Category: Writing

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Photography Writing

WWDC 2024 What Do I (Still….) Want To See?

A couple years ago I posted what I wanted for WWDC 2022. I figured that I’d go through the past list and cross off the items that have arrived over the past two major updates to iOS.

And then I’m going to sketch out how I’d like to see Apple actually adopt more AI/ML into their operating systems.

Photos

This was a low point in iOS and remains so. I really want Apple to improve the Photos application given how regularly I use it.

  • The ability to search photos by different cameras and/or focal lengths
  • The ability to select a point on a photo to set the white point for exposure balancing when editing photos
  • Better/faster sync across devices
  • Enable ability to edit geolocation
  • Enable tags in photos

All of these are basically just aiming to have the iOS Photos app getting brought up to the same standards as Photos on MacOS.

Camera

There is so much potential that’s in the Camera application. I look at this from the perspective of a photographer, while recognizing that Apple has done a lot to really improve the state of things for videographers.

  • Set burst mode to activate by holding the shutter button; this was how things used to be and I want the option to go back to the way things were!
  • Advanced metering modes, such as the ability to set center, multi-zone, spot, and expose for highlights!
  • Set and forget auto-focus points in the frame; not focus lock, but focus zones
  • Zone focusing
  • Working (virtual) spirit level!

Maps

I actually like Maps. I use it a lot. But I definitely want things to be much more collaborative and less focused on Yelp data. I really do like the privacy aspects associated with Maps over some competing applications.1

  • Ability to collaborate on a guide
  • Option to select who’s restaurant data is running underneath the app (I never will install Yelp which is the current app linked in Maps)

Music

Music is fine on the whole. Still want to have something like multiple libraries, though.

  • Ability to collaborate on a playlist
  • Have multiple libraries: I want one ‘primary’ or ‘all albums’ and others with selected albums. I do not want to just make playlists

Reminders

While it’s getting better there’s still some things to do, though apparently the second item may be coming this WWDC which would be pretty great.

  • Speed up sync across shared reminders; this matters for things like shared grocery shopping!
  • Integrate reminders’ date/time in calendar, as well as with whom reminders are shared

Messages

These are both covered off!

  • Emoji reactions
  • Integration with Giphy!

News

I’ll be honest: I’ve given up on the RSS feed idea and just rely on Reeder. But I use News a lot and so it’d be nice to more fully block publications from coming up.

  • When I block a publication actually block it instead of giving me the option to see stories from publications I’ve blocked
  • It’d be great to see News updated so I can add my own RSS feeds

Fitness

The number one issue with Fitness is that I can’t log rest days. I’ve actually started to use Streaks to be more forgiving and stopped worrying so much about maintaining my streaks in Fitness. But it’s absurd that Apple hasn’t integrated this feature that’s widely requested by its user base.

  • Need ability to have off days; when sick or travelling or something it can be impossible to maintain streaks which is incredibly frustrating if you regularly live a semi-active life

Health

This still isn’t great. There is no good year over year data that you can compare against. I don’t understand why the UI isn’t better and I hope that it gets better soon.

  • Show long-term data (e.g. year vs year vs year) in a user friendly way; currently this requires third-party apps and should be default and native

And one more thing…

There is a lot of time and attention being paid to how Apple will show off artificial intelligence functionality in forthcoming operating systems. I tend to agree with Joe Rosensteel about what Apple shouldn’t do: no spying AI systems and instead a focus on useful AI-enabled functionalities.

For Photos I want to propose a pretty useful option for people that would leverage some existing iPhone capabilities. Imagine if you could take a photo (or use the measurement application built into Apple’s mobile OSes) to determine how large a photo would fit in a frame along with the aspect ratio and, then, prompted you to select photos for the frame. That selection could either automatically select just photos of the right aspect range or could show what an AI-determined best aspect ratio crop would look like.

If something like this were bundled up in a kickass UI I can see this being phenomenally helpful and solving a real world annoyance for anyone who wants to print photos.

We create far too many digital photos and print far too few. Physical photos are part of building longterm and vibrant memories: Apple should lean into enabling its customers to make these kinds of mementos.

  1. Rather than requesting a route from A to B, Apple Maps sends off multiple requests with multiple identifiers that masks where you’re trying to go. The app also converts your precise location to a less-exact one after 24 hours, and Apple itself doesn’t store any information about where you’ve been or what you’ve been searching for. Plus none of the information that reaches an external server is associated with your Apple ID. Source: https://www.tomsguide.com/news/google-maps-vs-apple-maps ↩︎
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Writing

What Does It Mean To “Search”

Are we approaching “Google zero”, where Google searches will use generative AI systems to summarize responses to queries, thus ending the reason for people to visit website? And if that happens what is lost?

