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) …
"Data is the oil of the digital era," proclaims a 2017 Economist article. Big business--especially tech giants like Alphabet (Google's parent), Amazon, Facebook, and Apple among others-- are mining data like Standard Oil processed petroleum a century before. Why is the legal industry still running on gut and instinct while the businesses it serves are propelled by data? A recent survey by business analytics powerhouse RELX Group polled 1,000 U.S. senior executives across the health care, insurance, legal, science, banking industries as well as government. Law finished last among industries--just ahead of government--in utilizing big data in some form.
In an industry that is experiencing a steady rate of job creation, data science itself has moved from just a buzzword to a strategic component in organisations. In addition to this, data scientists are increasingly taking on more strategic roles as organisations employ a product-centric view of data. It is a field that promises tremendous job growth and higher earning potential. Our latest research posits 97,000 jobs are available in this buzzing field. On the hiring end, there is a significant overall growth in jobs in the field.
One can't read any news today without a barrage of articles about data science and machine learning and artificial intelligence. To better assess all of this talk and hype, I recently had the opportunity to sit down with Brian Sampsel, VP and Chief Analytics Innovator for the Columbus Collaboratory. Brian, when you look at all of this talk about machine learning, how are companies responding? Brian Sampsel: Given all the talk and hype, it is surprising to find that so many large enterprises are doing things the way they have always been done, especially as it pertains to back office functions. One glaring example of this is forecasting.
Pharmaceuticals and life sciences companies are experiencing a wave of competing challenges as part of what could be called the New Health Economy. They include consolidation among providers, especially hospitals, intended to produce efficiency gains; the changing demands and expectations of patients, who seek a greater role in their own care; increasing cost pressures from payors leading to calls for pricing reform; and the declining autonomy of the individual physician as rule-based, protocol-driven care becomes ascendant.
There's a significant issue with big data today: fully using it requires data scientists who are expensive and often a bottleneck on data utility. Big data is complex, and it takes specialist knowledge and skills to get a grip on the potential uses. For big data to fully reach its potential, businesses need to get beyond the data scientist. This relief is coming in the form of artificial intelligence. While AI won't replace the data scientist any time soon, it can assume some of the tasks currently handled by data scientists.
Across industries, Big Data and Artificial Intelligence (AI) have proven to be powerful tools when it comes to informing companies about their target customers. Gartner predicts that by 2019, more than 50% of organizations will redirect their investments to customer experience innovations. As a result, many organizations have built teams to collect and analyze data on every step of the customer journey – taking into account where, why and how customers interact with their channels. By analyzing this data in real time, companies are able to keep up with evolving customer demands. Dissecting every interaction to understand what drives customer behavior may seem like a gargantuan task for many.
We expect the landscape to be an integrated edge-to-core-to-cloud solution enabling what today is called IoT, Big Data, Fast Data and AI. Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything--until we reconcile how that technology fits into the bigger picture. Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring.
May Masoud is a Solution Specialist at SAS Canada, as part of the Data Sciences team. Leveraging her analytics background, she helps businesses visualize the potential of their data, and surface insights using modern data mining and machine learning techniques. With a Master of Business Analytics following a Bachelor in Statistics & Economics, May aims to create value at every step of the analytics lifecycle: data discovery, model build, model deployment, and business strategy. She has touched the analytics landscape in a variety of industries, whether it is oil production models for the energy sector or solving churn problems in the telecom industry. May's aim is to ubiquitize self-serve analytics and enable citizen data scientists.
This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. As a professional computer scientist and statistician, I hope to shed some useful light on the topic. I have potential bias: I've written four R-related books, I've given a keynote talk at useR!; I currently serve as Editor-in-Chief of the R Journal; etc. But I hope this analysis will be considered fair and helpful. This is subjective, of course, but having written (and taught) in many different programming languages, I really appreciate Python's greatly reduced use of parentheses and braces: This is of particular interest to me, as an educator.
Three years ago, if you told me that one day I would use python to analyze AI policy and make Guido van Rossum chuckle, I would think you are crazy. Three years later at PyCon 2019 in Cleveland, that's exactly what happened. I was by no means a tech person. I was trained as an economist (read: stats nerd), but somehow for the past three years I've been writing analysis on deep-tech fields including AI and 5G. What I hope to achieve with this post is not #humblebrag (ok, maybe a little happy dance) but to share with you all the struggles I had and am still experiencing on a daily basis and to reassure a fellow researcher somewhere feeling that he/she is faking it all the time, you are not alone.