2018/07/16: Machine Learning is eliminating the time wasted in traditional programming and instead gets computers to program themselves. In a nutshell – machine learning is like farming; nutrient is the main data, gardener is the farmer, seed is the algorithm while the plant is the program.
Thus the key areas machine learning plays its critical role is:
Computational biology – designing remedy to diseases or simply drugs;
Breeding and coming up with ideal traits;
Predicting the climate change;
Robotics and auto driven tractors
Soil nutrient evaluation; among others
2018/11/02: A new free website spearheaded by the Library Innovation Lab at the Harvard Law School makes available nearly 6.5 million state and federal cases dating from the 1600s to earlier this year, in an initiative that could alter and inform the future availability of similar areas of public-sector big data.
Led by the Lab, which was founded in 2010 as an arena for experimentation and exploration into expanding the role of libraries in the online era, the Caselaw Access Project went live Oct. 29 after five years of discussions, planning and digitization of roughly 100,000 pages per day over two years.
The effort was inspired by the Google Books Project; the Free Law Project, a California 501(c)(3) that provides free, public online access to primary legal sources, including so-called “slip opinions,” or early but nearly final versions of legal opinions; and the Legal Information Institute, a nonprofit service of Cornell University that provides free online access to key legal materials.
The conversion, done in-house at the Harvard Law School Library to preserve the chain of custody of millions of cases it had collected, used a hydraulic cutter to trim the binding from thousands of volumes; and a machine similar to those employed in the meatpacking industry to vacuum-seal them after scanning. Scanning costs were in the millions of dollars. Scanned, resealed volumes were shipped out-of-state for long-term storage underground at a former limestone mine in Louisville, Ky. Pages were subsequently uploaded to an optical character recognition (OCR) vendor for extraction into text files.
The project, which was funded by venture capital-backed startup Ravel Law and the Harvard Law School, doesn’t aggregate every court battle. Its legal trove primarily focuses on supreme court and appellate decisions, but is limited, the Lab’s director said, by the extent to which bygone officials “cared enough at the time” to compile decisions. Director Adam Ziegler said the project has a high concentration of federal trial opinions and lots of trial opinions from the state of New York, an early legal center, but fewer from some other states.
In standing up the project website, Ziegler said the Lab hopes to provide “anyone and everyone” with easy access to the law via court opinions, but noted that concept will have different meanings to different groups and “definitely means things we don’t even envision ourselves.”
2018/10/10: There is a "machine learning is hard" angle to this: while the flawed outcomes from the flawed training data was totally predictable, the system's self-generated discriminatory criteria were surprising and unpredictable. No one told it to downrank resumes containing "women's" -- it arrived at that conclusion on its own, by noticing that this was a word that rarely appeared on the resumes of previous Amazon hires.
The group created 500 computer models focused on specific job functions and locations. They taught each to recognize some 50,000 terms that showed up on past candidates’ resumes. The algorithms learned to assign little significance to skills that were common across IT applicants, such as the ability to write various computer codes, the people said.
Instead, the technology favored candidates who described themselves using verbs more commonly found on male engineers’ resumes, such as “executed” and “captured,” one person said.
Gender bias was not the only issue. Problems with the data that underpinned the models’ judgments meant that unqualified candidates were often recommended for all manner of jobs, the people said. With the technology returning results almost at random, Amazon shut down the project, they said.
Bear with me while I explain. A quick observation: Donald Trump is not like normal people. In particular, he doesn't have any principles to speak of, that might guide him. No moral compass. That doesn't mean he doesn't have a method. He does, but it's local rather than global. Instead of following some hidden but