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) …
Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.
Tuomas Sandholm, a computer scientist at Carnegie Mellon University, is not a poker player -- or much of a poker fan, in fact -- but he is fascinated by the game for much the same reason as the great game theorist John von Neumann before him. Von Neumann, who died in 1957, viewed poker as the perfect model for human decision making, for finding the balance between skill and chance that accompanies our every choice. He saw poker as the ultimate strategic challenge, combining as it does not just the mathematical elements of a game like chess but the uniquely human, psychological angles that are more difficult to model precisely -- a view shared years later by Sandholm in his research with artificial intelligence. WHAT I LEFT OUT is a recurring feature in which book authors are invited to share anecdotes and narratives that, for whatever reason, did not make it into their final manuscripts. In this installment, Maria Konnikova shares a story that was left out of "The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win" (Penguin Press). "Poker is the main benchmark and challenge program for games of imperfect information," Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh.
In the same way a smart video doorbell keeps a watchful eye over your home, a smart pool water monitor can help keep tabs on the quality of your precious pool water. It may seem like a trivial gadget to add to your home, but if you've spent any time schlepping water from your pool to the pool store every week, you know how time-consuming it can be. And, during a pandemic-riddled summer when social distancing is still in effect, taking matters into your own hands may just be the way to go. I've tried all sorts of smart gadgets before but nothing like the pHin smart pool water monitor. The pHin smart pool water monitor comes neatly packaged and includes everything you need, including a bridge, to set it up.
Cyberattacks on the likes of several tech giants have brought to the fore the challenge of bridging the skills gap in the cybersecurity space in India. And, artificial intelligence being the latest buzzword of the tech industry, is being touted as one of the key solutions to the cybersecurity skills gap. According to a report, it is estimated that there will be 3.5 million unfilled cybersecurity jobs globally by the year 2021. And therefore, companies are struggling to find adequate qualified people to assist in creating an intelligent cybersecurity framework. The challenge has become apparent in the last five to ten years with a sharp increase in cyberattacks, all the way from ransomware to zero-day malware to now sneaky crypto-mining attacks.
Your smartphone knows when you are at home or the office. At least, mine does, and can even tell me when to leave to get at one of my common destinations on time. We all accept that our smart devices collect information about our preferences and send them over to the cloud for processing. These come back as recommendations for shopping, food, mating, and when to leave the office and head home. What is the magic behind inferring a usual location?
The first day of The Rising 2020 started with an informal session with Sara Hooker, a researcher at Google Brain where she shared some of her personal reflections on how to navigate in the field of machine learning and why we need to celebrate failures as well as success. Sara started her session with a simple story where she shared her childhood dream of being featured in the magazine, The Economist. In fact, she mentioned that "one of my goals was to eventually be an economist." However, when that happened in 2016, it wasn't a pleasing feeling for Sara; instead, it was a feeling of "unease" and seemed problematic. A lot of this could be attributed to the article that The Economist did, which profiled the efforts of fast.ai, a course that's run by Jeremy Howard and Rachel Thomas, and utilised Sara as an example of their success.
The concept is pretty genius. Take a beefy smart lock and a video doorbell and mash them both into a single unit. It's one-stop security shopping for the exterior of your smart home, letting you not only see who comes and goes, but giving you the power to open the door for them, too. While products like Amazon Key let you cobble together a solution like this from a collection of disparate products, the Lockly Vision marks the first time it's been integrated into a single device. Camera aside, Lockly Vision is functionally similar in design to Lockly's other deadbolts, such as the Lockly Secure Pro, giving you myriad ways to open the lock.
If you have difficulty in understanding Bayes' theorem, trust me you are not alone. In this tutorial, I'll help you to cross that bridge step by step. Let's consider Alex and Brenda are two people in your office, When you are working you saw someone walked in front of you, and you didn't notice who is she/he. Now I'll give you extra information, Let's calculate the probabilities with this new information, Probability that Alex is the person passed by is 2/5 i.e, Probability that Brenda is the person passed by is 3/5 i.e, Probabilities that we are calculated before the new information are called Prior, and probabilities that we are calculated after the new information are called Posterior. Consider a scenario where, Alex comes to the office 3 days a week, and Brenda comes to the office 1 day a week.
In October 2019, an Amazon employee in Melbourne, Australia, bumped into another person while cycling on the road. As she was assuring that person that she would help, she realised he was deaf and mute and had no idea what she was saying. That awkward situation could have been avoided if assistive technology was on hand to facilitate communication between the two parties. Following the incident, a team led by Santanu Dutt, head of technology for Southeast Asia at Amazon Web Services, got down to work. Within 10 days or so, Dutt's team had built a machine learning model that was trained on sign languages.
The team's experiments indicated that adaptation in our brain is significantly accelerated with training frequency. "Learning by observing the same image 10 times in a second is as effective as observing the same image 1,000 times in a month," said Sardi, a main contributor to this work. "Repeating the same image speedily enhances adaptation in our brain to seconds rather than tens of minutes. It is possible that learning in our brain is even faster, but beyond our current experimental limitations," added Vardi, another main contributor to the research. Utilization of this newly-discovered, brain-inspired accelerated learning mechanism substantially outperforms commonly-used machine learning algorithms, such as handwritten digit recognition, especially where small datasets are provided for training.