Top 7 Resources To Learn Facial Recognition - Analytics India Magazine


Facial recognition is arguably the most talked-about technology within the artificial intelligence landscape due to its wide range of applications and biased outputs. Several countries are adopting this technology for surveillance purposes, most notably China and India. Both are among the first countries to make use of this technology on a large scale. Even the EU has pulled back from banning this technology for some years and has left it for the countries to decide. This will increase the demand for professionals who can develop solutions around facial recognition technology to simplify life and make operations efficient.

Automation May Take Jobs--but AI Will Create Them


Chances are you've already encountered, more than a few times, truly frightening predictions about artificial intelligence and its implications for the future of humankind. The machines are coming and they want your job, at a minimum. Scary stories are easy to find in all the erudite places where the tech visionaries of Silicon Valley and Seattle, the cosmopolitan elite of New York City, and the policy wonks of Washington, DC, converge--TED talks, Davos, ideas festivals, Vanity Fair, the New Yorker, The New York Times, Hollywood films, South by Southwest, Burning Man. The brilliant innovator Elon Musk and the genius theoretical physicist Stephen Hawking have been two of the most quotable and influential purveyors of these AI predictions. AI poses "an existential threat" to civilization, Elon Musk warned a gathering of governors in Rhode Island one summer's day.

Deploying machine learning models as serverless APIs Amazon Web Services


Machine learning (ML) practitioners gather data, design algorithms, run experiments, and evaluate the results. After you create an ML model, you face another problem: serving predictions at scale cost-effectively. Serverless technology empowers you to serve your model predictions without worrying about how to manage the underlying infrastructure. Services like AWS Lambda only charge for the amount of time that you run your code, which allows for significant cost savings. Depending on latency and memory requirements, AWS Lambda can be an excellent choice for easily deploying ML models.

The Big Reboot, Part 1 – Rethinking Education and Employment in an Automated Era Fast Future Publishing


The Big Reboot is a two-part exploration of how we prepare society for the potential impacts of technological disruption, job automation, and the continuing shifts taking place in the global economy. In this first discussion we look at practical strategies for i) raising skills and digital literacy across society, and ii) generating the new ventures and job openings required to fill the employment gap left by those that are displaced by technology. We are reaching peak hysteria in the debate about the potential impact of artificial intelligence (AI) and automation on tasks, roles, jobs, employment, and incomes. On an almost weekly basis, we see projections of wholesale job devastation through automation. These doom-laden forecasts vie with outlandishly optimistic forecasts from AI vendors and consultants suggesting that millions of new roles will be created because of our smart new tech toys.

Deploying a Deep Learning Model using Flask


I am creating the web deployment for a book I am writing for Manning Publications on deep learning with structured data. The audience for this book is interested in how to deploy a simple deep learning model. They need a deployment example that is straightforward and doesn't force them to wade through a bunch of web programming details. For this reason, I wanted a web deployment solution that kept as much of the coding as possible in Python. With this in mind, I looked at two Python-based options for web deployment: Flask and Django.

AI can't predict how a child's life will turn out even with a ton of data


McLanahan and her colleagues Matthew Salganik and Ian Lundberg then designed a challenge to crowdsource predictions on six outcomes in the final phase that they deemed sociologically important. These included the children's grade point average at school; their level of "grit," or self-reported perseverance in school; and the overall level of poverty in their household. Challenge participants from various universities were given only part of the data to train their algorithms, while the organizers held some back for final evaluations. Over the course of five months, hundreds of researchers, including computer scientists, statisticians, and computational sociologists, then submitted their best techniques for prediction.

Four-year-olds have the same overconfidence as bankers, study says

Daily Mail - Science & tech

Cocky children as young as four have the same levels of overconfidence as city bankers and business leaders, according to a new study. UK researchers demonstrated that high levels of confidence in one's own abilities – a trait common among high achievers – is apparent from an extremely early age. This suggests that cocky city types developed their'cognitive bias' from infancy rather than later life, they say. Researchers conducted a card game with young girls and boys with the objective of collecting as many stickers as possible, and compared their different strategies. More than 70 per cent of four-year-olds and half of five and six-year-olds were overconfident in their expectations - comparable to big shot bankers and traders.

Master of Computer Science in Data Science Coursera


Earn your Master's, learn from pioneering Illinois faculty, and gain the data science skills that are transforming business and society. Illinois Computer Science offers a specialized track that includes both MCS degree requirements and data science-focused coursework. This degree is right for anyone who not only wants to learn to extract knowledge and insights from massive data sets, but also wants full command of the computational infrastructure to do so. The Master of Computer Science in Data Science (MCS-DS) leads the MCS degree through a focus on core competencies in machine learning, data mining, data visualization, and cloud computing, It also includes interdisciplinary data science courses, offered in cooperation with the Department of Statistics and the School of Information Science. Data Visualization: Coursework designed to show you how to create effective and understandable data presentations.

Introduction to Artificial Intelligence (AI) Coursera


In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Future of Education and Work: Follow-up to Panel! by THRIVEinEDU by Rachelle Dene Poth • A podcast on Anchor


Welcome to the THRIVEinEDU podcast where learning happens and random thoughts related to all things education are shared. Listen in each week as I talk about some of the emerging trends and topics in education. Topics include Artificial Intelligence, AR/VR, Global Collaborations, Preparing Students for the Future, PBL, SEL, Coding, the Power of PLNS and more.