There are notable examples of online search improving over hand-coded or learned policies (e.g. AlphaZero) for sequential decision making. It is not clear, however, whether or not policy improvement is guaranteed for many of these approaches, even when given a perfect evaluation function and transition model. Indeed, simple counter examples show that seemingly reasonable online search procedures can hurt performance compared to the original policy. To address this issue, we introduce the choice function framework for analyzing online search procedures for policy improvement. A choice function specifies the actions to be considered at every node of a search tree, with all other actions being pruned. Our main contribution is to give sufficient conditions for stationary and non-stationary choice functions to guarantee that the value achieved by online search is no worse than the original policy. In addition, we describe a general parametric class of choice functions that satisfy those conditions and present an illustrative use case of the framework's empirical utility.

Traditional machine learning methods share a common hypothesis: training and testing datasets must be in a common feature space with the same distribution. However, in reality, the labeled target data may be rare, so that target space does not share the same feature space or distribution as an available training set (source domain). To address the mismatch of domains, we propose a Dual-Subspace Transfer Learning (DSTL) framework that considers both the common and specific information of the two domains. In DSTL, a latent common subspace is first learned to preserve the data properties and reduce the discrepancy of domains. Then, we propose a mapping strategy to transfer the sourcespecific information to the target subspace. The integration of the domain-common and specific information constructs the proposed DSTL framework. In comparison to the stateart-of works, the main contribution of our work is that the DSTL framework not only considers the commonalities, but also exploits the specific information. Experiments on three emotional speech corpora verify the effectiveness of our approach. The results show that the methods which include both domain-common and specific information perform better than the baseline methods which only exploit the domain commonalities.

Workshops such as Natural Language processing along with Data Scrapping & Machine Learning to name a few. Along with awesome national speakers from around Pakistan, this year's pyconPK proud to have an international speaker Van Lindberg, Director Python Software Foundation. Furthermore, You can check full list of agenda on pyconPK official website. If you still haven't booked your tickets for pyconPK 2019, do it while you can at happening.pk Core objective of PyCon is to pass on technical skills and knowledge to enable non-Python developers and industry outsiders to explore the language. You can follow their Facebook Page and Facebook event for daily updates. See ya at the conference fellas.

I wrote about Dropbox several times in the past week, motivated by meetings I had with Dropbox at the Work In Progress even in San Francisco. I attended Boxworks 19 and live-tweeted the keynote. ADP DataCloud Reaches New Heights with AI-Enhanced Features ADP is moving aggressively to augment companies' hiring efforts with increased capabilities based on machine learning applied to workforce data from over 30 million employees: ADP leverages insights from its immense pool of workforce data to design products that allow business leaders to make actionable decisions by eliminating the need for the manual analysis of a company's data. Previous enhancements such as Executive and Manager Insights (EMI) have helped clients seamlessly measure, compare and apply insights uncovered from ADP workforce data to identify specific issues including unnecessary overtime costs and productivity gaps, enabling them to take action within the flow of work. With AI-enhanced capabilities, ADP DataCloud can continue building upon its success and help leaders proactively address issues before they become larger problems.

Policies are the foundation for any successful organization. Policies are the rules, or laws, of an organization. Heck, one could argue that an organization's culture is better defined by its policies than it is by the character of its leadership team. Unfortunately, the management, creation and execution of policies haven't changed much since the days of "time-and-motion studies". In many cases, policies are nothing more than a static list of what-if rules that govern what workers are to do in well-defined situations.

This makes investment in AI one of the biggest commercial opportunities in today's fast-changing economy. However, recent Deloitte data reveals that while 82% of the UK's large businesses are pursuing some form of AI initiatives, only 15% can be considered'seasoned' or mature AI adopters – lower than the US (24%), Germany (22%), Canada (19%), Australia (17%) and France (16%). In this competitive international market, creating a favourable business environment for AI and investing in AI skills programmes is key for ensuring the long-term success of the UK's national AI strategy. While the UK has a thriving AI scene, it's important to ensure it continues to grow and that investment in AI is proportionally distributed across the country, both to make the most of existing AI talent and to encourage new talent development. Although 80% of the UK's top 50 AI start-ups are based in London, some notable AI success stories came from outside of the capital.

Artificial intelligence (AI) is here. The technology is already making an impact across many industries. According to IDC, worldwide spending on AI systems is forecast to reach $35.8 billion in 2019 -- a 44% increase over 2018. For any company that hasn't started its AI journey -- it's time to get started. That's because AI is shaking up the customer experience, bringing users and brands closer together than ever before.

There is some debate recently as to whether data is the new oil [1] or not [2]. Whatever the case, acquiring training data for our machine learning work can be expensive (in man-hours, licensing fees, equipment run time, etc.). Thus, a crucial issue in machine learning projects is to determine how much training data is needed to achieve a specific performance goal (i.e., classifier accuracy). In this post, we will do a quick but broad in scope review of empirical and research literature results, regarding training data size, in areas ranging from regression analysis to deep learning. The training data size issue is also known in the literature as sample complexity.

Automation has left nations driving on Robotic Process Automation (RPA) and Artificial Intelligence (AI) based technologies globally. According to an academic study conducted by Goldsmiths (University of London) in collaboration with the enterprise software provider Automation Anywhere, 71% of Indian respondents said that their employees used RPA and AI-based augmentation in its full potential, the highest percentage for any of the four markets surveyed. The findings were published by the Augmented Human Enterprise where 66% of Indians surveyed said they are empowered to take risks to embrace automation and RPA while 77% added that their organization prioritized employee development. The study pointed out that India is big on employee engagement too, with an impressive 84% saying employee listening is a priority among Indian enterprises. Mihir Shukla, the CEO at Automation Anywhere asserted that "Think of the human body breathing. It's a complex and critical mechanism but automated so our brains are freed to power everything else we do. I think for many organizations, all they can do is'breathe.' It's so important, it's all the employees can focus on," If the breathing is automated in the organization, then employees have the times to focus on other strategic issues and opportunities within the organization, thus leading to a greater return on investment.