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Oracle Artificial Intelligence Blog

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AI Events

Analytics and Data Summit, March 12-14, 2019

Oracle Conference Center, Redwood Shores, CA All Analytics, All Data, No Nonsense! Analytics and Data Summit 19 is a once-a-year opportunity for Oracle technology users.   Designed for Oracle technical professions, it is like a mini-Oracle World or Code One event held on the Oracle Campus for customers, users and partners, but focused on "novel and interesting" use cases of Oracle technologies.   The 3-day event is a great way to network with peers, share, and learn from “novel and interesting use cases” of Oracle’s amazing Analytics and Data technologies.    Analytics and Data Summit 19 delivers: More than 100 technical sessions delivered by technical experts, customers, partners, product managers and developers.  Oracle Database, Oracle Autonomous, Oracle Machine Learning, Oracle Advanced Analytics, Oracle Analytics Cloud, Oracle DVD, Oracle Business Intelligence EE, Oracle Spatial & Graph and more! IoT, Python, R, Blockchain, Kafka, Streaming, RDF and more! Hands on Labs training taught by Product Managers and technical experts Networking opportunities to meet with Oracle ACEs, Customers and Global Leader Customers, Product Managers, Developers and Partners and Consultants. Latest product updates and insider information from Oracle executive management, product managers and developers Partners and Consultants who can help you solve your specific challenges See this 2 min Analytics and Data Summit overview video on Twitter for more information. Click on the Analytics and Data Summit 19 Schedule for the full detailed 3-day Agenda. Analytics and Data Summit 19 is run by an independent user group (BIWA Inc.); not Oracle. Check out #AnD_Summit on Twitter for any current applicable discount codes to receive $50-$75 off the then current registration fee.  Please helps spread the word to friends, colleagues and users and Oracle technical professionals to register.  Being run by an independent user group (BIWA Inc.), we rely on word of mouth and grass roots user community initiatives—a lot, so really appreciate the community support! We have another great lineup this year and hope to have our biggest Analytics and Data Summit event ever!  Hope to see you there. 

Oracle Conference Center, Redwood Shores, CA All Analytics, All Data, No Nonsense! Analytics and Data Summit 19 is a once-a-year opportunity for Oracle technology users.   Designed for Oracle...

AI in Business

How AI is fueling smarter recruitment

  Prior planning and preparation prevents poor performance – a phrase that could be soon be out of date. With the advent of new technologies, and in particular Artificial Intelligence, businesses will soon be able to predict the outcome of different scenarios, before they even come to fruition.   From an HR standpoint, these technologies could transform how we look after team members, find new ones, and initiate HR measures in time to maximize the retention of your employees.   According to Deloitte’s 2018 Global Human Capital report, 72% of business leaders already recognize that AI, robotics and automation are important – but less than a third (31%) are ready to act. So, how can HR teams actually use AI and predictive analytics?   Know your teams Previously, HR professionals checked how happy people were during quarterly, biannual, or even annual reviews.   Now, managers and HR teams can monitor mood and attitude through chatbots with their teams each week. They can check on training programs to see who’s working on their development and who’s dropped off. And they can review skillsets and new opportunities in seconds, helping to keep employees engaged and feeling valued.   In other words, they can reduce staff turnover. With an intelligent Human Capital Management (HCM) system that uses predictive analytics, managers can draw on dozens of datasets to forecast the potential of employees leaving, work out what to do, and then act. And if new team members are needed, it can fuel smarter hiring too.   Anticipate your needs Through the filter of predictive analytics, data on current talent can help you work out which skills and people you’ll need next. You’ll be able to do workforce planning far in advance, finding the experience and capabilities that will matter in a year – or three – and molding the teams you know the business will need. Your AI-based system could also reveal where you should look for candidates and the possible impact on the team they’d be joining – not to mention the business as a whole. It could even reach out to the best people, making an automated but personalized first approach. People are the Business A prerequisite of these insights is a full transparency of skills and personality traits over all employees. This has been a wish for a long time for HR. Today we can realize this due to the almost endless capacities in managing complex data in Clouds. People are the business. Besides the needed equity capital means, the most important factor to secure future success of a corporation is having engaged people. AI capabilities as described above can help to identify what your people truly yearn for and assess the risks and chances of putting them in the position they are longing for. By using AI and predictive analytics to better understand the workforce, HR leaders can drive greater value and manage risk more effectively. But how do they currently view AI? Read the results of our latest survey to find out.

  Prior planning and preparation prevents poor performance – a phrase that could be soon be out of date. With the advent of new technologies, and in particular Artificial Intelligence, businesses will...

