I'd like to share Backprop, a Python library I've been co-authoring for the last few months. Our goal is to make finetuning and using models as easy as possible, even without extensive ML experience. We've currently got support for text and image-based tasks, with wrappers around models like Google's T5, OpenAI's CLIP, and Facebook's BART, among others. Once you've got your training data, you can just import your model/task, and then finetune with a single line of code. We've also got some features that make deployment for production easy, but for full transparency, deployment is through a paid platform we've developed that is by no means necessary to use the library.

TARGETED [digital] marketing is supposed to get advertiser's ads in front of the people most likely to find interest in them. According to Google, and Facebook, they know who these people are because they track and "analyze" the content of each and every one of their user's posts, so they claim to know what we like and don't, where we hang out, what attracts us, and what we'll pass on. However, even with all this data crunching, quantitative data doesn't really tell us qualitative reasons why anyone really does most anything. This is a fatal flaw with machine learning, often called AI (artificial intelligence), and why, even with all this data, response rates average between .05 - 5% with most digital marketing efforts, which is actually LOWER than with print, TV ads, and other'traditional' media.

George Miller's Mad Max: Fury Road may have spent almost 20 years stuck in development hell, but the filmmaker had always envisioned the project without Mel Gibson in the lead. The 1979 original was the breakthrough role of the actor's career, and the sequel remains one of the greatest action movies ever made, but Miller was adamant that the title hero be recast after the Lethal Weapon star had aged out of consideration. Tom Hardy, 22 years Gibson's junior, was chosen to play the new Max Rockatansky instead, but in terms of the franchise's canon, he's the same character, just at a different stage of his life. The choice to recast was a wise one, too, as Fury Road left audiences with their jaws on the floor when it exploded onto the scene and almost instantly gained a reputation as a modern action classic, scoring the rare combination of box office success, universal critical acclaim and awards season glory. The post-apocalyptic blockbuster earned ten Academy Award nominations including Best Picture and Best Director, and ended up walking away with six prizes in the technical categories.

When James Clarke went to work at London's legendary Abbey Road Studios in late 2009, he wasn't an audio engineer. He'd been hired to work as a software programmer. One day not long after he started, he was having lunch with several studio veterans of the 1960s and '70s, the pre-computer era of music recording when songs were captured on a single piece of tape. To make conversation, Clarke asked a seemingly innocent question: Could you take a tape from the days before multitrack recording and isolate the individual instruments? Could you pull it apart?

Forty years ago, the word "hacker" was little known. Its march from obscurity to newspaper headlines owes a great deal to tech journalist Steven Levy, who in 1984 defied the advice of his publisher to call his first book Hackers: Heroes of the Computer Revolution.11 Hackers were a subculture of computer enthusiasts for whom programming was a vocation and playing around with computers constituted a lifestyle. Hackers was published only three years after Tracy Kidder's The Soul of a New Machine, explored in my last column (January 2021, p. 32–37), but a lot had changed during the interval. Kidder's assumed readers had never seen a minicomputer, still less designed one. By 1984, in contrast, the computer geek was a prominent part of popular culture. Unlike Kidder, Levy had to make people reconsider what they thought they already knew. Computers were suddenly everywhere, but they remained unfamiliar enough to inspire a host of popular books to ponder the personal and social transformations triggered by the microchip. The short-lived home computer boom had brought computer programming into the living rooms and basements of millions of middle-class Americans, sparking warnings about the perils of computer addiction. A satirical guide, published the same year, warned of "micromania."15 The year before, the film Wargames suggested computer-obsessed youth might accidentally trigger nuclear war.

Some of these gaps can be plugged in by deploying AI and machine learning.

It is no secret that technology has been transforming the way we work. Every day, new tools and applications appear to automate processes, making them more agile and precise. In the area of finance, especially in the tax sector, the use of Artificial Intelligence has become increasingly fundamental to eliminate manual errors, increase productivity and make the business more strategic – mainly in the UK – where 1,958 hours are spent per year to meet all tax obligations, according to the World Bank. According to the survey conducted by Thomson Reuters in partnership with Live University, 56% of British companies intend to use Artificial Intelligence to optimize tax management. When the debate is about which technology is more functional for the sector, 61% of professionals point to Machine Learning as the innovation most capable of benefiting the segment; 31% bet on Data Science, and 10% prefer chatbots. The fact is that these combined technologies should revolutionize the finance area, as has already been happening with banks, in addition to other sectors such as e-commerce and general service, due to their high precision in collecting and data analysis, problem-solving, and responsiveness.

Machine learning and natural language processing technology have now advanced to the level that they can be used to determine what feedback …

The Global Artificial Intelligence and Machine Learning Market survey report gives a detailed forecast and prospects of the market where 2020 is set …

When a secretive start-up scraped the internet to build a facial-recognition tool, it tested a legal and ethical limit -- and blew the future of privacy in America wide open. In May 2019, an agent at the Department of Homeland Security received a trove of unsettling images. Found by Yahoo in a Syrian user's account, the photos seemed to document the sexual abuse of a young girl. One showed a man with his head reclined on a pillow, gazing directly at the camera. The man appeared to be white, with brown hair and a goatee, but it was hard to really make him out; the photo was grainy, the angle a bit oblique. The agent sent the man's face to child-crime investigators around the country in the hope that someone might recognize him. When an investigator in New York saw the request, she ran the face through an unusual new facial-recognition app she had just started using, called Clearview AI. The team behind it had scraped the public web -- social media, employment sites, YouTube, Venmo -- to create a database with three billion images of people, along with links to the webpages from which the photos had come. This dwarfed the databases of other such products for law enforcement, which drew only on official photography like mug shots, driver's licenses and passport pictures; with Clearview, it was effortless to go from a face to a Facebook account. The app turned up an odd hit: an Instagram photo of a heavily muscled Asian man and a female fitness model, posing on a red carpet at a bodybuilding expo in Las Vegas. The suspect was neither Asian nor a woman. But upon closer inspection, you could see a white man in the background, at the edge of the photo's frame, standing behind the counter of a booth for a workout-supplements company. On Instagram, his face would appear about half as big as your fingernail. The federal agent was astounded. The agent contacted the supplements company and obtained the booth worker's name: Andres Rafael Viola, who turned out to be an Argentine citizen living in Las Vegas.