When is using Artificial Intelligence on data different from Data Science? The more autonomous the algorithm, the more Artificial Intelligence it contains. Basic Artificial Intelligence: Learning a pattern from data with a single Machine Learning algorithm and fixed representation. At this level, the Data Scientist has to try different Machine Learning algorithms, try different tuning parameters, and evaluate different problem features/attributes.Most Data Science efforts are at this level. Advanced Artificial Intelligence: Learning a pattern from data with a meta-learning algorithm applied to the space of Machine Learning algorithms and representations.
I can remember as far back as the 1980s when the birth of the Personal Computer triggered a steep advance in the demand for programmers to develop software for the small machines. There were many attempts at "automated programming" and "code generators" designed to advance the idea of point-and-click software development. It never really took off because the goals weren't realistic, replacing human coders. Something similar is happening today with "automated machine learning" Should data scientists be concerned? A recent Pew Research Center study provided the percentage of U.S. adults who think certain professions will be replaced by robots or computers in their lifetimes showed that 53% of software developers believed that their jobs would be replaced "somewhat or very likely."
Professionals within larger organizations (25,000 employees or more) are significantly more satisfied with their machine learning progress than employees in smaller companies (500 employees or less), according to Algorithmia's 2018 State of Enterprise Machine Learning study released on Tuesday. The report surveyed 523 data science and machine learning professionals to learn how companies of different sizes are using machine learning technologies, said the release. Employees from larger companies were 300% more likely to consider their machine learning efforts "sophisticated" and 80% more likely to be "satisfied" or "very satisfied" with the progression of such efforts, in comparison to smaller companies, added the release. Some 92% of respondents from larger organizations said their organization's investment in machine learning has grown by at least 25% in the past year, said the release. Larger companies have been utilizing machine learning in three main ways: Increasing customer loyalty (59%), increasing customer satisfaction (51%), and interacting with customers (48%), according to the report.
Will data scientists disappear soon? I am asking the question as I see more and more papers about why data scientists may be a parenthesis in history. Latest I read is Will The'Best Job Of 2016' Soon Become Redundant? To his point, there is indeed a number of software and cloud services aiming at automating data science. Marr cites IBM Watson Analytics as a great example of this.
Enterprises of all sizes are looking to leverage machine learning, but not everyone is finding immediate success. A newly released report revealed larger organizations are finding more success compared to smaller ones. The report, the State of Enterprise Machine Learning, by Algorithmia surveyed more than 500 data science and machine learning professionals to gauge how companies are utilizing the technology. The report found data science and machine learning professionals in larger organizations with more than 2,500 employees feel more satisfied with their progress. According to Algorithmia, in larger organizations professionals are 300 percent more likely to consider their deployment model sophisticated, and are 80 percent more likely to say they are satisfied or very satisfied with their progress compared to smaller companies with 500 or less employees.