A few weeks after the tech giant admitted that it is already running its own machine learning investment fund, it now announced to the public its hands-on Launchpad Studio program which aims to provide support and resources to hungry AI startups. In the company's Google Developers Blog post on Wednesday, Google claimed that the program's mission is to "enable startups from around the world to build great companies." "In the last 4 years, we've learned a lot while supporting early and late-stage founders. From working with dynamic startups--such as teams applying Artificial Intelligence technology to solving transportation problems in Israel, improving tele-medicine in Brazil, and optimizing online retail in India--we've learned that these startups require specialized services to help them scale," a part of the post read. The Launchpad Studio is another initiative from Google that shows the company's commitment to artificial intelligence and machine learning studies.

Companies are able to save costs by tagging their content & other data using machine learning. Machine learning can understand your content automatically by either reading the text, image recognition & video processing capabilities. Once you automatically understand the content, then it becomes easy for companies to serve the right content to internal and external stakeholders such as sales teams – to help them engage better with prospects & customers. Forrester research found that 77% of consumers in the United States suggested that valuing their time is the most important aspect of the brands interaction with them. Machine learning allows you to understand the user/customer intent, and serve personalized actions at the precise time your customer expects.

'It's not a matter of there being a set of ethics for machines and another for human beings; we argue that there is just one thing called ethics. We want to make sure that machines have this ethics built into them,' says Professor Susan Anderson, who says this needs to be an exhaustive process. 'In order to try to capture the ethical principles needed we need to have a dialogue with the machine that is centred just around whatever the domain is that the machine will be functioning in, and try to discover the ethically-relevant features that the machine will have to encounter or deal with, the prima facie duties that the machine should be aware of and the decision principles that in the last analysis should govern its behaviour.' Professor Anderson says that in the course of an'interactive dialogue' between the machine and one or more ethicists, the machine would be able to'tease out' ethical elements that are relevant to its domain. 'Like, could someone be harmed?

Below are excerpts from a presentation I gave a few months ago in Europe as an invited speaker to a group of low profile but high net worth investors and traders. The subject was determine by the organizer to be about the impact of artificial intelligence and machine learning on trading and investing. The excerpts below are organized in four section and cover about 50% of the original presentation. Artificial Intelligence (AI) allows replacing humans with machines. In the 1980s, AI research focused primarily on expert systems and fuzzy logic.

The race to build mass-market autonomous cars is creating big demand for laser sensors that help vehicles map their surroundings. But cheaper versions of the hardware currently used in experimental self-driving vehicles may not deliver the quality of data required for driving at highway speeds. Most driverless cars make use of lidar sensors, which bounce laser beams off nearby objects to create 3-D maps of their surroundings. Lidar can provide better-quality data than radar and is superior to optical cameras because it is unaffected by variations in ambient light. You've probably seen the best-known example of a lidar sensor, produced by market leader Velodyne.

We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we propose instead to use more flexible code distributions. These distributions are estimated non-parametrically by reversing the generator map during training. The benefits include: more powerful generative models, better modeling of latent structure and explicit control of the degree of generalization.

We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively.

Graphs provide a natural mathematical abstraction for systems with pairwise interactions, and thus have become a prevalent tool for the representation of systems across various scientific domains. However, as the size of relational datasets continues to grow, traditional graph-based approaches are increasingly replaced by other modeling paradigms, which enable a more flexible treatment of such datasets. A promising framework in this context is provided by graphons, which have been formally introduced as the natural limiting objects for graphs of increasing sizes. However, while the theory of graphons is already well developed, some prominent tools in network analysis still have no counterpart within the realm of graphons. In particular, node centrality measures, which have been successfully employed in various applications to reveal important nodes in a network, have so far not been defined for graphons. In this work we introduce formal definitions of centrality measures for graphons and establish their connections to centrality measures defined on finite graphs. In particular, we build on the theory of linear integral operators to define degree, eigenvector, and Katz centrality functions for graphons. We further establish concentration inequalities showing that these centrality functions are natural limits of their analogous counterparts defined on sequences of random graphs of increasing size. We discuss several strategies for computing these centrality measures, and illustrate them through a set of numerical examples.

Domain adaptation (DA) is an important and emerging field of machine learning that tackles the problem occurring when the distributions of training (source domain) and test (target domain) data are similar but different. This kind of learning paradigm is of vital importance for future advances as it allows a learner to generalize the knowledge across different tasks. Current theoretical results show that the efficiency of DA algorithms depends on their capacity of minimizing the divergence between source and target probability distributions. In this paper, we provide a theoretical study on the advantages that concepts borrowed from optimal transportation theory [17] can bring to DA. In particular, we show that the Wasserstein metric can be used as a divergence measure between distributions to obtain generalization guarantees for three different learning settings: (i) classic DA with unsupervised target data (ii) DA combining source and target labeled data, (iii) multiple source DA. Based on the obtained results, we motivate the use of the regularized optimal transport and provide some algorithmic insights for multi-source domain adaptation. We also show when this theoretical analysis can lead to tighter inequalities than those of other existing frameworks. We believe that these results open the door to novel ideas and directions for DA.

The Cocktail Bot 4.0 consists of five robots with one high-level goal: Mix one more than 20 possible drink combination for you! But it isn't as easy as it sounds. After the customer composed his drink by combining liquor, soft drink and ice in a web interface. The robots start to mix the drink on their own. Five robot stations are preparing the order to deliver it to the guests.