Import AI

160,000 teenagers get graded by a machine:
…International Baccalaureate organization does a terrible thing…
Due to COVID, the International Baccalaureate educational foundation is going to predict grades for students based on their prior bodies of work, rather than giving them a score as an outcome of taking a test. 166,000 students will be affected by the experiment. This has already gone badly wrong and will continue to do so.

A terrible idea: This is a terrible idea. It’s almost a made-to-order example of how you shouldn’t deploy an AI system. To make that clear, let’s outline what is going on:
– Deploy an untested model against a large population
– Have this model make predictions that will have a massive influence over the target populations’ lives
– Have no plan for how to prevent your model learning to discriminate against students based on Gender, Race, etc.

A bad idea, executed mindlessly: Imagine being a teenager and having some opaque algorithm make a fundamental decision about your future educational career. Now imagine that this system’s prediction feels wrong to you. How do you live with that? I’m not sure – but people are going to need to. One UK teacher told the Financial Times that the automated grading has already created “really appalling injustices“.
  Read more about this here: 160k+ high schools students will only graduate if a statistical model allows them to (Ishan Dikshit, personal blog).
  Read more about the effects of the algorithm: Students and teachers hit at International Baccalaureate grading (Financial Times).

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Import (A)Idea – The Ethics of Cloning in the Generative Model Age:
Here’s a fun mental experiment: humans have recently discovered a technology that lets them cheaply and easily clone people to carry out tasks. Putting aside the ethical issues of cloning, it feels like most people would be comfortable with people being cloned to do highly specific, physical tasks for which they’re a demonstrable expert – think, 1,000 Michelin-grade chefs, or 1,000 supremely talented jewelery artisans, or 1,000 people working in construction. Now consider what happens if we cloned people to do tasks oriented around influencing mass culture – how might media be changed by the presence of 1,000 Andy Warhols or 1,000 Edward Bernays, or 1,000 Georgia O’Keeffe? And now, for the extra confounding factor, imagine that only a tiny number of entities on the planet have the ability to clone people, cloning is an imperfect process, and the ‘clones’ are about as interpretable as the people they were cloned from – aka, basically uninterpretable.

Now simply swap out the word ‘clones’ with ‘generative models’, and you might see what I mean. Today, large-scale generative models in text, image, and video are making it easy for (some) organizations to clone a swathe of culture, create an entity that reflects that culture outward, and then deploy that entity into a variety of different contexts. I think this is somewhat analogous to the ethics inherent to choosing to clone a person; once the person we clone starts doing more tasks that have a greater bearing on society, we might ask the cloners what values this person has and what process we used to decide that they were the right person to clone. I think the answers to both of these questions are low-resolution today and a challenge for AI researchers will be to figure out satisfying, detailed answers to these questions. The future of human culture will be the interplay between these AI artefacts that clone, warp, and reflect culture, and the humans who will likely create cultural products in response to the outputs of the ‘clones’. 

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Want AI systems that can better cope with adversarial examples? Tweak your activation function:
…Is there an easier way to deal with confounding images?…
Adversarial examples, aka optical illusions for computer systems (which could also come in the form of confusing audio or other datastreams), are a problem; the fact our AI-driven classification systems can get broken relatively easy makes it harder to trust the technology and also increases its potential harms. Now, a team of researchers with Google have published a paper about something that people have long desired – a simple way to tweak models during training so that they’re more resilient to adversarial examples.

What they did: The Google researchers have proposed to swap out the ReLU activation function for something they call ‘smooth adversarial training’ (SAT) in the backward pass during neural net training. By doing this, they’re able to train systems that are more resilient to adversarial examples, while exhibiting no fall in typical performance as well (a desirable, rare feature).

The key statistic: “Compared to standard adversarial training, SAT improves adversarial robustness for “free”, i.e., no drop in accuracy and no increase in computational cost. For example, without introducing additional computations, SAT significantly enhances ResNet-50’s robustness from 33.0% to 42.3%, while also improving accuracy by 0.9% on ImageNet.” In further tests, they show that SAT’s benefits continue to hold even in larger-scale training regimes – this is encouraging, as it’s often the case that new inventions break at large scales. They also show that they’re able to train ‘EfficientNet’ models using SAT instead of ReLU, and continue to see good performance.

