Results


AI in HR: Artificial intelligence to bring out the best in people

#artificialintelligence

Its main AI and HR analytics product is Cornerstone Insights, what CTO Mark Goldin called "machine learning in a box." The dispassionate analysis that AI brought to Expedia's recruiting practices can also be applied to performance management, which Holger Mueller, vice president and principal analyst at Constellation Research, considers talent management's core function -- and the part that's most broken. "The applications of AI basically are analytics applications, where the software is using history and algorithms and data to be smarter and smarter over time," Bersin explained. HR is a good target for AI because many HR practices are "handcrafted," cultural in nature and could be better at handling data, according to Josh Bersin, principal and founder of consulting firm Bersin by Deloitte.


Where Does Automated Customer Benchmarking Make Sense?

#artificialintelligence

A customer benchmarking engine is an emerging technology which uses an artificial intelligence approach to automate the reasoning that underlies data-driven benchmarking. Its benefits are discussed here, there, and elsewhere. Briefly, it uncovers comparative insights on customers which empower customer-focused employees to be more proactive, or which are shown directly to those customers as a premium information service. The business benefits include churn reduction, market differentiation, extra revenue, and deeper customer relationships. But, automated customer benchmarking doesn't always make sense.


How Digital Media Will Bring Out Our Best Selves in the Workplace

#artificialintelligence

Tomorrow's most effective individuals will combine their personal capabilities with customized digital boosters. Technology now touches and transforms every aspect of personal productivity in the workplace. Mobile devices, bots, and digital assistants are ubiquitous, while managers increasingly use key performance indicator (KPI) dashboards to monitor and measure employee performance. In industry after global industry, effectively collaborating with technology is as important as effectively collaborating with people. Continually boosting the value of employees in this environment -- especially knowledge workers -- poses a difficult design challenge.


An Introduction to Machine Learning Theory and Its Applications

#artificialintelligence

And more recently, in 1997, Tom Mitchell gave a "well-posed" definition that has proven more useful to engineering types: "A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E." So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E) and, if it has successfully "learned", it will then do better at predicting future traffic patterns (performance measure P). On this flat screen we can draw you a picture of, at most, a three-dimensional data set, but ML problems commonly deal with data with millions of dimensions, and very complex predictor functions. The goal of ML is never to make "perfect" guesses, because ML deals in domains where there is no such thing. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data.


An AI Shares My Office

#artificialintelligence

The Netflix-produced hit was the result of an algorithm coupled with Netflix's large collection of data on what viewers like to watch. Taking that as inspiration, ad agency McCann Erickson recently added the world's first artificial intelligence (AI)-based creative director to its team in Japan. The memorably named AI-CD β will use data on award-winning commercials to produce ideas for new campaigns. The company isn't the first to let the math do the thinking: In 2014, Hong Kong–based venture capital firm Deep Knowledge Ventures announced a new addition to its board of directors, VITAL, which uses data to vote on potential investments. More recently, Finnish tech company Tieto welcomed Alicia T., an AI complete with a conversational interface, to the board (a win for board diversity?) of its new data-driven business services unit.


An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

#artificialintelligence

Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.


Using Machine Learning Algorithms to Improve Your Business Workflows

#artificialintelligence

For example, when you apply machine learning algorithms to a sales workflow process, the technology is constantly learning from its mistakes and reprogramming itself to improve performance. The next generation of productivity software and machine learning might also include more intelligent document creation tools and processes. There's also the prospect of machine learning that complements traditional customer relationship management and collaboration platforms, helping users better capture and interact with customer data and internal content and saving them the time of searching for content across platforms. Applying machine learning to customer service enables organizations to offer a layer of proactive self-help tools that can provide customers with options to resolve their issues without having to call into the actual customer service department.


Adgorithms Artificial Intelligence Will Change the Way You Think About Your Job

#artificialintelligence

This viewpoint is oversimplified, write Ravin Jesuthasan, Tracy Malcolm, and George Zarkadakis for the Harvard Business Review, as it fails to consider that automation will affect only certain tasks within specific jobs of a given occupation (when evaluated this way, only 9% of jobs are at a high risk of automation). Because AI is changing all industries so rapidly, it may be more useful to measure the value of improved performance in a given position, or Return on Improved Performance, say Jesuthasan, Malcolm, and Zarkadakis. In this instance, a pilot's job description suddenly and dramatically changes, as does the value of her experience and skill. Although it will likely be years before unmanned commercial flights are a reality, commercial airfare is a perfect example of what Thomas H. Davenport and Julia Kirby of the Harvard Business Review call an "opportunity for augmentation."


Are You Ready for Artificial Intelligence Leadership? Centurysoft Blog

#artificialintelligence

With your human boss you always have to ensure that you are in the boss's good books. When you are working under the direction of artificial intelligence you could be working under an interactive conversational assistant who is most capable of providing all of the leadership you need to rely on to be a successful and valued employee. A truly good leader should be there to assist their staff and when artificial intelligence is integrated into the management role of a business it is an intelligent virtual assistant. Nick Gordon is a senior writer at Centurysoft Blog, where he covers topics such as Digital Media, Data Analytics, Chatbots, Artificial Intelligence and Business Intelligence.


An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples

#artificialintelligence

Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.