If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
If you like the Android TV ecosystem, NVIDIA's Shield TV is a great streaming option if you also want something a bit more powerful than a standard Chromecast. It's an even more compelling pick now that it's down to $129 in an early Black Friday sale -- that's an all-time-low price that has come around before, but not very often. This streaming device earned a spot in our holiday gift guide this year for being one of the more powerful dongles you can get thanks to NVIDIA's Tegra X1 processor. The company claims this chip is 25 percent faster than the X1 that came before it, and in the Shield TV, it's put to good use by helping power an AI-powered HD to 4K upscaler. That means lower-res footage will more closely resemble 4K footage when streamed with the Shield TV.
SK Telecom (SKT) today unveiled its self-developed artificial intelligence (AI) chip named'SAPEON X220' and shared its AI semiconductor business vision. SAPEON X220 is optimally designed to process artificial intelligence tasks faster, using less power by efficiently processing large amounts of data in parallel. Its deep learning computation speed is 6.7 kilo-frames per second, which is 1.5 times faster than that of Graphics Processing Units (GPUs) for inference that are being widely used by AI-service companies. At the same time, it uses 20% less power than GPU by consuming 60 watts of energy and is about half the price of a GPU. SKT explained that SAPEON X220 will enable the provision of high-quality AI services by enhancing the performance of AI data centers through speedy computation of massive amounts of data.
Search is one of the oldest technologies around. Ever since the dawn of the World Wide Web, a search engine has been the portal through which we obtain information. The search for a better search engine index kick started the Hadoop craze, and it continues to drive Google to push the limits of technology. But don't for a second think that search has been solved. "Search is far from being solved. It's the hardest thing we do. It's the hardest thing everybody does."
Think back to the first time you heard the phrase "neural networks" or "neural nets" -- perhaps it's right now -- and try to remember what your first impression was. As an Applied Math and Economics major with a newfound interest in data science and machine learning, I remember thinking that whatever neural networks are, they must be extremely important, really cool, and very complicated. I also remember thinking that a true understanding of neural networks must be on the other side of a thick wall of prerequisite knowledge including neuroscience and graduate mathematics. Through taking a machine learning course with Professor Samuel Watson at Brown, I have learned that three of the previous four statements are true in most cases -- neural nets are extremely important, really cool, and they can be very complicated depending on the architecture of the model. But most importantly, I learned that understanding neural networks requires minimal prerequisite knowledge as long as the information is presented in a logical and digestable way.
Artificial intelligence (AI), the use of human-like intelligence through software and mechanisms, enables the disruption of the most diverse segments. After all, this is an industry that has grown an average of 20% per year for the past 5 years, according to a survey by BBC Research. Many organizations have already joined the "future" and gained space by efficiently applying AI in everyday activities. For example, some banks started to perform financial services without the help of a human; farms use drones capable of identifying points in a crop that need more irrigation and automatically trigger sprinklers. AI is not set to replace the recruiter's work, the importance of the interview, the empathy, and the sparkle in the eye that we sometimes feel when interviewing a candidate.
In today's age of digitalization, keeping up with consumers' health insurance demands while constantly outdoing competition is a complex endeavor. With global health care spending expected to rise at a CAGR of 5% in 2019-23, helping consumers live a healthier life and offer simple and affordable health insurance options is tough. It requires healthcare payers to continually fine-tune their strategies and embrace the wave of technological innovations. Advancements in AI and machine learning are a great way to more efficiently identify at-risk individuals, streamline the insurance process, maximize revenue, and reduce costs. As the health insurance customer gets more and more empowered, healthcare payers need to modernize their approach to communicate and meet their customers' expectations.
Growth is fundamental to our personal and professional lives. It challenges us to become better people and make the most of every day. The idea of developing new soft and hard skills, although overwhelming at first, is much more manageable and achievable with the right structure and guidance. Those looking to embrace growth as we close out 2020, and head into a new calendar year, will want to check out this roundup of eLearning bundles on sale for Black Friday. Everything in this roundup is an additional 70% off for a limited time, which means now is a great time to pick up more skills and hit the ground running in 2021.
Dynamic programming is a method developed by Richard Bellman in 1950s. The main idea behind the dynamic programming is to break a complicated problem into smaller sub-problems in a recursive manner. In computer science and programming, the dynamic programming method is used to solve some optimization problems. The dynamic programming is a general concept and not special to a particular programming language. But, we will do the examples in Python.
Research being conducted by the U.S. Army Combat Capabilities Development Command (DEVCOM) is focused on a new machine learning approach that could improve radar performance in congested environments. Researchers from DEVCOM, Army Research Laboratory, and Virginia Tech have developed an automatic way for radars to operate in congested and limited-spectrum environments created by commercial 4G LTE and future 5G communications systems. The researchers claim they examined how future Department of Defense radar systems will share the spectrum with commercial communications systems. The team used machine learning to learn the behavior of ever-changing interference in the spectrum and find clean spectrum to maximize the radar performance. Once clean spectrum is identified, waveforms can be modified to best fit into the spectrum.
Autonomous a2z, a member company of Born2Global Centre and Sejong Technopark, has recently received a seed investment of US$1.9 million from angel investments by individuals and partner corporations. Autonomous a2z is a company specializing in autonomous mobility solutions. The startup announced that it has begun commercializing an ongoing project. It also stated that the infusion of capital is already serving as a stepping stone for future development of autonomous mobility solutions and for assuming a leading position in relevant markets. Founded in 2018, Autonomous a2z has advanced its cutting-edge technologies to test self-driving technologies throughout Korea. As implied by its name, the firm develops "everything from a to z" in the arena of self-driving cars, including its own systems and algorithms.