Tag Archives: learning

Book review – Life 3.0: Being Human in the Age of Artificial Intelligence, by Max Tegmark

Max Tegmark’s new book, Life 3.0: Being Human in the Age of Artificial Intelligence, introduces a framework for defining types of life based on the degree of design control that sensing, self-replicating entities have over their own ‘hardware’ (physical forms) and ‘software’ (“all the algorithms and knowledge that you use to process the information from your senses and decide what to do”).

It’s a relatively non-academic read and well worth the effort for anyone interested in the potential to design the next major forms of ‘Life’ to transcend many of the physical and cognitive constraints that have us now on the brink of self-destruction. Tegmark’s forecast is optimistic.

Neuroplasticity at the neuron and synapse level – Neurons sort into functional networks

“Until recently, scientists had thought that most synapses of a similar type and in a similar location in the brain behaved in a similar fashion with respect to how experience induces plasticity,” Friedlander said. “In our work, however, we found dramatic differences in the plasticity response, even between neighboring synapses in response to identical activity experiences.”

“Individual neurons whose synapses are most likely to strengthen in response to a certain experience are more likely to connect to certain partner neurons, while those whose synapses weaken in response to a similar experience are more likely to connect to other partner neurons,” Friedlander said. “The neurons whose synapses do not change at all in response to that same experience are more likely to connect to yet other partner neurons, forming a more stable but non-plastic network.”

Read more at: https://medicalxpress.com/news/2016-02-scientists-brain-plasticity-assorted-functional.html#jCp

all possible minds

18 October meeting topic – General AI: Opportunities and Risks

Artificial intelligence (AI) is being incorporated into an increasing range of engineered systems. Potential benefits are so desirable, there is no doubt that humans will pursue AI with increasing determination and resources. Potential risks to humans range from economic and labor disruptions to extinction, making AI risk analysis and mitigation critical.

Specialized (narrow and shallow-to-deep) AI, such as Siri, OK Google, Watson, and vehicle-driving systems acquire pattern recognition accuracy by training on vast data sets containing the target patterns. Humans provide the operational goals (utility functions) and curate the items in the training data sets to include only information directly related to the goal. For example, a driving AI’s utility functions involve getting the vehicle to a destination while keeping the vehicle within various parameters (speed, staying within lane, complying with traffic signs and signals, avoiding collisions, etc.).

Artificial general intelligence (AGI or GAI) systems, by contrast, are capable of learning and performing the full range of intellectual work at or beyond human level. AGI systems can achieve learning goals without explicitly curated training data sets or detailed objectives. They can learn ‘in the wild’, so to speak. For example, an AGI with the goal of maximizing a game score requires only a visual interface to the game (so it can sense the game environment and the outcomes of its own actions) and an ability to interact with (play) the game. It figures out everything on its own.

Some people have raised alarms that AGIs, because their ability to learn is more generalized, are likely to suddenly surpass humans in most or all areas of intellectual achievement. By definition, once AGI minds surpass ours, we will not be able to understand much of their reasoning or actions. This situation is often called the technological singularity–a sort of knowledge horizon we’ll not be able to cross. The concerns arise from our uncertainty that superintelligent AIs will value us or our human objectives or–if they do value us–that they will be able to translate that into actions that do not degrade our survival or quality of existence.

Multimedia Resources

• Demis Hassabis on Google Deep Mind and AGI (video, 14:05, best content starts a 3:40)

• Google Deep Mind (Alpha Go) AGI (video, 13:44)

• Extra: Nick Bostrom on Superintelligence and existential threats (video, 19:54) – part of the talk concerns biological paths to superintelligence

Print Resources

• Primary reading (long article): Superintelligence: Fears, Promises, and Potentials

• Deeper dive (for your further edification): Superintelligence; Paths, Dangers, and Strategies, by Nick Bostrom

Members may RSVP for this discussion at https://www.meetup.com/abq_brain_mind_consciousness_AI/events/234823660/. Based on participant requests, attendance is capped at 10 to promote more and deeper discussion. Those who want to attend but are not in the first 10 may elect to go on the waiting list. It is not unusual for someone to change a “Yes” RSVP to “No”, which will allow the next person on the waiting list to attend. If the topic attracts a large wait list, we may schedule additional discussion.

Members of this site who can’t attend the meeting are welcome to participate in the extended discussion by commenting on this announcement.