There didn't seem to be anything strange about the new teaching assistant, Jill Watson, who messaged students about assignments and due dates in professor Ashok Goel's artificial intelligence class at the Georgia Institute of Technology. Her responses were brief but informative, and it wasn't until the semester ended that the students learned Jill wasn't actually a "she" at all, let alone a human being. Jill was a chatbot, built by Goel to help lighten the load on his eight other human TAs. "We thought that if an AI TA would automatically answer routine questions that typically have crisp answers, then the (human) teaching staff could engage the students on the more open-ended questions," Goel told Digital Trends. "It is only later that we became motivated by the goal of building human-like AI TAs so that the students cannot easily tell the difference between human and AI TAs. Now we are interested in building AI TAs that enhance student engagement, retention, performance, and learning."

Two Los Angeles Unified teachers play a tabletop game created during a two-day professional development workshop last week at the Annenberg Space for Photography's Skylight Studios. Two Los Angeles Unified teachers play a tabletop game created during a two-day professional development workshop last week at the Annenberg Space for Photography's Skylight Studios. About 1,000 middle and high school students in Los Angeles are expected to design video games this school year that expose players to the importance of kindness, wildlife conservation or news literacy. The best student gamemakers could earn scholarships and other prizes. With the gaming effort, it recruited Riot Games, the Los Angeles company behind the hit computer game "League of Legends," to host participants on upcoming field trips.

Over the years, Andrew Ng has worn many hats -- Coursera co-founder, former Baidu chief scientist, founding lead of Google Brain team, and Stanford University adjunct professor. But lately, he has emerged as the leading influencer championing artificial intelligence (AI). Well over 1.5 million people have enrolled in his AI courses in Coursera. In a chat with ET, Ng talks about recent AI controversies: Elon Musk Versus Mark Zuckerberg spat on dangers of AI, Facebook AI chatbots creating their own language and job displacements. Edited excerpts: In an experiment recently, Facebook chatbots created their own language and had to be shut down.

Scientists who build artificial intelligence and autonomous systems need a strong ethical understanding of the impact their work could have. More than 100 technology pioneers recently published an open letter to the United Nations on the topic of lethal autonomous weapons, or "killer robots". These people, including the entrepreneur Elon Musk and the founders of several robotics companies, are part of an effort that began in 2015. The original letter called for an end to an arms race that it claimed could be the "third revolution in warfare, after gunpowder and nuclear arms". The UN has a role to play, but responsibility for the future of these systems also needs to begin in the lab. The education system that trains our AI researchers needs to school them in ethics as well as coding.

In many real-world machine learning problems the same data is comprised of several different representations or views. For example, same documents may be available in multiple languages [1] or different descriptors can be constructed from the same images [2]. Although each of these individual views may be sufficient to perform a learning task, integrating complementary information from different views can reduce the complexity of a given task [3]. Multi-view clustering seeks to partition data points based on multiple representations by assuming that the same cluster structure is shared across views. By combining information from different views, multi-view clustering algorithms attempt to achieve more accurate cluster assignments than one can get by simply concatenating features from different views. In practice, high-dimensional data often reside in a low-dimensional subspace. When all data points lie in a single subspace, the problem can be set as finding a basis of a subspace and a low-dimensional representation of data points. Depending on the constraints imposed on the lowdimensional representation, this problem can be solved using e.g.

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performance while maintaining accuracy. For DNNs, the model size is an important factor affecting performance, scalability and energy efficiency. Weight pruning achieves good compression ratios but suffers from three drawbacks: 1) the irregular network structure after pruning; 2) the increased training complexity; and 3) the lack of rigorous guarantee of compression ratio and inference accuracy. To overcome these limitations, this paper proposes CirCNN, a principled approach to represent weights and process neural networks using block-circulant matrices. CirCNN utilizes the Fast Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) from O(n2) to O(nlogn) and the storage complexity from O(n2) to O(n), with negligible accuracy loss. Compared to other approaches, CirCNN is distinct due to its mathematical rigor: it can converge to the same effectiveness as DNNs without compression. The CirCNN architecture, a universal DNN inference engine that can be implemented on various hardware/software platforms with configurable network architecture. To demonstrate the performance and energy efficiency, we test CirCNN in FPGA, ASIC and embedded processors. Our results show that CirCNN architecture achieves very high energy efficiency and performance with a small hardware footprint. Based on the FPGA implementation and ASIC synthesis results, CirCNN achieves 6-102X energy efficiency improvements compared with the best state-of-the-art results.

Typically, students work with robots that have been pre-programmed or program robots to undertake simple tasks for which the outcome is known. But a research project in Israel came up with a way for high schoolers and first-year engineering students to learn robot intelligence technologies by engaging them in teaching robots -- both physical and digital -- to learn. In a paper recently published by the International Journal of Online Engineering, three researchers described how students taught their robots to acquire skills by implementing a "reinforcement learning (RL) process" that used simulation modeling and cloud communication. The idea of RL is to use trial and error rather than direct instructions to help the robot determine appropriate performance criteria -- in this case, what angle it should situate itself in to lift varying weights. The project followed three phases.

This course will explain how to use scikit-learn to do advanced machine learning. If you are aiming to work as a professional data scientist, you need to master scikit-learn! It is expected that you have some familiarity with statistics, and python programming. It's not necessary to be an expert, but you should be able to understand what is a Gaussian distribution, code loops and functions in Python, and know the basics of a maximum likelihood estimator. The course will be entirely focused on the python implementation, and the math behind it will be omitted as much as possible.

If you work in tech or are even thinking about it, you've probably come across the term "machine learning". Google Trends shows that the popularity of the search term "Machine Learning" has grown by about 400% in the last three years. Clearly, there is a high demand for people with knowledge of machine learning–but what exactly is it? In this post, I'll describe what exactly is meant by the term "machine learning", and explain why it seems to have such importance to modern businesses. There is a famous definition by the computer scientist Tom M Mitchell which has often been used and which I will adapt here.

Over the years, has worn many hats -- Coursera co-founder, former Baidu chief scientist, founding lead of Google Brain team, and Stanford University adjunct professor. But lately, he has emerged as the leading influencer championing artificial intelligence (AI). Well over 1.5 million people have enrolled in his AI courses in Coursera. In a chat with ET, Ng talks about recent AI controversies: Elon Musk Versus Mark Zuckerberg spat on dangers of AI, Facebook AI chatbots creating their own language and job displacements.