I for one welcome our new AI overlods

[ihan tavallinen]

Tämä muuttaa kerralla kaiken!

Robotti sai luvan noutaa ruuat ja kaljat kaupasta



Yhdysvalloissa Virginia on ensimmäisenä osavaltiona antanut roboteille luvan käyttää jalkakäytäviä ja suojateitä jalankulkijoiden tapaan.

Mashable kertoo, että muutoksen taustalla on Starship Technologies -niminen yritys.

Moni yritys on suunnitellut lentäviä lähettirobotteja, mutta niiden turvallisuudessa on vielä mutkia matkassa. Starship Technologies ratkaisee asian toisella tavalla. Sen tarkoituksena on valmistamaa koppakuoriaismaisia robotteja, jotka voivat kuljettaa säiliössään ostokset kotiin tai mitä muuta niiden kuljetettavaksi annetaankaan.

Jalkakäytävällä kulkeva robotti ei saa viilettää yli juoksuvauhtia (16 kmh) eikä se saa painaa yli 27 kiloa.

Vastaava lakialoite on harkittavana myös Idahossa ja Floridassa. Ne ovat jo käytössä Redwood Cityn piirikunnassa Kaliforniassa ja Washington D.C.:ssä.

Virossa Starship Technologies toimii jo Woltin kautta kotiin tilattavien ruokien tuojana.

http://www.tivi.fi/Kaikki_uutiset/robot ... ta-6630422

[Sasse netä]

Kuvituskuva vähän johtaa kyllä harhaan tässä.



Edit: Saispa tuohon vaan istuimen ja luukun josta voi napata kylmän kaljan samalla kun robotti ajelee

[Mike Progehevi]

muuttaisitko Virginiaan tommosen takia

[Poistunut käyttäjä 98d6c7]

muuttaisitko Virginiaan tommosen takia

jos tossa olis vessapönttö mil vois istua päällä ja vääntää sontaa niin kyllä!

[Poistunut käyttäjä 22a646]

en tiedä onko tämä asiatopik mutta yritetään.

AI’s PR Problem

Had artificial intelligence been named something less spooky, we’d probably worry about it less.

HBO’s Westworld features a common plot device—synthetic hosts rising up against their callous human creators. But is it more than just a plot twist? After all, smart people like Bill Gates and Steven Hawking have warned that artificial intelligence may be on a dangerous path and could threaten the survival of the human race.

They’re not the only ones worried. The Committee on Legal Affairs of the European Parliament recently issued a report calling on the EU to require intelligent robots to be registered, in part so their ethical character can be assessed. The “Stop Killer Robots” movement, opposed to the use of so-called autonomous weapons in war, is influencing both United Nations and U.S. Defense Department policy.

Artificial intelligence, it seems, has a PR problem. While it’s true that today’s machines can credibly perform many tasks (playing chess, driving cars) that were once reserved for humans, that doesn’t mean that the machines are growing more intelligent and ambitious. It just means they’re doing what we built them to do.

The robots may be coming, but they are not coming for us—because there is no “they.” Machines are not people, and there’s no persuasive evidence that they are on a path toward sentience.

We’ve been replacing skilled and knowledgeable workers for centuries, but the machines don’t aspire to better jobs and higher employment. Jacquard looms replaced expert needleworkers in the 19th century, but these remarkable devices—programmed with punch cards for a myriad of fabric patterns—didn’t spell doom for dressmakers and tailors. Until the mid-20th century we relied on our best and brightest to do arithmetic—being a “calculator” used to be a highly respected profession. Now that comparably capable devices are given away as promotional trinkets at trade shows, the mathematically minded among us can focus on tasks that require broader skills, like statistical analysis. Soon, your car will be able to drive you to the office upon command, but you don’t have to worry about it signing up with Uber to make a few extra bucks for gas while you’re in a staff meeting (unless you instruct it to).

AI makes use of some powerful technologies, but they don’t fit together as well as you might expect. Early researchers focused on ways to manipulate symbols according to rules. This was useful for tasks such as proving mathematical theorems, solving puzzles, or laying out integrated circuits. But several iconic AI problems—such as identifying objects in pictures and converting spoken words to written language—proved difficult to crack. More recent techniques, which go under the aspirational banner of machine learning, proved much better suited for these challenges. Machine-learning programs extract useful patterns out of large collections of data. They power recommendation systems on Amazon and Netflix, hone Google search results, describe videos on YouTube, recognize faces, trade stocks, steer cars, and solve a myriad of other problems where big data can be brought to bear. But neither approach is the Holy Grail of intelligence. Indeed, they coexist rather awkwardly under the label of artificial intelligence. The mere existence of two major approaches with different strengths calls into question whether either of them could serve as a basis for a universal theory of intelligence.

