There is no doubt that artificial intelligence (AI) is on the cusp of achieving significant disruption across several sectors in the world -- one can simply look to companies like American company Alfi (NASDAQ: ALF) which is attempting to revolutionize the ad-tech industry with privacy-conscious AI --. It is becoming a key driver of productivity and gross domestic product growth for many nations and is pushing the boundaries of technology as we know it. According to a report, the United States leads the AI pack today, with China in a close 2nd and the European Union in 3rd. Out of 100 total available points in the report's scoring methodology, the United States leads with 44.2 points, China with 32.3, and the European Union with 23.5. Although it may seem like the U.S. has an unassailable lead, the fact is that China is rapidly catching up and stands today as a full-spectrum peer competitor of the U.S. in many applications of AI.

Some Uber and Lyft vehicles will soon have digital tablets in back seats that display ads and track riders' faces to gauge reactions. Alfi--a self-described'AI enterprise SaaS platform company powering computer vision with machine learning models'-- announced last week it is providing drivers of both ride-sharing companies with 10,000 camera equipped devices, However, it seems both Uber and Lyft are in the dark about this new venture. Noah Edwardsen, head of corporate communications at Uber, told DailyMail.com: So if this is happening, it is something they are doing with drivers individually.' While a Lyft spokes person told DailyMail.com:

We are an innovative big data company specializing in artificial intelligence and machine learning. Alfi uses computer vision incorporating custom built, ultra-precise, proprietary models to capture customer demographics and sentiment. The combination of our models with high levels of visualization accuracy allows Alfi unparalleled insight into human behaviour. With real-time metrics we are able to capture big data and deliver analytical reporting real time.

Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input parameter estimation of black-box generators. While previous works on likelihood-free inference introduces an implicit proposal distribution on the generator input, this paper analyzes theoretic limitations of the proposal distribution approach. On top of that, we introduce a new algorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate the analyzed limitations, so ALFI is able to find the posterior distribution on the input parameter for black-box generative models. We experimented ALFI with diverse simulation models as well as pre-trained statistical models, and we identified that ALFI achieves the best parameter estimation accuracy with a limited simulation budget.

Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a likelihood function, most of the existing inference methods optimize the simulator parameters through a handcrafted iterative procedure that tries to make the simulated data more similar to the observations. In this work, we explore whether meta-learning can be used in the likelihood-free context, for learning automatically from data an iterative optimization procedure that would solve likelihood-free inference problems. We design a recurrent inference machine that learns a sequence of parameter updates leading to good parameter estimates, without ever specifying some explicit notion of divergence between the simulated data and the real data distributions. We demonstrate our approach on toy simulators, showing promising results both in terms of performance and robustness.