We crafted a scalable, cost-effective approach for a new era of learning that puts the spotlight on --learning anytime, anywhere-- through digital technologies.-- With the Accenture Future Talent Platform, the client can now launch new services on its ecommerce site 75--percent faster than previously possible. The program will identify new roles and skills and build a training plan for a pilot, followed by a 40,000-person rollout. Accenture will also develop a curated, interactive curriculum for bank employees. Combining unmatched experience and specialized skills across more than 40 industries and all business functions -- underpinned by the world--s largest delivery network -- Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders.
We've spent so long wringing our hands and worrying about artificial and virtual intelligence that we forgot to roll out the welcome mat when they finally arrived. Now, when major tech companies give their annual keynotes, they can't help but pepper the narrative with phrases like "machine learning." What does it all mean, though? Should we crank up the worry now that it looks like every tent-pole feature of self-learning software could also be a critical flaw? The future is here -- and it's equal parts exciting and terrifying.
J.P. Morgan is one of the most advanced banks when it comes to data science and machine learning. It hired in Geoffrey Zweig from Microsoft in February 2017 as head of machine learning. It's actually launched a market-making product (LOXM) based on machine learning and it recently promoted Samik Chandarana, a former credit trader, to head its data science and analytics (effectively its machine learning) strategies. Chandarana hasn't actually started his new job yet – he's still a trader, but he'll be starting it soon and in an interview posted on J.P. Morgan's Youtube channel, he expresses various opinions about what it will entail. The bottom line, as Saeed Amen explained in a recent blog, is that machine learning and data science jobs in investment banks aren't necessarily as exciting as they seem.
Marketing Mix Modeling refers to statistical methods that attribute product performance to various marketing efforts. In the article below I describe the 10 most difficult challenges my team deals with when tackling these models. In subsequent articles I will discuss the different model choices along with their associated pros/cons. Enjoy the article and please comment at the bottom when you are finished. Below are the top 10 challenges faced by modelers of media mix.
A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead? Yoshua Bengio believes that understanding the basics of AI is within every citizen's reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all.
Microsoft on Wednesday announced new artificial intelligence features and functionality for several of its flagship products and services, including Office 365, Cortana and Bing, at an event in San Francisco. Building on the progress the company has made in integrating AI over the past year, the new enhancements are designed to help users perform increasingly complex and complicated tasks. "AI has come a long way in the ability to find information, but making sense of that information is the real challenge," said Kristina Behr, a partner design and planning program manager with Microsoft's Artificial Intelligence and Research group. One of the advances, machine reading comprehension, will improve an AI-based system's understanding of context -- for example, recognizing that one's cousin is a family member. Bing users will get more personalized answers, Microsoft said, such as restaurant recommendations based on travel destinations, or a greater variety of answers to offer different perspectives on a topic.
Most of the literature in ML that I've come across has been heavily based in either Computer Vision, Robotics1 or NLP. Are there any other lesser known, but really exciting applications of Machine Learning in Electrical Engineering, currently being researched? Some examples would be designing chips, circuits or control algorithms (not robotics based).