Monthly Archives: June 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.

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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.

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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.

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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."

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Apple's biggest announcements from WWDC

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

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

French startup Clustree just raised a $7.9 million Series A round (7 million) from Creandum with Idinvest Partners and Alven Capital also participating. Clustree leverages machine learning to help both good employees who feel stuck in their jobs and HR departments who might not think to check their own companies for the perfect candidate.

In order to do this, Clustree has structured more than 250 million career paths from various sources. Big French companies can then tap into this data to get recommendations about who they should hire next for this job opening. The service combines this data set with internal data as well as continuous feedback from HR managers.

Internal candidates are sometimes better for new job positions, which helps when it comes to employee retention. Clustree can also help you with external recruitment.

Our offer covers the employee lifecycle, from recruitment to succession plan, founder and CEO Bndicte de Raphlis Soissan told me On the recruitment part, Clustree focuses on the natural talent pool of the company: it means that we are analyzing the profiles of their existing employees and the resumes they naturally received. When we deliver recruitment recommendations, our artificial intelligence will analyze that whole unique pool to find interesting candidates.

Companies like Orange, Crdit Agricole, SNCF, Carrefour and LOral are all using Clustree. And they pay quite a lot of money to access the solution.

All our customers are French companies but with an international positioning. Our solution is used across 30 different countries, Bndicte de Raphlis Soissan said. It means that we help recruit and manage careers for American, Japanese, Chinese and German people for instance, even if they all work for a leading french company.

With todays funding round, the company plans to hire more people across the board. The plan is to get more clients in France and then think about international expansion.

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Clustree grabs $7.9 million for its AI-powered recruitment service - TechCrunch

Like the rest of the tech world, Apple wants to make AI on your mobile device as fast and powerful as possible. Thats why the company unveiled a new machine learning framework API for developers today named Core ML.

The key benefit of Core ML will be speeding up how quickly AI tasks execute on the iPhone, iPad, and Apple Watch. This could cover everything from text analysis to face recognition, and should have an effect on a broad category of apps. It means, says Apple, that image recognition on the iPhone will be six times faster than on Googles Pixel.

Core ML will support a number of essential machine learning tools, including all sorts of neural networks (deep, recurrent, and convolutional), as well as linear models and tree ensembles. And because this is Apple, theres also a privacy focus, too Core ML is for on-device processing, meaning the data that developers use to improve user experience wont leave customers phones and tablets.

Apple isnt the only tech company looking to make AI work better on mobile, though, and this announcement fits an industry-wide trend. Both Google and Facebook have previously announced versions of their machine learning frameworks optimized for mobile devices, and chip-maker Qualcomm has created its own software (named the Neural Processing Engine) to smooth the mobile AI experience. Machine learning: its not just happening in the cloud anymore.

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Apple announces new machine learning API to make mobile AI ... - The Verge

In BriefA startup operating out of Tel Aviv, Israel is looking tochange the way artificial intelligence is able to perceive theworld. Cortica boasts of having a transparent and verifiablemachine learning algorithm that makes it possible for machines tolearn, classify, and represent things without guidance. Seeing the World as We Do

On the second floor of a small office building in the middle of Tel Aviv, the bustling heart of Israels booming tech industry sits the world headquarters for Cortica, an AI company with the ambitiousgoal of getting machines to see the world as well as we do.Click to View Full Infographic

They are one of hundreds of AI startups that have sprouted up all over the world in the last few years. The global AI market has now exceeded a billion dollarsandthe tech giants are racing to acquire them as they are re-positioning themselves as AI companies first.

But its a convoluted space with a high knowledge barrier of entry. Most of these companies use buzz words like machine learning, deep learning or neural networks knowing that most consumers and investors have no idea what they really mean.For someone without a background in the field it can be hard to distinguish substance from snake oil.

What makes Cortica different is that unlike most machine learning algorithms that are essentially blackboxes, systems where even the programmers dont know what the system is doing, Corticas program is transparent and verifiable.

