Category Archives: Ai

The followingis a guest post by Kyle Evanoff,research associate for International Economics and U.S. Foreign Policy.

Today through Friday, artificial intelligence (AI) experts are meeting with international leaders in Geneva, Switzerland, for the inaugural AI for Good Global Summit. Organized by the International Telecommunications Union (ITU), a UN agency that specializes in information and communication technologies, and the XPRIZE Foundation, a Silicon Valley nonprofit that awards competitive prizes for solutions addressing some of the worlds most difficult problems, the gathering will discuss AI-related issues and promote international dialogue and cooperation on AI innovation.

The summit comes at a critical time and should help increase policymakers awareness of the possibilities and challenges associated with AI. The downside is that it may encourage undue optimism, by giving short shrift to the significant risks that AI poses to international security.

Although many policymakers and citizens are unaware of it, narrow forms of AI are already here. Software programs have long been able to defeat the worlds best chess players, and newer ones are succeeding at less-defined tasks, such as composing music, writing news articles, and diagnosing medical conditions. The rate of progress is surprising even tech leaders, and future developments could bring massive increases in economic growth and human well-being, as well as cause widespread socioeconomic upheaval.

This weeks forum provides a much-needed opportunity to discuss how AI should be governed at the global levela topic that has garnered little attention from multilateral institutions like the United Nations. The draft program promises to educate policymakers on multiple AI issues, from sessions on moonshots to ethics, sustainable living, and poverty reduction, among other topics. Participants will include prominent individuals drawn from multilateral institutions, nongovernmental organizations (NGOs), the private sector, and academia.

This inclusivity is typical of the complex governance models that increasingly define and shape global policymakingwith internet governance being a case in point. Increasingly, NGOs, public-private partnerships, industry codes of conduct, and other flexible arrangements have assumed many of the global governance functions once reserved for intergovernmental organizations. The new partnership between ITU and the XPRIZE Foundation suggests that global governance of AI, although in its infancy, is poised to follow this same model.

For all its strengths, however, this multistakeholder approach could afford private sector organizers excessive agenda-setting power. The XPRIZE Foundation, founded by outspoken techno-optimist Peter Diamandis, promotes technological innovation as a means of creating a more abundant future. The summits mission and agenda hews to this attitude, placing disproportionate emphasis on how AI technologies can overcome problems and too little attention on the question of mitigating risks from those same technologies.

This is worrisome, since the risks of AI are numerous and non-trivial. Unrestrained AI innovation could threaten international stability, global security, and possibly even humanitys survival. And, because many of the pertinent technologies have yet to reach maturity, the risks associated with them have received scant attention on the international stage.

One area in which the risk of AI is obvious is electioneering. Since the epochal June 2016 Brexit referendum, state and nonstate actors with varying motivations have used AI to create and/or distribute propaganda via the internet. An Oxford study found that during the recent French presidential election, the proportion of traffic originating from highly automated Twitter accounts doubled between the first and second rounds of voting. Some even attribute Donald J. Trumps victory over Hillary Clinton in the U.S. presidential election to weaponized artificial intelligence spreading misinformation. Automated propaganda may well call the integrity of future elections into question.

Another major AI risk lies in the development and use of lethal autonomous weapons systems (LAWS). After the release of a 2012 Human Rights Watch report, Losing Humanity: The Case Against Killer Robots, the United Nations began considering including restrictions on LAWS in the Convention on Certain Conventional Weapons (CCW). Meanwhile, both China and the United States have made significant headway with their autonomous weapons programs, in what is quickly escalating into an international arms race. Since autonomous weapons might lower the political cost of conflict, they could make war more commonplace and increase death tolls.

A more distant but possibly greater risk is that of artificial general intelligence (AGI). While current AI programs are designed for specific, narrow purposes, future programs may be able to apply their intelligence to a far broader range of applications, much as humans do. An AGI-capable entity, through recursive self-improvement, could give rise to a superintelligence more capable than any humanone that might prove impossible to control and pose an existential threat to humanity, regardless of the intent of its initial programming. Although the AI doomsday scenario is a common science fiction trope, experts consider it to be a legitimate concern.

