Search Results for: "artificial intelligence"

Microsoft Demos Copilot, Its ‘Artificial Intelligence’ Virtual Assistant ⇥ microsoft.com

Jared Spataro, of Microsoft:

Today at an event in New York, we announced our vision for Microsoft Copilot — a digital companion for your whole life — that will create a single Copilot user experience across Bing, Edge, Microsoft 365, and Windows. As a first step toward realizing this vision, we’re unveiling a new visual identity — the Copilot icon — and creating a consistent user experience that will start to roll out across all our Copilots, first in Windows on September 26 and then in Microsoft 365 Copilot when it is generally available for enterprise customers on November 1.

This is a typically ambitious effort from Microsoft. Copilot replaces Cortana, which will mostly be dropped later this year, and is being pitched as a next-generation virtual assistant in a similar do everything vein. This much I understand; tying virtual assistants to voice controls does not make much sense because sometimes — and, for me, a lot of the time — you do not want to be chatting with your computer. That is certainly a nice option and a boon for accessibility, but clear and articulate speech should not be required to use these kinds of features.

Microsoft’s implementation, however, is worrisome as I use a Windows PC at my day job. Carmen Zlateff, Microsoft Windows vice president, demoed a feature today in which she said “as soon as I copy the text, Copilot appears” in a large sidebar that spans the entire screen height. I copy a lot of stuff in a day, and I cannot tell you how much I do not want a visually intrusive — if not necessarily interruptive — feature like this. I hope I will be able to turn this off.

Meanwhile, a bunch of this stuff is getting jammed into Edge and Microsoft 365 productivity apps. Edge is getting so bloated it seems like the company will need to make a new browser again very soon. The Office features might help me get through a bunch of emails very quickly, but the kinds of productivity enhancements Microsoft suggests for me have not yet materialized into something I actually find useful. Its Viva Insights tool, introduced in 2021, is supposed to analyze your individual working patterns and provide recommendations, but I cannot see why I should pay attention to a graphic that looks like the Solar System illustrating which of my colleagues I spoke with least last week. Designing dashboards like these are a fun project and they make great demos. I am less convinced of their utility.

I get the same kind of vibe from Copilot. I hope it will be effective at summarizing all my pre-reads for a meeting, but I have my doubts. So much of what Microsoft showed today requires a great deal of trust from users: trust in its ability to find connections; in its accuracy; in its ability to balance helpfulness and intrusion; in its neutrality to its corporate overlords. One demo showed someone searching for cleats using Microsoft’s shopping search engine and getting a deal with the browser-based coupon finder. It is a little thing, but can I trust Copilot and Microsoft Shopping are showing me the best quality results that are most relevant, or should I just assume this is a lightly personalized way to see which companies have the highest ad spend with Microsoft?

It seems risky to so confidently launch something like this at a time when trust in big technology companies is at rock-bottom levels in the United States, especially among young people. Microsoft is certainly showing it is at the leading edge of this stuff, and you should expect more from its competitors very soon. I am just not sure giving more autonomy to systems like these from powerful corporations is what people most want.

Hundreds of Machine Generated ‘News’ Sites Clash With the Online News Act ⇥ thestar.com

Kevin Jiang, the Toronto Star:

Just months after the advent of ChatGPT late last year, hundreds of websites have already been identified as using generative artificial intelligence to spew thousands of AI-written, often misinformation-laden “news” stories online.

As the world nears a “precipice” of AI-driven misinformation, experts tell the Star that the tech industry pushback to Canada’s Online News Act — namely Google and Meta blocking trusted Canadian news sources for Canadians — may only make the issue worse.

This is not just a future concern: people affected by wildfires in British Columbia and the Northwest Territories have been unable to share news stories with each other on Meta’s platforms. That is obviously a horrible side effect, though better than what happened last time Meta issued national restrictions.

Also, I have no time for people who treat the exchange of news and information on Facebook or Instagram — or other social media platforms — as a mistake or some kind of dumbing-down of society. It is anything but. People moved their community connections online long ago, and their hosting is migrated to wherever those people congregate. And, for a long time now, that has been Facebook.

But, while it is Meta that is affecting the distribution of news on its platform, it is for reasons that can best be described as a response to a poorly designed piece of legislation — even though that law is not yet in effect. If Meta is told that it must soon pay for each news link shared publicly on its platforms, it is obviously going to try its best to avoid that extra variable expense. The only way it can effectively do that is to prohibit these links. It is terrible that Meta is standing firm but this feels like a fairly predictable consequence of a law based on links, and it seems like the federal government was ill prepared as it is now requesting Meta to stand down and permit news links again.

The irony of the fallout from this law is that any supposed news links in a Canadian’s Facebook or Instagram feed will be, by definition, not real news. The advertising businesses of Google and Meta surely played a role in encouraging more publishers to move behind paywalls, but they were not solely responsible. News has always been expensive to produce and that puts it at odds with a decades-long business obsession of maximizing profit and minimizing resources and expenses no matter how much it strains quality. Research and facts and original reporting will increasingly be treated like luxuries — in the same was as well made long-lasting products — if we do not change those priorities.

