Quantitative technology forecasting uses quantitative methods to understand and project technological changes. It is a broad field encompassing many different techniques and has been applied to a vast range of technologies. A widely used approach in this field is trend extrapolation. Based on the publications available to us, there has been little or no attempt made to systematically review the empirical evidence on quantitative trend extrapolation techniques. This study attempts to close this gap by conducting a systematic review of technology forecasting literature addressing the application of quantitative trend extrapolation techniques. We identified 25 studies relevant to the objective of this research and classified the techniques used in the studies into different categories, among which growth curves and time series methods were shown to remain popular over the past decade, while newer methods, such as machine learning-based hybrid models, have emerged in recent years. As more effort and evidence are needed to determine if hybrid models are superior to traditional methods, we expect to see a growing trend in the development and application of hybrid models to technology forecasting.

Indoor positioning technologies are one of the driving forces behind the digital transformation of the industrial sector. The ability to track objects, assets and people accurately and cheaply could save resources, time and money for companies in various sectors, from logistics to health care. Xavier Vilajosana, the Professor in the Faculty of Computer Science, Multimedia and Telecommunications and leader of the Wireless Networks group (WINE) at the Internet Interdisciplinary Institute (IN3) at the UOC (Universitat Oberta de Catalunya) is coordinating the university's participation in a new European project which is developing innovative solutions to improve location in indoor environments. DUNE uses deep learning techniques combined with distributed computing systems, which take advantage of both cloud and edge computing. In other words, these are computing architectures that operate both on remote servers and near where the data are generated.

You have heard that AI can be useful in various industries to do tasks. AI is a group of many different technologies working together to enable machines to sense, act and learn with human-like levels of intelligence. Maybe that's why it seems the definition of artificial intelligence is different. Meanwhile, technologies like machine learning and natural language processing are all parts of artificial intelligence. Each one is revolving along its own path.

As a teenager, I discovered a worn copy of the book Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter on a bookshelf at home. It still had a computer punch card in it that my Mom had used as a bookmark, back when she briefly worked as a programmer in the early 1980s. Reading that book was like falling into another world. I found myself thinking about the mind and computers in brand new ways. I learned about Alan Turing's work for the first time.

Cerebras Systems said today that the company has achieved the computational equivalent of the human brain, or the equivalent of 100 trillion synapses. Cerebras manufactures what it calls the Wafer Scale Engine-1 and -2, a massive 46,225 sq. The company essentially mounts that chip inside of a standalone CS-2 system, about the size of a dorm refrigerator. Now, the company says that it's been able to surround the CS-2 with several different technologies to enable the brain-scale computational power to reach 120 trillion synapse equivalents, also called parameters. Cerebras isn't alone in trying to model machine learning at the chip level, in an effort to duplicate how the human brain works.

Research and development in new technologies for migration management are rapidly increasing. To quote certain migration examples, big data was used to predict population movements in the Mediterranean, AI lie detectors used at the European border, and the recent one is the government of Canada using automated decision-making in immigration and refugee applications. Artificial intelligence in migration is helping countries to manage international migration. Every corner of the world is encountering an unprecedented number of challenging migration crises. As an increasing number of people are interacting with immigration and refugee determination systems, nations are taking a stab at artificial intelligence. AI in global immigration is helping countries to automate a plethora of decisions that are made almost daily as people want to cross borders and look for new homes.

When Charles Rosen, the A.I. pioneer who founded SRI International's Artificial Intelligence Center, was asked to come up with a name for the world's first general -purpose mobile robot, he thought for a moment and then said: "Well, it shakes like hell when it moves. Let's just call it Shakey." Some variation of this idea has pervaded for much of the history of modern robotics. Robots, we often assume, are clunky machines with as much grace as an atheist's Sunday lunch. Even science fiction movies have repeatedly imagined robots as ungainly creations that walk with slow, halting steps. Recently, a group of researchers from the Dynamic Robotics Laboratory at Oregon State took one of the university's Cassie robots, a pair of walking robot legs that resembles the lower extremities of an ostrich, to a sports field to try out the lab's latest "bipedal gait" algorithms.

From drones for food delivery and robots for automation to COVID-19 contact tracing apps, and online education learning platforms, we've seen a great acceleration in adoption of different technologies in the past few months. Technology has been a great pillar of strength during the pandemic and it's also going to help redefine the post COVID-19 world. Now, different businesses and industries will benefit from different technologies, but there are some common ones that are likely to dominate the world after COVID-19. Nuff said, let's take a look at some of the tech trends that are likely to see a surge in adoption post COVID-19. We know this one's too obvious but that's for a reason - AI is playing a massive role in helping us all get through the pandemic and it will see a greater adoption after the pandemic is over.

Developing artificial intelligence expertise is a challenge. There's a huge global demand for practitioners with the right skills and knowledge and a lack of people who can actually deliver what's needed. It's difficult because many of the most talented engineers are being hired by the planet's leading tech companies on salaries that simply aren't realistic for many organizations. Ultimately, you have two options: form an in-house artificial intelligence development team or choose an external software development team or consultant with proven artificial intelligence expertise. Let's take a closer look at each strategy. If you want to develop your own AI capabilities, you will need to bring in strong technical skills in machine learning.

A chat with AI instructor Chris Mohritz Christopher Mohritz is a lifelong entrepreneur and technologist with a number of successful businesses under his belt; bringing a unique blend of technology know-how coupled with creative thinking and business acumen to each of his projects. Since 2009, Chris has been building and leveraging artificial intelligence systems to cognify a wide range of business functions -- marketing, sales, customer support and decision automation to name a few. And over the past five years, he has been building and operating a business accelerator for web/mobile startups, helping other entrepreneurs launch exceptional "AI-first" businesses. Chris draws heavily from a deep background in technology -- from operating nuclear reactors in the U.S. Navy to designing datacenters at Lockheed Martin. Complemented by a broad range of business experience -- from technical sales for the Fortune 500 to project management in the public sector.