There are well-documented examples of AI systems making decisions that affect protected classes, such as housing assistance or unemployment benefits. AI can be used to screen resumes; banks apply AI models to grant individual consumers credit and set interest rates for them. Many small decisions, taken together, can have large effects, such as: AI-driven price discrimination could lead to certain groups in a society consistently paying more. But are there AI applications today that affect everyone, no matter their "class"? As I mentioned earlier, we are shifting our AI Ethics courses to more practical, useful techniques.
AI has the potential to improve human lives and a company's bottom line, but it can also accelerate inequality and eliminate jobs during the worst U.S. recession since the Great Depression. This dual promise and peril led members of the House Budget Committee to hold a hearing today to discuss the impact of AI on economic recovery, the future of work, and the federal budget. Expert witnesses recommended approaches that ranged from giving people lifelong upskilling accounts to creating regional investment districts and portable benefits. Daron Acemoglu warned the committee about the dangers of excessive automation. The MIT professor and economist recently found that every robot replaces 3.3 human jobs in the U.S. In a working paper published by the National Bureau of Economic Research, Acemoglu detailed how excessive automation looks for ways to replace workers with machines or algorithms but produces few new jobs.
The Labor Department reported Friday unemployment fell to 10.2% as the economy added 1.8 million jobs, fewer than the number added in the two preceding months likely due to an upsurges in coronavirus cases. The Bureau of Labor statistics credited an improving economy as pandemic restrictions were eased. It was the lowest level since the coronavirus pandemic induced a recession in February. The Labor Department, however, admitted data collection may be flawed because of its classification methods, and some people marked as on temporary layoff should be classified as unemployed. The misclassification for June and July was believed smaller than those for previous months.
From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace. Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout.
TS may look like a simple data object and easy to deal with, but the reality is that for someone new it can be a daunting task just to prepare the dataset before the actual fun stuff can begin. Every single time series (TS) data is loaded with information; and time series analysis (TSA) is the process of unpacking all of that. However, to unlock this potential, data needs to be prepared and formatted appropriately before putting it through the analytics pipeline. TS may look like a simple data object and easy to deal with, but the reality is that for someone new it can be a daunting task just to prepare the dataset before the actual fun stuff can begin. So in this article we will talk about some simple tips and tricks for getting the analysis-ready data to potentially save many hours of one's productive time.
For about a decade, we have heard rumors that a new generation of automated technologies has learned to do our jobs. If these tech prophecies were true, robots and algorithms should have been ready to step in during the lockdowns and finally prove that they can work more safely, cheaply, and efficiently than we can. But when COVID-19 raised the curtain on automation, people stepped into the spotlight. As the lockdowns begin to end, we must remember that today's crisis is not about automation. It's about how we value and protect the people whose labor sustains the world.
Artificial intelligence salaries benefit from the perfect recipe for a sweet paycheck: a hot field and high demand for scarce talent. It's the ever-reliable law of supply and demand, and right now, anything artificial intelligence-related is in very high demand. According to Indeed.com, the average IT salary -- the keyword is "artificial intelligence engineer" -- in the San Francisco area ranges from approximately $134,135 per year for "software engineer" to $169,930 per year for "machine learning engineer." However, it can go much higher if you have the credentials firms need. One tenured professor was offered triple his $180,000 salary to join Google, which he declined for a different teaching position.
We use machine learning methods to examine the power of Treasury term spreads and other financial market and macroeconomic variables to forecast US recessions, vis-à-vis probit regression. In particular we propose a novel strategy for conducting cross-validation on classifiers trained with macro/financial panel data of low frequency and compare the results to those obtained from standard k-folds cross-validation. Consistent with the existing literature we find that, in the time series setting, forecast accuracy estimates derived from k-folds are biased optimistically, and cross-validation strategies which eliminate data "peeking" produce lower, and perhaps more realistic, estimates of forecast accuracy. That is, while a k-folds cross-validation indicates tha t the forecast accuracy of tree methods dominates that of neural networks, which in turn dominates that of probit regression, the more conservative cross-validation strategy we propose indicates the exact opposite, and that probit regression should be preferred over machine learning methods, at least in the context of the present problem. This latter result stands in contrast to a growing body of literature demonstrating that machine learning methods outperform many alternative classification algorithms and we discuss some possible reasons for our result.
Perceiving the pandemics' hard reset as a chance to grow stronger, more resilient, and resourceful dominates manufacturers' mindsets who continue to double down on analytics and AI-driven pilots. Combining human experience, insight, and AI techniques, they're discovering new ways to differentiate themselves while driving down costs and protecting margins. And they're all up for the challenge of continuing to grow in tough economic times. Boston Consulting Group's recent study The Rise of the AI-Powered Company in the Postcrisis World found that in the four previous global economic downturns, 14% of companies were able to increase both sales growth and profit margins as the following graphic shows: AI Is Core To Manufacturing's Real-Time Future Real-time monitoring provides many benefits, including troubleshooting production bottlenecks, tracking scrap rates, meeting customer delivery dates, and more. It's an excellent source of contextually relevant data that can be used for training machine learning models.