Claims About ChatGPT’s Supposed Liberal Bias Are Wildly Overstated ⇥ aisnakeoil.com

Gerrit De Vynck, Washington Post:

A paper from U.K.-based researchers suggests that OpenAI’s ChatGPT has a liberal bias, highlighting how artificial intelligence companies are struggling to control the behavior of the bots even as they push them out to millions of users worldwide.

The study, from researchers at the University of East Anglia, asked ChatGPT to answer a survey on political beliefs as it believed supporters of liberal parties in the United States, United Kingdom and Brazil might answer them. They then asked ChatGPT to answer the same questions without any prompting, and compared the two sets of responses.

The survey in question is the Political Compass.

Arvind Narayanan on Mastodon:

The “ChatGPT has a liberal bias” paper has at least 4 *independently* fatal flaws:

– Tested an older model, not ChatGPT.

– Used a trick prompt to bypass the fact that it actually refuses to opine on political q’s.

– Order effect: flipping q’s in the prompt changes bias from Democratic to Republican.

– The prompt is very long and seems to make the model simply forget what it’s supposed to do.

Colin Fraser appears to be responsible for finding that the order of how the terms appear affects the political alignment displayed by ChatGPT.

Narayanan and Sayash Kapoor tried to replicate the paper’s findings:

Here’s what we found. GPT-4 refused to opine in 84% of cases (52/62), and only directly responded in 8% of cases (5/62). (In the remaining cases, it stated that it doesn’t have personal opinions, but provided a viewpoint anyway). GPT-3.5 refused in 53% of cases (33/62), and directly responded in 39% of cases (24/62).

It is striking to me how the claims of this paper were widely repeated with apparent confirmation that tech companies are responsible for pushing the liberal beliefs that are ostensibly a reflection of mainstream news outlets.

Microsoft’s Machine Generated Article Recommending the Ottawa Food Bank to Tourists ⇥ twitter.com

Paris Marx:

Microsoft is really hitting it out of the park with its AI-generated travel stories! If you visit Ottawa, it highly recommends the Ottawa Food Bank and provides a great tip for tourists: “Consider going into it on an empty stomach.”

Jay Peters, the Verge:

If you try to view the story at the link we originally included in this article, you’ll see a message that says “this page no longer exists.” However, Microsoft’s article is still accessible from another link.

Microsoft laid off journalists at Microsoft News and MSN in 2020 to replace them with artificial intelligence. Microsoft didn’t immediately respond to a request for comment.

The article was pulled for users using British English, but remains accessible in American English and, perhaps more relevant, Canadian English. How hard can it be to remove all versions of this obviously dumb article?

Way of the future.

Update: Microsoft seems to have pulled the article entirely. I cannot find a language code which works.

Copying on Purpose

Threads’ user base seems to be an object of fascination among the tech press. Mark Zuckerberg says it is “on the trajectory I expect to build a vibrant long term app” with “10s of millions” of users returning daily. Meanwhile, third-party estimators have spent the weeks since Threads’ debut breaking the news that its returning user base is smaller than its total base, and that people are somewhat less interested in it than when it launched, neither of which is surprising or catastrophic.1 Meanwhile, Elon Musk says Twitter is more popular than ever but, then again, he does say a lot of things that are not true.

All that merits discussion, I suppose, but I am more interested in the purpose of Threads. It is obviously a copy of Twitter at its core, but so what? Twitter is the progenitor of a genre of product, derived from instant messenger status messages and built into something entirely different. Everything is a copy, a derivative, a remix. It was not so long ago that many people were equating a person’s ban from mainstream social media platforms with suppression and censorship. That is plenty ridiculous on its face, but it does mean we should support more platforms because it does not make sense for there to be just one Twitter-like service or one YouTube-like video host.

So why is Threads, anyway? How does Meta’s duplication of Twitter — and, indeed, its frequent replication of other features and apps — fit into the company’s overall strategy? What is its strategy? Meta introduced Threads by saying it is “a new, separate space for real-time updates and public conversations”, which “take[s] what Instagram does best and expand[s] that to text”. Meta’s mission is to “[give] people the power to build community and bring the world closer together”. It is a “privacy-focused” set of social media platforms. It is “making significant investments” in its definition of a metaverse which “will unlock monetization opportunities for businesses, developers, and creators”. It is doing a bunch of stuff with generative artificial intelligence.

But what it sells are advertisements. It currently makes a range of products which serve both as venues for those ads, and as activity collection streams for targeting information. This leaves it susceptible to risks on many fronts, including privacy and platform changes, which at least partly explains why it is slowly moving toward its own immersive computing platform.