These are common questions that have been building month over month as more advanced foundational models are built, deployed, and iterated upon. But there has been relatively little assessment in public forums around the social dimensions of making a web search. Instead, the focus has tended to be on loss of traffic and subsequent economic effects of this transition.

A 2022 paper entitled “Situating Search” identifies what a search engine does, and what it is used for, in order for the authors to argue that search that only provides specific requested information (often inaccurately) fails to account for the broader range of things that people use search for.

Specifically, when people search they:

  • lookup
  • learn
  • investigate

When a ChatGPT or Gemini approach to search is applied, however, it limits the range of options before a user. Specifically, in binding search to conversational responses we may impair individuals from conducting search/learning in ways that expand domain knowledge or that rely on sensemaking of results to come to a given conclusion.

Page 227 of the paper has a helpful overview of the dimensions of Information Seeking Strategies (ISS), which explain the links between search and the kinds of activities in which individuals engage. Why, also, might chat-based (or other multimodal) search be a problem?

  • it can come across as too authoritative
  • by synthesizing data from multiple sources and masking the available range of sources, it cuts the individual’s ability to expose the broader knowledge space
  • LLMs, in synthesizing text, may provide results that are not true

All of the above issues are compounded in situations where individuals have low information literacy and, thus, are challenged in their ability to recognize deficient responses from an AI-based search system.

The authors ultimately conclude with the following:

…we should be looking to build tools that help users find and make sense of information rather than tools that purport to do it all for them. We should also acknowledge that the search systems are used and will continue to be used for tasks other than simply finding an answer to a question; that there is tremendous value in information seekers exploring, stumbling, and learning through the process of querying and discovery through these systems.

As we race to upend the systems we use, today, we should avoid moving quickly and breaking things and instead opt to enhance and improve our knowledge ecosystem. There is a place for these emerging technologies but rather than bolting them onto–and into–all of our information technologies we should determine when they are or are not fit for a given purpose.

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Writing

Establishing Confidence Ratings in Policy Assessments

Few government policy analysts are trained in assessing evidence in a structured way and then assigning confidence ratings when providing formal advice to decision makers. This lack of training can be particularly problematic when certain language (“likely,” “probable,” “believe,” etc) is used in an unstructured way because these words can lead decision makers to conclude that recommended actions have a heft of evidence and assessment that, in fact, may not be present in the assessment.

Put simply: we don’t train people to clinically assess the evidence provided to them in a rigorous and structured way, and upon which they make analyses. This has the effect of potentially driving certain decisions that otherwise might not be made.

The government analysts who do have this training tend to come from the intelligence community, which has spend decades (if not centuries) attempting to divine how reliable or confident assessments are because the sources of their data are often partial or questionable.

I have to wonder just what can be done to address this kind of training gap. It doesn’t make sense to send all policy analysts to an intelligence training camp because the needs are not the same. But there should be some kind of training that’s widely and commonly available.

Robert Lee, who works in private practice these days but was formerly in intelligence, set out some high-level framings for how private threat intelligence companies might delineate between different confidence ratings in a blog he posted a few years ago. His categories (and descriptions) were:

Low Confidence: A hypothesis that is supported with available information. The information is likely single sourced and there are known collection/information gaps. However, this is a good assessment that is supported. It may not be finished intelligence though and may not be appropriate to be the only factor in making a decision.

Moderate Confidence: A hypothesis that is supported with multiple pieces of available information and collection gaps are significantly reduced. The information may still be single sourced but there’s multiple pieces of data or information supporting this hypothesis. We have accounted for the collection/information gaps even if we haven’t been able to address all of them.

High Confidence: A hypothesis is supported by a predominant amount of the available data and information, it is supported through multiple sources, and the risk of collection gaps are all but eliminated. High confidence assessments are almost never single sourced. There will likely always be a collection gap even if we do not know what it is but we have accounted for everything possible and reduced the risk of that collection gap; i.e. even if we cannot get collection/information in a certain area it’s all but certain to not change the outcome of the assessment.

While this kind of categorization helps to clarify intelligence products I’m less certain how effective it is when it comes to more general policy advice. In these situations assessments of likely behaviours may be predicated on ‘softer’ sources of data such as a policy actor’s past behaviours. The result is that predictions may sometimes be based less on specific and novel data points and, instead, on a broader psychographic or historical understanding of how an actor is likely to behave in certain situations and conditions.

