Daily Archives: June 6, 2017

Ai is a poet noted for her uncompromising poetic vision and bleak dramatic monologues which give voice to marginalized, often poor and abused speakers. Though born Florence Anthony, she legally changed her name to Ai which means love in Japanese. She has said that her given name reflects a scandalous affair my mother had with a Japanese man she met at a streetcar stop and has no wish to be identified for all eternity with a man she never knew. Ais awareness of her own mixed race heritageshe self-identifies as Japanese, Choctaw-Chickasaw, Black, Irish, Southern Cheyenne, and Comancheas well as her strong feminist bent shape her poetry, which is often brutal and direct in its subject matter. In the volumes of verse she published since her first collection, Cruelty (1973), Ai provoked both controversy and praise for her stark monologues and gruesome first-person accounts of non-normative behavior. Dubbed All womanall human by confessional poet Anne Sexton, Ai has also been praised by the Times Literary Supplement for capturing the cruelty of intimate relationships and the delights of perverse spontaneitye.g. the joy a mother gets from beating her child. Alicia Ostriker countered Sextons summation of Ai, writing: All womanall human; she is hardly that. She is more like a bad dream of Woody Allens, or the inside story of some Swinburnean Dolorosa, or the vagina-dentata itself starting to talk. Woman, in Ais embodiment, wants sex. She knows about death and can kill animals and people. She is hard as dirt. Her realitiesvery small onesare so intolerable that we fashion female myths to express our fear of her. She, however, lives the hard life below our myths.

Ai explained her use of the dramatic monologue as an early realization that first person voice was always the stronger voice to use when writing. Her poems depict individuals that Duane Ackerson characterized in Contemporary Women Poets as people seeking transformation, a rough sort of salvation, through violent acts. The speakers in her poems are struggling individualsusually women, but occasionally menisolated by poverty, by small-town life, or life on a remote farm. Killing Floor (1978), the volume that followed Cruelty, includes a poem called The Kid which is spoken in the voice of a boy who has just murdered his family. Sin (1986) contains more complex dramatic monologues as Ai assumes actual personae, from Joe McCarthy to the Kennedy brothers. Ais characters tend to speak in a flat demotic, stripped of nuance or emotion. Poet and critic Rachael Hadas has noted that although virtually all the poems present themselves as spoken by a particular character, Ai makes little attempt to capture individual styles of diction [or] personal vocabularies. For Hadas, however, this makes the poems all the more striking, as her stripped-down diction conveys an underlying, almost biblical indignationnot, at times, without compassionat human misuses of power and the corrupting energies of various human appetites.

Fate (1991) and Greed (1993), like Sin before them, contain monologues that dramatize public figures. Readers confront the inner worlds of former F.B.I. director J. Edgar Hoover, missing-and-presumed-dead Union leader Jimmy Hoffa, musician Elvis Presley, and actor James Dean as voices from beyond-the-grave who yet remain out of sync with social or ethical norms. Noting that Ai reinvents each of her subjects within her verse, Ackerson added that, through each monologue, what these individuals say, returning after death, expresses more about the American psyche than about the real figures. Vice: New and Selected Poems (1999) contained work from Ais previous five books as well as 18 new poems. It was awarded the National Book Award for Poetry. Ais next book, Dread (2003), was likewise praised for its searing and honest treatment of, according to a Publishers Weekly reviewer, violent or baroquely sexual life stories. In the New York Times Book Review, Viijay Seshadri wrote that Dread has the characteristic moral strength that makes Ai a necessary poet. Aiming her poetic barbs directly at prejudices and societal ills of all types, Ai has been outspoken on the subject of race, saying People whose concept of themselves is largely dependent on their racial identity and superiority feel threatened by a multiracial person. The insistence that one must align oneself with this or that race is basically racist. And the notion that without a racial identity a person cant have any identity perpetuates racismI wish I could say that race isnt important. But it is. More than ever, it is a medium of exchange, the coin of the realm with which one buys ones share of jobs and social position. This is a fact which I have faced and must ultimately transcend. If this transcendence were less complex, less individual, it would lose its holiness.

In addition to the National Book Award, Ais work was awarded an American Book Award from the Before Columbus Foundation, for Sin, and the Lamont Poetry Award of the Academy of American Poets, for Killing Floor. She received grants from the Guggenheim Foundation, the Bunting Fellowship Program at Radcliffe College and the National Endowment for the Arts. She taught at Oklahoma State University. She died in 2010.

Continue reading here:

Ai | Poetry Foundation

Executive Summary

For tomorrows workers, AI will be more than a tool; AIs will be their co-workers and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively if theyre to understand their inherent limitations, and build even better platforms and intelligent systems we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level. The U.S. is woefully behind many of our peer nations, and President Obamas Computer Science for All initiative may flounder amid budget cuts proposed by the Trump administration. Another major hurdle is that our schools face a severe shortage of teachers who are trained in computer science. This is where U.S. tech companies could help immensely. Investing in how the next generation understand and interacts with big data and AI is an investment that will pay off in the long run for all of us.

Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary. For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives.

If the next generation is to use AI and big data effectively if theyre to understand their inherent limitations, and build even better platforms and intelligent systems we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level.

For example, consider how kids are currently interacting with AI and automated technologies: Right now, it might seem magical to tell Siri, Show me photos of celebrities in orange dresses, and see a photo of Taylor Swiftpop up on a smartphone less than a second later. But its clearly not magic. People design AI systems by carefully decomposing a problem into lots of small problems, and enabling the solutions to the small problems to communicate with each other. In this example, the AI program divides the audio into chunks, sends them into the cloud, analyzes them to determine their probable meaning and translates the result into a set of search queries. Then millions of possible answers to those queries are sorted and ranked. Thanks to the scalability of the cloud, this takes just a few dozen milliseconds.

This isnt rocket science. But it requires a lot of components waveform analysis to interpret the audio, machine learning to teach a machine how to recognize a dress, encryption to protect the information, etc. While many are standard components that are used and re-used in any number of applications, its not something a solitary genius cooks up in a garage. People who create this type of technology must be able to build teams, work in teams, and integrate solutions created by other teams. These are the skills that we need to be teaching the next generation.

Also, with AI taking over routine information and manual tasks in the workplace, we need additional emphasis on qualities that differentiate human workers from AI creativity, adaptability, and interpersonal skills.

At the elementary level, that means that we need to emphasize exercises that encourage problem solving and teach children how to work cooperatively in teams. Happily, there is a lot of interest in inquiry-based or project-based learning at the K-8 level, though its hard to know how many districts are pursuing this approach.

Ethics also deserves more attention at every educational level. AI technologies face ethical dilemmas all the time for example, how to exclude racial, ethnic, and gender prejudices from automated decisions; how a self-driving car balances the lives of its occupants with those of pedestrians, etc. and we need people and programmers who can make well-thought-out contributions to those decision making processes.

Were not obsessed about teaching coding at the elementary levels. Its fine to do so, especially if the kids enjoy it, and languages such as Snap! and Scratch are useful. But coding is something kids can pick up later on in their education. However, the notion that you dont need to worry at all about learning to program is misguided. With the world becoming increasingly digital, computer science is as vital in the arts and sciences as writing and math are. Whether a person chooses to become a computer scientist or not, coding is something that will help a person do more in whatever field they choose. Thats why we believe a basic computer programming course should be required at the 9th grade level.

Only about 40% of U.S. schools now teach programming and the quality and rigor of these courses varies widely. The number of students taking Advanced Placement exams in computer science is growing dramatically, but the 58,000 students taking the AP Computer Science A (APCS-A) test last year still pales in comparison to the 308,000 who took the AP Calculus AB test. A third of our states dont even count computer science course credits toward graduation requirements.

The U.S. is woefully behind many of our peer nations. Israel notably has integrated computer science into its pre-college curriculum. The UK has made good progress lately with its Computing at School program and Germany and Russia have leapt ahead as well. President Obamas Computer Science for All initiative, announced in his 2016 State of the Union, was a belated step in the right direction, but may flounder amid budget cuts proposed by the Trump administration.

Expanding computer science at the high school level not only benefits the students, but could help the field of computer science by encouraging more students and a more diverse group of students to consider computer science as a career. Though we were thrilled last fall when almost half of our incoming first-year class at Carnegie Mellon was female, the field of computer science is still struggling to increase the number of women and minorities. Engineering intelligence into systems, and finding insights in a ubiquitous sea of data, is a task that cries out for a diverse workforce.

To be successful, however, it is critical that we update the way programming is taught. Were too often teaching programming as if it were still the 90s, when the details of coding (think Visual Basic) were considered the heart of computer science. If you can slog through programming language details, you might learn something, but its still a slog and it shouldnt be. Coding is a creative activity, so developing a programming course that is fun and exciting is eminently doable. In New York City, for instance, The Girl Scouts have a program that teaches girls to use Javascript to create and enhance videos an activity that kids already want to do because its fun and relevant to their lives. Why cant our schools follow suit?

Beyond 9th grade, we believe schools should provide electives such as robotics, computational math, and computational art to nurture students who have the interest and the talent to become computer scientists, or who will need computers to enhance their work in other fields. Few U.S. high schools now go beyond the core training necessary to prepare for the APCS-A exam, though we have a few stunning success stories Stuyvesant High School in New York City, Thomas Jefferson High School for Science and Technology in Alexandria, Virginia, and TAG (The School for the Talented and Gifted) in Dallas, among others. These schools all boast committed faculty members who have a background or training in computer science.

We also urge high school math departments to place less emphasis on continuous math, including advanced calculus, and more on the math that is directly relevant to computer science, such as statistics, probability, graph theory and logic. Those will be the most useful skills for tomorrows data-driven workforce.