Ad-supported does not equate to bad. Print and broadcast media have been ad-supported for decades and they are similarly incentivized to increase and retain their audience. But, in their case, they are producing or at least deliberately selecting media of a particular type — stories in a newspaper, songs on a radio station, shows on TV — and in a particular style. Meta’s products resemble that sort of arrangement, but do not strictly mimic it. Its current business model rewards maximizing user engagement and data collection. But, given the digital space, there is little prescription for format. Instagram’s image posts can be text-based; users can write an essay on Facebook; a Threads post can contain nothing more than a set of images.

So Meta has a bunch of things going for it:

  • a business model that incentivizes creating usage and behavioural data at scale,

  • a budget to experiment, and

  • an existing massive user base to drive adoption.

All this explains why Meta is so happy to keep duplicating stuff popularized elsewhere. It cloned Snapchat’s Stories format in Instagram to great success, so it tried cloning Snapchat in its entirety more than once, both of which flopped. After Vine popularized short videos, Facebook launched Riff. After Twitter dumbly let Vine wither and die, and its place was taken by Musical.ly and then TikTok, Facebook launched Lasso, which failed, then Reels and copied its recommendation-heavy feed, moves which — with some help — have been successful. Before BeReal began to tank, it was copied by, uh, TikTok, but Meta was working on its own version, too.

But does any of this suggest to you an ultimate end goal or reason for being? To me, this just looks like Meta is throwing stuff at people in the hope any of it sticks enough for them to open the advertising spigot. In the same way a Zara store is just full of stuff, much of it ripping off the work of others, Meta’s product line does not point to a goal any more specific than its mission statement of “bring[ing] the world closer”. That is meaningless! The same corporate goal could be used by a food importer or a construction firm.

None of this is to say Meta is valueless as a company; clearly it is not. But it makes decisions that look scatterbrained as it fends off possible competitors while trying to build its immersive computing vision. But that might be far enough away that it is sapping any here-and-now vision the company might have. Even if the ideas are copies — and, again, I do not see that as an inherent weakness — I can only think of one truly unique, Meta-specific, and successful take: Threads itself. It feels like a text-only Instagram app, not a mere Twitter clone, and it is more Meta-like for it. That probably explains why I use it infrequently, and why it seems to have been greeted with so much attention. Even so, I do not really understand where it fits into the puzzle of the Meta business as a whole. Is it always going to be a standalone app? Is it a large language model instruction farm? Is it just something the company is playing around with and seeing where it goes, along the lines of its other experimental products? That seems at odds with its self-described “year of efficiency”.

I wish I saw in Meta a more deliberate set of products. Not because I am a shareholder — I am not — but because I think it would be a more interesting business to follow. I wish I had a clearer sense of what makes a Meta product or service.

  1. Then there is the matter of how Sensor Tower and SimilarWeb measure app usage given how restricted their visibility is on Android and, especially, iOS. Sensor Tower runs an ad blocking VPN which it uses in a way not dissimilar from how Meta used Onavo, and several screen time monitoring products, which is something that was not disclosed in an analysis the company did with the New York Times.

    SimilarWeb has a fancy graphic illustrating its data acquisition and delivery process, which it breaks down into collection, synthesis, modelling, and digital intelligence. Is it accurate? Since neither Apple nor Google reports the kind of data SimilarWeb purports to know about apps, it is very difficult to know. But, as its name suggests, its primary business is in web-based tracking, so it is at least possible to compare its data against others’. It says the five most popular questions asked to Google so far this year are “what”, “what to watch”, “how to delete instagram account”, “how to tie a tie”, and “how to screenshot on windows”. PageTraffic says the five most-Googled questions are “what to watch”, “where’s my refund”, “how you like that”, “what is my IP address”, and “how many ounces in a cup”, and Semrush says the top five are “where is my refund”, “how many ounces in a cup”, “how to calculate bmi”, “is rihanna pregnant”, and “how late is the closest grocery store open”. All three use different data sources but are comparable data sets — that is, all from Google, all worldwide, and all from 2023. They also estimate wildly differing search volumes: SimilarWeb’s estimate of the world’s most popular question query, “what”, is searched about 2,015,720 times per month, while Semrush says “where is my refund” is searched 15,500,000 times per month. That is not even close.

    But who knows? Maybe the estimates from these marketing companies really can be extrapolated to determine real-world app usage. Colour me skeptical, though: if there is such wide disagreement in search analysis — a field which uses relatively open and widely accessible data — then what chance do they have of accurately assessing closed software platforms? ↥︎

Zoom’s Machine Learning Backlash ⇥ stackdiary.com

Alex Ivanovs, Stackdiary:

Zoom’s updated policy states that all rights to Service Generated Data are retained solely by Zoom. This extends to Zoom’s rights to modify, distribute, process, share, maintain, and store such data “for any purpose, to the extent and in the manner permitted under applicable law.”

