The governing regulations over the use of data and who has access to it will change the landscape of how we move about in the online world. Over the past decade, data has emerged as "the new oil" – a driving force behind the world's economy. Because of the sheer amount of data, new concerns for its use have driven innovation within the privacy and security sphere. Let's take a look at how some of these situations will shape what we understand of privacy and how it appears in industry use cases. Widespread consensus with artificial intelligence is that people still don't trust AI.

At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process. Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we're not so sure. We are huge believers in the power of AI to transform business: We've put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don't have the same economic construction as software businesses. At times, they can even look more like traditional services companies.

Google Maps celebrated its 15th birthday today by announcing a new milestone: in the last year, the company mapped as many buildings as it did in the previous decade. The service reached this landmark through a two-step process. Firstly, staff worked with Google's data operations team to manually trace common building outlines. They then trained machine learning models to recognize the edges and shapes of buildings. Another recent deployment of machine learning enabled Maps to recognize handwritten building numbers that were so unclear that even a passerby in a car couldn't see them.

Using machine learning, a Stanford-led research team has slashed battery testing times – a key barrier to longer-lasting, faster-charging batteries for electric vehicles. Using a new machine learning method, a Stanford-led research team has slashed battery testing times – a key barrier to longer-lasting, faster-charging batteries for electric vehicles – by nearly fifteenfold. Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology. For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times.

Would you be surprised to learn a 120-year-old company is transforming its business with artificial intelligence and technology? Akron, Ohio-based tire maker Goodyear might not be the first company you think of when discussing technological innovation, but they continue to announce intriguing developments and offer proof via new initiatives and products that they are altering operations to be competitive in the future. Regardless if it's an autonomous, electric, or a traditional vehicle, they all need a solid foundation of the right tire for the specific demands of the vehicle. Goodyear uses internet of things technology in its Eagle 360 Urban tire. The tire is 3D printed with super-elastic polymer and embedded with sensors.

Police chief says mannequins will have facial recognition cameras to fight crime, spot traffic offenders, fine drunk drivers; American and French police show interest in the new tech Disruptive technologies such as Artificial Intelligence (AI) will soon empower mannequins to fight crime, spot traffic offenders, fine drunk drivers and rein in criminals across the city, a top official said. "We will soon have artificial eyes fixed in mannequins as cameras with a small AI-linked computing device inside them for facial recognition through a well-connected central server," City Police Commissioner Bhaskar Rao said. The mannequins, however, will not be permanent fixtures at a given place but operate in a hide-and-seek mode. "The AI software will locate the culprits, tip off the police about the number of violations one has committed, count the traffic slips registered against the same vehicle, estimate the penalty amount and alert the police," said Rao. On how futuristic dummies and connected police officers work, Rao said a drunk driver caught on MG Road will be identified by the mannequin even at a far-away junction to relay information to the control room through facial recognition.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

Thompson sampling is a methodology for multi-armed bandit problems that is known to enjoy favorable performance in both theory and practice. It does, however, have a significant limitation computationally, arising from the need for samples from posterior distributions at every iteration. We propose two Markov Chain Monte Carlo (MCMC) methods tailored to Thompson sampling to address this issue. We construct quickly converging Langevin algorithms to generate approximate samples that have accuracy guarantees, and we leverage novel posterior concentration rates to analyze the regret of the resulting approximate Thompson sampling algorithm. Further, we specify the necessary hyper-parameters for the MCMC procedure to guarantee optimal instance-dependent frequentist regret while having low computational complexity. In particular, our algorithms take advantage of both posterior concentration and a sample reuse mechanism to ensure that only a constant number of iterations and a constant amount of data is needed in each round. The resulting approximate Thompson sampling algorithm has logarithmic regret and its computational complexity does not scale with the time horizon of the algorithm.

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model (predicting some outcome) that accounts for agents' incentives to "game" their features in order to receive better decisions. Whereas the strategic classification literature generally assumes that agents' outcomes are not causally dependent on their features (and thus strategic behavior is a form of lying), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. Our formulation is the first to incorporate a crucial phenomenon: when agents act to change observable features, they may as a side effect perturb hidden features that causally affect their true outcomes. We consider three distinct desiderata for a decision-maker's model: accurately predicting agents' post-gaming outcomes (accuracy), incentivizing agents to improve these outcomes (improvement), and, in the linear setting, estimating the visible coefficients of the true causal model (causal precision). As our main contribution, we provide the first algorithms for learning accuracy-optimizing, improvement-optimizing, and causal-precision-optimizing linear regression models directly from data, without prior knowledge of agents' possible actions. These algorithms circumvent the hardness result of Miller et al. (2019) by allowing the decision maker to observe agents' responses to a sequence of decision rules, in effect inducing agents to perform causal interventions for free.

In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their interaction at all. In our framework, complexities are captured by the reconstruction error of an autoencoder that operates on the quantiles of the distribution. Comparing the reconstruction errors of the two autoencoders, one for each variable, is shown to perform surprisingly well on the accepted causality directionality benchmarks. Hence, the decision as to which of the two is the cause and which is the effect may not be based on causality but on complexity. In the multivariate case, where one can ensure that the complexities of the cause and effect are balanced, we propose a new adversarial training method that mimics the disentangled structure of the causal model. We prove that in the multidimensional case, such modeling is likely to fit the data only in the direction of causality. Furthermore, a uniqueness result shows that the learned model is able to identify the underlying causal and residual (noise) components. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.