If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
If you ask, many people will say we are in a new era of higher education, one where machine learning and big data analytics are driving rapid change. From the influx of adaptive learning technologies to the automated student support services and predictive analytics models driving new interventions, there are fewer spaces of college and university life that are not being touched by these technological innovations. These technological opportunities could offer a lot to higher education. Indeed, if we ignore the opportunities that machine learning and big data analytics might provide to complement our human capacities, we will do a disservice to those we claim to serve -- our students. But if we treat them as an opportunity to downsize the work force or largely replace human social interactions with automated ones, we are going to lose a lot more than we gain.
If you just returned from HIMSS this year you most likely hear one of these acronyms every hour you were there. What is the difference between them? If you google these questions you will get all kinds of definitions and explanations. Here are the basics, from the leading text book around the world, Artificial Intelligence: A Modern Approach. To build a generally intelligent agent, you need machine learning in addition to the other aspects mentioned above.
Every time you read a trade journal, an article on LinkedIn or attend a conference you can bet there'll be something about AI and Big Data (it's always capital B and capital D too). It's also probable that many businesses will be able to get along fine without either. However, anyone wanting to profit from these innovations will be finding out exactly how they can assist them. On the one hand, AI will undoubtedly help in processes, transactions and compliance. Machine learning will reduce time, cost and complexity from many arduous jobs within companies, businesses and firms.
JDA software (a leading supply chain software company) has formed a Data Science Lab dedicated to the promise and power of Machine Learning. I spoke with Marie-Claude Cote Head Data Scientist at JDA. She described the lab as a group of data science PhD's and practitioners partnered with supply chain domain experts. Cote cited several examples of where JDA labs has successfully applied or is looking to apply machine learning to a Supply Chain business problem. Identify a problem, decide on an approach, execute a Proof of Concept and when you see that it works you scale the solution to work with all your data at production levels.
Some predict that Artificial Intelligence will drive the next industrial revolution. What is certain is that over the next few years AI will become more important to marketers. But to unlock AI's huge potential you need an AI strategy. Here are five Ps to help you develop yours. If you're interested in marketing applications of AI, Econsultancy's Supercharged conference takes place in London on May 1, 2018 and is chocked full of case studies and advice on how to build out your data science capability.
This post is an initial analysis of opportunities in Artificial Intelligence (AI) as early systems start to come into range of being useful to enterprises other than the big data analytics based businesses like Google and Facebook. To say the sector is overhyped is putting it mildly, but there are some babies among the frothy bathwater, but hopefully we can sort the wheat from the crap. Maybe a better term for AI is "Automated Intelligence" – essentially it is just another wave of (digital) automation, chipping away at "white collar" knowledge work, just at the next level up compared to the previous waves.
LAGUNA NIGUEL, Calif.--For decades, innovators have been holding a candle for the potential of Big Data to one day revolutionize health care. Entrepreneurs have developed ways for people to track their own biological metrics, and for companies to use advances like artificial intelligence to provide more targeted care. Are we finally at the point where data can lead to tangible changes in health care? Or is still an unfulfilled promise? Medical experts at Fortune magazine's Brainstorm Health conference discussed the promise and continued challenges of mining health data for insights.
Although data scientists can gain great insights from large data sets -- and can ultimately use these insights to tackle major challenges -- accomplishing this is much easier said than done. Many such efforts are stymied from the outset, as privacy concerns make it difficult for scientists to access the data they would like to work with. In a paper presented at the IEEE International Conference on Data Science and Advanced Analytics, members of the Data to AI Lab at the MIT Laboratory for Information and Decision Systems (LIDS) Kalyan Veeramachaneni, principal research scientist in LIDS and the Institute for Data, Systems, and Society (IDSS) and co-authors Neha Patki and Roy Wedge describe a machine learning system that automatically creates synthetic data -- with the goal of enabling data science efforts that, due to a lack of access to real data, may have otherwise not left the ground. While the use of authentic data can cause significant privacy concerns, this synthetic data is completely different from that produced by real users -- but can still be used to develop and test data science algorithms and models. "Once we model an entire database, we can sample and recreate a synthetic version of the data that very much looks like the original database, statistically speaking," says Veeramachaneni.
IBM is turning its cognitive computing platform Watson into a new voice assistant for enterprises. The Watson Assistant combines two Watson services -- Watson Conversation and Watson Virtual Agent -- to offer businesses a more advanced conversational experience that can be pre-trained on a range of intents. IBM said the aim was to make Watson Assistant an out-of-the-box, white-label type service that's easier for organizations to embed into their consumer-facing offerings than the Watson API. "Everyone was trying to build an assistant using the API, but building a conversational intelligent assistant from the API alone was really hard," said Bret Greenstein, IBM's global vice president for IoT products. "We realized we needed to build a hosted offering for the types of brands that engage with consumers."