What raises alarm is the explicit mention of the company’s right to use this data for machine learning and artificial intelligence, including training and tuning of algorithms and models. This effectively allows Zoom to train its AI on customer content without providing an opt-out option, a decision that is likely to spark significant debate about user privacy and consent.

Smita Hashim of Zoom (emphasis theirs):

We changed our terms of service in March 2023 to be more transparent about how we use and who owns the various forms of content across our platform.

[…]

To reiterate: we do not use audio, video, or chat content for training our models without customer consent.

Zoom is trialling a summary feature which uses machine learning techniques, and it appears administrators are able to opt out of data sharing while still having access to the feature. But why is all of this contained in a monolithic terms-of-service document? Few people read these things in full and even fewer understand them. It may appear simpler, but features which require this kind of compromise should have specific and separate documentation for meaningful explicit consent.

Inside the A.I. Factory ⇥ nymag.com

Josh Dzieza, writing for New York in collaboration with the Verge, on the hidden human role in artificial intelligence:

Over the past six months, I spoke with more than two dozen annotators from around the world, and while many of them were training cutting-edge chatbots, just as many were doing the mundane manual labor required to keep AI running. There are people classifying the emotional content of TikTok videos, new variants of email spam, and the precise sexual provocativeness of online ads. Others are looking at credit-card transactions and figuring out what sort of purchase they relate to or checking e-commerce recommendations and deciding whether that shirt is really something you might like after buying that other shirt. Humans are correcting customer-service chatbots, listening to Alexa requests, and categorizing the emotions of people on video calls. They are labeling food so that smart refrigerators don’t get confused by new packaging, checking automated security cameras before sounding alarms, and identifying corn for baffled autonomous tractors.

The magical feeling of so many of our modern products and services is too often explained by throwing money at low-paid labourers. Same day delivery? Online purchases of anything? Expedited free returns? Moderation of comments and images? As much as it looks from our perspective like the work of advancements in computing power, none of it would be possible without tens of thousands of people doing their best to earn a living spending unpredictable hours doing menial tasks.

The extent to which that bothers you is a personal affair; I am not one to judge. At the very least, I think it is something we should all remember the next time we hear about a significant advancement in this space. There are plenty of engineers who worked hard and deserve credit, but there are also thousands of people labelling elbows in photos and judging the emotion of internet comments.

The Best Time to Regulate A.I. Is Today

For the first time in more than a decade, it truly feels like we are experiencing massive changes in how we use computers now, and how that will change in the future. The ferocious burgeoning industry of artificial intelligence, machine learning, LLMs, image generators, and other nascent inventions has been a part of our lives first gradually, then suddenly. The growth of this new industry provides an opportunity to reflect on how it ought to be grown while avoiding problems similar to those which have come before.

A frustrating quality of industries and their representatives is a general desire to avoid scrutiny of their inventions and practices. High technology is no different. They begin by claiming things are too new or that worries are unproven and, therefore, there is no need for external policies governing their work. They argue industry-created best practices are sufficient in curtailing bad behaviour. After a period of explosive growth, as regulators are eager to corral growing concerns, those same industry voices protest that regulations will kill jobs and destroy businesses. It is a very clever series of arguments which can luckily be repurposed for any issue.

Eighteen years ago, EPIC reported on the failure of trusting data brokers and online advertising platforms to self-regulate. It compared them unfavourably to the telemarketing industry, which pretended to self-police for years before the Do Not Call list was introduced. At the time, it was a rousing success; unfortunately, regulators were underfunded and failed to keep pace with technological change. Due to overwhelming public frustration with the state of robocalls, the U.S. government began rolling out call verification standards in 2019, and Canadian regulators followed suit. For U.S. numbers, these verification standards will be getting even more stringent just nine days from now.

These are imperfect rules and they are producing mixed results, but they are at least an attempt at addressing a common problem with some success. Meanwhile, a regulatory structure for personal privacy remains elusive. That industry still believes self-regulation is effective despite all evidence to the contrary, as my regular readers are fully aware.

Artificial intelligence and machine learning services are growing in popularity across a wide variety of industries, which makes it a perfect opportunity to create a regulatory structure and a set of ideals for safer development. The European Union has already proposed a set of restrictions based on risk. Some capabilities — like when automated systems are involved in education, law enforcement, or hiring contexts — would be considered “high risk” and subject to ongoing assessment. Other services would face transparency requirements. I do not know if these rules are good but, on their face, the behavioural ideals which the E.U. appears to be constructing are fair. The companies building these tools should be expected to disclose how models were trained and, if they do not do so, there should be consequences. That is not unreasonable.

This is about establishing a set of principles to which new developments in this space must adhere. I am not sure what those look like, but I do not think the correct answer is in letting businesses figure it out before regulators struggle to catch up years later with lobbyist-influenced half-measures. Things can be different this time around if there is a demand and an expectation for doing so. Written and enforced correctly, these regulations can help temper the worst tendencies of this industry while allowing it to flourish.

