AI in Business

Machine learning will redesign, not replace, work

The conversation around artificial intelligence and automation seems dominated by either doomsayers who fear robots will supplant all humans in the workforce, or optimists who think there’s nothing new under the sun. But MIT Sloan professor Erik Brynjolfsson and his colleagues say that debate needs to take a different tone.

“Our findings suggest that a shift is needed in the debate about the effects of AI: away from the common focus on full automation of entire jobs and pervasive occupational replacement toward the redesign of jobs and reengineering of business practices,”

Read more at: https://phys.org/news/2018-06-machine-redesign.html#jCp

AI is having a large effect on the economy

A review of the evidence that artificial intelligence (AI) is having a large effect on the economy. Across a variety of statistics—including robotics shipments, AI startups, and patent counts—there is evidence of a large increase in AI-related activity. We also review recent research in this area which suggests that AI and robotics have the potential to increase productivity growth but may have mixed effects on labor, particularly in the short run. In particular, some occupations and industries may do well while others experience labor market upheaval. We then consider current and potential policies around AI that may help to boost productivity growth while also mitigating any labor market downsides including evaluating the pros and cons of an AI specific regulator, expanded antitrust enforcement, and alternative strategies for dealing with the labor-market impacts of AI, including universal basic income and guaranteed employment.

By Jason Furman - Harvard Kennedy School; Peterson Institute for International Economics and Robert Seamans - New York University (NYU) - Leonard N. Stern School of Business

Read more here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3186591

AI in Hardware

This New Chip Design Could Make Neural Nets More Efficient and a Lot Faster

Neural networks running on GPUs have achieved some amazing advances in artificial intelligence, but the two are accidental bedfellows. IBM researchers hope a new chip design tailored specifically to run neural nets could provide a faster and more efficient alternative.

Read more at: https://singularityhub.com/2018/06/11/this-new-chip-design-could-make-neural-nets-more-efficient-and-a-lot-faster/#sm.000013xw6q33p2csvu2wogtep1rj3

AI in Practice

Machine Learning Challenges from Kaggle CEO, Anthony Goldbloom

Anthony John Goldbloom is the founder and CEO of Kaggle, a Silicon Valley start-up which has used predictive modeling competitions to solve problems for NASA, Wikipedia, Ford and Deloitte.  Take a look at his talk on this YouTube video:

DeepPhish: Simulating Malicious AI

If AI is being used to prevent attacks, what is stopping cyber criminals from using the same technology to defeat both traditional and AI-based cyber-defense systems? This hypothesis is of urgent importance due to the lack of research on the potential consequences of the weaponization of Machine Learning as a threat actor tool.

Read the paper here: https://albahnsen.com/wp-content/uploads/2018/05/deepphish-simulating-malicious-ai_submitted.pdf

Who’s singing? Automatic bird sound recognition with machine learning – Dan Stowell

Bird sounds are complex and fascinating. Can we automatically “understand” them using machine learning? Dan describes his academic research into “machine listening” for bird sounds. He tells you why it’s important, methods used, Python libraries, open code and open data that you can use. Examples of the latest research, and a successful commercial recognition app (Warblr).

Machines that learn by doing

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016.

AI in Code

Dense Human Pose Estimation In The Wild

Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. DensePose-RCNN is implemented in the Detectron framework and is powered by Caffe2.

Fork the code from Github – https://github.com/facebookresearch/DensePose

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