There have been significant research efforts to address the issue of unintentional bias in Machine Learning (ML). Many well-known companies have dealt with the fallout after the deployment of their products due to this issue. In an industrial context, enterprises have large-scale ML solutions for a broad class of use cases deployed for different swaths of customers. Trading off the cost of detecting and mitigating bias across this landscape over the lifetime of each use case against the risk of impact to the brand image is a key consideration. We propose a framework for industrial uses that addresses their methodological and mechanization needs. Our approach benefits from prior experience handling security and privacy concerns as well as past internal ML projects. Through significant reuse of bias handling ability at every stage in the ML development lifecycle to guide users we can lower overall costs of reducing bias.

Given a collection of strings belonging to a context free grammar (CFG) and another collection of strings not belonging to the CFG, how might one infer the grammar? This is the problem of grammatical inference. Since CFGs are the languages recognized by pushdown automata (PDA), it suffices to determine the state transition rules and stack action rules of the corresponding PDA. An approach would be to train a recurrent neural network (RNN) to classify the sample data and attempt to extract these PDA rules. But neural networks are not a priori aware of the structure of a PDA and would likely require many samples to infer this structure. Furthermore, extracting the PDA rules from the RNN is nontrivial. We build a RNN specifically structured like a PDA, where weights correspond directly to the PDA rules. This requires a stack architecture that is somehow differentiable (to enable gradient-based learning) and stable (an unstable stack will show deteriorating performance with longer strings). We propose a stack architecture that is differentiable and that provably exhibits orbital stability. Using this stack, we construct a neural network that provably approximates a PDA for strings of arbitrary length. Moreover, our model and method of proof can easily be generalized to other state machines, such as a Turing Machine.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An email from a Missouri woman has prompted Merriam-Webster to update its definition of "racism" to include the systemic aspects that have contributed to discrimination, according to a report. Kennedy Mitchum, 22, of Florissant, told KMOV-TV that she was inspired to email the dictionary publisher after getting into arguments with others about the definition of racism. Merriam-Webster defines racism as "a belief that race is the primary determinant of human traits and capacities and that racial differences produce an inherent superiority of a particular race."

IBM's CEO said the company will no longer offer facial recognition software while questioning how similar technology is used by law enforcement. In a letter to Congress submitted Monday, IBM CEO Arvind Krishna said the company will not support any technology that could lead to mass surveillance, racial profiling or "violations of basic human rights and freedoms." "We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies," wrote Krishna. The letter follows protests demanding change among police forces nationwide in the wake of the death of George Floyd, who died while in police custody in Minneapolis last month. It's official:The US is in a recession, ending longest expansion in history The CEO also advocates for federal rules to hold police more accountable, as well as a national policy to improve technology used to maintain transparency, including body cameras and modern data analytics techniques.

Facial recognition software is nothing if not fallible. In 2019, the National Institute of Standards and Technology demonstrated this with a study on A.I. systems used by police departments to identify alleged criminals. The study found that these algorithms falsely identified Asian and black faces 10 to 100 times more often than Caucasian faces. It is these sorts of findings that have led activists to call for bans on facial recognition technology and for technology companies not to develop such products. That movement scored a win on Monday, when IBM CEO Arvind Krishna announced in a letter to Congress that the company will no longer develop, research, or sell facial recognition technology.

A bipartisan cadre of tech-focused legislators in the House and Senate have introduced legislation that would direct the federal government to develop a national cloud computing infrastructure for artificial intelligence research.Introduced by Sens. Rob Portman, R-Ohio, and Martin Heinrich, D-N.M., Thursday, the National Cloud Computing Task Force Act would convene a mix of technical experts across academic, industry and government. The group would develop a nuanced roadmap for how the nation should build, deploy, govern and sustain a national research cloud for AI."With China focused on toppling the United States' leadership in AI, we need to redouble our efforts with a sustained commitment to the best and brightest by developing a national research cloud to ensure our technical researchers get the tools they need to succeed," Portman said in a statement. "By democratizing access to computing power we ensure that any American with computer science talent can pursue their good ideas."The A report submitted to Congress by the National Security Commission on Artificial Intelligence outlined how quickly China is closing in on the United States' tech research hold.

When an apple begins to rot it creates a chemical called ethylene. If that apple happens to be in a barrel with a bunch of other apples, and the rotting causes its skin to break, the ethylene will immediately cause the other apples to start rotting. That's why the proverb "one bad apple spoils the bunch" is meant as a warning. If you find one bad apple, all the apples around it are already rotting. Obviously, the smartest thing to do is to locate, isolate, and remove bad apples before they can poison others.

Ironically, the same innovations we tend to regard as "creepy" (e.g., AI, algorithms, and Big Data) may help leaders make their workplace more inclusive. But there are reasons to be skeptical. I don't consider myself a techno-enthusiast, and I'm definitely not optimistic by nature. So, NO, this isn't another overhyped post on how AI will save the world, or how Big Data (does anyone still use the term?) will make our world better by eliminating racism from society. Sadly, the only way to achieve that would be to eliminate humans, too.

At the end of May 2020, Microsoft took the decision to sack dozens of journalists in favor of replacing them with artificial intelligence (AI). The journalists and editors fired ran the MSN News website, the automatic homepage of the Microsoft Edge browser. The site doesn't write news itself, but draws news from other sources and splits advertising revenue with the original publishers. It used to be curated by humans, who would select stories adhering to their editorial guidelines and edit articles, photos, and headlines wherever necessary. Now, just a few weeks after replacing those humans with software, robots at MSN News have been accused of racism by UK band Little Mix member Jade Thirlwall, after MSN posted a story of her opening up about the racism she experienced at school, accompanied by an image of fellow Little Mix bandmate Leigh-Anne Pinnock.

With creative AI emerging, art creation doesn't seem to be unique to humans, not anymore. Creativity is one of the few traits that make humans different from other species. We alone can make music and art that speak to our experiences or illuminate truths about our world. But suddenly, humans' artistic abilities have some competition--and from a decidedly non-human source; Artificial Intelligence. Over the last couple of years, there have been some remarkable examples of art produced by deep learning algorithms.