If you're not in the know, artificial intelligence and data science may sound like especially nerdy subsets of the already pocket-protector infused field of computer science. But anyone who is serious about expanding computer science education--a list that includes Fortune 500 company CEOs and policymakers on both sides of the aisle--should be thinking carefully about emphasizing AI, in which machines are trained to perform tasks that simulate some of what the human brain can do, and data science, in which students learn to record, store, and analyze data. That means making sure kids have access to well-designed resources to learn those subjects, bolstering professional development for those who teach them, exposing career counselors to information about how to help students pursue jobs in those fields, and much more. That imperative is at the heart of a list of recommendations by CSforALL, an education advocacy group presented last month at the International Society for Technology in Education's annual conference. Leigh Ann DeLyser, CSforALL's co-founder and executive director, spoke with Education Week about some big picture ideas around the push for a greater focus on AI and data science within computer science education.

If you're not in the know, artificial intelligence and data science may sound like especially nerdy subsets of the already pocket-protector infused field of computer science. But anyone who is serious about expanding computer science education--a list that includes Fortune 500 company CEOs and policymakers on both sides of the aisle --should be thinking carefully about emphasizing AI, in which machines are trained to perform tasks that simulate some of what the human brain can do, and data science, in which students learn to record, store, and analyze data. That means making sure kids have access to well-designed resources to learn those subjects, bolstering professional development for those who teach them, exposing career counselors to information about how to help students pursue jobs in those fields, and much more. That imperative is at the heart of a list of recommendations by CSforALL, an education advocacy group presented last month at the International Society for Technology in Education's annual conference. Leigh Ann DeLyser, CSforALL's co-founder and executive director, spoke with Education Week about some big picture ideas around the push for a greater focus on AI and data science within computer science education.

Last week, we completed an eye-opening activity in one of my introductory graduate school courses. For some context, the class is designed to provide an introduction to different research paradigms within human-computer interaction (HCI) and related fields. We spent the first half of the quarter discussing the high-level elements of quality research and have recently been discussing methods to gauge the ethics and trustworthiness of scholarly research. For the activity, our professor had each of us analyze a research paper of choice and write a short 300-word snippet discussing the ethical issues either directly present in or implied from the research. We then compiled all of our articles together into a little "virtual magazine" of sorts, usable as a quick future reference when reading scholarly papers. The end result was fascinating, in particular because we were able to find a number of ethical concerns still present in actual, published research.

Andrés Pérez Soderi has a joke he likes to tell when asked to describe how his company came together. "A Russian, a Chinese and a Venezuelan walked into a bar, and they end up making software so they can understand each other," Soderi says. Venezuelan-born Soderi is one of the three founders of US-based artificial intelligence startup, Sanas. Together with Chinese-born co-founders, Shawn Zhang, and Russian-born, Maxim Serebryakov, and their team, he has been working to unpick the nuances of accents to make communication easier for people from different backgrounds. That could have huge implications for any marketer who wants to improve customer experience through voice channels by helping both customers and staff be better understood.

Mobile developers are often asked to implement the latest available features for their platforms, and demand for ML models in production application has increased dramatically over the last couple of years. The creation of production-ready Neural Networks requires a big dataset and lots of time, so our models in this course will have some reasonable limitations. But you will be able to train Semantic Segmentation Neural Network fast and understand critical concepts of how these models are trained and how they can be integrated into your apps. In this tutorial, we will look at the "Wanna Nails" case, and we will show you how to train a model that will detect nails in a couple of hours. "Wanna Nails" is an app that uses Object Segmentation to detect nails and try on different polish colors.

Can you interpret a deep neural network? Building a complex and dense machine learning model has the potential of reaching our desired accuracy, but does it make sense? Can you open up the black-box model and explain how it arrived at the final result? These are critical questions we need to answer as data scientists. A wide variety of businesses are relying on machine learning to drive their strategy and spruce up their bottomline. Building a model that we can explain to our clients and stakeholders is key.

Let me start by thanking Antti [Kaikkonen, Minister of Defence of Finland] and all the Finnish colleagues for an excellent couple of days – 24 hours - of this informal meeting of the European Union Member States' Defence Ministers. It has been extremely productive and intense. Our agenda has been very heavy – heavy in terms of content, but light in terms of the kind of approach and relations we have had. The wonderful Helsinki sun has helped establishing a friendly atmosphere and I would say that the exchanges have been extremely consensual, productive and positive. Thank you for that, because your hospitality has contributed to set a positive and constructive tone.

Can you interpret a deep neural network? Building a complex and dense machine learning model has the potential of reaching our desired accuracy, but does it make sense? Can you open up the black-box model and explain how it arrived at the final result? These are critical questions we need to answer as data scientists. A wide variety of businesses are relying on machine learning to drive their strategy and spruce up their bottomline. Building a model that we can explain to our clients and stakeholders is key.

Are you ready to compete as intelligent technology meets human ingenuity to create the future workforce? Ryan Shanks from Accenture has some recommendations. A recent Accenture Strategy report, Reworking the Revolution: Are you ready to compete as intelligent technology meets human ingenuity to create the future workforce?, estimates that if businesses invest in artificial intelligence (AI) and human-machine collaboration at the same rate as top-performing companies, they could boost revenues by 38pc by 2022 and raise employment levels by 10pc. Collectively, this would lift profits by $4.8trn globally over the same period. For the average S&P 500 company, this equates to $7.5bn of revenues and a $880m lift to profitability.

Machine Learning (ML) is now a de-facto skill for every quantitative job and almost every industry embraced it, even though fundamentals of the field is not new at all. However, what does it mean to teach to a machine? Unfortunately, for even moderate technical people coming from different backgrounds, answer to this question is not apparent in the first instance. This sounds like a conceptual and jargon issue, but it lies in the very success of supervised learning algorithms. What is a machine in machine learning First of all here, machine does not mean a machine in conventional sense, but computational modules or set of instructions.