes: AI Deployment Challenges: 5 Tips To Help Overcome The Hurdles More….
  • Futurum Research: Make 2018 the Year You Upgrade Legacy Systems More….
  • Gartner: What AI can and cannot do More….
  • ARM: What AI needs to go mainstream More….
  • Forbes: Three Steps To Embedding Artificial Intelligence In Enterprise Applications More….


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