Memory-Based Learning

As data people, we very typically spend a great deal of time summarizing our findings to stakeholders in a clear, concise and impactful way. Often times, due to the lack of infrastructure, we end up using presentation files with chart images. This can become a real pain when we need to make modifications or when the analysis needs to "live on". Typically, this is where BI (business intelligence) or dashboard tools shine. Unfortunately, this can be a major stumbling block for smaller shops who rely on a lot of local analysis and may not have the budget for a BI tool.

The power of artificial intelligence (AI) to transform the consumer journey has dominated industry conversations in recent years. At its lowest common denominator, AI enables brands to better synthesize mounds of data and incorporate those learnings to improve the commerce experience. This is brought to life in a variety of ways, from empowering store associates to enabling more powerful search to providing the personal touch. Artificial intelligence is expected to be the most impactful technology for commerce over the next five years.Getty AI, which refers to technologies capable of performing tasks normally requiring human intelligence, goes back centuries. The idea of cognitive computing gained steam in the 1930s when Alan Turing suggested that a machine could simulate any conceivable act of mathematical deduction.

Workday held its European Rising conference last year. One of the key themes from the event was how it is embedding AI into its solutions. Having spoken to Chano Fernandez, Co-President Workday early in the week, we also spoke to Barbry McGann, SVP Product Management at Workday later in the conference. The conversation centred around the main message that Workday delivered at its latest conference, AI. One of its recent product innovations was Skills Cloud.

The TDA models have by far the richest functionality and are, unsurprisingly, what we use in our work. They include all the capabilities described above. TDA begins with a similarity measure on a data set X, and then constructs a graph for X which acts as a similarity map or similarity model for it. Each node in the graph corresponds to a sub-collection of X. Pairs of points which lie in the same node or in adjacent nodes are more similar to each other than pairs which lie in nodes far removed from each other in the graph structure. The graphical model can of course be visualized, but it has a great deal of other functionality.

Decision Optimization is now available in the Watson Studio ecosystem with a seamless integration of the CPLEX solvers in the Python runtime environment. Watson Studio now provides everything you need to describe your data, gain insight, and make an optimal decision in the very same ecosystem. Get started right away and learn how to make more intelligent marketing and targeting decisions. Decision Optimization is a subset of data science techniques frequently used for prescriptive analytics. Most documented data science use cases are dedicated to revealing or predicting unknown or future data that is not under your control.

Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. That is why ensemble methods placed first in many prestigious machine learning competitions, such as the Netflix Competition, KDD 2009, and Kaggle. The Statsbot team wanted to give you the advantage of this approach and asked a data scientist, Vadim Smolyakov, to dive into three basic ensemble learning techniques.

Called IQcast, the feature tells users whether they have a low, medium or high chance of dropping below the target blood glucose range within the next one to four hours. These individual-specific predictions are generated by analyzing data collected through Sugar.IQ app and the Guardian Connect device. The Sugar.IQ app is currently available in the App Store for free download. The FDA-cleared app uses IBM Watson Health's AI and analytics tools to help users see how their glucose levels change during the day, and includes a smart food logging system, motivational insights, a glycemic assistant, a data tracker and a glycemic insights feature. Hypoglycemia -- defined by the American Diabetes Association as a blood glucose level lower than 70 mg/dL -- can lead to symptoms ranging from lightheadedness and lethargy to vision impairment and seizures.

The firm's chief strategy officer on how artificial intelligence is changing the game.

Companies from a wide range of industries use machine learning data to do everyday business. From consumer marketing and workforce management to healthcare treatment decision solutions and public safety and policing solutions, whether you realize it or not your life is increasingly more affected by the outcomes of machine learning algorithms. Machine learning algorithms make decisions like who gets a bonus, a job interview, whether or not your credit card limit (or interest) is raised, and who gets into a clinical trial. Machine learning algorithms even help make decisions about who gets parole and who languishes in prison. The result is that people's lives and livelihood are affected by the decisions made by machines.

Do you want to learn how you can accelerate your AI strategy or get ahead of the latest AI trends? Or are you more curious to learn what results businesses are achieving by adopting AI? Either way, make sure you attend Think 2019 and experience Watson AI technology first-hand. Here's a sneak peek at five sessions you can't miss: Being able to explain the decisions your AI makes and have trust in them is crucial to accelerating adoption of AI in your business. In these sessions, you'll learn how AI OpenScale provides businesses with confidence in AI decisions and infuses AI throughout its full lifecycle with trust and transparency, explains outcomes, and automatically mitigates bias. However, there are still a variety of hurdles businesses need to overcome to scale and automate their AI.