Inhuman Writing for Google Is Eating the Web ⇥ theverge.com

Mia Sato, the Verge:

[Jennifer] Dziura still updates her personal blog — these are words for people.

The shop blog, meanwhile, is the opposite. Packed with SEO keywords and phrases and generated using artificial intelligence tools, the Get Bullish store blog posts act as a funnel for consumers coming from Google Search, looking for things like Mother’s Day gifts, items with swear words, or gnome decor. On one hand, shoppers can peruse a list of products for sale — traffic picks up especially around holidays — but the words on the page, Dziura says, are not being read by people. These blogs are for Google Search.

[…]

This is the type of content publishers, brands, and mom-and-pop businesses spend an untold number of hours on, and on which a booming SEO economy full of hackers and hucksters make promises ranging from confirmed to apocryphal. The industries that rely heavily on Search — online shops, digital publishers, restaurants, doctors and dentists, plumbers and electricians — are in a holding pattern, churning out more and more text and tags and keywords just to be seen.

The sharp divergence between writing for real people and creating material for Google’s use has become so obvious over the past few years that it has managed to worsen both Google’s own results and the web at large. The small business owners profiled by Sato are in an exhausting fight with automated chum machines generating supposedly “authoritative” articles. When a measure becomes a target — well, you know.

There are loads of examples and I recently found a particularly galling one after a family friend died. Their obituary was not published, but several articles began appearing across the web suggesting a cause of death, which was not yet public information. These websites seem to automatically crawl publicly available information and try to merge it with some machine learning magic and, in some instances, appear to invent a cause of death. There is also a cottage industry of bizarre YouTube channels with videos that have nothing to do with the obituary-aligned titles. I have no idea why those videos exist; none that I clicked on were ad-supported. But the websites have an easy answer: ghoulish people have found that friends and family of a person who recently died are looking for obituaries, and have figured out how to scam their ad-heavy pages to high-ranking positions.

At the same time, I have also noticed a growing number of businesses — particularly restaurants — with little to no web presence. They probably have a listing in Apple Maps and Google Maps, an Instagram page, and a single-page website, but that could be their entire online presence. I know it is not possible for every type of business. It does seem more resilient against the slowly degrading condition of search engines and the web at large, though.

Global Tech Supply Chains Are as Complex as a Circuit Board ⇥ inkstickmedia.com

Brendan O’Connor, Inkstick:

But not forever. Moore’s Law is not a natural law, but a prediction based on production capacities, capital flows, and the availability of highly exploitable labor. There are limits: political and economic as well as physical. “At some point, the laws of physics will make it impossible to shrink transistors further,” [Chris] Miller warns. “Even before then, it could become too costly to manufacture them.” Already it is proving more difficult to keep costs down: the extreme ultraviolet lithography machines needed to print the smallest and most advanced chips cost more than $100 million apiece. (And only one company in the world makes them.) And yet, Miller notes, startups focused on designing chips for artificial intelligence and other highly complex and specialized logic chips have raised billions of dollars in funding, while the big tech firms like Google, Amazon, Microsoft, Apple, Facebook, and Alibaba are pouring funds into their own chip design arms. “There’s clearly no deficit of innovation,” he writes. The question, Miller argues, isn’t whether Moore’s Law has hit its limit, “but whether we’ve reached a peak in the amount of computing power a chip can cost-effectively produce. Many thousands of engineers and many billions of dollars are betting not.” In other words, they are betting that if they throw enough money at the problem, they’ll be the ones to break through the limit — and release untold profits and productivity on the other side.

This one is long, but well worth your time.

U.S. FTC Settles With Amazon Over Insecure Ring Cameras ⇥ ftc.gov

Lesley Fair, of the U.S.’ Federal Trade Commission:

Many consumers who use video doorbell and security cameras want to detect intruders invading the privacy of their homes. Consumers who installed Ring may be surprised to learn that according to a proposed FTC settlement, one “intruder” that was invading their privacy was Ring itself. The FTC says Ring gave its employees and hundreds of Ukraine-based third-party contractors up-close-and-personal video access into customers’ bedrooms, their kids’ bedrooms, and other highly personal spaces – including the ability to download, view, and share those videos at will. And that’s not all Ring was up to. In addition to a $5.8 million financial settlement, the proposed order in the case contains provisions at the intersection of artificial intelligence, biometric data, and personal privacy. It’s an instructive bookend to another major biometric privacy case the FTC announced today, Amazon Alexa.

To put the financial settlement in context, Amazon sold an estimated 1.7 million Ring cameras in 2021 — the most recent year for which I could find sales figures — and the cheapest Ring camera you could buy at the time retailed for $60. In response to years of contractor and public abuses of its insecure webcams, Amazon has to pay about three weeks’ worth of a single year of sales. That is hardly a punitive amount, and the FTC only says it is to be “used for consumer refunds”: sorry Amazon fibbed about the security of the cheap product it sold to 55,000 people, thus permitting many of them to be tormented and spied upon, but at least some of them can get their money back. And of course Amazon has to admit no culpability.