Example from Kent’s Words of Estimative Probability

Lee, also, provided the estimation probability that was developed in the early 1980s for CIA assessments. And I think that I like the Kent Word approach more if only because it provides a broader kind of language around “why” a given assessment is more or less accurate.

While I understand and appreciate that threat intelligence companies are often working with specific datapoints and this is what can lead to analytic determinations, most policy work is much softer than this and consequently doesn’t (to me) clearly align with the more robust efforts to achieve confidence ratings that we see today. Nevertheless, some kind of more robust approach to providing recommendations to decision makers is needed so that executives have a strong sense of an analyst’s confidence in any recommendation, and especially when there may be competing policy options at play. While intuition drives a considerable amount of policy work at least a little more formalized structure and analysis would almost certainly benefit public policy decision making processes.

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Aside Links

Liberal Fictions, AI technologies, and Human Rights

Although we talk the talk of individual consent and control, such liberal fictions are no longer sufficient to provide the protection needed to ensure that individuals and the communities to which they belong are not exploited through the data harvested from them. This is why acknowledging the role that data protection law plays in protecting human rights, autonomy and dignity is so important. This is why the human rights dimension of privacy should not just be a ‘factor’ to take into account alongside stimulating innovation and lowering the regulatory burden on industry. It is the starting point and the baseline. Innovation is good, but it cannot be at the expense of human rights.

— Prof. Teresa Scassa, “Bill C-27 and a human rights-based approach to data protection”

It’s notable that Prof. Scassa speaks about the way in which Bill C-27’s preamble was supplemented with language about human rights as a way to assuage some public critique of the legislation. Preambles, however, lack the force of law and do not compel judges to interpret legislation,action in a particular way. They are often better read as a way to explain legislation to a public or strike up discussions with the judiciary when legislation repudiates a court decision.

For a long form analysis of the utility of preambles see Prof. Kent Roaches, “The Uses and Audiences of Preambles in Legislation.”

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Links

Instagram’s Ongoing Trust and Safety Problem

A New York Times investigation reveals how Instagram promotes posts that include young girls to male users, including sexual predators.

Aside from reaching a surprisingly large proportion of men, the ads got direct responses from dozens of Instagram users, including phone calls from two accused sex offenders, offers to pay the child for sexual acts and professions of love.

The results suggest that the platform’s algorithms play an important role in directing men to photos of children. And they echo concerns about the prevalence of men who use Instagram to follow and contact minors, including those who have been arrested for using social media to solicit children for sex.

…

… though The Times chose topics that the company estimated were dominated by women, the ads were shown, on average, to men about 80 percent of the time, according to a Times analysis of Instagram’s audience data. In one group of tests, photos showing the child went to men 95 percent of the time, on average, while photos of the items alone went to men 64 percent of the time.

These findings are deeply disturbing to say the absolute least.

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Links

New York City’s Chatbot: A Warning to Other Government Agencies?

A good article by The Markup assessed the accuracy of New York City’s municipal chatbot. The chatbot is intended to provide New Yorkers with information about starting or operating a business in the city. The journalists found the chatbot regularly provided false or incorrect information which could result in legal repercussions for businesses and significantly discriminate against city residents. Problematic outputs included incorrect housing-related information, whether businesses must accept cash for services rendered, whether employers can take cuts of employees’ tips, and more. 

While New York does include a warning to those using the chatbot, it remains unclear (and perhaps doubtful) that residents who use it will know when to dispute outputs. Moreover, the statements of how the tool can be helpful and sources it is trained on may cause individuals to trust the chatbot.

In aggregate, this speaks to how important it is to effectively communicate with users, in excess of policies simply mandating some kind of disclosure of the risks associated with these tools, as well as demonstrates the importance of government institutions more carefully assessing (and appreciating) the risks of these systems prior to deploying them.

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Photography Writing

Tecumseth & Niagara, Toronto, 2023

Tecumseth & Niagara, Toronto, 2023

Toronto is a city of destruction and construction: destruction of the previous era’s architecture (and often industrial buildings) and the construction of housing or glass office towers in their stead. This image by Tecumseth & Niagara shows the destruction of an abattoir that was removed to make room for condos, and the buildings in the background are new rentals in Toronto’s Liberty Village. When I landed in Toronto, in Liberty Village over a decade ago, the land those rentals are on were home to a few artist spaces where the big Toronto samba schools practiced and massive parade puppets were made. Nothing has replaced those artist spaces, to the detriment of artists across the city.