AI News

H2O.ai Driverless AI Cruises on Oracle Cloud Infrastructure GPUs

One of the things I'm most excited about at Oracle Cloud Infrastructure is the opportunity to do cool things with our partners in the artificial intelligence (AI)/machine learning (ML) ecosystem. H2O.ai is doing some really innovative things in the ML space that can help power these sorts of use cases and more. Their open source ML libraries have become the de facto standard in the industry, providing a simple way to run a variety of ML methods, from logistic regressions and GBT to an AutoML capability that tunes the model automatically. H2O.ai has continued to build on this functionality with GPU support with what I think might be the best-named product of all time, Sparkling Water. (Yes, it's H2O running on Spark. Get it?). The latest H2O.ai product is Driverless AI. The name is perhaps a bit misleading. Driverless AI isn't related to driverless cars. Instead, it's an ML platform that provides a GUI on top of the H2O ML libraries that we already know. The GUI provides support for a significant chunk of the ML lifecycle: Data loading Visualization Feature engineering Model creation Model evaluation Deployment for scoring Software to do all this simply wasn't available five years ago. Instead, a highly skilled person would have had to put everything together by hand over a period of weeks or months. There are still some gaps. For example, data wrangling is still a mess even with the time series support and automatic feature generation abilities of Driverless AI. That said, building accurate ML models has never been easier. So, what does this all have to do with Oracle Cloud Infrastructure? We're building data centers all over the world, and they're being populated with some nifty hardware, including cutting-edge GPU boxes. The new BM.GPU3.8 is the top of that range with 8 NVIDIA Volta cards. This is the perfect machine to handle the compute demands of DAI, and we're pricing them to be significantly less expensive than any competing platform. For our provisioning plane, Oracle Cloud Infrastructure has made an open choice. Rather than building a proprietary technology such as Amazon Web Services CloudFormation, we've chosen to adopt the open source industry standard of Terraform. We've joined the Cloud Native Computing Foundation (CNCF) as a Platinum member and contributed our Terraform provider to the open source project. We've partnered with H2O.ai to write some Terraform modules that deploy H2O.ai Driverless AI on Oracle Cloud Infrastructure. The first module deploys on GPU machines. I worked with our team to record this video that demonstrates how to use the module. It also includes a very basic demo.   This is just the beginning of our partnership with H2O.ai. We're working on several activities with them: Oguz Pastirmaci from the Oracle Cloud Infrastructure data and AI team is working to enhance the Terraform module. Building a model is fast with 8 GPUs. It's going to be a lot faster with a whole cluster of those machines humming in parallel. We're discussing how we might be able to simplify deployment even further, providing a more integrated experience with a higher-level interface. We'll be at H2O World San Francisco 2019 on Feb. 4-5. Although the event won't have booths, a number of us should be wandering around the conference. Say hi! If you're interested in learning more about H2O.ai on Oracle Cloud Infrastructure or about our AI/ML partnerships in general, reach out to me at ben.lackey@oracle.com. You can also follow me on Twitter @benofben.

One of the things I'm most excited about at Oracle Cloud Infrastructure is the opportunity to do cool things with our partners in the artificial intelligence (AI)/machine learning (ML) ecosystem. ...