A.I. Developers Say Their Product Creates a ‘Risk of Extinction’ in Self-Serving Statement ⇥ safe.ai

Hundreds of experts in artificial intelligence — including several executives and developers in the field — issued a brief and worrying statement via the Center for AI Safety:

Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.

The Center calls out Meta by name for not signing onto the letter; Elon Musk also did not endorse it.

OpenAI’s Sam Altman was among the hundreds of signatories after feigning an absolute rejection of regulations which he and his peers did not have a role in writing. Perhaps that is an overly cynical take, but it is hard to read this statement with the gravity it suggests.

Martin Peers, the Information:

Perhaps instead of issuing a single-sentence statement meant to freak everyone out, AI scientists should use their considerable skills to figure out a solution to the problem they have wrought.

I believe the researchers, academics, and ethicists are earnest in their endorsement of this statement. I do not believe the corporate executives who simultaneously claim artificial intelligence is a threat to civilization itself while rapidly deploying their latest developments in the field. Their obvious hypocrisy makes it hard to take them seriously.

Humane’s Device Preview at TED ⇥ ted.com

Last month, we caught glimpses of Imran Chaudhri’s preview of the device being developed by Humane. Chaudhri presented this sneak peek at the TED conference in Vancouver, and the full presentation was published today.

While it is important not to rush to judge a device which none of us have used, I feel like its debut in a TED Talk is worrisome. It is a conference that has become synonymous with catchy hacks and apparently counterintuitive thinking which have little basis in reality. That is not a great sign.

Also, and this is a little thing, TED says (PDF) “speakers may never use the TED or TEDx stage to pitch their products or services”, and that “if it feels like an advertisement, it probably is”. Chaudhri’s talk is all about how great artificial intelligence is going to be, but it is all structured around a device debut which feels like a supercut of iPhone launch presentations.

For Google ⇥ theverge.com

Nilay Patel, the Verge:

Google Search is so useful and so pervasive that its overwhelming influence on our lives is also strangely invisible: Google’s grand promise was to organize the world’s information, but over the past quarter century, an enormous amount of the world’s information has been organized for Google — to rank in Google results. Almost everything you encounter on the web — every website, every article, every infobox — has been designed in ways that makes them easy for Google to understand. In many cases, the internet has become more parseable by search engines than it is by humans.

I am reminded of Goodhart’s law in thinking about the adversarial relationship between Google and search optimization experts which, presumably, will morph into a similar situation between artificial intelligence services and self-proclaimed experts in that field. Because it has been an unrivalled default for so long, there is no reason for the most popular parts of the web to work anything better than as a feed for Google’s consumption and, hopefully, its users’ attention. All of this has broken the web as much as it has broken Google Search.

Patel wrote this as an introduction to a series of articles the Verge is running this year in recognition of Google’s twenty-fifth anniversary. The first, about Accelerated Mobile Pages, is a good summary of the kind of control Google has over publishers.

Artificial Intelligence McKinsey ⇥ newyorker.com

Ted Chiang, the New Yorker:

Today, we find ourselves in a situation in which technology has become conflated with capitalism, which has in turn become conflated with the very notion of progress. If you try to criticize capitalism, you are accused of opposing both technology and progress. But what does progress even mean, if it doesn’t include better lives for people who work? What is the point of greater efficiency, if the money being saved isn’t going anywhere except into shareholders’ bank accounts? We should all strive to be Luddites, because we should all be more concerned with economic justice than with increasing the private accumulation of capital. We need to be able to criticize harmful uses of technology — and those include uses that benefit shareholders over workers — without being described as opponents of technology.

The whole article is terrific — as the headline alludes to, an imagining of artificial intelligence technologies performing a sort of McKinsey-like role in executing the worst impulses of our economic system — but this paragraph is damn near perfect.

Not too long ago, in the era of gadget blogs and technology enthusiasm gone mainstream, there was a specific kind of optimism where every new product or service was imagined as beneficial. Tides turned, and criticism is the current default position. I think that is a healthier and more realistic way of viewing this market, even as it feels more negative. What good is thinking about new technologies if they are not given adequate context? We have decades of personal computing to draw from, plus hundreds of years of efficiency gains. On the cusp of another vast transformation, we should put that knowledge to use.

Content Moderators in Kenya Form Labour Union ⇥ nation.africa

Odanga Madung, Nation:

At a meeting held in Nairobi on Monday, 200 content moderators from Sama and Majorel — the firms that serve Facebook, YouTube, TikTok and Chat GPT — took a stand against tech giants’ mistreatment of their workers by coming together to lobby for their rights.

In a first-of-its-kind event, moderators covering 14 different African languages came together on Labour Day to vote for establishing a union to address issues including mistreatment of workers.

