engineer


Two-year-olds should learn to code, says computing pioneer

The Guardian

Dame Stephanie Shirley, whose company was one of the first to sell software in the 1960s, said that engaging very young children – in particular girls – could ignite a passion for puzzles and problem-solving long before the "male geek" stereotype took hold. "I don't think you can start too early," she said, adding that evidence suggested that the best time to introduce children to simple coding activities was between the ages of two and seven years. "Companies run by women still have extraordinary difficulty in getting venture capital," she said. Such technology is already being tested at Priors Court in Berkshire, a residential school for autistic children that Shirley founded.


How To Become A Machine Learning Engineer: Learning Path

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Learning will be better if you work on theoretical and practical materials at the same time to get practical experience on the learned material. Fast Style Transfer Network This will show how you can use neural network to transfer styles from famous paintings to any photo. So don't try to figure out solution by yourself -- search for papers, projects, people that can help you. How can I improve tuning of hyperparameters of the models?


R: Complete Machine Learning Solutions - Udemy

@machinelearnbot

With machine learning, the analysis of business operations and processes is not limited to human scale thinking; machine scale analysis enables businesses to capture hidden values in big data. By taking this course, you will gain a detailed and practical knowledge of R and machine learning concepts to build complex machine learning models. Dipanjan Sarkar is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. He is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development.


It's All Corner Cases: Teaching Computers to Drive Safely

@machinelearnbot

It could be argued there is only one proven Big Data application -- web search. During my tenure as a Sr Fellow at Yahoo, I long argued the only company that truly had solved a deep learning problem (and by its nature, a BIG data problem) was Google (also, maybe Facebook?). While the Google way is probably the only proven success in building true big data deep learning applications (emphasis here being on truly BIG), most companies are not trying to replicate "the Google way" and thinking at scale. I would argue that today most AI companies suffer from one or both of two symptoms: (1) building models using expensive, small, manually annotated datasets and (2) taking months -- or even years -- to go from model development to production.


An inside look at Ford's $1 billion bet on Argo AI

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Instead, he comes off as optimistic about the company he founded with Peter Rander, who, as former engineering lead at the Uber Advanced Technologies Group, helped bring the ride-hail company's first-generation self-driving prototypes to public roads. In broad terms, Argo is developing self-driving technology that Ford can use to deploy fully autonomous Level 4-capable vehicles for commercial on-demand service. Ford has also charged Argo with how to create high-definition maps, keep them "fresh," and sustain that over time, Ford's CTO and vice president of research and advanced engineering Ken Washington said during a presentation at The Information's autonomous vehicle summit in June. Not long after leaving Google, Salesky and Rander started their new company with a small investment from an undisclosed source; neither Salesky or Ford will identify the source of the seed money.


Flipboard on Flipboard

#artificialintelligence

Instead, he comes off as optimistic about the company he founded with Peter Rander, who, as former engineering lead at the Uber Advanced Technologies Group, helped bring the ride-hail company's first-generation self-driving prototypes to public roads. In broad terms, Argo is developing self-driving technology that Ford can use to deploy fully autonomous Level 4-capable vehicles for commercial on-demand service. Ford has also charged Argo with how to create high-definition maps, keep them "fresh," and sustain that over time, Ford's CTO and vice president of research and advanced engineering Ken Washington said during a presentation at The Information's autonomous vehicle summit in June. Not long after leaving Google, Salesky and Rander started their new company with a small investment from an undisclosed source; neither Salesky or Ford will identify the source of the seed money.


Humans are an autonomous car's biggest problem. The new Audi A8 has a solution

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In 2012 the engineers working on Google's self-driving car realised they had a problem. And before those fully autonomous cars arrive and are widely adopted, hundreds of thousands of lives will be lost that might have been saved. Decades from now, when fully autonomous vehicles are available everywhere, these stopgap measures won't be necessary. A truly autonomous car won't care if its passengers are watching the road.


A.I. Business Applications (and How It May Impact You)

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Technology companies of all sizes and in locations all around the world are developing AI-driven products aimed at reducing operating costs, improving decision-making and enhancing consumer services across a range of client industries. The sum of these drivers -- new programming techniques, more data and faster chips -- has seen AI converge with human-level performance in the key areas of image classification and speech recognition over recent years (see EXHIBIT 2). Chipmakers stand to benefit from increased demand for processing power, particularly makers of graphical processing units for AI program training. And internet companies with AI at the core of their consumer services (such as digital assistants and new software features) stand to benefit directly from improvements in speech recognition and image classification.


The top 10 skills required for IoT developers

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Building an IoT system requires a team effort. A basic IoT team includes an electrical engineer, a mechanical engineer, an industrial designer, an embedded systems designer, one back-end developer, one front-end developer and a product manager. Needed skill sets include sensor data analysis, data center management, predictive analytics, and programming in Hadoop and NoSQL. Big data drives IoT, and the job of software engineers, network engineers, and UX engineers is to make the data work seamlessly for users.


Do doctors fear AI? Not the hundreds I work with around the world - Watson Health Perspectives

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Forty-two clinical and health experts have joined me on the Watson Health team to shape how AI will benefit physicians and patients, and we are part of a broader IBM clinical community that engages with key clinical and health leaders worldwide in a variety of ways. In addition to offering clinical expertise, advisory board members are invited to be candid about the strengths and weaknesses of AI today, its value to medicine, and how it needs to advance. Our advisory boards complement the clinical, health services, and health systems expertise that we have on the team at IBM and Watson Health. IBM Watson Health will continue to work with clinical communities to advance cognitive technology as clinicians aim to improve the value of care in the U.S. and globally.