Artificial Intelligence (AI) for social good is a field of work which, broadly speaking, uses AI to make the world a better place. I had a chance to interview two leaders in the field, Dr. Bryan Wilder, who recently received his Ph.D. from Harvard (and will be joining the faculty at Carnegie Mellon next fall) and current Harvard Ph.D. student, Lily Xu. Both Bryan and Lily have been advised by Dr. Tambe, Gordon McKay Professor of Computer Science and Director of the Center for Research in Computation and Society (CRCS) at Harvard University and Director of AI for Social Good at Google Research India. While Bryan and Lily are both working at the intersection of AI and social good, they arrived at this junction via different paths. Bryan was studying computer science and looking for a field to apply his knowledge; his search led him to public health.
One of the reasons I decided to learn data science is the power of the word Machine Learning. When I started my research on what type of data science/software engineering course I should take, I encountered many cases where data science and machine learning were thrown together. This gave me a question "Why do websites use them together so often, and how similar are those two fields?" I mean, it was evident that those two were related in some part, but as a new kid on the block who just started to get interested in data science, it took a while for me to understand the difference between the two. So what is the difference?
The latest ZDNet survey on AI actionability and accountability finds that IT teams are taking a direct lead, with most companies building in-house systems. However, oversight of AI-generated decisions is lagging. Can businesses trust decisions that artificial intelligence and machine learning are churning out in increasingly larger numbers? Those decisions need more checks and balances -- IT leaders and professionals have to ensure that AI is as fair, unbiased, and as accurate as possible. This means more training and greater investments in data platforms.
Can businesses trust decisions that artificial intelligence and machine learning are churning out in increasingly larger numbers? Those decisions need more checks and balances -- IT leaders and professionals have to ensure that AI is as fair, unbiased, and as accurate as possible. This means more training and greater investments in data platforms. A new survey of IT executives conducted by ZDNet found that companies need more data engineers, data scientists, and developers to deliver on these goals. The survey confirmed that AI and ML initiatives are front and center at most enterprises.
Although data engineers and data scientists have overlapping skill sets, they fulfill different roles within the fields of big data and AI system development. Data scientists develop analytical models, while data engineers deploy those models in production. As such, data scientists focus primarily on analytics, and data engineers focus more heavily on programming. To launch your data career, you'll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard's Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship.
Can robots and workers co-exist? Workers, policymakers, and the media are concerned with the idea that automation, or technological change, will displace millions of American workers--and they are partially right. Andrew Yang, an early 2020 Presidential hopeful is already running on the idea that "the robots are coming" – though the story is not so simple. There have been, and will continue to be, technological breakthroughs that replace workers and reshape our economy. The next big worker-displacing technology is supposedly artificial intelligence (AI), which is thought to have the potential to replace millions of workers performing routine and menial tasks.
Get well versed with Machine learning and AI by working on Hands-on Projects. Do you feel overwhelmed going through all the AI and Machine learning study materials? These Machine learning and AI projects will get you started with the implementation of a few very interesting projects from scratch. The first one, a Web application for Object Identification will teach you to deploy a simple machine learning application. The second one, Dog Breed Prediction will help you building & optimizing a model for dog breed prediction among 120 breeds of dogs.
The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Databases have always been able to do simple, clerical work like finding particular records that match some given criteria -- say, all users who are between 20 and 30 years old. Lately database companies have been adding artificial intelligence routines into databases so the users can explore the power of these smarter, more sophisticated algorithms on their own data stored in the database. The AI algorithms are also finding a home below the surface, where the AI routines help optimize internal tasks like re-indexing or query planning. These new features are often billed as adding automation because they relieve the user of housekeeping work.
A technology-led revolution, dubbed Industry 4.0, is gathering pace in the industrial world where traditional processes and legacy technologies are being replaced by smart devices, automated machines and advanced forms of computing. The rise of Cyber Physical Systems (CPS), owing to exponential growth in technologies like the Internet of Things (IoT), artificial intelligence (AI), cloud, robots, drones, sensors, etc., is helping manufacturers improve efficiencies, productivity and the autonomous operation of production lines. Businesses are pouring billions of dollars in AI and automation, and the Industrial IoT (IIoT) alone is set to become a $500 billion market by 2025. IT/OT convergence could spell disaster for industries. As smart factories and supply chains connect the production line to the outside world via IIoT, digitally connected industries are becoming increasingly appealing to cybercriminals, who now have the opportunity to hijack high-value targets.
Machine learning and AI can transform unstructured dark data into valuable business insights. Learn how to process dark data and use the information to your advantage. To compete in modern digital environments, machine learning, deep learning and AI are increasingly accessible. By using machine learning and AI, companies can use dark data to acquire more competitive business insights. Dark data consists millions of unstructured data points that businesses accrue and store in multiformat data lakes.