Just looking today at the main page of the paper that I read, The Guardian, I have found the following articles. I’m sure there are more, but let’s have a look at what’s being shown to me.

Police AI chief admits crime-fighting tech will have bias but vows to tackle it
Here’s an article that states that while it acknowledges that there is bias inherent in AI tools deployed for crime-fighting they ‘pledge’ that the risks will be mitigated (‘combated’). There is known bias in these tools because there is bias in the data.

“Bias in use of AI in policing could result in instances where algorithms – often trained on historical data reflecting past human prejudices – systematically produce unfair outcomes, such as overtargeting minority communities or misidentifying individuals based on race, gender, or socioeconomic status.”

Although I don’t necessarily fall within these categories it concerns me that these factors are part of the system, because the data used to train these models have these flaws. Garbage in, garbage out is the saying I was taught many years ago.

Anlife: what does an unusual evolution simulator have to say about AI?
A game that utilises AI for the movement of the block creatures that are created and manipulated in the game. What interests me is where and how AI appears and reactions to it.

As we enter the age of the AI-rranged marriage, here’s why I hate Fate, Van Badham
This is an opinion piece about one of the new AI dating apps. It’s worth reading. These paragraphs are particularly insightful:

“You know, how writers just wanted contextual proofing tools from AI but and got machines insisting on the superiority of rewritten, flattened text. Or how academics just wanted a tool to index their references and got hallucinations that invented a few sources that didn’t actually exist, but the machine thought maybe should.

Insert your own industry experience here, and all of us in sad recognition that the forced AI-ification of everyday life continues with a robotic efficiency that, dear Christ, is outsourcing the messy human weirdness that made us fascinating and exotic to one another – and sexy and wonderful.”

Sadly, that does seem to be the direction of travel.

US AI giant accuses Chinese rivals of mass data theft
This is the other side of AI – the business of it. How do you train a model? Do you pay for the data, try to gather that data yourself, or do you find another way to get the data from somebody else? This links into the battle for supremacy in this field.