For the most part, the AI achievements touted in the media aren’t evidence of great improvements in the field. The AI program from Google that won a Go contest last year was not a refined version of the one from IBM that beat the world’s chess champion in 1997; the car feature that beeps when you stray out of your lane works quite differently than the one that plans your route. Instead, the accomplishments so breathlessly reported are often cobbled together from a grab bag of disparate tools and techniques. It might be easy to mistake the drumbeat of stories about machines besting us at tasks as evidence that these tools are growing ever smarter—but that’s not happening.

Public discourse about AI has become untethered from reality in part because the field doesn’t have a coherent theory. Without such a theory, people can’t gauge progress in the field, and characterizing advances becomes anyone’s guess. As a result the people we hear from the most are those with the loudest voices rather than those with something substantive to say, and press reports about killer robots go largely unchallenged.

I’d suggest that one problem with AI is the name itself—coined more than 50 years ago to describe efforts to program computers to solve problems that required human intelligence or attention. Had artificial intelligence been named something less spooky, it might seem as prosaic as operations research or predictive analytics.

Perhaps a less provocative description would be something like “anthropic computing.” A broad moniker such as this could encompass efforts to design biologically inspired computer systems, machines that mimic the human form or abilities, and programs that interact with people in natural, familiar ways.

We should stop describing these modern marvels as proto-humans and instead talk about them as a new generation of flexible and powerful machines. We should be careful about how we deploy and use AI, but not because we are summoning some mythical demon that may turn against us. Rather, we should resist our predisposition to attribute human traits to our creations and accept these remarkable inventions for what they really are—potent tools that promise a more prosperous and comfortable future.

[lortto]

^ The Atlanticissa melko samankaltaisia huomioita toissapäivältä:

https://www.theatlantic.com/technology/ ... ce/518547/

‘Artificial Intelligence’ Has Become Meaningless

It’s often just a fancy name for a computer program.

In science fiction, the promise or threat of artificial intelligence is tied to humans’ relationship to conscious machines. Whether it’s Terminators or Cylons or servants like the “Star Trek” computer or the Star Wars droids, machines warrant the name AI when they become sentient—or at least self-aware enough to act with expertise, not to mention volition and surprise.

What to make, then, of the explosion of supposed-AI in media, industry, and technology? In some cases, the AI designation might be warranted, even if with some aspiration. Autonomous vehicles, for example, don’t quite measure up to R2D2 (or Hal), but they do deploy a combination of sensors, data, and computation to perform the complex work of driving. But in most cases, the systems making claims to artificial intelligence aren’t sentient, self-aware, volitional, or even surprising. They’re just software.

* * *

Deflationary examples of AI are everywhere. Google funds a system to identify toxic comments online, a machine learning algorithm called Perspective. But it turns out that simple typos can fool it. Artificial intelligence is cited as a barrier to strengthen an American border wall, but the “barrier” turns out to be little more than sensor networks and automated kiosks with potentially-dubious built-in profiling. Similarly, a “Tennis Club AI” turns out to be just a better line sensor using off-the-shelf computer vision. Facebook announces an AI to detect suicidal thoughts posted to its platform, but closer inspection reveals that the “AI detection” in question is little more than a pattern-matching filter that flags posts for human community managers.

AI’s miracles are celebrated outside the tech sector, too. Coca-Cola reportedly wants to use “AI bots” to “crank out ads” instead of humans. What that means remains mysterious. Similar efforts to generate AI music or to compose AI news stories seem promising on first blush—but then, AI editors trawling Wikipedia to correct typos and links end up stuck in infinite loops with one another. And according to human-bot interaction consultancy Botanalytics (no, really), 40 percent of interlocutors give up on conversational bots after one interaction. Maybe that’s because bots are mostly glorified phone trees, or else clever, automated Mad Libs.
AI: Making computers act like they do in the movies.