It also relies on a branch of AI research called unsupervised learning. Most AI companies rely on algorithms that have to be taught by humans how to characterize and represent whatever they are learning. It is a much slower and more tedious process that has far less chance of revealing anything novel. Unsupervised learning algorithms, such as the ones Cortica uses, are capable of learning, classifying and representing things without guidance.Cortica was founded by Professor Josh Zeevi and two of his doctoral graduates, Igal Raichelgauz and Karina Odinaev from Technion Israel Institute of Technology. Israel, a tiny country of just over 8 million people, has emerged as a global hub of technical innovation. Israel now has more companies listed on the New York stock exchange than any country besides America and China and 50% of those Israeli firms can trace their origins to Technion. If Corticas aspirations come to fruition, it will catapult itself to the top of a long list of successful

Cortica was founded by Professor Josh Zeevi and two of his doctoral graduates, Igal Raichelgauz and Karina Odinaev from Technion Israel Institute of Technology. Israel, a tiny country of just over 8 million people, has emerged as a global hub of technical innovation. Israel now has more companies listed on the New York stock exchange than any country besides America and China and 50% of those Israeli firms can trace their origins to Technion. If Corticas aspirations come to fruition, it will catapult itself to the top of a long list of successfulIsraeli startups that have earned the country its moniker as the startup nation.

As Prof. Zeevi states, There is no objective reason why computers should not do better than the human brain. Although no one is there yet, Cortica will get there. Their team, with offices in New York and Beijing, believe they have figured out how to reverse engineer the biological visual cortexto enable machines to see the world as well as we can.

Doing so is no trivial task as an incredible amount of processing goes on in your head whenever you open your eyes. As soon as your eyelids flicker open receptors in the back of your eye take in the visible light waves and convert it to electrical signals that relay that message to the back of your brain to sort and analyze the immense amount of information contained into the size, shape, depth, and color of all the objects in view. All that data then gets sent for further processing to your cortex which classifies everything into objects by comparing them to every other object you have ever encountered. This is how you recognize things, make sense of them and determine their function. This happens tens of times per second and gives you a sense of motion as each frame is represented by a selected group of neurons that constitutes a clique a fundamental concept in Corticas technology that was motivated by the neurophysiological function exhibited by cortical networks of neurons. This visual response is triggered instantaneously and gets compared with Corticas extensive data base of previously triggered clustered and stored image concepts represented by highly-compressed signatures of neural cliques. All told, it is a mind boggling amount of processing that happens at every moment your eyes are open and is a process that Cortica believes they have managed to accurately simulate in silicon.

A number of companies now claim to be on the verge of getting machines to do this task with the same ease and fluidity that humans can. Cortica believes that their unique approach to the problem puts them ahead of the crowd and that they will be the first to come up with a visual system on par with our own.

This will have a wide range of applications and revolutionize a number of different fields. Everything from security cameras to autonomous vehicles to satellite imagery to medical diagnostics will be vastly improved by the application of this technology.

But that is just the beginning of what the people at Cortica believe their system will be capable of.

Endowing computers with a sense of sight will be a monumental step forward in AI research. Coupled with advances in natural language processing, AI will then have the fundamental pieces needed to understand and interact with the world. This will enable AI research to move into its next phase which is the search for the holy grail of computer science; AGI, artificial general intelligence.

Unlike narrow AI that is designed to take in and spit out specific information, AGI will be able to take in a variety of inputs and give a variety of outputs. In many ways this is what separates humans from computers, computers need specific instructions and can only output what they are directed to output. But humans can take in a wide range of inputs through our various senses and then do different tasks with that information. In the eyes of programmers this makes us general purpose computers.

But we too are limited, mostly by the amount of information that we can process and the speed at which we can output information. People can really only do one task effectively at a time and can only send information at the speed at which we can talk or write. AGI has the potential to far exceed us in both.

Cortica believes that in time their technology will be a vital part of bringing about AGI. If successful, it will be the last tool we will ever need to create.

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An Israeli AI Company Is Giving Machines The Gift Of Sight - Futurism