The value of distinguishing AI’s from humans

What will happen when we can no longer distinguish human tweets from AI tweets? Does it matter? Should we care? Will there be a verified human status?

Renée DiResta, writing for The Atlantic:

Amid the arms race surrounding AI-generated content, users and internet companies will give up on trying to judge authenticity tweet by tweet and article by article. Instead, the identity of the account attached to the comment, or person attached to the byline, will become a critical signal toward gauging legitimacy. Many users will want to know that what they’re reading or seeing is tied to a real person—not an AI-generated persona. . . .

. . . . .

The idea that a verified identity should be a precondition for contributing to public discourse is dystopian in its own way. Since the dawn of the nation, Americans have valued anonymous and pseudonymous speech: Alexander Hamilton, James Madison, and John Jay used the pen name Publius when they wrote the Federalist Papers, which laid out founding principles of American government. Whistleblowers and other insiders have published anonymous statements in the interest of informing the public. Figures as varied as the statistics guru Nate Silver (“Poblano”) and Senator Mitt Romney (“Pierre Delecto”) have used pseudonyms while discussing political matters on the internet. The goal shouldn’t be to end anonymity online, but merely to reserve the public square for people who exist—not for artificially intelligent propaganda generators.

The Supply of Disinformation Will Soon Be Infinite

The idea that we should reserve the public square for humans is remarkable, in just the sense that this technology is now upon us. Human sentiments have value; AI facsimiles do not.

An optimistic take is that perhaps we will instead pay attention to the useful content of such messages, rather than inflammatory rhetoric. A good idea is a good idea, AI or not.

Ransomware causes death

The Associated Press:

German authorities said Thursday that what appears to have been a misdirected hacker attack caused the failure of IT systems at a major hospital in Duesseldorf, and a woman who needed urgent admission died after she had to be taken to another city for treatment.

German Hospital Hacked, Patient Taken to Another City Dies

Looks like this was not intended, but the story is an illustration of how dependent we are on software.

Trillions of parameters

Maria Deutscher, writing for Silicon Angle:

Microsoft Corp. has released a new version of its open-source DeepSpeed tool that it says will enable the creation of deep learning models with a trillion parameters, more than five times as many as in the world’s current largest model.

Microsoft AI tool enables ‘extremely large’ models with a trillion parameters

That’s a lot of transformations. If there’s a pattern, a trillion parameters should be able to find and store it.

Portland bans facial recognition by private entities

34.10.030 Prohibition.

Except as provided in the Exceptions section below, a Private Entity shall not use Face Recognition Technologies in Places of Public Accommodation within the boundaries of the City of Portland.

34.10.040 Exceptions.

The prohibition in this Chapter does not apply to use of Face Recognition Technologies:

1. To the extent necessary for a Private Entity to comply with federal, state, or local laws;

2. For user verification purposes by an individual to access the individual’s own personal or employer issued communication and electronic devices; or

3. In automatic face detection services in social media applications.

Prohibit the use of Face Recognition Technologies by Private Entities in Places of Public Accommodation in the City (via PRIVACY & INFORMATION SECURITY LAW BLOG)

Note the exception for use in “social media applications.”

What does it mean for AI to be “explainable”?

A NIST paper attempts to answer this question:

Briefly, our four principles of explainable AI are:

Explanation: Systems deliver accompanying evidence or reason(s) for all outputs. 

Meaningful: Systems provide explanations that are understandable to individual users. 

Explanation Accuracy: The explanation correctly reflects the system’s process for generating the output. 

Knowledge Limits: The system only operates under conditions for which it was designed or when the system reaches a sufficient confidence in its output. 

Four Principles of Explainable Artificial Intelligence

Stating this differently: there should be an explanation, it should be understandable and accurate, and the system should stop when it’s generating nonsense.

These are very reasonable principles, but likely tough to deliver with current technology.

Indeed, the paper discusses that humans are often unable to explain why they have taken a certain action:

People fabricate reasons for their decisions, even those thought to be immutable, such as personally held opinions [24, 34, 99]. In fact, people’s conscious reasoning that is able to be verbalized does not seem to always occur before the expressed decision. Instead, evidence suggests that people make their decision and then apply reasons for those decisions after the fact [95]. From a neuroscience perspective, neural markers of a decision can occur up to 10 seconds before a person’s conscious awareness [85]. This finding suggests that decision making processes begin long before our conscious awareness. 

Id. at 14.

And it is well documented that even experts generally cannot predict their own accuracy.

What hope do the AI’s have?

AlphaDogfight wins 5-0 in F-16 battle vs human

Will Knight, writing for Wired:

Last week, a technique popularized by DeepMind was adapted to control an autonomous F-16 fighter plane in a Pentagon-funded contest to show off the capabilities of AI systems. In the final stage of the event, a similar algorithm went head-to-head with a real F-16 pilot using a VR headset and simulator controls. The AI pilot won, 5-0.

A Dogfight Renews Concerns About AI’s Lethal Potential

This is an under-discussed issue, but is inevitable. DeepMind is convinced that its AlphaZero DNN can master any two-player, turn-based game that shows perfect information. And its AlphaStar DNN shows what it can do in real-time games as well. It is a natural, and inevitable, extension to war capabilities.

Is this ok? Does that question even matter? How long before human-in-the-loop is the unacceptable bottleneck?

Freedom vs. Security continued…

Kashmir Hill for the NYT:

Floyd Abrams, one of the most prominent First Amendment lawyers in the country, has a new client: the facial recognition company Clearview AI.

Litigation against the start-up “has the potential of leading to a major decision about the interrelationship between privacy claims and First Amendment defenses in the 21st century,” Mr. Abrams said in a phone interview. He said the underlying legal questions could one day reach the Supreme Court.

Facial Recognition Start-Up Mounts a First Amendment Defense

Is everything known to the public truly available for any use whatsoever? We are trending away from that view, and this will be a battle to watch closely.

Facial recognition software countermeasures

Software that tweaks photos to hide them from facial recognition:

A start-up called Clearview AI, for example, scraped billions of online photos to build a tool for the police that could lead them from a face to a Facebook account, revealing a person’s identity.

Now researchers are trying to foil those systems. A team of computer engineers at the University of Chicago has developed a tool that disguises photos with pixel-level changes that confuse facial recognition systems.

This Tool Could Protect Your Photos From Facial Recognition

This is of course just an arms race. The facial recognition will improve, the hiding software will get tweaked.