In Figure 1, we see the high-level description of the lifelong UDA approach. Lifelong learning is an iterative process in which the model is updated persistently. Upon training on a source domain with labeled data, the input data is transformed into a multi-modal distribution in the embedding space.

Note: False positive is also known as type one error whereas false negative is known as type two error. Confusion Matrix: The matrix of all combinations of the predicted label and a true label is called a confusion matrix. Precision: Precision is an evaluation metric that reflects the situation and obtained by dividing the number of true positives by the sum of true positives and false positives. Note: To increase precision we must either increase the number of true positives the classifier predicts or negative instances in positive class. F1 Score: Combining precision and recall into a single number.

Computers are crunching more numbers than ever to crack the most complex problems of our time -- how to cure diseases like COVID and cancer, mitigate climate change and more. These and other grand challenges ushered computing into today's exascale era when top performance is often measured in exaflops. An exaflop is a measure of performance for a supercomputer that can calculate at least 1018 or one quintillion floating point operations per second. In exaflop, the exa- prefix means a quintillion, that's a billion billion, or one followed by 18 zeros. Similarly, an exabyte is a memory subsystem packing a quintillion bytes of data.

As artificial intelligence (AI) and machine learning gain momentum, an increasing number of government agencies are considering or starting to use them to improve decision making. Additionally, COVID-19 has suddenly put an emphasis on speed. In these uncharted waters, where the tides continue to shift, it's not surprising that analytics, widely recognized for its problem-solving and predictive prowess, has become an essential navigational tool. Some examples of compelling applications include those that identify tax-evasion patterns, sort through infrastructure data to target bridge inspections, or sift through health and social-service data to prioritize cases for child welfare and support, or predicting the spread of infectious diseases. They enable governments to perform more efficiently, both improving outcomes and keeping costs down.

Businesses that deploy artificial intelligence (AI) can expect a significant boost to their working culture, a new report from MIT Sloan Management Review and Boston Consulting Group claims. Based on a survey of 2,197 managers from 106 countries and 28 industries, and complemented by 18 in-depth interviews with executives, the report states that the majority of firms saw improvements in collective learning, team morale and collaboration. The report also says that cultural benefits also result in financial benefits; a rising tide lifts all boats, as the saying goes. Using artificial intelligence allows companies to re-think effectiveness, as thinking of new business objectives leads to new ways to measure performance, and new behaviors. Businesses that reported AI-powered financial benefits are ten times more likely to change how they measure success.

Digital Marketing is constantly changing and evolving and 2019 was no different. We can see new trends in digital marketing, new products, tools and technologies. Below I'm presenting 20 digital marketing ideas for you and your business and some tips on how to effectively use them to improve your marketing efforts in 2020. Pick and choose ideas that suit you and your business the most for the best results. Creating a website is an obvious thing to do for everybody that is doing digital marketing.

This is the final blog in a four-part series detailing the components necessary for AI success. You can read my earlier posts about cultural willingness, data and infrastructure readiness, and workforce skilling, before or after considering these four steps toward AI ethics, risk and compliance. Successful AI adoption requires forethought and preparation. Although AI itself requires a culture open to experimentation and learning from mistakes, when it comes to ethics, risk, and compliance, you can't simply wing it. Allocating the resources and planning for ethics is often a second thought, met by a certain amount of resistance in many organizations.