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AI & National Security: A Primer

More troublingly: AI-enabled mass surveillance and tracking could make operating in repressive regimes ever more difficult, and would almost certainly necessitate changes in HUMINT disguise doctrine to incorporate counter-AI principles.

The DOD does have policy establishing standards for review of AI systems for approval, but that policy as written has no requirement for a “human in the loop.” Such a requirement is soft;

When Army Contracting Command — Aberdeen Proving Ground Belvoir Division recently posted a federal business opportunity for a program titled ATLAS (Advanced Targeting and Lethality Autonomous System), many people were concerned — (I mean, it has “lethality” and “autonomous” in the name!) — but the DOD was quick to clarify that ATLAS did not mean “building autonomous weapons,” but was instead looking at ways to use AI to improve other parts of military tanks, not the weapon part itself.

While fully autonomous weapons may not be on the horizon with the DOD, that does not mean adversaries will refrain from creating them, and the introduction of autonomous weapons on the battlefield has major strategic implications.

How do we maintain situational awareness of the battlespace in the context of credible falsified materials, or adversarial attacks against AI situational awareness systems?

Sex Robots and the End of Civilization

I recently came from an Engineering school and the female representation was so horrid that even classes with >50 students would frequently lack ANY female presence despite the department TRYING to get women in the department and always highlighting female students in all of our events at the expense of higher achieving men.

Another great example, where you reasoning would have come to the wrong conclusion, is the big running races like the 5k, 10k, half marathon and full marathon, that are a routine event in every major city, and now even smaller communities too, as they bring in a lot of hotel and restaurant revenue for one thing.

And these are endurance and strength events, and you'll see that many of the women come across the finish line well BEFORE many men who also appear to be in good shape, after running more than 10 miles.

Apparently your memory is short -- as I outlined previously, part of that is because in the old days, women were simply not allowed or even taken seriously in high paying male-dominated fields, so why should women waste time getting degrees in fields that won't allow them jobs?

All you've done is quote your own made-up nonsense, like women's physical abilities being less than half men's, which is spectacularly wrong, and was the way they thought 100 years ago.

But you're right that they would be pushed out because of people like you, who, as we have already PROVEN here, got your facts wrong, and so you'd assume that women shouldn't be hired, thereby self-perpetuating and falsely confirming the very bias you already have.

Gotta tell you my friend, just based on personal experience in high tech, and so this is not a large scientific example, but as a manager I've seen more than enough lazy entitled guys who call in sick, have all kinds of excuses, hung over from the game, sexually harassed women, dug in their heels when a woman was a manager on a project because they just couldn't stand it, in short, and creating a lot of BS that slowed down work.

Uh huh, because in the USA, if you're a man and you insist on doing this, you know damn well that a lot of the guys at the office, and your manager (if he's anywhere as biased as you are) will think you're a loser for 'thinking like a woman' and wanting time off to be at home with the newborn.

Ten-Thousand First Dates: Reinforcement Learning Romance

New Zealand — where I live — has historically not had a “dating” culture so much as a “binge drinking” culture.

The commonly accepted mating ritual of my youth was to get blind drunk, wake up in the company of a complete stranger and then — if you liked the look of them — sheepishly suggest a repeat engagement.

Dating apps and an increasingly globalised culture has brought the concept of the “date” into greater currency in New Zealand, and if one wants to attract a beau in these modern times, one must adapt.

It’s quite different from the classification and regression approaches we’ve seen earlier, where a set of observations are used to derive rules to make predictions about unseen cases.

It’s also different from the more unstructured algorithms we’ve seen, such as the data transformations that let us make knitting pattern recommendations or find similar films.

The applications of reinforcement learning are quite broad, and include complex controllers for robotics, scheduling lifts in buildings, and teaching computers to play video games.

At every step the algorithm records the state of the environment, the choice it made, and the outcome of that choice in terms of whether it generated a reward or a penalty.

The simulation is repeated many times, and the computer learns over time which choices in which states lead to the greatest chance of reward.

It has learnt what is called a “policy” — given a particular game state, it knows precisely which action will maximise its chance of a reward.

In every “round” of the date, the agent chooses a conversational gambit from one of four commonly accepted topics of conversation: Sports, popular music, history, and machine learning and artificial intelligence.

Once the partner has been sufficiently pleased by witty conversation, the agent might try “flirting”, which will hopefully bring the date to a successful conclusion.

At first it will choose actions at random, but as it succeeds at dates, it will weight those choices more highly, exploring these successful paths in greater detail.

At the end of the training period, we can check how well the agent performs — in the course of a hundred dates, how many end in success?

That means that, even after ten thousand dates, the agent doesn’t learn enough to reliably charm his partner — his successes in dating never improve beyond random chance.

We need to reward the agent for taking small steps towards a successful date, like choosing a good topic of conversation;

Armed with this new approach, we’ll send the agent on another ten thousand dates, and test him again, leading us to… …disaster again!

At its best, this is a great strength of artificial intelligence — free of preconceptions, it can discover novel approaches to problems, and discard anything irrelevant.

Examining the agent’s policy shows that he adopts a fairly conventional approach: he attempts various topics until he finds something that appeals, and then sticks to that until he has received sufficient positive feedback.

I’ve learned from my panel of dating experts that representing romantic advances as an adversarial encounter with only one kind of successful outcome is “unhealthy” and “kinda creepy”.

Furthermore, both in how I built my simulation, and how I shaped the rewards that helped the agent learn, I was building into the experiment my own assumptions about how dating works.

When artificial intelligence is applied to real-world problems, if we want its outcomes to accord with a human sense of safety and equity, we have to rely on human judgement, and human empathy.

Why Artificial Intelligence Is a Lot Like Romance: Eye on A.I.

For large companies like Google that have embedded machine learning throughout their businesses and apps, a modest 2% improvement on a particularly important metric could result in financial gains in the hundreds of millions of dollars.

The ties between the U.S. firms and Chinese startups comes amid a trade war between the two countries as well as China’s controversial use of facial recognition technology to detain ethnic minorities, the report noted.

Foreign investors plowed $2 billion into the Russian Direct Investment Fund, Russia’s sovereign wealth fund created in 2011, to help grow Russian companies specializing in A.I., according to The Moscow Times, which cited a report by the Vedomosti business daily.

Spokespeople from the sovereign wealth firm told Vedomosti that the foreign investors included “partners from large sovereign funds and global corporations from the Middle East and Asia.” Sheldon Fernandez, the CEO of Canadian startup DarwinAI, talks to tech publication ITPro Today about the challenges businesses face with machine learning: “While A.I.

has accomplished some dazzling things in an academic or research capacity, businesses are still figuring out how to translate its predictive capabilities into more mundane but practical use cases for the enterprise.” Walmart hired Suresh Kumar to become its chief technology and chief development officer.

Kumar was previously Google’s vice president and general manager of display and video ads, a corporate vice president for Microsoft’s cloud infrastructure and operations, and a vice president of worldwide retail systems for Amazon.

Researchers from Texas Tech University published a paper about using reinforcement learning—in which computers learn through repetition—to automatically detect malicious websites used for so-called phishing attacks.

The report said “Through thousands of hours of game play, the agents learned very particular skills, like racing toward the opponent’s home base when a teammate was on the verge of capturing a flag.” Despite the feat, however, the report cites an A.I.

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