Quotation-as-title from a Chinese proverb. Image from top-linked post.
Tag: artificial intelligence
As an historian, I’m surprisingly bad at recalling facts and dates. However, I’d argue that the study of history is actually about the relationship between those facts and dates — which, let’s face it, so long as you’re in the right ballpark, you can always look up.
Understanding the relationship between things, I’d argue, is a demonstration of higher-order competence. This is described well by the SOLO Taxonomy, which I featured in my ebook on digital literacies:
This is important, as it helps to explain two related concepts around which people often get confused: ‘artificial intelligence’ and ‘machine learning’. If you look at the diagram above, you can see that the ‘Extended Abstract’ of the SOLO taxonomy also includes the ‘Relational’ part. Similarly, the field of ‘artificial intelligence’ includes ‘machine learning’.
There are some examples of each in this WIRED article, but for the purposes of this post let’s just leave it there. Some of what I want to talk about here involves machine learning and some artificial intelligence. It’s all interesting and affects the future of tech in education and society.
If you’re a gamer, you’ll already be familiar with some of the benefits of AI. No longer are ‘CPU players’ dumb, but actually play a lot like human players. That means with no unfair advantages programmed in by the designers of the game, the AI can work out strategies to defeat opponents. The recent example of OpenAI Five beating the best players at a game called Dota 2, and then internet teams finding vulnerabilities in the system, is a fascinating battle of human versus machine:
“Beating OpenAI Five is a testament to human tenacity and skill. The human teams have been working together to get those wins. The way people win is to take advantage of every single weakness in Five—some coming from the few parts of Five that are scripted rather than learned—gradually build up resources, and most importantly, never engage Five in a fair fight.” OpenAI co-founder Greg Brockman told Motherboard.
Deepfakes, are created via “a technique for human image synthesis based on artificial intelligence… that can depict a person or persons saying things or performing actions that never occurred in reality”. There’s plenty of porn, of course, but also politically-motivated videos claiming that people said things they never did.
There’s benefits here, though, too. Recent AI research shows how, soon, it will be possible to replace any game character with one created from your own videos. In other words, you will be able to be in the game!
It only took a few short videos of each activity — fencing, dancing and tennis — to train the system. It was able to filter out other people and compensate for different camera angles. The research resembles Adobe’s “content-aware fill” that also uses AI to remove elements from video, like tourists or garbage cans. Other companies, like NVIDIA, have also built AI that can transform real-life video into virtual landscapes suitable for games.
It’s easy to be scared of all of this, fearful that it’s going to ravage our democratic institutions and cause a meltdown of civilisation. But, actually, the best way to ensure that it’s not used for those purposes is to try and understand it. To play with it. To experiment.
Algorithms have already been appointed to the boards of some companies and, if you think about it, there’s plenty of job roles where automated testing is entirely normal. I’m looking forward to a world where AI makes our lives a whole lot easier and friction-free.
Also check out:
- AI generates non-stop stream of death metal (Engadget) — “The result isn’t entirely natural, if simply because it’s not limited by the constraints of the human body. There are no real pauses. However, it certainly sounds the part — you’ll find plenty of hyper-fast drums, guitar thrashing and guttural growling.”
- How AI Will Turn Us All Into Filmmakers (WIRED) — “AI-assisted editing won’t make Oscar-worthy auteurs out of us. But amateur visual storytelling will probably explode in complexity.”
- Experts Weigh in on Merits of AI in Education (THE Journal) — “AI systems are perfect for analyzing students’ progress, providing more practice where needed and moving on to new material when students are ready,” she stated. “This allows time with instructors to focus on more complex learning, including 21st-century skills.”
It’s really interesting to do philosophical thought experiments with kids. For example, the trolley problem, a staple of undergradate Philosophy courses, is also accessible to children from a fairly young age.
You see a runaway trolley moving toward five tied-up (or otherwise incapacitated) people lying on the tracks. You are standing next to a lever that controls a switch. If you pull the lever, the trolley will be redirected onto a side track, and the five people on the main track will be saved. However, there is a single person lying on the side track. You have two options:
- Do nothing and allow the trolley to kill the five people on the main track.