Why this matters: It’s (relatively) general inventions like this that can sometimes have the largest effect. Keep your eyes out for future papers that propose using SAT instead of ReLU or another activation function.
  Read more: Smooth Adversarial Training (arXiv).

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Anduril gets $200 million for its military AI vision:
…Want to understand the future of defense<>AI? Look at what startups do…
Startups are worth tracking because they’re usually founded by people with idiosyncratic visions of the future – and if they become successful, that vision has a greater chance of coming true. That’s why AI-defense-tech startup Anduril getting $200 million in new funding is notable, because if the startup sees further success and executes on its vision, then the U.S government and other countries will acquire increasingly power AI capabilities, using them to police their borders and make various strategic (and, eventually, kinetic) decisions using AI tools.

What is Anduril? Anduril has raised $241 million in venture capital funding since it was founded in 2017, according to Crunchbase. Its  key staff include include Palmer Luckey, the DIY VR headset wunderkind who sold Oculus to Facebook for $2bn; Trae Stephens, a partner at Peter Thiel’s ‘Founders Fund’ venture capital firm and early Palantir employee; and Chris Brose, the former staff director of the Senate Armed Services Committee.

What does Anduril want and why does this matter? Anduril’s vision of the future is embodied by the tech it builds today, which includes AI-infused sentry towers that can be deployed to autonomously scan and survey areas (like national borders), the ‘Ghost sUAS‘ autonomous helicopter; and its ‘Anvil sUAS‘ an anti-drone weapon. The more successful companies Anduril are, the more likely it is that our future will consist of ‘invisible’ walsall made up of numerous smart machines, working together for purposes set by those who can pay.
  Read more: Anduril Raises $200 Million To Fund Ambitious Plans To Build A Defense Tech Giant (Forbes).
  Read more about Anduril (official website).

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China’s CCTV giant Hikvision builds one camera with six onboard AI algos:
…6 AI algorithms + cheap sensors + enterprise sales = the world will change quite quickly…
Hikvision, a Chinese AI surveillance startup, has built a new camera line called ‘DeepinView’, where the products “come equipped with multiple dedicated algorithms”, including sub-systems that can:
– Perform automatic number plate recognition with vehicle attribute recognition
– Conduct facial recognition
– Face counting
– Count hard hats on construction sites
– “detecting multiple targets and multiple types of targets at once”
– Perimeter protection
– Queue monitoring

Think of it as an all-in-one surveillance camera, and a sketch of a future where more powerful AI technologies get deployed onto specialized sensor systems, with the data fed back to massive data farms (the DeepinView line already permits “third-party platforms to receive data from Hikvision cameras for real-time video analysis”.

Why this matters: Imagine what it’s going to be like when there’s some equivalent of a vertically integrated ‘surveillance operating system’ that stitches products like these together along with various other systems built by Hikvision and others – the company is already thinking about this, based on its ‘Safe City’ ‘Hikvision AI Cloud‘ systems.
  Read more: Hikvision introduces dedicated series in its DeepinView camera line (PRNewswire).
  Check out more details about HikVision products, e.g, the new DeepinView Face Recognition Indoor Moto Varifocal Dome Camera (Hikvision official product page).

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You can train AI systems, but can you ship them? This course will teach you how:
A bunch of AI practitioners loosely connected to UC Berkeley have developed Full Stack Deep Learning, a course “aimed at people who already know the basics of deep learning and want to understand the rest of the process of creating production deep learning systems,” according to the organizers.

What does production require? The course includes a bunch of areas that are frequently undercovered (or not mentioned at all) during typical college classes. For instance, Full Stack Deep Learning outlines things like:

• How to set up machine learning projects with decent data collection.
• What kinds of testing and development pipelines are needed to troubleshoot models.
• How to set teams of humans working on ML systems up for success

Read more: Full Stack Deep Learning (official course website).

