"Investors for the most part are familiar with whistleblowing systems and protections and really prize whistleblower protections because, at the end of the day, they do protect long-term investors like Trillium," said Jonas Kron, the firm's chief advocacy officer. The Boston-based firm's proposal, which was submitted in December and could be voted on during Alphabet's shareholder meeting this year, cited several examples of alleged retaliation against workers, saying the incidents were red flags about potential internal problems related to culture, ethics and human rights. Trillium's proposal calls for the company to make the report public. Our Morning Risk Report features insights and news on governance, risk and compliance. A spokeswoman for Alphabet declined to comment.

A few weeks ago, we covered ARM's announcement that it would be delivering a suite of AI hardware IP for Deep Learning, called Project Trillium. ARM announced at the time that third party IP could be integrated with the Trillium platform, and now ARM and NVIDIA have teamed up to do just that. Specifically the two companies will integrate NVIDIA's IP for the acceleration of Convolutional Neural Networks (CNNs), the bread and butter for image processing and visually guided systems such as vehicles and drones. Without a lot of fanfare, NVIDIA's Deep Learning Accelerator (NVDLA) was open-sourced last fall, providing free Intellectual Property (IP) licensing to anyone wanting to build a chip that uses CNNs for inference applications (inference, for those unfamiliar, is the processing of a trained neural network). The crying sound you're now hearing around the world is probably a bunch of well-funded startups and their investors who thought that a dozen guys in a garage could out-engineer NVIDIA when it came to CNN accelerator chips.

Two important changes to the datacenter are happening in the same year--one on the hardware side, another in software. And together, they create a force big enough to blow away the clouds, at least over the long haul. As we covered this year from a datacentric (and even supercomputing) point of view, 2018 is the time for Arm to shine. With a bevy of inroads to commercial markets at the high-end all the way down to the micro-device level, the architecture presents a genuine challenge to the processor establishment. And now, coupled with the biggest trend since cloud or big data (which ironically could be replaced in various ways by this contender) machine learning comes steamrolling along, changing the way we could think about the final product of training--inference.

We are victims of one common superstitionthe superstition that we understand the changes that are daily taking place in the world because we read about them and know what they are. The anthropological stories and the concept of memes were brought to my attention several years ago by Lynn Conway Much of the vision and some of the material was drawn from a paper that we worked on together but never published. The important distinction between process and product, was made crisp for me by John Seely Brown, who also has encouraged and made possible projects like Trillium, which I watched with interest, and like Colab, in which I participated. Joshua Lederberg kindled my interest in biological issues and a respect for knowledge processes and their partial automation that has not faded Dan Bobrow listened to my ramblings on several runs, agonized over my confusions, helped to get the kinks out of the arguments, and suggested the title for the article Sanjay Mittal and I have spent many hours speculating together on the issues in building community knowledge bases and knowledge servers and in understanding the principles of knowledge competitions Austin Henderson helped me to understand the Trillium story and to report it accurately. Austin and Sanjay hounded me to say, more precisely, what a knowledge medium is Agustin Araya and Mark Miller participated in a Colab session in which we tried to jointly lay out these ideas, and together asked me to make the prescriptions clearer Ed Feigenbaum persuaded me to be more precise in the discussion of the limits of today's expert systems technology Thanks to Agustin Araya, Dan Bobrow, John Seely Brown, Lynn Conway, Bob Engelmore, Ed Feigenbaum, Felix Frayman, Gregg Foster, Austin Henderson, Ken Kahn, Mark Miller, Sanjay Mittal, Julian Orr, Allen Sears, Lucy Suchman, and Paul Wallich for reading early drafts of this paper and for helping to clarify the ideas and improve the article's readability Stephen Cross triggered the writing of this article when he invited me to give the keynote address at the Aerospace Applications of Artificial Intelligence Conference in Dayton, Ohio, in September 1985.

Public opinion about artificial intelligence is schizophrenic. "It will never work" versus "It might cost me changes that are daily taking place in the This dichotomy of attitudes reflects a collective world because we read about them and know confusion about AI.