AI News, 2019 artificial intelligence
Katherine Cross on moderating online gaming communities and artificial intelligence
In Katherine Cross’ short story “Machine of Loving Grace” — the final installment in our Better Worlds anthology — Alexandra and Phoebe must deal with their creation Ami, an artificial intelligence that was designed to moderate online communities, as it fights fire with fire.
The inspiration for Ami came from a real product on the market — Spirit AI’s Ally — which does something very similar, though Ally is meant to supplement mods rather than replace them outright.
It’s the only solution they can see to the scalability problem that’s particularly acute on social media platforms or huge games where human mods can’t keep up with the actions of millions of users.
Black Twitch streamers, for instance, have been harassed by people using monkey, fried chicken, or banana emoji, whose racist semiotics are easily understood by other humans, but that would be totally baffling to even the most sophisticated machine learning algorithm.
The most abusive fans expect to be able to hurl vitriol and threats at the people who make their games, but they are shocked, shocked when the targets of their ire clap back.
Games have also been sold as a fantasy of consequence-free indulgence, which is fair to a degree — we all need escapism — but in social environments, that notion is often extended to social interactions between players with no regard for how, say, racist or sexual harassment may impede someone else’s fantasy, someone else’s escape.
And, as I said, many game studios are reluctant to tell their most toxic fans “no.” Community moderation is an obvious necessity, and its mere presence can placate worried parents or gamers.
Skynet gets name-dropped a couple of times, but what strikes me about this particular robot takeover is that it really is acting in a sort of best-interest scenario, going beyond gamers yelling in games and demanding that entire businesses restructure.
What sets Ami apart is that, unlike so many other AI in fiction that aspire only to slaughter all humans (which, I’d argue, is more a reflection of our guilt about our own priorities), Ami comes into her power and wants to use it to alleviate the suffering she sees around her.
AAAI 2019 Conference | Thirty-Third AAAI Conference on Artificial Intelligence
The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines.
AAAI-19 will have a diverse technical track, student abstracts, poster sessions, invited speakers, tutorials, workshops, and exhibit and competition programs, all selected according to the highest reviewing standards.
Explore the rich history of the Islands at one of the many museums or historical sites, take a hike in a lush tropical forest, or admire the unparalleled views from the conference site.
Trends 2019: Artificial intelligence goes mainstream | ITWeb
From next-level recommendation marketing and virtual agents, to self-learning data analytics, there's no escaping the touch of the machine in our everyday lives.
But we're already seeing the same patterns take place as the first industrial revolution, where AI isn't replacing us at all, but instead allowing us to tap into our more human strengths.
Just look at how AI is being rolled out in the medical world, not replacing doctors but giving them the power to make faster and more accurate diagnoses, help more patients and offer more personal care.
If you're looking for a yardstick on how much of a foothold AI is getting in South Africa, you need look no further than Michael Jordaan staking his retirement on NMRQL Research, an investment management company powered by machine learning.
As more intuitive tools, learning resources, communities and hardware became available, it became much more accessible and attractive to the general public.
the perfect storm of familiarity, data skills, infrastructure and software all advancing to a point where you no longer need a high level of specialisation to take advantage.
There are now a wide variety of APIs available that sit on top of the more baseline tools, allowing for greater complexity while lowering barriers to entry.
Anyone with basic programming skills can take advantage of more intuitive deep learning platforms like MXNet and TensorFlow, and deploy new models with a single click.
We're also seeing the rise of machine-learning-as-a-service models, where you no longer need to invest in a dedicated data science and AI team to access high-level machine learning.
The proliferation of resources and use cases, combined with greater adoption in both consumer-facing and enterprise environments, means we are effectively at Point Zero for learning to work with AI.
CLEVVA, for example, helps improve customer service staff's decision-making abilities rather than replacing them, while Xineoh uses machine learning to predict customer behaviour.
Now is also the time for organisations to start testing out new AI applications in pilot environments, and I'm not just talking about rolling out a chatbot and calling it a day.
- On 4. december 2020
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