Topaz Labs created JPEG to RAW AI to help solve a problem that they believe many people have. Let's say you were sent a JPG file, but it was highly compressed, and you want to edit it. Under normal circumstances, you couldn't edit the photo too much. But after running it through JPEG to RAW AI you have much more potential for editing.

Interesting. I think I’ll try the JPEF to RAW AI software. I have a bunch of 3MP images I shot on a Sony point-and-shoot before I bought my first DSLR (2006). Maybe JPEFG to RAW AI can improve those JPEG images.

Understanding China's AI Strategy by Gregory C. Allen (
In the second half of 2018, I traveled to China on four separate trips to attend major diplomatic, military, and private-sector conferences focusing on Artificial Intelligence (AI). During these trips, I participated in a series of meetings with high-ranking Chinese officials in China’s Ministry of Foreign Affairs, leaders of China’s military AI research organizations, government think tank experts, and corporate executives at Chinese AI companies. From these discussions – as well as my ongoing work analyzing China’s AI industry, policies, reports, and programs – I have arrived at a number of key judgments about Chinese leadership’s views, strategies, and prospects for AI as it applies to China’s economy and national security. Of course, China’s leadership in this area is a large population with diversity in its views, and any effort to generalize is inherently presumptuous and essentially guaranteed to oversimplify. However, the distance is large between prevailing views in American commentary on China’s AI efforts and what I have come to believe are the facts. I hope by stating my takeaways directly, this report will advance the assessment of this issue and be of benefit to the wider U.S. policymaking community.

Gregory C. Allen at the Center for a New American Security has produced a report with analysis and insights into China’s AI strategy with national and cyber-security implications for the commercial, government, and military sectors.

BUILT BY HUMANS. RULED BY COMPUTERS by an author (The Michigan Engineer)
In 2014, a computer system called MiDAS plucked his file out of the Michigan Unemployment Insurance Agency database and calculated, without any human review, that he had defrauded the unemployment system and owed the state of Michigan approximately $22,000 in restitution, penalties and interest – the result of a supposed $4,300 overpayment, plus Michigan’s customary 400 percent penalty and 12 percent interest. Then, still untouched by humans, MiDAS began to collect. It seized more than $10,000 from Russell by electronically intercepting his tax refunds in 2015 and 2016. He knew nothing about the fraud determination until his 2015 tax refund disappeared.

How do you beat something you can’t see? It’s like swinging in the dark. What are the laws that apply to a computer system? And what about us humans? Brian Russell Russell simply couldn’t afford the five-figure hit to his income. For the next two years, he made ends meet the best he knew how – he cancelled family trips, cut back on medical care for his diabetes, worked odd jobs. For a time, he lived in a friend’s basement.

While Russell struggled in the aftermath of the fraud determination, MiDAS kept rolling. An algorithm-based administration and fraud collection system implemented by the state of Michigan, it ran without human intervention for nearly two years between 2013 and 2015. During that time, it accused about 50,000 Michiganders of unemployment fraud. A 2017 review by the state found that more than 90 percent of those accusations were false.

Russell still doesn’t know why MiDAS accused him of fraud. He collected unemployment on and off a few years back when he was working as a journeyman electrician. Like generations of electricians before him, his union filed for unemployment on his behalf when he was between jobs. He can’t see the system, can’t touch it, can’t talk to it, can’t ask it why it has taken his money. The Michigan Unemployment Insurance Agency hasn’t shared any information with him.

“How do you beat something you can’t see?” Russell said. “It’s like swinging in the dark. What are the laws that apply to a computer system? And what about us humans?”

We admit that humans are flawed and make mistakes. But we also know when mistakes have been made and we can correct. I wonder if it’s rational to be optimistic that flawed humans can design automated systems to find and correct for flaws in the system?