A mission of Buoy, one of the latest startups to attack the smart home, is "to enjoy water without worry." For the vast majority of Americans, that seems like a solution in search of a problem. Water usage may be the least top-of-mind concern of the modern US homeowner, so much so that it is often held up as a model of a nearly-free commoditized modern convenience. But that may be changing. A Michigan State study this year has found that water prices have risen 41 percent since 2010 and are set to rise dramatically in the next five years.
Data scientists and machine learning engineers in India make about one-tenth of what their counterparts in the United States do, a leading global survey shows. The median annual salary in India, based on 450 responses, is $11,715 (Rs 7.5 lakhs), a fraction of the comparable annual earnings in the US ($110,000). The median for all respondents from 52 countries, whose data was considered in the calculations, is $55,441. Kaggle, the world's largest global online community of data scientists, statisticians and machine learning engineers, published its The State of Data Science & Machine Learning annual survey earlier this week, deriving insights on 16,000 respondents in a report that polled the data science and machine learning industry. The Google-owned platform currently boasts of over a million members and is known to attract the world's smartest data scientists by holding public and private data science competitions.
Quantitative research has been defined in various ways. Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to predict or explain a particular phenomenon. In marketing research, "quant" historically has meant consumer surveys. Analysis of consumer survey data has typically been limited to reporting numbers, perhaps broken down by age group, gender and a few other respondent groups of interest.
There is massive excitement -- and some fear -- about AI. The buzz is definitely building, as more and more people start talking about it. But although there is enthusiasm about the potential of AI and its building blocks, there is much less discussion about how organisations should start to invest in AI. Few are talking about their experience of AI, even though we know that there are plenty of organisations out there who are already dipping their toes in the water. We decided to fill this gap in our recent study on enterprise readiness for AI.
Artificial Intelligence (AI) and automation are already improving the capabilities and productivity of the CFO and the Finance team. But what impacts will these advanced approaches have on your organization in the future? From July to August 2017, we surveyed 200 accounting professionals to find out what they know, hope for and love about one of the hottest topics in Finance today.
While there is nearly universal agreement that artificial intelligence offers the promise of revolutionary benefits, recent survey findings from Gartner reveal almost 60 percent of organizations surveyed have yet to take advantage of the benefits of AI. Perhaps even more surprisingly, only a little more than 10 percent of surveyed businesses have deployed or implemented any AI solution at all. Based on the survey, there appears to be a gap between AI's promise and the ability for an enterprise to implement it. A further confirmation of that point is the finding that close to half of the surveyed organizations state they prefer to buy pre-packaged AI solutions or use AI capabilities already embedded in their applications. This shouldn't be a surprise as end-user organizations are looking to use AI to help better solve business problems.
Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses.
Like it or not, it appears that the continuing skills gap that continues to plague many sections of the software world, including development, testing and more, has found a new victim: digital transformation through the use of machine learning. A survey conducted by ServiceNow looked at the eagerness of organizations to incorporate machine learning as part of their digital transformation. Mainly, senior executives want to buy into machine learning in order to support faster and more accurate decision making. But the survey polled some interesting numbers that point to what appears to be a significant lack of machine learning skills needed to manage intelligent machines within organizations. The report shows that 72% of CIOs surveyed said they are leading their company's digitalization efforts, and just over half agree that machine learning plays a critical role in that.
The Mozilla Foundation has released the results of a new study which claims that while consumers are ready to use mobile and Internet of Things (IoT) devices, users remain divided in opinion over a connected future. On Wednesday, Mozilla revealed the results of the survey, of which 190,000 people from dozens of countries participated. The research found that the more tech savvy people are, the more optimistic they feel about a connected future but the loss of privacy worries citizens worldwide. According to the research, respondents who identified themselves as proficient with technology were the most likely to be optimistic about a connected future, while those who considered themselves least proficient -- 31 percent in total -- said they were "scared as hell" of the idea. Respondents in India, Mexico, and Brazil stood out as being the most optimistic about the benefits of connected technologies, while those in Belgium, France, the UK, Switzerland, and the US were more likely to express concerns.
The majority of employed data scientists gained their skills through self-learning or a Massive Open Online Course (MOOC) rather than a traditional computer science degree, according to a survey from data scientist community Kaggle, which was acquired by Google Cloud earlier this year. Some 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% said that they began picking up the needed skills on their own, the 2017 State of Data Science & Machine Learning Survey report found. Some 30% got their start in data science at a university, according to the survey of more than 16,000 people in the field. More than half of currently employed data scientists still use MOOCs for ongoing education and skillbuilding, the report found, demonstrating the potential of these courses for helping people gain real world skills. Data scientist took the no. 1 spot in Glassdoor's Best Jobs in America list in 2016 and 2017, and reports a median base salary of $110,000.