programming


There's a huge opportunity in robotics for early career computer scientists and serious software engineers

ZDNet

There's a major roadblock to deeper market penetration of enterprise robotics, and a new generation of early career computer scientists and more seasoned software engineers may hold the answer. I recently had a chance to speak with Maya Cakmak, assistant professor at the University of Washington, Computer Science & Engineering Department, where she directs the Human-Centered Robotics Lab. To understand PbD, consider collaborative robots from companies like ABB and Kuka. The units consist of articulated arms that can be programmed to help workers do a variety of things, such as pick and place objects, test devices and components, and perform simple but precise manufacturing tasks. So-called "cobots" are relatively inexpensive and operate alongside humans, and many of the use cases involve small- to mid-sized businesses.


Don't Fear The AI Future Of TV, Film

#artificialintelligence

It's not just taking over practical or empirical tasks; creative work like screenwriting and film editing will be part of the AI future too. Bottom line, from the perspective of audience perspective and creator profit, artificial intelligence makes sense. Already, we're seeing how AI can make more interactive viewing experiences, while helping creators personalize content. AI offers the potential to take much of the perspiration out of making TV and film, leaving creators more time to make more compelling shows, more exciting videos, and "stickier" advertising content.


Artificial Intelligence on the menu as Nokia chairman goes back to school

#artificialintelligence

HELSINKI: He runs a company that is a byword for technological innovation -- but Nokia's chairman had no qualms about going back to school to learn more about artificial intelligence (AI). Risto Siilasmaa, 51, said he signed up this summer for online courses on AI programming run by Stanford University. Siilasmaa received plaudits for transforming the Finnish company from an ailing mobile phone manufacturer into one of the world's biggest telecoms network equipment makers. Nokia sold the phone business to Microsoft in 2014, which has largely abandoned the mobile device market.


Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition: Amazon.co.uk: Sebastian Raschka, Vahid Mirjalili: 9781787125933: Books

@machinelearnbot

Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. While Vahid's broad research interests focus on deep learning and computer vision applications, he is especially interested in leveraging deep learning techniques to extend privacy in biometric data such as face images so that information is not revealed beyond what users intend to reveal.


Certificate in Introduction to Data Mining and Machine Learning using Python

@machinelearnbot

This is a programming oriented, hands-on training for starting a career in Data Mining and Machine Learning, and to acquire the necessary skills in statistical and inferential thinking. After this course, many of the things you read and hear about Data Science, Artificial Intelligence and Machine learning would make a lot more sense. The applications of this field span from marketing analysis and forecasts, predicting demands for products, making intelligent business decisions, cyber security and threat detection, predicting poll and survey results, and too many others to mention here. This course will enable participants to learn the foundation skills through programming, in arguably the most popular Data Science language today--Python.


'Blade Runner 2049' dives deeper on AI to transcend the original

Engadget

That led to a ban on replicants altogether, which was lifted only when Wallace Corporation, a successor to the original replicant maker, Tyrell, proved that he could make models that were more obedient than the Nexus 8. K is one of these newer replicants, which still have longer lifespans but differ from older models by their increased reliance on embedded memories. They just wanted more life, as Roy Batty explained to his creator, Tyrell (before gouging his eyes out). Blade Runner 2049 takes that existential question a step further.


Should you use Python for data science?

@machinelearnbot

For those first venturing into the world of data science, it's important to master one language first, rather than looking to be a Jack of all trades from the offset. And there's no shortage of languages that you can pick as your weapon of choice for doing so – when it comes to data science, there's plenty on offer, including (but not limited to): Java, C, C, Scala, Perl, Clojure, Julia, and more. Whether you're an experienced data scientist or analyst, a software engineer who's starting to work more closely with machine learning, or even a complete beginner, Python is an easy programming language to pick up. From building web services, data mining, Python is a programming language that gives you the opportunity to solve data problems end-to-end.


Python vs R: Which programming language is better for data science?

@machinelearnbot

For those first venturing into the world of data science, it's important to master one language first, rather than looking to be a Jack of all trades from the offset. And there's no shortage of languages that you can pick as your weapon of choice for doing so -- when it comes to data science, there's plenty on offer, including (but not limited to): Java, C, C, Scala, Perl, Clojure, Julia, and more. Whether you're an experienced data scientist or analyst, a software engineer who's starting to work more closely with machine learning, or even a complete beginner, Python is an easy programming language to pick up. From building web services, data mining, Python is a programming language that gives you the opportunity to solve data problems end-to-end.


Text Analysis with R for Students of Literature – Book Review

@machinelearnbot

Many people, especially the long-term practitioners in humanities and similar disciplines, find this change worrying, and in many ways exactly contrary to the spirit of these disciplines. However, the aim of this book is neither to teach R or programming, but to give the Literature students just the most basic tools needed to do some relatively straightforward textual analysis. The book takes the freely available text file of "Moby Dick" and runs a variety of textual analysis on it: simple word count and word frequencies, correlations between various "special" words, context analysis, etc. Even though this is primarily a book intended for literature students, I would actually strongly recommend it to anyone interested in text mining, text analysis and natural language processing.


The Last Invention of Man - Issue 53: Monsters

Nautilus

When they launched, Prometheus was slightly worse than them at programming AI systems, but made up for this by being vastly faster, spending the equivalent of thousands of person-years chugging away at the problem while they chugged a Red Bull. For each such task category, the Omegas had Prometheus design a lean custom-built narrow AI software module that could do precisely such tasks and nothing else. It simply boiled down to maximizing their rate of return on investment, but normal investment strategies were a slow-motion parody of what they could do: Whereas a normal investor might be pleased with a 9 percent return per year, their MTurk investments had yielded 9 percent per hour, generating eight times more money each day. If this brought in $250 million in a week, they would have doubled their investment eight times in eight days, giving a return of 3 percent per hour--slightly worse than their MTurk start, but much more sustainable.