- Pull the lever, diverting the trolley onto the side track where it will kill one person.
Which is the more ethical option?
With the advent of autonomous vehicles, these are no longer idle questions. The vehicles, which have to make split-second decision, may have to decide whether to hit a pram containing a baby, or swerve and hit a couple of pensioners. Due to cultural differences, even that’s not something that can be easily programmed, as the diagram below demonstrates.
For two countries that are so close together, it’s really interesting that Japan and China are on the opposite ends of the spectrum when it comes to saving passengers or pedestrians!
The authors of the paper cited in the article are careful to point out that countries shouldn’t simply create laws based on popular opinion:
Edmond Awad, an author of the paper, brought up the social status comparison as an example. “It seems concerning that people found it okay to a significant degree to spare higher status over lower status,” he said. “It’s important to say, ‘Hey, we could quantify that’ instead of saying, ‘Oh, maybe we should use that.’” The results, he said, should be used by industry and government as a foundation for understanding how the public would react to the ethics of different design and policy decisions.
This is why we need more people with a background in the Humanities in tech, and be having a real conversation about ethics and AI.
Of course, that’s easier said than done, particularly when those companies who are in a position to make significant strides in this regard have near-monopolies in their field and are pulling in eye-watering amounts of money. A recent example of this, where Google convened an AI ethics committee was attacked as a smokescreen:
Academic Ben Wagner says tech’s enthusiasm for ethics paraphernalia is just “ethics washing,” a strategy to avoid government regulation. When researchers uncover new ways for technology to harm marginalized groups or infringe on civil liberties, tech companies can point to their boards and charters and say, “Look, we’re doing something.” It deflects criticism, and because the boards lack any power, it means the companies don’t change.
“It’s not that people are against governance bodies, but we have no transparency into how they’re built,” [Rumman] Chowdhury [a data scientist and lead for responsible AI at management consultancy Accenture] tells The Verge. With regard to Google’s most recent board, she says, “This board cannot make changes, it can just make suggestions. They can’t talk about it with the public. So what oversight capabilities do they have?”
As we saw around privacy, it takes a trusted multi-national body like the European Union to create a regulatory framework like GDPR for these issues. Thankfully, they’ve started that process by releasing guidelines containing seven requirements to create trustworthy AI:
- Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.
- Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.
- Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.
- Transparency: The traceability of AI systems should be ensured.
- Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.
- Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.
- Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.
The problem isn’t that people are going out of their way to build malevolent systems to rob us of our humanity. As usual, bad things happen because of more mundane requirements. For example, The Guardian has recently reported on concerns around predictive policing and hospitals using AI to predict everything from no-shows to risk of illness.
When we throw facial recognition into the mix, things get particularly scary. It’s all very well for Taylor Swift to use this technology to identify stalkers at her concerts, but given its massive drawbacks, perhaps we should restrict facial recognition somehow?
Human bias can seep into AI systems. Amazon abandoned a recruiting algorithm after it was shown to favor men’s resumes over women’s; researchers concluded an algorithm used in courtroom sentencing was more lenient to white people than to black people; a study found that mortgage algorithms discriminate against Latino and African American borrowers.
Facial recognition might be a cool way to unlock your phone, but the kind of micro-expressions that made for great television in the series Lie to Me is now easily exploited in what is expected to become a $20bn industry.
The difficult thing with all of this is that it’s very difficult for us as individuals to make a difference here. The problem needs to be tackled at a much higher level, as with GDPR. That will take time, and meanwhile the use of AI is exploding. Be careful out there.
Also check out:
- How to hack your face to dodge the rise of facial recognition tech (WIRED) — “The real solution to the issues around FR? The tech community working with other industries and sectors to strike an appropriate balance between security and privacy in public spaces.”
- U.S., U.K. embrace autonomous robot spy subs that can stay at sea for months (Digital Trends) — “According to a department spokesperson, these autonomous subs will most likely not carry weapons, although a final decision has not been made.”
- The world’s first genderless AI voice is here. Listen now (Fast Company) — “In other words, because Siri cannot be gender neutral, she reinforces a dated tradition of gender norms.”