Given rapid recent advances in AI and the magnitude of potential risks, the time to begin multilateral discussions on international rules is now. AGI may seem far off, but many experts believe that it could become a reality by 2050. This makes the timeline for AGI similar to that of climate change. The stakes, though, could be much higher. Waiting until a crisis has occurred to act could preclude the possibility of action altogether.

Rather than allocating their limited resources to summits promoting AI innovation (a task for which national governments and the private sector are better suited), multilateral institutions should recognize AIs risks and work to mitigate them. Finalizing the inclusion of LAWS in the CCW would constitute an important milestone in this regard. So too would the formal adoption of AI safety principles such as those established at the Beneficial AI 2017 conference, one of the many artificial intelligence summits occurring outside of traditional global governance channels.

Multilateral institutions should also continue working with nontraditional actors to ensure that AIs benefits outweigh its costs. Complex governance arrangements can provide much-needed resources and serve as stopgaps when necessary. But intergovernmental organizations, as well as the national governments that govern them, should be careful in ceding too much agenda-setting power to private organizations. The primary danger of the AI for Good Global Summit is not that it distorts perceptions of AI risk; it is that Silicon Valley will wield greater influence over AI governance with each successive summit. Since technologists often prioritize innovation over risk mitigation, this could undermine global security.

More important still, policymakers should recognize AIs unprecedented transformative power and take a more proactive approach to addressing new technologies. The greatest risk of all is inaction.

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The Inaugural AI for Good Global Summit Is a Milestone but Must Focus More on Risks - Council on Foreign Relations (blog)

Slide: 1 / of 1. Caption: Stephanie Berger

A metronome ticks time. Not for the student, but for the teacher, who plays a short piano melody. Without missing a measure, the student follows with an improvised, yet derivative, cello run. The student plays the same run again, and then again. I have it looping, actually, so you can hear the response over and over again, says the teacher, Jesse Engel, a computer scientist with Google Brain. And you can hear some similarities with what I played, but its not doing the job of trying to replicate what I played. Its trying to continue it in a meaningful way.

The student here is an artificial intelligence algorithm; the instrument, a synthesizer. And the reallesson is teaching an audience of hundreds how computers might someday become capable of producing real works of art. Engels is onstage at NYUs Skirball Center for the Performing Arts as part of the 2017 World Science Festival, along with three likeminded experts. Eachof themis there to showcasehow they nurture creativity in computers.

Which begs the question: What is creativity? The broadest definition is any nonlinear solution to a problem. Music is a creative way of making noises that sound pleasant. Language is creative communication. Airplanes are a creative solution to the problem of flight. But the fact that we can build airplanes that fly faster and higher than birds does not necessarily explain how birds fly, or how they evolved to fly, says Peter Ulric Tse, a neuroscientist at Dartmouth College. Tse is onstage with Engel, but rather than using AI to tackle a creative endeavor, such as music, he believes they are a vehicle for understanding the nature of creativity itself.

In humans, creativity evolved mysteriously. Homo sapiens became a distinct species around 200,000 years ago. Our ancestors characteristic (or, sapient, if you will) feature wastheir huge foreheads: the site ofthe frontal cortex, where high-level reasoning occurs. But the earliest indications of creativity in humans didnt appearuntil relatively recently. Asculpture of a human with a lions headone of the earliest examplesdates to around 40,000 years ago. That, and other archaeological evidence from the same time period meanswe Homo sapienslikely spent most of ourevolutionary history with unrealized creative potential. However, no physical evidence exists toexplain whatflipped the switch. Thoughts dont leave fossils, neurocircuits dont leave fossils, says Tse. All we have are bones and skulls and artifacts.

Artificial intelligences path towards creativity probably wont ever fully explain how it evolved in humans. At most, it will give neuroscientists like Tse ways to examine the problem laterally.But it could help scientists understand creativitys theoretical limits. Lav Varshney, another member of the onstage panel, is working on a mathematical theory of creativity. The way Ive been defining it is things that are both novel, and of high quality in their domain, says Varshney, an engineering theorist at the University of Illinois Urbana-Champaign. For example, a new kind of food.

In the case of cuisine, Varshney says he trains his AI to measure goodness based on things like hedonic psychophysicsa branch of research that studies the molecular properties of human flavor perception. He does similar work in fashion, feeding his algorithm information on color matching, and so on. And according to his research, creativity has theoretical limits. Varshney says that as you increase the value of both quality and novelty, you get more and more noise. That is, it becomes harder and harder to distinguish the newness, and the goodness, of a thing. This probably explains why the avant garde is so well, avant garde.