AI has also become a fashion for corporate strategy. The Bloomberg Intelligence economist Michael McDonough tracked mentions of “artificial intelligence” in earnings call transcripts, noting a huge uptick in the last two years. Companies boast about undefined AI acquisitions. The 2017 Deloitte Global Human Capital Trends report claims that AI has “revolutionized” the way people work and live, but never cites specifics. Nevertheless, coverage of the report concludes that artificial intelligence is forcing corporate leaders to “reconsider some of their core structures.”

And both press and popular discourse sometimes inflate simple features into AI miracles. Last month, for example, Twitter announced service updates to help protect users from low-quality and abusive tweets. The changes amounted to simple refinements to hide posts from blocked, muted, and new accounts, along with other, undescribed content filters. Nevertheless, some takes on these changes—which amount to little more than additional clauses in database queries— conclude that Twitter is “constantly working on making its AI smarter.”

* * *

I asked my Georgia Tech colleague, the artificial intelligence researcher Charles Isbell, to weigh in on what “artificial intelligence” should mean. His first answer: “Making computers act like they do in the movies.” That might sound glib, but it underscores AI’s intrinsic relationship to theories of cognition and sentience. Commander Data poses questions about what qualities and capacities make a being conscious and moral—as do self-driving cars. A content filter that hides social media posts from accounts without profile pictures? Not so much. That’s just software.

Isbell suggests two features necessary before a system deserves the name AI. First, it must learn over time in response to changes in its environment. Fictional robots and cyborgs do this invisibly, by the magic of narrative abstraction. But even a simple machine-learning system like Netflix’s dynamic optimizer, which attempts to improve the quality of compressed video, takes data gathered initially from human viewers and uses it to train an algorithm to make future choices about video transmission.

Isbell’s second feature of true AI: what it learns to do should be interesting enough that it takes humans some effort to learn. It’s a distinction that separates artificial intelligence from mere computational automation. A robot that replaces human workers to assemble automobiles isn’t an artificial intelligence, so much as machine programmed to automate repetitive work. For Isbell, “true” AI requires that the computer program or machine exhibit self-governance, surprise, and novelty.
AI can remind creators and users of an essential truth: today’s computer systems are nothing special.

Griping about AI’s deflated aspirations might seem unimportant. If sensor-driven, data-backed machine learning systems are poised to grow, perhaps people would do well to track the evolution of those technologies. But previous experience suggests that computation’s ascendency demands scrutiny. I’ve previously argued that the word “algorithm” has become a cultural fetish, the secular, technical equivalent of invoking God. To use the term indiscriminately exalts ordinary—and flawed—software services as false idols. AI is no different. As the bot author Allison Parrish puts it, “whenever someone says ‘AI’ what they're really talking about is ‘a computer program someone wrote.’”

Writing at the MIT Technology Review, the Stanford computer scientist Jerry Kaplan makes a similar argument: AI is a fable “cobbled together from a grab bag of disparate tools and techniques.” The AI research community seems to agree, calling their discipline “fragmented and largely uncoordinated.” Given the incoherence of AI in practice, Kaplan suggests “anthropic computing” as an alternative—programs meant to behave like or interact with human beings. For Kaplan, the mythical nature of AI, including the baggage of its adoption in novels, film, and television, makes the term a bogeyman to abandon more than a future to desire.

* * *

Kaplan keeps good company—when the mathematician Alan Turing accidentally invented the idea of machine intelligence almost 70 years ago, he proposed that machines would be intelligent when they could trick people into thinking they were human. At the time, in 1950, the idea seemed unlikely; Even though Turing’s thought experiment wasn’t limited to computers, the machines still took up entire rooms just to perform relatively simple calculations.

But today, computers trick people all the time. Not by successfully posing as humans, but by convincing them that they are sufficient alternatives to other tools of human effort. Twitter and Facebook and Google aren’t “better” town halls, neighborhood centers, libraries, or newspapers—they are different ones, run by computers, for better and for worse. The implications of these and other services must be addressed by understanding them as particular implementations of software in corporations, not as totems of otherworldly AI.

On that front, Kaplan could be right: abandoning the term might be the best way to exorcise its demonic grip on contemporary culture. But Isbell’s more traditional take—that AI is machinery that learns and then acts on that learning—also has merit. By protecting the exalted status of its science-fictional orthodoxy, AI can remind creators and users of an essential truth: today’s computer systems are nothing special. They are apparatuses made by people, running software made by people, full of the feats and flaws of both.

[Tomhet]

Ihan hyvä että on spooky nimi, kun kaikki menee kumminkin vituiksi.