Sample-selection approaches, which attempt to pick up clean instances from the noisy training data set, have become one promising direction to robust learning from corrupted labels. These methods all build on the memorization effect, which means deep networks learn easy patterns first and then gradually over-fit the training data set. In this paper, we show how to properly select instances so that the training process can benefit the most from the memorization effect is a hard problem. Specifically, memorization can heavily depend on many factors, e.g., data set and network architecture. Nonetheless, there still exist general patterns of how memorization can occur. These facts motivate us to exploit memorization by automated machine learning (AutoML) techniques. First, we design an expressive but compact search space based on observed general patterns. Then, we propose to use the natural gradient-based search algorithm to efficiently search through space. Finally, extensive experiments on both synthetic data sets and benchmark data sets demonstrate that the proposed method can not only be much efficient than existing AutoML algorithms but can also achieve much better performance than the state-of-the-art approaches for learning from corrupted labels.

IBM Watson is considered to be the first-ever commercialized cognitive computing platform, designed specifically for underpinning the development of various enterprise solutions. IBM Watson services continue to tap immense opportunity in the rapidly evolving cognitive computing field, which has been reshaping the nature of business operations, thereby determining their growth. Fact.MR's recent study projects the IBM Watson services market to record a spectacular rise in the period of forecast (2018-2028). Over US$ 20,000 Mn worth of IBM Watson services are estimated to be sold globally by 2028-end. Although cognitive computing is yet at its nascent phase, the technology is expected to have a significant influence on transformation of various businesses and industrial sectors.

Multi-channel is the new norm – consumers are not completely abandoning brick-and-mortar stores. Instead, they expect seamless shopping experiences across online, mobile and offline stores. They might first browse and research online, then purchase or pick-up in-store--or the other way around. Successful retailers who can gain customer loyalty are those who can deliver a superior seamless experience across all channels. Data is the new gold – The additional touchpoints mean retailers have greater opportunity and more data to identify their customers and discern their preferences. However, without a proper data and analytics infrastructure, many retailers struggle to mine and analyze huge volumes of data generated daily to gain valuable insights that can help them innovate.

Businesses today are increasingly certain that AI will be a driving force in the evolution of their industries over the next few years. To successfully infuse AI into your product or solution, there are many factors that challenge its widespread adoption in the business–and to achieving your expected outcomes. Building trust – Organizations and businesses tend to be skeptical about AI because of its "black box" nature, resulting in many promising models not going into production. Algorithm bias – Another inherent problem with AI systems is they're only as good–or as bad–as the data they're trained on. If the input data is filled with racial, gender, communal or ethnic biases, your model's accuracy is going to eventually drift away.

--In recent years, huge amounts of unstructured textual data on the Internet are a big difficulty for AI algorithms to provide the best recommendations for users and their search queries. Since the Internet became widespread, a lot of research has been done in the field of Natural Language Processing (NLP) and machine learning. Almost every solution transforms documents into V ector Space Models (VSM) in order to apply AI algorithms over them. One such approach is based on Case-Based Reasoning (CBR). Therefore, the most important part of those systems is to compute the similarity between numerical data points. In 2016, the new similarity TS-SS metric is proposed, which showed state-of-the-art results in the field of textual mining for unsupervised learning. However, no one before has investigated its performances for supervised learning (classification task). In this work, we devised a CBR system capable of finding the most similar documents for a given query aiming to investigate performances of the new state-of- the-art metric, TS-SS, in addition to the two other geometrical similarity measures -- Euclidean distance and Cosine similarity -- that showed the best predictive results over several benchmark corpora. The results show surprising inappropriateness of TS-SS measure for high dimensional features.

There are many reasons why you would want to leverage Watson Assistant to make your Einstein Bot "better". In a previous blog, I spoke to just some of the key reasons why you would need to do so. I will provide additional detail here but first, let's look at how you integrate Watson into your Einstein Bot. The obvious table stakes, you need a Watson Assistant service to integrate with Bots. If you don't already have one, you can get a free IBM Cloud account to deploy a Watson Assistant service, which you can do in about a minute, also for free.

Microsoft started using machine learning (ML) to manage the rollout of Windows 10 feature updates with the Windows 10 April 2018 Update (version 1803). In a new blog post by Microsoft's Archana Ramesh and Michael Stephenson, both data scientists for Microsoft Cloud and AI, the company outlines improvements made since then. Microsoft has been having a tough time recently with the quality of cumulative updates (CU) and feature updates for Windows 10. While the tech media tends to blow things out of proportion sometimes, I think it's fair to say that quality has taken a knock since internal testers were dismissed in favor of the Windows Insider Program. Biannual feature updates haven't been without their issues either. But because of the diversity of the Windows ecosystem, regardless of how much testing is done, there is always the potential for issues when making changes to a complex piece of software like Windows.

At the front lines of business communications lies the customer service team, a vital link between customers and your organization. With rising customer expectations and multiple communications channels available, connecting with customers in their preferred method is essential to a positive experience. Customer experience (CX) improvements are driven by new technology, and each customer interaction impacts the user's relationship with your organization. AI is one of these technologies that can improve your customer experience and contact center. For example, AI can connect the dots between the maze of data in your contact center and change the way your teams interact with your customers.