IBM has laid off a number of employees in its Watson Health unit, but says initial reports that as much as 50 percent to 70 percent of the unit's workforce was furloughed are not accurate and that the reductions will not hurt its core cognitive computing business. A company representative, however, would not provide additional details or give the specific number of employees being let go. The company also refused to say how many people are employed in the Watson Health unit. "IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM," said the vendor in a written statement. "This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes and automation to drive speed."

IBM has laid off a number of employees in its Watson Health unit, but says initial reports that as much as 50 percent to 70 percent of the unit's workforce was furloughed are not accurate and that the reductions will not hurt its core cognitive computing business. A company representative, however, would not provide additional details or give the specific number of employees being let go. The company also refused to say how many people are employed in the Watson Health unit. "IBM is continuing to reposition our team to focus on the high-value segments of the IT market, and we continue to hire aggressively in critical new areas that deliver value for our clients and IBM," said the vendor in a written statement. "This activity affects a small percentage of our Watson Health workforce, as we move to more technology-intensive offerings, simplified processes and automation to drive speed."

Artificial intelligence may seem futuristic, but smart brands are already focusing on creating practical, measurable consumer-facing applications with the technology. Brand and agency leaders are using this new technology for everything from shifting how media dollars are deployed, to customer service, to using artificial intelligence for content creation. Competitors enter and stakes rise. And consumers expect more from the companies they buy from. It is not about just being the most convenient option, or the cheapest option.

Their conversation focuses on Watson Services for Core ML, which allows developers to build Watson Machine Learning models in the cloud and deploy applications on Apple iOS devices. IBM Watson Services allows developers to build applications and let Watson do the heavy lifting when it comes to AI and Machine Learning. Watson Studio--with its simple steps and drag-and-drop features--allows developers to create models without being Machine Learning experts. Sridhar discusses how this enables users, developers, and businesses to build better applications. The bottom line is that putting Watson in your pocket is a big deal.

Apple recently announced their partnership with IBM to leverage IBM's Watson service to train machine learning models for CoreML. So that mean you now can build apps that leverage Watson machine learning models on iPhone and iPad, even when your device is offline. Your apps can quickly analyze images, accurately classify visual content, and easily train models using Watson Services. With this video series you will learn to onboard with not only pre-trained Watson models but customize and train models that continuously learn over time. In Apple's own words "You can build apps that seamlessly integrate with IBM Cloud using the IBM Cloud Developer Console for Apple. This allows you to quickly tap into Watson Services for Core ML, as well as other IBM cloud services including authentication, data, analytics, and more. The console provides a catalog of starter kits designed for common frameworks that integrate with IBM Cloud."

IBM has laid off approximately 50 and 70 per cent of staff this week in its Watson Health division, according to inside sources. The axe, we're told, is largely falling on IBMers within companies the IT goliath has taken over in the past few years to augment Watson's credentials in the health industry. These include medical data biz Truven, which was acquired in 2016 for $2.6bn, medical imaging firm Merge, bought in 2015 for $1bn, and healthcare management business Phytel, also snapped up in 2015. Yesterday and today, staff were let go at IBM's offices in Dallas, Texas, as well as in Ann Arbor, Michigan, Cleveland, Ohio, and Denver, Colorado, in the US, and elsewhere, it is claimed. A spokesperson for Big Blue was not available for comment.

When dealing with audio files a lot of work is required to index them properly so that is easy to lookup for them when needed. In this example we'll show how to use IBM Watson Speech to Text to recognize speech from audio files stored on Box and enrich their metadata with the extracted text. Go to Service Credentials and copy the username & password values, we're going to use them soon. In less then 1 minute you can have this up and running by using the project Blueprint, a pre-built template to help you get started with proven integration solutions. To get started with the Blueprint just click here.

Learning from Observation involves creating agents that observe experts performing tasks and imitate them. Case-Based Reasoning (CBR) is a tool that can be used for this purpose. Regular CBR can only learn memoryless behavior: behavior that doesn't rely on the past. Temporal Backtracking (TB) is an approach to learning state-based behavior that uses recency as its inductive bias, which may or may not be relevant to the agent behavior. We show how TB can be viewed as a particular case of a more generalized case-based approach to learning state-based behavior that can accommodate other inductive biases. We then propose five alternative similarity metrics to learn three different state-based behaviors in a 2D vacuum cleaner domain, and compare their performance to the TB algorithm's performance. We show that none of the proposed metrics (nor TB) is a one-size-fits all algorithm for learning state-based behavior.

We propose a Case-Based Reasoning(CBR) approach for content selection, which is an intermediate step towards generating textual summaries of time series data in the weather prediction domain. Specifically, we handle two significant challenges, the first involving multivariate data that warrants modeling of the interaction of two `channels' (wind speed and direction in our context) and the second involving the effective integration of domain-specific knowledge in the form of rules with data from a case library of past instances of content selection. We present an approach that uses domain knowledge to transform a given raw time series instance into a representation that facilitates effective retrieval of relevant cases, which are then used for change point prediction. We empirically demonstrate that our approach combining CBR and domain rules outperforms classical content selection mechanisms that are based on rules or heuristics alone as well as those that are purely data-driven.

The development of microarray technology has made it possible to assemble biomedical datasets that measure the expression profile of thousands of genes simultaneously. However, such high-dimensional datasets make computation costly and can complicate the interpretation of a predictive model. To address this, feature selection methods are used to extract biological information from a large amount of data in order to filter the expression dataset down to the smallest possible subset of accurate predictor genes. Feature selection has three main advantages: it decreases computational costs, mitigates the possibility of overfitting due to high inter-variable correlations, and allows for an easier clinical interpretation of the model. In this paper we compare three methods of feature selection: iterative Bayesian Model Averaging (BMA), Random Survival Forest (RSF) and Cox Proportional Hazard (CPH) and five methods of survival analysis: Analysis RandomSurvival Forest (RSF), Cox Proportional Hazard (CPH), Alan Additive Filter (AAF), DeepSurv (neural network), andCbrSurv (case-based reasoning), which we introduce in this paper. Features selected by these methods are compared with a hand selected set of features. All the data we used came from the Metabric breast cancer dataset. Our results indicate that feature selection improves the performance of survival analysis methods. Overall, the best survival analysis performance was obtained by combining RSF for feature selection and CbrSurv, closely followed by DeepSurv, for survival prediction