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) …
One of the major challenges in most BI projects is to figure out a way to get clean data. This is true for both BI and Predictive Analytics projects. To improve the effectiveness of the data cleaning process, the current trend is to migrate from the manual data cleaning to more intelligent machine learning-based processes. Before we dig into figuring out how to handle missing values, it's critical to figure out the nature of the missing values. There are three possible types, depending on if there exists a relationship between the missing data with the other data in the dataset.
Artificial-intelligence researcher Oren Etzioni has suggestions for keeping enough AI faculty members around to train the next generation.Credit: Bret Hartman/TED Oren Etzioni is chief executive of the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington, and is on leave from the nearby University of Washington. He offers some recommendations for how to stem the outflow of artificial-intelligence (AI) researchers from academia to industry -- a loss that is damaging academia's ability to teach incoming undergraduates. It is a very sizeable trend for fresh PhD graduates and faculty members. In machine learning, you see some significant departures. Industry compensation packages are highly variable.
Europe is not a static entity but here is what it looks like in 2019: The European Union is made up of 28 countries. The capital is in Brussels, Belgium, and the presidency is shared among EU members each semester. In 2019, the first semester sees Romania holding presidency until June, then Finland until the end of the year. An estimated 551.8 million people live in the EU, speaking 24 official languages. Approximately 72% of the population is employed,a which is greater than the pre-economic-crisis peak of 2008.
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.
The gender pay gap has always been a topic of debate but never has it ever been able to bring the much-required change in the system. Time and again, feminists have raised their voices against such inequalities. Infact, they have been very right in stating that the women are sharing responsibilities equally then why not authority? Besides, many compensation guidelines and policies have also been articulated by the government but little did all of that benefit. Otherwise, the rate at which the global economy is embracing the removal of the gender pay gap can take the next 202 years to hit the equilibrium.
Businesses are finally starting to get value out of using chatbots. Companies like JPMorgan Chase, Bank of America, Marriott, and Coca-Cola are starting to see massive returns. All in all, bots are finally starting to win businesses over and, according to an Oracle survey, 80 % of businesses want chatbots by 2020. Here's a look at where the biggest opportunities are and how to take advantage of them. If your company is looking to leverage chatbot technology, you'll need a good place to start, and one of the biggest tips is focusing on ROI.
Want to build skills in artificial intelligence (A.I.) and deep learning? Udacity and Google are launching a free introductory course on the subject, which naturally leans into TensorFlow, the open-source library for deep learning software developed by Google. "Intro to TensorFlow for Deep Learning" is a two-month course, and now open to enrollment. Its goal is to help developers build A.I. applications that can scale (using TensorFlow, of course). It's the second TensorFlow-based collaboration between the two firms; in 2016, Udacity and Google launched a TesnorFlow course that taught students the basics of the platform.
Employment contracts haven't changed much since ancient Roman times: they're all about showing up and doing the job, not about whether the job will be satisfying or whether the employee will get the tools and authority they need to do the job. Startups may have free snacks and lots of perks, but once they grow into large companies it tends to be business as usual, with hierarchies and managers whose priorities might not actually help employees deliver what the business needs. Could a CEO taking a radically different approach do better? That's what Charles Towers-Clark explores in The WEIRD CEO: How to lead in a world dominated by Artificial Intelligence. 'Weird' doesn't mean strange in this case: it's an acronym for principles that are intended to make employees more independent and, at the same time, more involved and invested in the success of the organisation.
Hiring developer talent is a business priority, but not all roles are created equal. As startups introduce new ways to apply technologies and large enterprises continue their quest to digitally transform, hiring needs to evolve for all companies looking to hire top tech talent. Data from Hired's marketplace reveals that global demand for blockchain engineers is through the roof, at a 517% increase year over year. For developers interested in blockchain roles, don't let the titles fool you. For engineers with an expertise in blockchain, they typically hold titles such as backend engineer, systems engineer or solutions architect, with blockchain being listed as a desired skill for the role.
In the nascent field of Data Science, myths are abound. Here's my top 10, scoured from the internet (where better than to find a myth or two?). This one is only part myth. Historically, women have been discouraged from entering the computing sciences for many reasons unrelated to talent (see my previous post, On being a Female Data Scientist), and in 1975, the Good Ol' Boy's Club was in full swing. But this isn't 1975, it's 2019--a new era where women are welcome.