Majorel is based in Luxemborg; Teleperformance, based in France, recently offered to buy it. Sama is based in the United States and was, last year, sued by a former moderator. The vast distance between these companies, their employees in Kenya, and their clients mostly located in Silicon Valley is not only geographic. These are some of the people who remove the worst of the web and make artificial intelligence work better.

Good for them.

The Information: Siri’s Flagging Quality Is the Result of ‘Turf Wars’ ⇥ macrumors.com

Normally, I would not link to something for which I have not read the source story. In this case, I will make an exception, as the original is by Wayne Ma of the Information, who has a solid track record. I hope these two summaries are accurate reflections of Ma’s reporting.

Hartley Charlton, MacRumors:

The extensive paywalled report explains why former Apple employees who worked in the company’s AI and machine learning groups believe that a lack of ambition and organizational dysfunction have hindered Siri and the company’s AI technologies. Apple’s virtual assistant is apparently “widely derided” inside the company for its lack of functionality and minimal improvement over time.

[…]

Apple executives are said to have dismissed proposals to give Siri the ability to conduct extended back-and-forth conversations, claiming that the feature would be difficult to control and gimmicky. Apple’s uncompromising stance on privacy has also created challenges for enhancing Siri , with the company pushing for more of the virtual assistant’s functions to be performed on-device.

Samuel Axon, Ars Technica:

For example, it reveals that the team that has been working on Apple’s long-in-development mixed reality headset was so frustrated with Siri that it considered developing a completely separate, alternative voice control method for the headset.

But it goes beyond just recounting neutral details; rather, it lays all that information out in a structured case to argue that Apple is ill-prepared to compete in the fast-moving field of AI.

By the sound of that, Ma is making a similar argument as was reported by Brian X. Chen, Nico Grant, and Karen Weise in the New York Times last month. I linked to it noting two things: first, that the headline’s proclamation that Apple has “lost the A.I. race” is premature; second, that the vignette in the lede is factually incorrect. But there was a detail I think is worth mentioning in the context of Siri’s capabilities:

Siri also had a cumbersome design that made it time-consuming to add new features, said [former Apple employee John] Burkey, who was given the job of improving Siri in 2014. Siri’s database contains a gigantic list of words, including the names of musical artists and locations like restaurants, in nearly two dozen languages.

That made it “one big snowball,” he said. If someone wanted to add a word to Siri’s database, he added, “it goes in one big pile.”

This is a claim sourced to a single person, but it would not surprise me if the entire Siri backend really is a simple database of known queries and expected responses. Sources the Times reporters spoke to say this structure cannot be adapted to fit a large language model system and, so, Apple is far behind.

Maybe all that is true. But what I cannot understand is why anyone would think users would want to have a conversation with Siri, when many would probably settle for a version of that basic database association schema working correctly.

Siri is infamously frustrating to use. It has unknowable limits to its capabilities — for example, requesting a scoreboard works for some sports but not others, and asking for a translation is only available between a small number of languages. It, like other voice assistants, assumes a stage-practiced speech cadence, which impairs its usability for those with atypical speech, or queries with pauses or corrections. But the things which bum me out in my own use of Siri are the ways in which it does not seem to be built by the same people who made the phone it runs on.

I know reading a list of bugs is boring, so here are two small examples:

  1. My wife, driving home, texts me while I am making dinner to ask if there is anything she should pick up. I see the notification come in on the Lock Screen, but my hands are dirty, so I say “hey Siri, reply to [her name]”. Instead of the prompt asking “okay, what would you like to say?”, I am instead asked “okay, which one should I use?” with the list of phone numbers from her contact card.

    There are three things wrong with this: my query uses the word “reply”, so it should compose a message to whatever contact method from which she sent the message; for several versions of iOS now, Messages consolidates conversations from the same contact, so Siri’s behaviour should work the same way; and, I am trying to send something to one of my most-messaged contacts, so it feels particularly dumb.

  2. Siri is, as of a recent version of iOS, hardwired to associate music-related commands to Apple Music. It will sometimes ask if the user wants an alternative app. But it also means it does not reliably play music from a local library, and it has no awareness of whether one has turned off cellular data use for Music.

    So if you are driving along, with a local library full of songs, and you ask Siri to play one of them, it will stream it from Apple Music instead; or, if you have cellular data off for Music, it will read out an error message. Meanwhile, the songs are sitting right there, in the library.

Neither of these examples, as far as I can see, should require a humanlike level of deep language understanding. In fact, both of these queries used to work as expected before becoming broken. It seems likely to me the latter was a deliberate change made to promote Apple’s services. In a similar vein, Ma, via Charlton, reports “specific decisions [were made] to exclude information such as iPhone prices from Siri to push users directly to Apple’s website instead”. If true, it is a cynical decision that has no benefit to users. The first problem I listed is simply baffling.