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AI Policy with Matthew van der Merwe:
…Matthew van der Merwe brings you views on AI and AI policy; I (lightly) edit them…

US face recognition round-up:
  Federal ban proposed — Senators have introduced a new bill prohibiting federal use of face recognition and other biometrics without special authorization from Congress, and withholding funding to local and state entities that fail to pass similar moratoria on these technologies. The Senate bill has no Republican sponsors, and therefore seems unlikely to pass, though a companion bill has been introduced in the Democrat-controlled House of Representatives.
  Local ban — Boston has become the latest major city to ban the use of face recognition by local agencies. It joins San Francisco, which became the first city to place a moratorium on the technology in early 2019 (see Import 147).
  Wrongful arrest — NYT has reported on the first known case of an innocent person being arrested due to a face recognition mishap. The victim, a black man and Michigan resident, was arrested and held overnight in jail. He had been misidentified in surveillance footage of a robbery, which had been analysed using software provided by DataWorks Plus. The ACLU is filing a complaint against Detroit police on the victim’s behalf, highlighting the risk posed to individuals  — particularly people of colour — by being wrongfully targeted by law enforcement.
  Clearview AI — UK and Australian authorities have launched a joint investigation into Clearview AI. The US company was revealed to have built up a database of more than 3 billion photos scraped from social media and other semi-public sources without individuals’ consent (see Import 182).
  Read more: Lawmakers Introduce Bill to Ban Federal Use of Facial Recognition Tech (NextGov).
  Read more: Wrongfully accused by an algorithm (NYT); Man Wrongfully Arrested Because Face Recognition Can’t Tell Black People Apart (ACLU).
  Read more: Boston bans facial recognition due to concern about racial bias (VentureBeat).
  Read more: UK and Australian regulators launch probe into Clearview AI (FT)

Tech policy job in Washington, DC:

The Center for Security and Emerging Technology (CSET) at Georgetown University is taking applications for research fellows. Founded in 2019, CSET has quickly become the leading DC think tank working on AI policy, and has been producing consistently excellent research. They are seeking applicants with graduate degrees and experience in research and policy analysis. Applications for this round close on Friday July 17th.
  Find out more and apply here.

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Tech Tales:

[2028, San Francisco bay area, California]

We called them ghosts at first.
As in: hey, did you hear my ghost? Did you see the ghost I dropped on our school path? How about the one in the subway station?
As in: I saw a ghost the other day and it was so funny – they’d dropped an arcade game inside the New York Public Library and get this – the game that you could play was called book burner!
As in: I saw your ghost the other day and it made me love you all the more.
As in: Hey, we’ve never spoken before, but I listened to your ghost when I got into port and it made me cry. Thank you. Keep it up.

After awhile, they became memories. We’d find ourselves walking around old parts of towns we used to live in, so we could trip the proximity sensors and hear or see the ghosts of our past. And now we’d leave new ghosts; different, because we’d got old:
As in: I remember how we used to throw beer bottles from this parking lot into a trash bin that used to sit across the creek. We were so young then. There isn’t a creek to see anymore – you all know why. Anyway, if you pass through and you’re from the old days, Davey says hello. I had kids, if you can believe it.
As in: A photograph of some old polaroids laid out in front of a house, then a photograph of one of the people in the polaroids, scarred by a couple of decades of hard living. “Still here and still doing it. Rattlesnakes forever!”, then a zoomed-in picture of a tattoo of a rattlesnake on the arm of the older person.
As in: I fell in love with you here, when we left our ghosts all up and down these streets during those crazy years. I still love you. I don’t know if you’ll ever pass through here, and I’m not going to tell you I left these messages – think of it as a surprise for us.

And long after that, they became relics. The same way MySpace and Facebook profiles turned into memorials to the dead, the ghosts became real ghosts.
As in: a thicket of ghosts, all containing old pictures of a person, and some kind of message of love. “You were always the joker of the class,” one would say. “I believed all your stories because they made me happy,” said another.
As in: A widower walking along a path, picking up the ghosts of their former partner.
As in: People leaving memories around the town, after getting a diagnosis. “It’s not that I’m angry, it’s that I’m confused,” one said (with a picture of a sun going behind clouds, above a sign for a medical center.
As in: A ghost from someone who went on to become famous – after they died, the ghost became very popular, and eventually it was almost impossible to find on the app, surrounded as it was by so many other ghosts made in tribue of it.

Things that inspired this story: Playing Pokemon Go during the pandemic; augmented reality; audio recordings; social media;