Data Science


Machine Learning Making Big Moves in Marketing

#artificialintelligence

This means that ML will be applied to customer interaction data in order to find and exploit patterns in different types of user interactions that occur at different times and locations: purchase histories, emails, call center interactions, social media, website searches, previous marketing campaigns, and even location data and/or "emotion data" from wearable sensors while the customer is shopping. The number one hottest trend (where ML-based marketing is moving) is behavioral analytics, including both predictive and prescriptive analytics modeling. ML is first and foremost a set of algorithms that learn to detect and recognize patterns in data, and to learn from experience when making decisions or taking actions based on those patterns – i.e., the ML process learns (from both successes and failures, including failed models and failed classifications) how to improve over time. ML (including social network analysis and social graph mining) is now helping marketers identify the key influencers within their market domain.


We need to talk about data science skills. Real talk about AI, deep and machine learning - Enterprise Irregulars

#artificialintelligence

To Bardin's point Silicon Valley firms have a halo, and the fact they're held to very different standards by equities analysts means they can spend a lot more on compensation. Google and Facebook are making money with machine learning, not for machine learning. So far the model is kind of similar to open source in that respect – Web companies are making money with open source. Before going back to the skills shortage a couple of conversations with IBM and Google recently are worth noting, with respect to the AI/ML market.


Artificial Intelligence in Enterprise – Using Meta-Vision to Disrupt Business Models for Products and Service

@machinelearnbot

For a compilation of business intelligence, Meta-Vision instantly understands what's important, automatically finds context, curates supporting facts (drawn from the data source), and creates a custom topology of machine-generated hashtags – mapping subject matter by Context Discriminants[2] – in seconds. Meta-Vision identified product weaknesses of the Lenovo Yoga 910 were a noisy PC fan and poor keyboard design. Meta-Vision topology corroborated the findings from the Earned Media analysis – a noisy PC fan and poor keyboard design – also negatively impacted the user experience. From product managers to sales and marketing, Meta-Vision empowers business teams to fully leverage information to elevate their execution, connect the dots, and tactically maximize outcomes and mitigate risk with disruptive decision-making.


Putting AI into action

#artificialintelligence

According to Gartner's IT Glossary, AI is technology that appears to emulate human performance – typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialog with people, and enhancing human cognitive performance. Indeed, AI represents a force of differentiation that will enable service providers to more effectively drive value across the business – from network optimisation and data analytics through to customer care and marketing engagement. AI can keep pace with these demands with solutions (both machine learning and deep learning) that include analytics tools and automation that can systematically respond, operate and improve operational and business support systems. It can take network optimisation to new levels, bringing advanced intelligence to data analytics, while making customer-facing operations and services more effective than ever before.


Registration Special for @CloudExpo Silicon Valley #IoT #AI #DX #DevOps

#artificialintelligence

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, 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 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, 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.


[slides] @KineticaDB Informercial: #FinTech Analytitcs @CloudExpo #AI #BI #DX #InsurTech

#artificialintelligence

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, 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 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, 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.


AI's First Stop: The IoT Edge

#artificialintelligence

Machine learning and other forms of artificial intelligence will likely infiltrate all levels of the IT infrastructure stack, but some architectures will take to it more readily than others. Indeed, with increased data democratization, even long-standing centralized business intelligence platforms are starting to cede ground to smaller, more targeted approaches to data analysis, such as SQL query, predictive data modeling and auto-generated discovery visualization. With NXP's technology, Greengrass can support functions like real-time data gathering and simultaneous management, analysis and storage. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly.


When KM meets AI: An interview with Fireman & Co senior consultant Sally Gonzalez Legal IT Insider

#artificialintelligence

I've been fortunate to have a long and stimulating career in KM and IT management, with about 15 years spent in-house in top IT leadership positions at several global law firms, most recently Dentons, and about 18 years consulting to law firms and law departments in the US, Canada, and the UK. One of the great tragedies of the early days of this period was to observe firms struggling to implement Client Relationship Management systems without recognizing them as fundamentally KM initiatives requiring all of KM's change management, content management, and process improvement competencies in order to succeed. Once again, KM professionals had the blend of skills necessary to provide significant contributions to these initiatives, and the KM fundamentals established in KM 1.0 were key building blocks to optimize legal service delivery. They understand that the path to successful AI implementations will follow the path of all successful KM solutions--blending people, process, and technology into self-sustaining initiatives and driving change to promote adoption.


AI is targeting some of the world's biggest problems: homelessness, terrorism, and extinction

#artificialintelligence

"[These projects] bring up completely new kinds of AI problems because working with low resource communities, data is sparse, as opposed to being plentiful. The USC Center for AI in Society is a collaboration between computer science and social science schools at USC, an ambitious initiative created to cross-pollinate ideas between the two disciplines in order to solve some of the world's biggest problems. Created in 2013, the program focuses on problems found in the 12 Grand Challenges of social work and the United Nations' Sustainable Development Goals. So we're really collaborating across schools in terms of engineering and AI and social work, and it's bringing up completely new sets of challenges to the core in terms of problems that the AI community has tackled," Tambe told VentureBeat in a phone interview.


artificial-intelligence-is-the-new-business-intelligence

#artificialintelligence

Let's tackle those fears and myths by understanding more realistically what AI can and cannot do to enable business applications: To understand AI in ways that drive business, we must start with something that business is familiar with -- business intelligence (BI). Simply defined, predictive analytics use your existing data to predict data that you don't, or can't, have. Prescriptive analytics in advanced BI can recommend actions to optimize business processes, marketing effectiveness, ad targeting and many other business operations. Machine learning can now train models to produce results that closely match those obtained by human experts.