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Deep Neural Networks Help to Explain Living Brains

#artificialintelligence

In the winter of 2011, Daniel Yamins, a postdoctoral researcher in computational neuroscience at the Massachusetts Institute of Technology, would at times toil past midnight on his machine vision project. He was painstakingly designing a system that could recognize objects in pictures, regardless of variations in size, position and other properties -- something that humans do with ease. The system was a deep neural network, a type of computational device inspired by the neurological wiring of living brains. "I remember very distinctly the time when we found a neural network that actually solved the task," he said. It was 2 a.m., a tad too early to wake up his adviser, James DiCarlo, or other colleagues, so an excited Yamins took a walk in the cold Cambridge air. "I was really pumped," he said. It would have counted as a noteworthy accomplishment in artificial intelligence alone, one of many that would make neural networks the darlings of AI technology over the next few years.


6 changes needed to upskill workers for the robotics age

#artificialintelligence

While the state of industrial robotics education is in disarray, the issue does not lay with educators trying to develop curriculums to teach students robotics. Educators are following a decades-old approach that focuses on difficult to use, brand-specific robot skills. Educators need a new approach, one that leverages advances in technology to make robot programming easier rather than doubling down on just delivering an outdated version of robotics education. The challenges stem from a stubborn industry, where vendors create their own unique walled gardens, with their own robot programming language and associated interfaces. Such approaches make it difficult to teach the full set of skills students need to deploy automation when they get to their new jobs.


Sentiment Analysis in 10 Minutes with BERT and Hugging Face

#artificialintelligence

I prepared this tutorial because it is somehow very difficult to find a blog post with actual working BERT code from the beginning till the end. They are always full of bugs. So, I have dug into several articles, put together their codes, edited them, and finally have a working BERT model. So, just by running the code in this tutorial, you can actually create a BERT model and fine-tune it for sentiment analysis. Natural language processing (NLP) is one of the most cumbersome areas of artificial intelligence when it comes to data preprocessing.


Automation: A data scientist's new best friend?

#artificialintelligence

Founder and CEO of DotData, Ryohei Fujimaki, explains how automation can help the data science industry become more efficient. Of the many technologies that will shape how we work in the future, automation is one of the most hotly debated. Some look forward to the new avenues it will open up while others fear it will make their skills redundant. Dr Ryohei Fujimaki, founder and CEO of data science company DotData, believes that data scientists are among those that will benefit the most. Fujimaki's team at DotData is helping companies accelerate their data science process.


How Chatbots Help Business Avoid the Fear of a Black-Box AI Planet

#artificialintelligence

The rise of AI in business largely goes unquestioned, until a poor decision comes out of a black box that no one can fathom or that causes actual damage. To avoid this, businesses need to adopt AI tools that are provable and customer-friendly, with chatbots paving the way until AI can be truly trusted. In most business cases, artificial intelligence helps companies progress when it comes to their varied use cases. From understanding us humans and our convoluted languages, recovering data from forms, predicting outcomes etc., AI helps spot meaning, intent and value, and provides the power for chatbots, analytic services and other digital business tools. However, as with 5G and 4G before it, as with robots in factories, and those pesky vaccines that keep us alive, there is a narrative in the media that AI is here to destroy us, to wipe out jobs, to weaken employees and other negative outcomes.


Python : Zero to Hero with Examples

#artificialintelligence

This article will help many python learners who feel confused about where to start--the concept and examples of python from basic will clear the doubts of every beginner. I hope this long article will not make you bore with learning, lets the fun begin. I think there is no need for an introduction to what python language is and how useful it is in various applications from web development to artificial intelligence -- covering all domains. It is a name and storage container in which we assign some values in the form of int or string. It is a Unicode character in the form of arrays of bytes.


NSF funds K-State research on artificial intelligence-based cyber-physical systems

#artificialintelligence

MANHATTAN, Kan. (WIBW) - The National Science Foundation is funding a K-State professor's research on artificial intelligence-based cyber-physical systems. Kansas State University says Pavithra Prabhakar, an associate professor and Peggy and Gary Edwards chair in engineering in its computer science department, has been awarded $450,000 from the National Science Foundation to work on artificial intelligence-based controllers in the 3-year long project titled, "Scalable Formal Verification of ANN Controlled Cyber-Physical Systems." According to K-State, artificial intelligence-based controllers are increasingly used for modern-day cyber-physical and autonomous systems like driverless cars. It said these systems have been called on to perform sophisticated functions and operate in dynamic environments. It said the use of such controllers in driverless cars is highly safety-critical, where the vehicle is expected to not only stay in the right lane but avoid accidents with other cars and pedestrians crossing roadways under different lighting conditions.


One of the most popular Roomba models is $100 off

Mashable

Save $101: The iRobot Roomba 675 is on sale on Amazon for $179 as of Nov. 28. Yes, Thanksgiving was a few days ago. Yes, we are still finding crumbs of corn bread on the floor. No shame in admitting that not every mess is easy to clean but definitely don't let the crumbs sit there any longer. Get yourself a Roomba so you can stop guessing if you got every spot and instead be sure that your robot vacuum buddy left no corner untouched.


"The Suicide of Our Troubles"

Slate

Please come out, we'd all love to see you.--Andrea Boyczuk She hadn't driven on 75 since before Christmas. There were lots of cars and self-driving trucks on the road, and in MR the sky had sprouted thousands of virtual signs, labels, and guides. It seemed a lot was going on. Eventually the silence made her edgy and she said, "So you're Lake Erie. How long have you been awake?" "I've been a legal person since 2017." The lake had a smooth, masculine voice, with none of the artificiality she'd heard in Mercury's on those occasions when she'd spoken to it directly and not through Donna. "I was made one so that the citizens of Ohio could litigate on my behalf. But I have a lot more resources since I have the actants' network attached to me."


Graph Neural Networks for Multiple Object Tracking

#artificialintelligence

Multiple object tracking(MOT) is the task of studying object appearance and movements to analyze their trajectories. For a given input video the algorithm is supposed to output which portions of the image represent the same object in different frames of the video. Algorithms like these can be used to solve some exciting problems like analyzing a particular soccer player's movements during the game, predicting whether a person is going to cross the street or not, or to track and analyze the movement of microscopic organisms in time-lapse microscopy images, etc. In this article, we will go through a state of the art Offline tracking framework for solving the problem of MOT. The approach that we are about to discuss was published in a paper by the researchers at the Dynamic Vision and Learning Group at TUM. Their proposed algorithm achieved SOTA on MOT15, MOT16, and MOT17 challenges.