Like Engel, Varshney is also teaching algorithms to compose music. On stage, he demonstrates one that is learning to compose in the style of Bach. But, he points out, this is not pure creativity. The computer learns by having another algorithma teacherprogressively introduce constraintshere are different available instruments, these are chords, this what it means to sing in soprano. In essence, the algorithm is replicating Bachs creativity based, not evolving its own creative genius. As such, AI algorithms are best suited to be creative collaborators.

Which is exactly whatSougwen Chungdisplays next. Chung is a visual artist, currently an in residence at Bell Labs, who draws with a robotic arm assistant. Ive had a lot of human collaborators, and thought it was time to switch it up a little bit, she says. Watching the pairwoman and machinework together is mesmerizing. At first it looks like the arm is mirroring her strokes. But as a piece progresses, you see that the arm has its own style. Yes, a style that is derivativeof Chungsbut still not the same.

When Chung first started using the robotic armcalled DOUGshe thought the collaboration itself might be part of the artistic performance. However, she now believes the arm is pushing her to consider new creative frontiers. When I collaborate with this algorithm, theres a real randomness and sense of unpredictability to it, and a lack of understanding thats kind of exciting, she says.

If that kind of freedomis at the heart of creativity, the next logical question is whether algorithms could ever eclipse human creativity.Engel, who has settled back into his seat after his performance, seems to think the answer is no. The intentionality is human on both ends of the spectrum, he says. That is, humans are both the input and the consumer for anything a computer creates. You can treat it more like a garden, he says. You control the garden at a high level: planting seeds, watering it, pruning as necessary. But the garden grows on its own.

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Want to Understand Creativity? Enlist an AI Collaborator - WIRED

In Brief Researchers at the University of Adelaide have developed an AI that can analyze CT scans to predict if a patient will die within five years with 69 percent accuracy. This system could eventually be used to save lives by providing doctors with a way to detect illnesses sooner. Predicting the Future

While many researchers are looking for ways to use artificial intelligence (AI) to extend human life, scientists at the University of Adelaidecreated an AI that could help them better understand death. The system they created predicts ifa person will die within five years after analyzingCT scans of their organs, and it was able to do sowith 69 percent accuracy a rate comparable to that of trained medical professionals.

The system makes use of thetechnique of deep learning, and it was tested using images taken from 48 patients, all over the age of 60. Its the first study to combine medical imaging and artificial intelligence, and the results have been published in Scientific Reports.

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, explained lead authorLuke Oakden-Rayner in a university press release. This method of analysis can explore the combination of genetic and environmental risks better than genome testing alone,according to the researchers.

While the findings are only preliminary given the small sample size, the next stage will apply the AI to tens of thousands of cases.

While this study does focus on death, the most obvious and exciting consequence of it is how it could help preserve life. 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, said Oakden-Rayner. Because it encourages more precise treatment using firmer foundational data, the system has the potential to save many lives and provide patients with less intrusive healthcare.

An added benefit of this AI is its wide array of potential uses. Because medical imaging of internal organs is a fairly routine part of modern healthcare, the data is already plentiful. The system could be used to predict medical outcomes beyond just death, such as the potential for treatment complications, and it could work with any number of images, such as MRIs or X-rays, not just CT scans. Researchers will just need to adjustthe AItotheir specifications, andtheyll be able to obtain predictions quickly and cheaply.

AIsystems are becoming more and more prevalentin the healthcare industry.Deepmind is being usedto fight blindness in the United Kingdom, and IBM Watson is already as competent as human doctors at detecting cancer. It is in medicine, perhaps more than any other field, that we see AIs huge potential to help the human race.

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An AI Can Now Predict How Much Longer You'll Live For - Futurism

Forbes

Nvidia Steps Up AI Data Center Push Forbes Recently, Nivida unveiled Volta, the most advanced data-center graphics-processing unit ever built. With 21.1 billion transistors and a massive 815 mm2 footprint, it will facilitate the next generation of artificial intelligence. Nvidia is still ...

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Nvidia Steps Up AI Data Center Push - Forbes

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

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

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

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