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) …
Data science has made key contributions in the battle against COVID-19, from tracking cases and deaths to understanding how populations move during travel restrictions to vaccine design. The Harvard Data Science Initiative is working to support faculty members, students, and fellows in designing and applying the tools of statistics and computer science and creating a community to foster the flow of ideas. The year-old Harvard Data Science Review published a special issue online this summer dedicated to COVID-19 that will be updated with the latest findings, with a goal of fostering innovation and keeping the conversation going about how data science can help meet the COVID-19 challenge. The Gazette spoke with Francesca Dominici, Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health and co-director of the initiative, and Xiao-Li Meng, the review's editor in chief and the Whipple V.N. Jones Professor of Statistics in the Faculty of Arts and Sciences, about how data science can be used to meet today's challenges, and in turn, challenges facing the field. GAZETTE: How is data science important to our understanding of and response to COVID-19? DOMINICI: Data science is on the front page of The New York Times probably every single day.
TLDR; The Azure ML Python SDK enables Data scientists, AI engineers,and MLOps developers to be productive in the cloud. This post highlights 10 examples every cloud AI developer should know, to be successful with Azure ML. If you are new to Azure you can get a free subscription using the link below. The scripts in this example are used to classify iris flower images to build a machine learning model based on scikit-learn's iris dataset the code can easily be adapted to any scikit-learn estimator. This example shows you how to run your TensorFlow training scripts at scale using Azure Machine Learning's TensorFlow estimator class.
The recruitment process has come a long way since the days of paper CVs. Thanks to a decade-long digital transformation, online job sites, virtual portfolios, and even Skype interviews are now staples in global talent acquisition, but could artificial intelligence (AI) elevate the hiring landscape and take the recruitment process one step further? AI has become somewhat of a buzzword lately. When we think of AI, we often think of human-like robots which can mimic our behaviour (and potentially take over the world someday). However, although artificially intelligent robots do exist, the term AI typically applies to any self-learning machine that can analyse data and provide insights that make us smarter, more efficient and better at the things we do every day.
Algorithms can search the game trees to determine the best move to make from the current state. The most well known is called the Minimax algorithm. The minimax algorithm is a useful method for simple two-player games. It is a method for selecting the best move given an alternating game where each player opposes the other working toward a mutually exclusive goal. Each player knows the moves that are possible given a current game state, so for each move, all subsequent moves can be discovered.
Bottom Line: Dexcom and Micron adopting a single AI platform for talent management that adapts to their specific HR strategies and provides new insights is delivering significant results. AI-based platforms provide new insights, intelligence and guidance to CHROs and HR leaders, helping them close the growing talent gaps their organizations face. By integrating hiring, internal mobility, diversity & inclusion, contingent workforces, training & development and performance management all on a single AI platform, HR leaders gain greater insights into closing talent gaps. And it's encouraging to see how AI platforms evaluate candidates on their capabilities while anonymizing factors that might lead to hiring bias. Interested in learning more about why AI platforms are gaining adoption, I recently attended a webinar co-hosted by Talent Tech Lab (TTL) and Eightfold.ai The webinar is titled An AI-First Approach to Recruiting with Eightfold and TTL.
Imagine that you are working on a project, with a team of 10 people. All members of this team, have to work from home now, because of the ongoing pandemic, so all of them have different laptops, different system specifications, different operating systems, etc. Now one fine day, a team member pushes a new change to GitHub, that adds some new functionality to your project. Unfortunately, these new changes do not work for some people, maybe because of different versions of the software installed on the different computers. So you have a very common problem, that many teams often face. "It works for him, but not for me" Docker was made specifically to solve this problem.
Meet the Toadi: an autonomous lawn mower robot that cuts your grass, so you don't have to. It comes with a 4K camera and AI for autonomous navigation. It cuts your grass but avoids objects and animals. The Toadi can maintain up to 1.2 acres of landscape. It has 4 easily replaceable titanium coated blades. The mowing height can be adjusted from 1.29″ to 3.54″.
There is no doubt that on the whole, the economic impacts from the lockdown and pandemic will be devastating. But while most leisure activities were throttled by the lockdown, others thrived -- just ask any of your friends that did Yoga With Adrienne (probably the same mates that brew their own kombucha). Tinder and Bumble usage alone spiked by over 20%, with Tinder registering 3 billion swipes on 28 March alone. However, the pandemic only accelerated a trend that was already in full force: finding love via apps. "Met online" is now the most common way that people report finding their significant other, streets ahead of boring old classics like "met in church" or "met in the neighbourhood". While there are a range of massively popular dating apps, including Bumble and Grindr, Tinder continues to be the most popular platform by a significant margin.