Lifeboat Foundation

Stuart Russell’s latest book examines how artificial intelligence could spin out of control. David Leslie critiques it.

DARPA expects to have a new commercial partner lined up for the Robotic Servicing of Geosynchronous Satellites spacecraft by the end of the year.

If you thought Cambridge Analytica had scary tech, wait until you see this. A new form of AI modelling promises accurate simulation of the behaviour of entire cities, countries and one day perhaps, the world.

Please no, we don’t need a machine-learning troll farm.

The idea that humans should merge with AI is very much in the air these days. It is offered both as a way for humans to avoid being outmoded by AI in the workplace, and as a path to superintelligence and immortality. For instance, Elon Musk recently commented that humans can escape being outmoded by AI by “having some sort of merger of biological intelligence and machine intelligence.”¹ To this end, he’s founded a company, Neuralink. One of its first aims is to develop “neural lace,” an injectable mesh that connects the brain directly to computers. Neural lace and other AI-based enhancements are supposed to allow data from your brain to travel wirelessly to one’s digital devices or to the cloud, where massive computing power is available.

For many transhumanists, uploading is key to the mind-machine merger.

Perhaps these sorts of enhancements will turn out to be beneficial, but to see if this is the case, we will need to move beyond all the hype. Policymakers, the public, and even AI researchers themselves need a better idea of what is at stake. For instance, if AI cannot be conscious, then if you substituted a microchip for the parts of the brain responsible for consciousness, you would end your life as a conscious being. You’d become what philosophers call a “zombie”—a nonconscious simulacrum of your earlier self. Further, even ifmicrochips could replace parts of the brain responsible for consciousness without zombifying you, radical enhancement is still a major risk. After too many changes, the person who remains may not even be you. Each human who enhances may, unbeknownst to them, end their life in the process.

We all know that dogs are a man’s best friend, but has the world really come to this?

On a particularly blustery day in New York City, I found myself (as one with the income bracket of a writer sporadically does) on the Upper East Side, amidst tribes of cooler-than-thou high school students, dedicated dog walkers and women wearing hats that looked like a Shar-Pei had potentially suffered in the making of it.

Nonetheless, I braved the chilly air and found solace in the Cooper Hewitt Museum, the design institution that is part of the Smithsonian. Upon entering, visitors are greeted with a magic wand-looking pen tool, that serves as an interactive notekeeper for items you are interested in. “How innovative.” Perfect for a museum about innovation, am I right? With my magic wand in hand, I entered the Narnia of objects, with the first stop being an exhibition titled “Access and Ability.” Featuring “artifacts” designed for people with disabilities, I was surprised to find among the various innovations, a very cute-looking puppy that I instinctively wanted to pet. But I did not, for fear of being arrested, a la Ocean’s 12.

They may not have fur, but we love them anyway.

Biomedical engineers at Duke University have devised a machine learning approach to modeling the interactions between complex variables in engineered bacteria that would otherwise be too cumbersome to predict. Their algorithms are generalizable to many kinds of biological systems.

In the new study, the researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture. The system worked 30,000 times faster than the existing computational model.

To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers. Then they used it to solve a second biological system that is computationally demanding in a different way, showing the algorithm can work for disparate challenges.

Thinking of #Upskilling? Check this out: If you want to learn machine learning and artificial intelligence, start here:

Two years ago, I started learning machine learning online on my own. I shared my journey through YouTube and my blog. I had no idea what I was doing. I’d never coded before but decided I wanted to learn machine learning.

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