In Hungary, Latvia, and Greece, travelers will be given an automated lie-detection test—by an animated AI border agent. The system, called iBorderCtrl, is part of a six-month pilot led by the Hungarian National Police at four different border crossing points. “We’re employing existing and proven technologies—as well as novel ones—to empower border agents to increase the accuracy and efficiency of border checks,” project coordinator George Boultadakis of European Dynamics in Luxembourg told the European Commission. “iBorderCtrl’s system will collect data that will move beyond biometrics and on to biomarkers of deceit.”
Posts tagged ML
Some of these skilled lawyers did question whether their profession could ever entirely trust automation to make skilled legal decisions. For a small number, they suggested they would be sticking to “reliable” manual processes for the immediate future. However, most of the participants stressed that high-volume and low-risk contracts took up too much of their time, and felt it was incumbent on lawyers to automate work when, and where, possible. For them, the study was also a simple, practical demonstration of a not-so-scary AI future. However, lawyers also stressed that undue weight should not be put on legal AI alone. One participant, Justin Brown, stressed that humans must use new technology alongside their lawyerly instincts. He says: “Either working alone is inferior to the combination of both.”
“The video, called “Alternative Face v1.1”, is the work of Mario Klingemann, a German artist. It plays audio from an NBC interview with Ms Conway through the mouth of Ms Hardy’s digital ghost. The video is wobbly and pixelated; a competent visual-effects shop could do much better. But Mr Klingemann did not fiddle with editing software to make it. Instead, he took only a few days to create the clip on a desktop computer using a generative adversarial network (GAN), a type of machine-learning algorithm. His computer spat it out automatically after being force fed old music videos of Ms Hardy. It is a recording of something that never happened.”
AlphaGo is made up of a number of relatively standard techniques: behavior cloning (supervised learning on human demonstration data), reinforcement learning (REINFORCE), value functions, and Monte Carlo Tree Search (MCTS). However, the way these components are combined is novel and not exactly standard. In particular, AlphaGo uses a SL (supervised learning) policy to initialize the learning of an RL (reinforcement learning) policy that gets perfected with self-play, which they then estimate a value function from, which then plugs into MCTS that (somewhat surprisingly) uses the (worse!, but more diverse) SL policy to sample rollouts. In addition, the policy/value nets are deep neural networks, so getting everything to work properly presents its own unique challenges (e.g. value function is trained in a tricky way to prevent overfitting). On all of these aspects, DeepMind has executed very well. That being said, AlphaGo does not by itself use any fundamental algorithmic breakthroughs in how we approach RL problems.
Until a few years ago, machine learning algorithms simply did not work very well on many meaningful tasks like recognizing objects or translation. Thus, when a machine learning algorithm failed to do the right thing, this was the rule, rather than the exception. Today, machine learning algorithms have advanced to the next stage of development: when presented with naturally occurring inputs, they can outperform humans. Machine learning has not yet reached true human-level performance, because when confronted by even a trivial adversary, most machine learning algorithms fail dramatically. In other words, we have reached the point where machine learning works, but may easily be broken.
In this paper, we demonstrated techniques for generating accessories in the form of eyeglass frames that, when printed and worn, can effectively fool state-of-the-art face-recognition systems. Our research builds on recent research in fooling machine-learning classifiers by perturbing inputs in an adversarial way, but does so with attention to two novel goals: the perturbations must be physically realizable and inconspicuous. We showed that our eyeglass frames enabled subjects to both dodge recognition and to impersonate others. We believe that our demonstration of techniques to realize these goals through printed eyeglass frames is both novel and important, and should inform future deliberations on the extent to which ML can be trusted in adversarial settings. Finally, we extended our work in two additional directions, first, to so-called black-box FRSs that can be queried but for which the internals are not known, and, second, to defeat state-of-the-art face detection systems.