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6 Design Principles for Artificial Intelligence in Digital Business
Artificial intelligence (AI) can augment or automate decisions and tasks today performed by humans, making it indispensable for digital business transformation.
“To overcome this hurdle, CIOs must ensure that applications intended to serve a strategic business purpose, such as increasing revenue or scaling services, are designed for strategic plans,” says Jorge Lopez, Distinguished Vice President Analyst, Gartner.
AI generates insights that lead directly to business execution Lopez outlines six design principles that will help CIOs and organizations evaluate all proposed AI applications with strategic intent — that is, applications intended to help achieve business results, not just operational improvements.
A strategic AI application can produce granular insights into what customers, markets or other entities are likely to do in specific future situations and what the enterprise can do to influence them.
A strategic AI application that acts autonomously can operate without human direction, producing significant productivity gains as it augments the work done by humans and frees them for more personalized tasks.
When designing AI applications for autonomous operations, ensure the AI applications are located as close as possible to the work being done, have near-real-time understanding of what’s going on and have the intelligence to make decisions on the spot.
Examples of Artificial Intelligence in Education
Content Technologies, Inc., an artificial intelligence development company specializing in automation of business processes and intelligent instruction design, has created a suite of smart content services for secondary education and beyond. Cram101, for example, uses AI to help disseminate and breakdown textbook content into digestible “smart”
Learning platforms for the modern workplace are designed to allow employees to master additional skills and receive continuous and automated feedback, and when used strategically have the potential to help improve performance and increase production.
software, for example, uses cognitive science and AI technologies to provide personalized tutoring and real-time feedback for post-secondary education students, particularly incoming college freshman who would otherwise need remedial courses.
Carnegie Learning’s Mika platform Pearson, in collaboration with University College London Knowledge Lab, notes that today’s model-based adaptive systems are also increasingly transparent, allowing educators to understand how a system arrived at a next-step decision and rendering them more effective tools for classroom teaching.
For example, the iTalk2Learn system16, a system engineered and tested by Carnegie Mellon University to assess its effects on young students learning fractions, applied a learner model that explicitly included information about an individual’s mathematics knowledge, cognitive needs, emotional state, as well as feedback received and the students’
While it seems obvious that no one in education is eager for virtual humans to come and replace educators, the idea of creating virtual human guides and facilitators for use in a variety of educational and therapeutic environments is a promising area of development.
SimCoach Prototype Captivating Virtual Instruction for Training (CVIT), for example, is a distributed learning strategy that aims to integrate live classroom methods with best-fit virtual technologies—including virtual facilitators, augmented reality, intelligent tutor, and others—in remote learning and training programs. It’s worth visiting USC’s Creative Technologies prototype page and exploring the many other initiatives currently under development, from immersive training counseling for Army leaders to a personal assistant (PAL3) for life-long learning.
AI’s ability to analyze large amounts of data in real-time (a student’s performance in a particular skill across subjects over the course of a year, for example) and automatically provide new content or specified learning parameters, helps meet students’
Woolf, et al., (2013) has proposed five key areas for ongoing researching in educating using AI: The above seems a useful framework for framing objectives and generating aligned ideas, as researchers and companies continue to move forward in developing AI applications in education.
How Artificial Intelligence Will Transform Business
Many people still associate artificial intelligence with science fiction dystopias, but that characterization is waning as artificial intelligence develops and becomes more commonplace in our daily lives.
The modern field of artificial intelligence came into existence in 1956, but it took decades of work to make significant progress toward developing an artificial intelligence system and making it a technological reality.
'Artificial intelligence' is a broad and general term that refers to any type of computer software that engages in humanlike activities, including learning, planning and problem-solving.
Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the internet of things – into a digestible context for humans.
If a machine in the manufacturing plant is working at a reduced capacity, a machine learning algorithm can catch it and notify decision-makers that it's time to dispatch a preventive maintenance team.
The development of artificial neural networks, an interconnected web of artificial intelligence 'nodes,' has given rise to what is known as 'deep learning.'
Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in 5-10 seconds.
Older machine learning algorithms tend to plateau in their capability once a certain amount of data has been captured, but deep learning models continue to improve their performance as more data is received.
Although artificial intelligence currently has a difficult time completing commonsense tasks in the real world, it is adept at processing and analyzing troves of data far more quickly than a human brain could.
Those traits make artificial intelligence highly valuable throughout many industries, whether it's simply helping visitors and staff make their way around a corporate campus efficiently or performing a task as complex as monitoring a wind turbine to predict when it will need repairs.
The troves of data are then contextualized by machine learning algorithms and delivered to human decision-makers to better understand energy usage and maintenance demands.
Dr. Hossein Rahnama, founder and CEO of artificial intelligence concierge company Flybits and visiting professor at the Massachusetts Institute of Technology, worked with TD Bank to integrate artificial intelligence into regular banking operations, such as mortgage loans.
'Using this technology, if you have a mortgage with the bank and it's up for renewal in 90 days or less … if you're walking by a branch, you get a personalized message inviting you to go to the branch and renew purchase,' Rahnama said.
'What's going to happen now with artificial intelligence and a combination of [the internet of things] is that the display won't be the main interface – the environment will be.
Sounds like a job for augmented reality.] With all these new artificial intelligence use cases comes the daunting question of whether machines will force humans into obsolescence.
The jury is still out: Some experts vehemently deny that artificial intelligence will automate so many jobs that millions of people find themselves unemployed, while other experts see it as a pressing problem.
It might be a little bit theoretical, but I think if you have to worry about artificial intelligence and robots replacing our jobs, it's probably algorithms replacing white-collar jobs such as business analysts, hedge fund managers and lawyers.'
[For example,] a tax accountant won't one day receive a pink slip and meet the robot that is now going to sit at her desk.
Husain pointed to self-driving trucks and artificial intelligence concierges like Siri and Cortana as examples, stating that as these technologies improve, widespread use could eliminate as many as 8 million jobs in the U.S. alone.
As this technology develops, the world will see new startups, numerous business applications and consumer uses, as well as the displacement of certain jobs and the creation of entirely new ones.
Artificial Intelligence and Legal Drafting
In this brief note, we discuss efforts to develop a foundation in mathematics and logic for artificial intelligence (AI) applications used in legal drafting.
The answer is that as we proceed into the 4th Industrial Revolution, the convergence of technologies, including mathematics, logic, AI, data science, and computer science, among others, is changing the way we practice law generally, and in particular the way we draft legal documents.
We have a choice: We can embrace the new technologies and develop best practices, or we can watch others do so and risk being left behind and losing market share.
Applying the New Logic of the Law to Legal Drafting To illustrate the practical application of the “mathematics of ideas” and logic to the law, I wrote four brief articles where I convert various types of legal writing to a data format, where the ideas in the legal document are stored as fields of data in a Boolean Lattice: Why Convert Contracts to Data?
For example, we can create software programs that summarize, report on, and even provide preliminary advice at the idea level regarding the contents of contracts in a large portfolio.
- On Monday, August 19, 2019
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