Weirdly I have very intimate memories of the abattoir. Toronto hosts an annual sunset-to-sunrise art festival, Nuit Blanche, and a couple interesting art exhibits were hosted at the abattoir over the years, and I have photos of them that I regularly return to re-experience. After the buildings were designated for destruction a number of community vegetable gardens were maintained on the outside lots. It was always a striking place to come and make images, and was a reminder of the Toronto-that-once-was and was yet-to-become.

For many street photographers, we take images and it is decades later that ‘difference’ is registered because many cities take a long time for major changes to become visible. It’s part of why the habits of the population —what people are wearing, holding, or driving — resonate so strongly with viewers; people and culture change while the built environment persists.

Toronto, by way of contrast, is in a moment of hyper-growth and so an attentive and active street photographer can document things today that may literally be different tomorrow. It turns the street photographer, almost by default, into an urban documentarian. And, also, is one of the many reasons why I think that Toronto offers a subset of street photographers a real opportunity to do novel and rapidly impactful work, as compared to those working in cities that aren’t undergoing the same tempo of destruction and re-construction.

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Photography Quotations

Moments of Thinking and Photography

Thinking should be done beforehand and afterwards—never while actually taking a photograph. Success depends on the extension of one’s culture, on one’s set of values, one’s clarity of mind and vivacity.

Henri Cartier-Bresson
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Links Writing

RCMP Found to Unlawfully Collect Publicly Available Information

The recent report from Office of the Privacy Commissioner of Canada, entitled “Investigation of the RCMP’s collection of open-source information under Project Wide Awake,” is an important read for those interested in the restrictions that apply to federal government agencies’ collection of this information.

The OPC found that the RCMP:

  • had sought to outsource its own legal accountabilities to a third-party vendor that aggregated information,
  • was unable to demonstrate that their vendor was lawfully collecting Canadian residents’ personal information,
  • operated in contravention to prior guarantees or agreements between the OPC and the RCMP,
  • was relying on a deficient privacy impact assessment, and
  • failed to adequately disclose to Canadian residents how information was being collected, with the effect of preventing them from understanding the activities that the RCMP was undertaking.

It is a breathtaking condemnation of the method by which the RCMP collected open source intelligence, and includes assertions that the agency is involved in activities that stand in contravention of PIPEDA and the Privacy Act, as well as its own internal processes and procedures. The findings in this investigation build from past investigations into how Clearview AI collected facial images to build biometric templates, guidance on publicly available information, and joint cross-national guidance concerning data scraping and the protection of privacy.

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Links Writing

Near-Term Threats Posed by Emergent AI Technologies

A cyberpunk image of a series of hackers that represent the 1970s, the 1980s, the 1990s, the 2000s, and the 2050s.

In January, the UK’s National Cyber Security Centre (NCSC) published its assessment of the near-term impact of AI with regards to cyber threats. The whole assessment is worth reading for its clarity and brevity in identifying different ways that AI technologies will be used by high-capacity state actors, by other state and well resourced criminal and mercenary actors, and by comparatively low-skill actors.

A few items which caught my eye:

  • More sophisticated uses of AI in cyber operations are highly likely to be restricted to threat actors with access to quality training data, significant expertise (in both AI and cyber), and resources. More advanced uses are unlikely to be realised before 2025.
  • AI will almost certainly make cyber operations more impactful because threat actors will be able to analyse exfiltrated data faster and more effectively, and use it to train AI models.
  • AI lowers the barrier for novice cyber criminals, hackers-for-hire and hacktivists to carry out effective access and information gathering operations. This enhanced access will likely contribute to the global ransomware threat over the next two years.
  • Cyber resilience challenges will become more acute as the technology develops. To 2025, GenAI and large language models will make it difficult for everyone, regardless of their level of cyber security understanding, to assess whether an email or password reset request is genuine, or to identify phishing, spoofing or social engineering attempts.

There are more insights, such as the value of training data held by high capacity actors and the likelihood that low skill actors will see significant upskilling over the next 18 months due to the availability of AI technologies.

The potential to assess information more quickly may have particularly notable impacts in the national security space, enable more effective corporate espionage operations, as well as enhance cyber criminal activities. In all cases, the ability to assess and query volumes of information at speed and scale will let threat actors extract value from information more efficiently than today.

The fact that the same technologies may enable lower-skilled actors to undertake wider ransomware operations, where it will be challenging to distinguish legitimate versus illegitimate security-related emails, also speaks to the desperate need for organizations to transition to higher-security solutions, including multiple factor authentication or passkeys.