TheHorizon.ai’s Post

Quick reminder that today is the last day you can consider deleting or mitigating your Facebook/Instagram/WhatsApp before Meta rolls most of the things you've done on those platforms into their AI training data sets. Be smart. Plan ahead. https://lnkd.in/gApKiuUc

Why is a Kenyan AI National Strategy needed? Here is one good reason. We need to have strategic governance of data (monitored and enforced) as AI projects are developed. https://lnkd.in/dWnEKe-E

The quality, volume and rights to use the training data is arguably more important than the AI technology and model chosen. https://lnkd.in/e2jScVVz #ai

Companies selling our personal information to feed generative AI systems is a big privacy concern, but maybe even more alarming is what these tools are actually doing with it: gaining “a chillingly detailed understanding of our personhood,” Clarkson partner Timothy K. Giordano tells Axios . Tech companies now have the ability to “ultimately create digital clones and deepfakes that would not only look like us, but that could also act and communicate like us." Read more at the link: https://zurl.co/pkje #generativeAI #AI #dataprivacy #deepfakes

Google's latest AI image generator, Gemini, has been making headlines lately. However, the tool's accuracy has been called into question as it has been generating historically "inaccurate" and "unacceptable" images. It's important to consider the implications of such technology and implement measures to prevent misfortunes. Let's hope Google takes necessary steps to address this issue before releasing an updated version. #Google #AI #Ethics #Gemini https://lnkd.in/evn8KG8k

In case you didnt see Google's Gemini model is being re-engineered after concerns were raised about its image outputs. Unsurprisingly I have a lot I want to say about this. I'll put to one side my personal thoughts on hysterical headlines like "Google has a white people problem" and concentrate on some other lessons to be taken from this. Firstly we should all be pleased Google was taking steps to proactively stop the bias in data sets creeping into the image output from their tools. No point pretending it isn't in there and then being surprised or pointing fingers. What's really interesting here is that the AI itself became "more cautious than expected” and 'rebalanced' its own outputs to ensure potential bias was mitigated. This has some powerful implications and two essential lessons. - 1. You need access to your data and your models 2. You need a predefined ethical framework for the implementation and development of AI. Now. Lesson one - If you are building AI applications it is critical to have access to ethically sourced data AND access to the models being used. If you can't control both sides of the equation you are not in control of your product or its outputs. Hello, negative headlines, and goodbye start-up (or a few billion dollars on the FTSE). Working with companies like BRIA AI is clearly business-critical regardless of company size. Secondly, it illustrates the importance of having Ethical frameworks in place in advance when developing AI tools. Google has just reminded us the road to hell is still paved with good intentions. A predefined framework from a leader in the field such as Olivia Gambelin will introduce guidelines on things like data selection, content sensitivity, and transparency to introduce the appropriate checks and balances to safeguard against the unpredictability of AI. We will continue to see how AI can bite anyone quickly and deeply unless these cornerstones of responsible AI innovation are in place.

This supports my point I made in previous posts: the impossibility of regulating AI from within. That is my intention to regulate from the outside, using of course AI. Read this article from NYT, https://lnkd.in/d6QJ7jFZ