#intlligence_artificielle

  • Opinion | A.I. Will Change Education. Don’t Let It Worsen Inequality. - The New York Times
    https://www.nytimes.com/2022/12/15/opinion/chatgpt-education-ai-technology.html

    It is in high schools and even college where some of ChatGPT’s most interesting and troubling aspects will become clear.

    Essay writing is most often assigned not because the result has much value — proud parents putting good grades on the fridge aside — but because the process teaches crucial skills: researching a topic, judging claims, synthesizing knowledge and expressing it in a clear, coherent and persuasive manner. Those skills will be even more important because of advances in A.I.

    When I asked ChatGPT a range of questions — about the ethical challenges faced by journalists who work with hacked materials, the necessity of cryptocurrency regulation, the possibility of democratic backsliding in the United States — the answers were cogent, well reasoned and clear. It’s also interactive: I could ask for more details or request changes.

    But then, on trickier topics or more complicated concepts, ChatGPT sometimes gave highly plausible answers that were flat-out wrong — something its creators warn about in their disclaimers.

    Unless you already knew the answer or were an expert in the field, you could be subjected to a high-quality intellectual snow job.

    In flipped classrooms, students wouldn’t use ChatGPT to conjure up a whole essay. Instead, they’d use it as a tool to generate critically examined building blocks of essays. It would be similar to how students in advanced math classes are allowed to use calculators to solve complex equations without replicating tedious, previously mastered steps.

    Teachers could assign a complicated topic and allow students to use such tools as part of their research. Assessing the veracity and reliability of these A.I.-generated notes and using them to create an essay would be done in the classroom, with guidance and instruction from teachers. The goal would be to increase the quality and the complexity of the argument.

    This would require more teachers to provide detailed feedback. Unless sufficient resources are provided equitably, adapting to conversational A.I. in flipped classrooms could exacerbate inequalities.

    Some school officials may treat this as a problem of merely plagiarism detection and expand the use of draconian surveillance systems. During the pandemic, many students were forced to take tests or write essays under the gaze of an automated eye-tracking system or on a locked-down computer to prevent cheating.

    In a fruitless arms race against conversational A.I., automated plagiarism software may become supercharged, making school more punitive for monitored students. Worse, such systems will inevitably produce some false accusations, which damage trust and may even stymie the prospects of promising students.

    Educational approaches that treat students like enemies may teach students to hate or subvert the controls. That’s not a recipe for human betterment.

    As societies responded to previous technological advances, like mechanization, by eventually enacting a public safety net, a shorter workweek and a minimum wage, we will also need policies that allow more people to live with dignity as a basic right, even if their skills have been superseded. With so much more wealth generated now, we could unleash our imagination even more, expanding free time and better working conditions for more people.

    The way forward is not to just lament supplanted skills, as Plato did, but also to recognize that as more complex skills become essential, our society must equitably educate people to develop them. And then it always goes back to the basics. Value people as people, not just as bundles of skills.

    #Education #Intlligence_artificielle #Zeynep_Tufekci

  • Alexa Prize: Amazon’s Battle to Bring Conversational AI Into Your Home | WIRED
    https://www.wired.com/story/inside-amazon-alexa-prize

    Amazon, in case you haven’t noticed, has spent the past few years pursuing voice AI with a voraciousness rivaling that of its conquest of retail. The company has more than 5,000 people working on the Alexa platform. And since just 2015, it has reportedly sold more than 20 million Echoes. One day, Amazon believes, AIs will do much more than merely control lights and playlists. They will drive cars, diagnose diseases, and permeate every niche of our lives. Voice will be the predominant interface, and conversation itself—helpful, informative, companionable, entertaining—will be the ultimate product.

    Alexa does well enough setting alarms and fulfilling one-off commands, but speech is an inherently social mode of interaction. “People are expecting Alexa to talk to them just like a friend,” says Ashwin Ram, who leads Alexa’s AI research team. Taking part in human conversation—with all its infinite variability, abrupt changes in context, and flashes of connection—is widely recognized as one of the hardest problems in AI, and Amazon has charged into it headlong.

    The Alexa Prize is hardly the first contest that has tried to squeeze more humanlike rapport out of the world’s chatbots. Every year for the better part of three decades, a smattering of computer scientists and hobbyists has gathered to compete for something called the Loebner Prize, in which contestants try to trick judges into believing a chatbot is human. That prize has inspired its share of controversy over the years—some AI researchers call it a publicity stunt—along with plenty of wistful, poetic ruminations on what divides humans from machines. But the Alexa Prize is different in a couple of ways. First, the point isn’t to fool anyone that Alexa is a person. Second, the scale of the competition—the sheer human, financial, and computational firepower behind it—is massive. For several months of 2017, during an early phase of the contest, anyone in the US who said “Alexa, let’s chat” to their Amazon voice device was allowed to converse with a randomly selected contest bot; they were then invited to rate the conversation they’d had from one to five stars. The bots had millions of rated interactions, making the Alexa Prize competition, by orders of magnitude, the largest chatbot showdown the world has ever seen.

    THE FEVERED QUEST for conversational AI has pitted Amazon, Apple, Facebook, Google, and Microsoft in a battle for two vital resources. The first is finite: top-shelf PhDs in computer science, who, owing to their scarcity, now command starting salaries well into the six figures. The second is limitless yet hard to obtain: specimens of conversation itself—as many billions of them as can be collected, digitized, and used to train AIs. Against this backdrop, the Alexa Prize was a masterstroke for Amazon. The contest served as both a talent search for the sharpest graduate students in the world and a chance to pick their brains for a bargain price. And it provided Amazon with an opportunity to amass a conversational data trove that no other technology company has.

    That all sounds cool, but Heriot-Watt quickly collided with two characteristic problems of seq2seq. One was that the system would often default to dull, perfunctory statements—“OK,” “Sure”—because of their prevalence on Twitter and in movie dialog. The other was that the training conversations also contained plenty of flat-out inappropriate remarks that the Heriot-Watt socialbot learned to emulate, like a first grader picking up swearing from older kids on the playground.

    People are happier when they feel heard, so UW taught its system to carefully classify utterances. Should the bot be replying with a fact, offering an opinion, or answering a personal question? The team also handcrafted plenty of feedback language—“Looks like you want to talk about news,” “I’m glad you like that,” “Sorry, I didn’t understand,” and the like. Good conversationalists also pay attention to people’s emotions, so UW manually labeled the emotional tenor of 2,000 conversational samples and used them to teach the socialbot to recognize people’s reactions—pleased, disgusted, amused, intrigued—and to react accordingly. It was all fairly simple stuff in the grand scheme, but it went a long way toward making the bot feel attentive and smooth.

    #Intlligence_artificielle #Alexa #Amazon #Dialogue