Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: "If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up." I believe that this statement also applies to CMOs. Machine Learning-based personalization (SegOne Segment of One Marketing) is hotter than ever, especially when marketers select context-specific content to be presented to an individual consumer.
If you ask ten data scientists, "Which machine learning tool is best?" you'll likely get many different answers. But slightly more surprising is that if you ask any one data of those data scientists the same question, you'll likely still get many different answers. Here's a quick preview of two key observations about what makes machine learning successful: Instead they usually keep three to five machine learning options in their tool box. Let's start with the first observation. Turns out that at any point in time many organizations have adopted a range of several machine learning tools.
Saw this story after it was tweeted by Puni Rajah – Bosch to invest 300m euros in AI, employ 100 experts from India, USA, Germany. As ever India has done great job building its native skills base. But the fact Bosch needs to look outside Germany is telling. We're seeing a lot of pressure owing to skills shortages, and companies, countries and cities everywhere are going to need to up their game to avoid brain drain. Silicon Valley is still the main place data scientists, in particular machine learning and AI specialists, are ending up. Pierre Etienne Bardin of Société Générale expressed the need to be more active in hiring and training succinctly in a recent post on data transformation as the new digital transformation.
Enterprise business models evolve over time for many reasons. The Internet has been a key factor driving enterprise business model change in recent years. The recent popularity of smartphones has disrupted consumer habits in travel, investment, entertainment, communication, social engagement, dining, shopping and many daily activities. Consequently, enterprises have been forced to change their business models. While some changes are progressive, others are disruptive.
Indian-origin researchers have developed a new system that uses Artificial Intelligence algorithms and a smartphone app to instantly distinguish between genuine and fake versions of the same product. The system works by deploying a dataset of three million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. "The classification accuracy is more than 98 per cent, and we show how our system works with a cellphone to verify the authenticity of everyday objects," said Lakshminarayanan Subramanian, Professor at New York University. The system is scheduled to be presented on August 14 at the annual KDD Conference on Knowledge Discovery and Data Mining in Halifax, Nova Scotia, Canada. The system described in the presentation is commercialised by Entrupy Inc., an New York University start-up founded by Ashlesh Sharma, Vidyuth Srinivasan, and Subramanian.
The financial services market is one of the most data-driven industries in the world, yet it's bogged down by legacy CPU technologies that simply can't keep up with the task of querying and visualizing billions of records. In his session at 20th Cloud Expo, Karthik Lalithraj, a Principal Solutions Architect at Kinetica, discussed how the advent of advanced in-database analytics on the GPU makes it possible to run sophisticated data science workloads on the same database that is housing the rich information needed to drive trading decisions. With the unique multi-core architecture of the GPU, financial computations can be processed efficiently and quickly, making it ideal for financial services streaming datasets. He shared how several financial institutions' quantitative science groups are specifically using GPUs to accelerate analytics, deep learning/machine learning, and converging AI and BI. With over 18 years of software experience in a variety of roles and responsibilities, he takes a holistic view at software architecture with special emphasis on helping enterprise IT organizations improve their service availability, application performance and scale.
Sally Gonzales joined Fireman & Company in May as the Toronto-headquartered legal management consultant's newest senior consultant. Recognised as an authority in knowledge management and strategic technology planning, here Gonzales, who has held senior IT and KM roles at firms including Dentons, Norton Rose Fulbright, Akin Gump and Jones Day, tells us what led to her move to Fireman; how KM has evolved in the legal space; the major KM trends ahead; and discusses some of the biggest questions around AI. "I think we are on the cusp of KM 4.0," she says. What led you on this path to Fireman & Company? 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. I recently returned to the US after two years in London working for Norton Rose Fulbright as KM Program Manager for their global enterprise search implementation.
Making AI models at the University of Southern California (USC) Center for AI in Society does not involve a clean, sorted dataset. Sometimes it means interviewing homeless youth in Los Angeles to map human social networks. Sometimes it involves going to Uganda for better conservation of endangered species. "With AI, we are able to reach 70 percent of the youth population in the pilot, compared to about 25 percent in the standard techniques. So AI algorithms are able to reach far more youth in terms of spreading HIV information compared to traditional methods," said Milind Tambe, a professor at the USC Viterbi School of Engineering and cofounder of the Center for AI in Society.
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.
Because IoT devices are deployed in mission-critical environments more than ever before, it's increasingly imperative they be truly smart. In his session at @ThingsExpo, John Crupi, Vice President and Engineering System Architect at Greenwave Systems, will discuss how IoT artificial intelligence (AI) can be carried out via edge analytics and machine learning technologies that enable things to process event data at the source, learn patterns of behavior over time for taking independent action, and deliver more accurate results in real-time. This opens the door to limitless possibilities, enabling businesses to make better decisions with far less effort. Speaker Bio John Crupi is Vice President and Engineering System Architect at Greenwave Systems, where he guides development on the edge-based visual analytics and real-time pattern discovery environment AXON Predict. He has over 25 years of experience executing enterprise systems and advanced visual analytics solutions.