business process management

When artificial intelligence meets business process management


There's a lot of discussion among IT professionals about what digital transformation actually is. From my perspective as a business process management expert, digital transformation results from applications that provide better user experiences for customers and employees. State-of-the-art user interfaces, combined with streamlined backend operations, enable improved business efficiency, smoother processes, faster reaction to market changes, and better adaptation to rapidly changing business environments. As business process-based applications get more sophisticated, however, the effect of delays--or blocked processes--remains a problem. Therefore, I've spent a good amount of effort looking at how to manage these blockages and delays.

The Next Era in Business Process Management: Artificial Intelligence - RTInsights


By using supervised machine learning, a BPM tool could find valuable patterns in data and automate business processes. In the past, the goals of digital transformation were met primarily with business process management (BPM) tools, which aim to help companies orchestrate resources, route work to the right people, automate routine manual tasks, and enable self-service where none existed before. The idea is that by connecting AI to existing BPM tools, and delivering the data generated by digitized processes to AI systems, companies could do even more work to cut human latency (and thus costs) out of processes while also delivering a better end product to customers. By eliminating the manual processes from determining the best targets for marketing, the BPM tool allows human employees to focus on more complex processes that drive more productivity and revenue, such as fine-tuning those personalized campaigns for higher conversion rates.

Google Assistant To Overtake Apple's Siri, Samsung's Bixby, With Over 1 Billion Installations By 2021: Survey

International Business Times

Google Assistant's wide presence is not unexpected, owing to the company's large ecosystem of Android devices. It is even installed on the Samsung Galaxy S8, which also comes with the company Bixby voice assistant pre-installed. According to a report, AI-based voice assistants will be installed on 7.5 billion devices by 2021. This growth is owed to their presence of voice assistants on a large number of connected devices including smart speakers, wearable devices, smart TVs, smart home devices and other smart devices.

Big Data Terminology: 16 Key Concepts Everyone Should Understand (Part II)


However, since it has become apparent that a huge amount of value can be locked away in this unstructured data, great efforts have been made to create applications that are capable of understanding unstructured data--for example, visual recognition and natural language processing. Recently, there has been a big push for the development of systems that are capable of processing and offering insights in real time (or near-real time), and advances in computing power, as well as development of techniques such as machine learning, have made it a reality in many applications. Spark is another open-source framework like Hadoop (discussed in my Part 1 post), but more recently developed and more suited to handling cutting-edge Big Data tasks involving real time analytics and machine learning. A subfield of reporting (see above), visualizing is now often an automated process, with visualizations that are customized by algorithm to be understandable to the people who need to act or take decisions based on them.

AI and IoT at SAP - Yin and Yang - Epikonic


SAP has been fairly quiet on the former and fairly vocal on the latter, although the first announcement was about machine learning powered intelligent business applications, back in November 2016. It's time for SAP Leonardo, the SAP system for digital innovation.' With this approach they even go beyond only connecting two technologies but they also add Blockchain, Big Data, Data Intelligence and Analytics into one single platform. This massive amount of data also explains why SAP moved Big Data, Data Intelligence and Analytics into SAP Leonardo.

How Machine Learning Unlocks the Power of BI - DZone Big Data


The pair joined forces to deliver an in-depth webinar on Machine Learning and business intelligence, which you can view in full here. Or, put another way: when does it make sense to invest in Machine Learning projects for my business? One of the most exciting applications, says Boaz, is Natural Language Processing (NLP). For example, Sisense Everywhere uses bots and NLP to deliver data insights outside of the usual dashboard environment.



It also adheres to good variable scoping practice and common tensorflow conventions I've observed in the documentation and source code, which has nice side effects such as clean graph visualizations in TensorBoard. Also employs a sampled softmax loss function to allow for larger vocabulary sizes (page 54 of notes). Instead of using the feed_dict argument to input data batches to the model, it is substantially faster encode the input information and preprocessing techniques in the graph structure itself. Rather the model uses a sequence of queues to access the data from files in google's protobuf format, decode the files into tensor sequences, dynamically batch and pad the sequences, and then feed these batches to the embedding decoder.

20 Questions With Google's Assistant and Apple's Siri

The Atlantic

Tuesday, Google made its artificial-intelligence powered Assistant available for the iPhone. The move brings the company's voice interface into direct competition with Apple's own Siri. For the first time, you can now have both assistants on the same phone in your palm. They serve as a platform for promoting the way Google's executives see their company and the world.

Ongoing #DigitalTransformation @CloudExpo #Agile #CIO #IoT #AI #ML #DL


According to a Forrester Research market study, the mainframe is leveraged by 92 of the top 100 banks worldwide, 23 of the top 25 U.S. retailers, all 10 of the world's largest insurers, and 23 of the world's 25 largest airlines. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA. Join Cloud Expo / @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 20th Cloud Expo / @ThingsExpo June 6-8, 2017, at the Javits Center in New York City, NY and October 31 - November 2, 2017, Santa Clara Convention Center, CA will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.

Styles of Deep Learning: What You Need to Know - TDWI Upside


If we look at the job of attempting to determine letters in the alphabet, an analytics solution would start with a model of each letter, then analyze how close images came to that model using a set of relatively simple formulae. A deep neural network would be capable of viewing the space as a set of pixels on one level, linking the pixels to a next layer of abstracted line connections (edges), linking those lines to a next layer of features, linking those features to a next layer of feature sets, and so forth, until -- over many iterations -- it was able to determine alphabetical characters on its own. It is this ability to independently analyze complex feature sets with relatively sparse advance modeling that sets deep learning apart and makes it increasingly important. We can also expect deep learning to be incorporated into other forms of analysis, such as big data, business analysis, predictive analytics, model-based analytics, and standard database searches or statistical analysis.