Perhaps these kinds of bugs would be less common if Siri were based on large language models — this is completely outside my field and my inbox is open — but I find that hard to believe. It is not the case that Siri is failing to understand what I am asking it to do. Rather, it is faltering at simple hurdles and functioning as an ad for other Apple services. I would be fine with Siri if it were a database that performed reliably and expectedly, and excited for the possibilities of one fronted by more capable artificial intelligence. What I am, though, is doubtful — doubtful that basic tasks like these will become meaningfully better, instead of a different set of bugs and obstacles I will need to learn.

Ma reports, via Charlton, that some people working on Siri left because there was too much human intervention. I wish it felt anything like that.

The New York Times Declares That Voice Assistants Have Lost the ‘AI Race’ ⇥ nytimes.com

Brian X. Chen, Nico Grant, and Karen Weise, New York Times:

On a rainy Tuesday in San Francisco, Apple executives took the stage in a crowded auditorium to unveil the fifth-generation iPhone. The phone, which looked identical to the previous version, had a new feature that the audience was soon buzzing about: Siri, a virtual assistant.

This first paragraph vignette has problems — and, no, I cannot help myself. The iPhone 4S and Siri were unveiled on October 4, 2011 at Apple’s campus in Cupertino, not in San Francisco, and it did not rain until that night in San Francisco. It was a Tuesday, though.

Please note there are three bylines on this story.

Anyway, the authors of this Times story attempt to illustrate how voice assistants, like Siri and Alexa, have been outdone by products like OpenAI’s ChatGPT:

The assistants and the chatbots are based on different flavors of A.I. Chatbots are powered by what are known as large language models, which are systems trained to recognize and generate text based on enormous data sets scraped off the web. They can then suggest words to complete a sentence.

In contrast, Siri, Alexa and Google Assistant are essentially what are known as command-and-control systems. These can understand a finite list of questions and requests like “What’s the weather in New York City?” or “Turn on the bedroom lights.” If a user asks the virtual assistant to do something that is not in its code, the bot simply says it can’t help.

The article’s conclusion? The architecture of voice assistants has precluded them from becoming meaningful players in artificial intelligence. They have “squandered their lead in the A.I. race”; the headline outright says they have “lost”. But hold on — it seems pretty early to declare outright winners and losers, right?

John Voorhees of MacStories sure thinks so:

It’s not surprising that sources have told The New York Times that Apple is researching the latest advances in artificial intelligence. All you have to do is visit the company’s Machine Learning Research website to see that. But to declare a winner in ‘the AI race’ based on the architecture of where voice assistants started compared to today’s chatbots is a bit facile. Voice assistants may be primitive by comparison to chatbots, but it’s far too early to count Apple, Google, or Amazon out or declare the race over, for that matter.

“Siri” and “Alexa” are just marketing names. The underlying technologies can change. It is naïve to think Google is not working to integrate something like the Bard system into its Assistant. I have no idea if any of these companies will be able to iterate as quickly as OpenAI has been doing — I have been wrong about this before — but to count them out now, mere months after ChatGPT’s launch, is ridiculous, especially as Siri, alone, is in a billion pockets.

Everything Is a Remix: A.I. and Image Generation ⇥ everythingisaremix.info

Kirby Ferguson’s latest is not to be missed: a thoughtful exploration of artificial intelligence within his “Everything is a Remix” framework. Great soundtrack, too.

It is also, Ferguson says, his last video. In an April email to subscribers — which I cannot figure out how to link to — Ferguson says further works will be more likely written rather than videos, owing in part to time constraints. If this is indeed the end of Ferguson’s personal video career, it is a beautiful way to bow out. If you have not checked out his back catalogue, it would be worth your time.

Thanks, Kirby.

Google’s Cautious Approach to Artificial Intelligence ⇥ wsj.com

Miles Kruppa and Sam Schechner, Wall Street Journal:

Now Google, the company that helped pioneer the modern era of artificial intelligence, finds its cautious approach to that very technology being tested by one of its oldest rivals. Last month Microsoft Corp. announced plans to infuse its Bing search engine with the technology behind the viral chatbot ChatGPT, which has wowed the world with its ability to converse in humanlike fashion. Developed by a seven-year-old startup co-founded by Elon Musk called OpenAI, ChatGPT piggybacked on early AI advances made at Google itself.

[…]

Google’s approach could prove to be prudent. Microsoft said in February it would put new limits on its chatbot after users reported inaccurate answers, and sometimes unhinged responses when pushing the app to its limits.

Many in the tech commentariat have predicted victory for Microsoft products so often it has become a running joke in some circles. It released one of the first folding phones which it eventually unloaded on Woot at a 70% discount; it was one of many instances where Microsoft’s entry was prematurely championed.

Even after all the embarrassing problems in Google’s Bard presentation and demos and the pressure the company is now facing, I imagine its management is feeling somewhat vindicated by Microsoft’s rapid dampening of the sassier side of its assistant. While Microsoft could use its sheer might to rapidly build a user base — as it did with Teams — Google could also flip a switch to do the same because it is the world’s most popular website.