Instead of looking at individual neurons, they look at combinations of neurons that form patterns or features.
Artificial neural networks are like digital versions of our brains. They learn from data, not rules, and they can perform extraordinary tasks, from translating languages to playing chess. But how do they do it? What is the logic behind their calculations? And how can we trust them to be safe and reliable?
XH4D/iStock.
AI brains: How do they work?
Have you recently been thinking about the Roman Empire? According to a viral social media trend, the answer is decidedly yes, assuming that you are a man. The backstory is that an online video postulated that men daily tend to think about the Roman Empire and a follow-up by women asking their male friends, partners, or relatives began to flood the Internet. Seemingly, most men insisted that they did indeed have frequent thoughts about the Roman Empire. A hashtag associated with the Roman Empire has ballooned to incurring over a billion hits.
Before I get into some further details on the contentious hubbub, a question that immediately struck me and has now been rattling around in the AI… More.
A viral trend online is that men are supposedly thinking daily about the Roman Empire. If so, this begs the question of whether generative AI might be doing likewise.
Understanding causality can’t come from passive observation, because the relevant counterfactuals often do not arise. If X is followed by Y, no matter how regularly, the only way to really know that is a causal relation is to intervene in the system: to prevent X and see if Y still happens. The hypothesis has to be tested. Causal knowledge thus comes from causal intervention in the world. What we see as intelligent behavior is the payoff for that hard work.
The implication is that artificial general intelligence will not arise in systems that only passively receive data. They need to be able to act back on the world and see how those data change in response. Such systems may thus have to be embodied in some way: either in physical robotics or in software entities that can act in simulated environments.
Artificial general intelligence may have to be earned through the exercise of agency.
Posted in robotics/AI
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The most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta-learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987. In the past decade, it has become a driver of many other people’s research as well. Here I summarize our work starting in 1994 on meta-reinforcement learning with self-modifying policies in a single lifelong trial, and — since 2003 — mathematically optimal meta-learning through the self-referential Gödel Machine. This talk was previously presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. Many additional publications on meta-learning can be found at https://people.idsia.ch/~juergen/metalearning.html.
Jürgen Schmidhuber.
Director, AI Initiative, KAUST
Scientific Director of the Swiss AI Lab IDSIA
Co-Founder & Chief Scientist, NNAISENSE
http://www.idsia.ch/~juergen/blog.html.
This video discusses what it would mean to live forever or to have a greatly expanded lifespan. Would we inevitably grow bored with existence? Why do we even want to live longer?
We also discuss the philosophy of the Ship of Theseus as it applies to mind upload or body transfer. If you transfer your mind to a new body, are you still the same person? We conclude that the answer is yes if and only if you experience no discontinuities in consciousness.
Finally, we discuss a few different ways that new bodies might be formed as technology advances. Digital avatars and robotic bodies are just a few of the possibilities.
Can You Upload Your Mind & Live Forever?
Posted in robotics/AI
James Somers on how A.I. chatbots such as ChatGPT and Google’s Bard will continue to learn and the contradictions entailed in their mission.
Algorithmic governance covers the rules and practices for the construction and use of algorithms embedded in AI technologies. But how should these be applied?
To make its weather predictions, it analyzes 60 million daily observations from satellite, aircraft, and ground-based reports, using what we know about atmospheric physics to determine what the weather is likely to be like across the globe over the next 15 days.
This can literally save lives — if people know in advance that hurricanes or winter storms are heading their way, they can take action to prepare — but because the model is so complex, it must be run on a supercomputer over the course of several hours, which also makes it expensive.
The AIs: AI-based weather forecasting models are starting to catch up with traditional ones, like the European Model.
AI systems assume humans are always certain. Teaching them to anticipate and understand uncertainty may be key to limiting human error.
