Krishna’s letter is here[0]. IBM will cease to sell related products and services. One might speculate they might resume sales once there are strong regulations and limitations in place.
[0] https://www.ibm.com/blogs/policy/wp-content/uploads/2020/06/...
>>> IBM no longer offers general purpose IBM facial recognition or analysis software. IBM firmly opposes and will not condone uses of any technology, including facial recognition technology offered by other vendors, for mass surveillance, racial profiling, violations of basic human rights and freedoms, or any purpose which is not consistent with our values and Principles of Trust and Transparency. We believe now is the time to begin a national dialogue on whether and how facial recognition technology should be employed by domestic law enforcement agencies.
Even if the phrasing is limited to "general purpose" and "software", it still seems a pretty strong stance to me.
Can you provide more details/proof?
Yikes, hopefully you're not suggesting that the length of time is a virtue? This may be a stereotype but I thought Northrop Grumman was one of those "attach" defense contractors that do work for years and years but never actually deliver any software.
They would be ultra secret.
Please take the "sophisticated" and "state-of-the-art" phrases as marketing.
I doubt they had RNNs 15 years ago.
> "sophisticated" and "state-of-the-art" phrases as marketing^W ways of charging more money
https://en.wikipedia.org/wiki/W%5EX
This is why I am thinking that.
With neural network use in image recognition (if not facial recognition) being used at least as early as 1983 (I'm not in the field, so just did 5s of research and found something related) - https://ieeexplore.ieee.org/document/6313076
"A recognition with a large-scale network is simulated on a PDP-11/34 minicomputer and is shown to have a great capability for visual pattern recognition. The model consists of nine layers of cells. ..."
another paper from 1990 using neural networks for facial sex identification:
https://www.researchgate.net/publication/221619290_SEXNET_A_...
and software facial recognition itself dates to the 60s.
despite what the 'data science and AI and by AI I really only mean the subset of AI referring to ML' hypetrain tells you, these things are not new, the processors are just faster, and the tools are more accessible, which means the relevant application space is larger.
The core to my argument that most of contemporary ML isn't new or different in an "algorithmic breakthrough" sense, but in that we've massively sped up GIGO. An AI researcher in the 60s/70s teleported to today isn't going to have a rough time figuring out what's going on, they are going to whistle in shock at how massively bloated our "sample sets" and data corpuses have grown, how fast we're churning through that data (regularly), and how much of it we are leaving unsupervised or "self-supervised", and with maybe some questions as to what we did with our (lack of) QA/QR processes along the way.