Data Science

Types of Machine Learning and Top 10 Algorithms Everyone Should Know

  From detecting skin cancer to sorting corn cobbs to predicting early equipment maintenance, machine learning has granted computer systems entirely new abilities.  Algorithms are the methods used to extract patterns from data for the purpose of granting computers the powers to predict and draw inferences. It will be interesting to learn how machine learning really works under the hood.  Let's walk through a few examples and use it as an excuse to talk about the process of getting answers from your data using machine learning. Here are top 10 machine learning algorithms that everyone involved in Data Science, Machine Learning, and AI should know about. Before we go further it is worth explaining the Taxonomy. Machine learning algorithms are divided into three broad categories: Supervised learning Unsupervised learning Reinforcement learning Supervised Learning Supervised learning is the task of inferring a function from the training data. The training data consists of a set of observations together with its outcome. This is used when you have labeled data sets available to train e.g. a set of medical images of human cells/organs that are labeled as malignant or benign. Supervised learning can be further subdivided into: Regression analysis Classification analysis Regression Analysis Regression analysis is used to predict numerical values. The top regression algorithms are: Linear Regression Linear regression model relationships between observation and outcome using a straight-line.  Root mean squared error and gradient descent is used to fit the best possible line. The methodology provides insights into the factors that have a greater influence on the outcome, for example, the color of an automobile may not have a strong correlation to its chances of breaking down, but the make/model may have a much stronger correlation. Polynomial Regression Polynomial regression it is a form of regression analysis in which the relationship between the observation and the outcome is modeled as an nth degree polynomial, the method is more reliable when the curve is built on a large number of observations that are distributed in a curve or a series of humps, and not linear. Classification analysis Classification analysis is a series of techniques used to predict categorical values, i.e. assign data points to categories e.g. Spam Email vs Non-Spam Email, or Red vs Blue vs Green objects. The top classification algorithms are: Logistic Regression Logistic regression is a misleading name even though the name suggests regression but in reality, it is a classification technique.  It is used to estimate the probability of a binary (1 or 0) response e.g. malignant or benign. It can be generalized to predict more than two categorical values also e.g. is the object an animal, human, or car. K-Nearest Neighbor K nearest neighbors is a classification technique where an object is classified by a majority vote. Suppose you are trying to classify the image of a flower as either Sunflower or Rose, and if K is chosen as 3, then 2 or all 3 of the 3 nearest classified neighbors should belong to the same flower class for the test sample image to be assigned that flower class. Nearness is measured for each dimension that is used for classification, for example, color and how close the color of the test sample to the color of other pre-classified flower images. It is neighbors the observation is assigned to the class which is most common among its K nearest neighbors. The best choice of K depends upon the data generally. The larger value of K reduces the effect of noise on the classification number. Decision Trees Decision trees is a decision support tool that uses a tree-like model of decisions. The possible consequences decision trees aim to create is a model that predicts by learning simple decision rules from the training data.   Unsupervised Learning Unsupervised learning is a set of algorithms used to draw inferences from data sets consisting of input data without using the outcome. The most common unsupervised learning method is cluster analysis which is used for exploratory data analysis to find hidden patterns or groupings in data. The popular unsupervised learning algorithms are: K-means Clustering K-means clustering aims to partition observations into K clusters, for instance, the item in a supermarket are clustered in categories like butter, cheese, and milk - A group dairy products. K-means algorithm does not necessarily find the most optimal configuration, the k-means algorithm is usually run multiple times to reduce this effect.   Principal Component Analysis Principal component analysis is a technique for feature extraction when faced with too many features or variables. Say you want to predict the GDP of United States, you have many variables to consider – Inflation, stock data for index funds as well as individual stocks, interest rate, ISM, jobless claims, unemployment rate, and the list goes on. Working with too many variables is problematic for machine learning as there can be risk of overfitting, lack of suitable data for each variable, and degree of correlation of each variable on the outcome. The first principal component has the largest possible variance that accounts for as much of the variability in the data as possible, each succeeding component, in turn, has the highest variance possible under the constraint that it is orthogonal to the preceding component.   Reinforcement Learning Reinforcement learning is different from both supervised and unsupervised learning. The goal in supervised learning is to find the best label based on past history of labeled data, and the goal in unsupervised learning is to assign logical grouping of the data in absence of outcomes or labels. In reinforcement learning, the goal is to reward good behavior, similar to rewarding pets for good behavior in order to reinforce that behavior. Reinforcement learning solves the difficult problem of correlating immediate actions with the delayed outcomes they create. Like humans, reinforcement learning algorithms sometimes have to contend with delayed gratification to see the outcomes of their actions or decisions made in the past, for example, the rewarding for a win in a game of chess or maximizing the points won in a game of Go with AlphaGo over many moves. Top reinforcement learning algorithms include Q-Learning, State–Action–Reward–State–Action (SARSA), Deep Q-Network (DQN), and Deep Deterministic Policy Gradient (DDPG). The explanation for these algorithms gets fairly involved and is worthy of its own dedicated blog post in future. Oracle offers a complete data science and machine learning frameworks, algorithms in its data science platform and also embedded in its SaaS applications and database. Click here to learn more about Oracle’s AI and Machine Learning offerings. p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Helvetica Neue'; color: #000000} li.li2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Helvetica Neue'; color: #e4af0a} span.s1 {color: #454545} span.s2 {color: #e4af0a} ol.ol1 {list-style-type: decimal} Image Sources/Credits (in order of appearance): http://datasciencecentral.com http://en.wikipedia.org https://stats.stackexchange.com https://medium.com/@adi.bronshtein/a-quick-introduction-to-k-nearest-neighbors-algorithm-62214cea29c7 https://canopylabs.com/resources/interpreting-complex-models-with-shap/ https://towardsdatascience.com/k-means-clustering-identifying-f-r-i-e-n-d-s-in-the-world-of-strangers-695537505d https://deepmind.com/research/alphago/            

  From detecting skin cancer to sorting corn cobbs to predicting early equipment maintenance, machine learning has granted computer systems entirely new abilities.  Algorithms are the methods used to...