A major hurdle is that our schools face a severe shortage of teachers who are trained in computer science. This is where U.S. tech companies could help immensely. Microsoft, for instance, sponsors the TEALS program, which pairs computer professionals with high school teachers for a few hours a week. But we need thousands of educators teaching millions of students. Even greater commitments will be necessary going forward. On the academic side, The University of Texas at Austins UTeach program is a model for preparing STEM teachers and has expanded to 44 universities in 21 states and the District of Columbia.

Much more is needed. As with science and math, we need governmental standards driving K-12 computer science education, along with textbooks, courses and ultimately a highly trained national cadre of computer science teachers that are tied to those standards. The Computer Science Teachers Association has been a leader in this area, promulgating a standards framework and an interim set of standards.

Investing in how the next generation understand and interacts with big data and AI is an investment that will pay off in the long run for all of us.

Read the rest here:

How to Prepare the Next Generation for Jobs in the AI Economy - Harvard Business Review

Luckily, foretelling such dire consequences may help doctors to stave them off. "Predicting the future of a patient is useful because it may enable doctors to tailor treatments to the individual," lead author Dr. Luke Oakden-Rayner told the University of Adelaide. "Instead of focusing on diagnosing diseases, the automated systems can predict medical outcomes in a way that doctors are not trained to do, by incorporating large volumes of data and detecting subtle patterns."

For this study, the system was looking for things like emphysema, an enlarged heart and vascular conditions like blood clotting.The deep learning system was trained to analyze over 16,000 image features that could indicate signs of disease in those organs. Machines have become adept at it surprisingly quickly, even though it's "something that requires extensive training for human experts," said Oakden-Rayner.

The goal was not to build a grim diagnostic system, and the AI only analyzed retrospective patient data. Rather, the team is looking to lay the groundwork for algorithms that can diagnose your overall health, rather than just spotting a single disease. They also want to "motivate the use of routinely collected, high resolution radiologic images as sources of high quality data for precision medicine," according to the paper. In other words, they're encouraging more scans as a way to improve the results of future diagnostic systems.

"Our research opens new avenues for the application of artificial intelligence technology in medical image analysis, and could offer new hope for the early detection of serious illness, requiring specific medical interventions," says Oakden-Rayner.

See original here:

AI can predict if you'll die soon by examining your organs - Engadget

Apple's Craig Federighi touts new machine learning and AI features coming to iPhones at WWDC.

Your Apple hardware is about to get a notch smarter as the company builds new artificial intelligence abilities into Macs and iPhones -- and lets other programmers tap into that power.

AI technology will mean Siri better understands what you want and speaks with a computer voice that Apple says sounds natural. Craig Federighi, senior vice president in charge of Mac and iPhone software, announced the AI technology Monday at the company's annual WWDC event for developers in San Jose, California. On Macs, it'll monitor your web browsing behavior to block advertising companies from tracking some of what you do online.

And that's not all. A new interface will let third-party programmers tap into Apple AI abilities, including speech recognition and image processing. The iPhone will better accommodate AI technology prepared ahead of time on massive data centers before being brought to phones and PCs.

"We want to make powerful machine learning easy to use in your apps," Federighi said. He shied away from the term "AI," though, preferring instead the more specific terms "machine learning" and "deep learning."

AI is making computing devices dramatically more useful by, for example, recognizing your friends' faces in photos or letting you dictate text messages. AI powerhouses like Google and Facebook use massive data centers to teach AI systems what to do, but the resulting AI smarts then can be squeezed into phones.

AI has an important role to play at Apple, a company whose core mission has been to make technology more accessible to everybody. AI can make computers understand what humans want and then package the results in a way we can use.

"We're moving from an era where you need to be technology-literate, where people need to understand the computers, to an AI era where technology understands the people," said Gartner analyst Tuong Nguyen. "That's what sets Apple apart from everybody else, and it's why they've been doing so well."

AI is a hype-heavy term these days, but there's real technology behind the buzzword. Traditional programming is very rigid; under this circumstance, do that. The machine learning behind AI, though, is trained by feeding raw data into a massive "neural network" and letting the computer when it gets the right answer. You don't have to worry about encoding all the details about what exactly a kitten looks like, you just have to have a lot of photos of kittens to train the system.

Apple already uses machine learning for a number of tasks, including rejection of stray palm swipes, grouping photos into events called memories, and extending phone battery life by better understanding what you're doing.

Apple has used a human to record Siri's voice, but AI is bringing a new sound with a computer-generated male or female voice. Federighi demonstrated how the voice used different inflections when repeating the word "sunny" three times in a row in a weather report. A male Siri voice offered a little pun: "I want machine learning, especially since I'm a machine, learning."

49

Apple's biggest announcements from WWDC

First published June5, 11:50 a.m. PT. Update, 12:55 p.m.: Adds comment from Gartner analyst.

See more here:

Macs, iPhones, Siri getting new AI brain power - CNET