The computational power of smartphones and tablets has skyrocketed to the point where they approach the level of desktop computers on the market not long ago. Although it’s easy for mobile devices to run all the standard smartphone apps, today’s artificial intelligence algorithms can be too compute-heavy for even high-end devices to handle.
Google AI lead Jeff Dean recently posted a link to his 1990 senior thesis on Twitter, which set off a wave of nostalgia for the early days of machine learning in the AI community. Parallel Implementation of Neural Network Training: Two Back-Propagation Approaches may be almost 30 years old and only eight pages long, but the paper does a remarkable job of explaining the methods behind neural network training and the modern development of artificial intelligence.
Neural networks can be notoriously difficult to debug, but a Google Brain research team believes it may have come up with a novel solution. A paper by Augustus Odena and Ian Goodfellow introduces Coverage-Guided Fuzzing (CGF) methods for neural networks. The team also announced an open source software library for CGF, TensorFuzz 1.
The dearth of AI talents capable of manually designing neural architecture such as AlexNet and ResNet has spurred research in automatic architecture design. Google’s Cloud AutoML is an example of a system that enables developers with limited machine learning expertise to train high quality models. The trade-off, however, is AutoML’s high computational costs.
Last week, Singh posted a YouTube video, Making Amazon Alexa respond to Sign Language using AI, in which he smoothly communicates with Alexa using sign language. Alexa, which is installed in a laptop with a built-in camera, interprets Singh’s signed queries in real-time, converting them into text and delivering appropriate responses.
Farming is becoming a data-centric business powered by artificial intelligence. China’s big tech firms are using neural network-backed computer vision, wearable devices, and predictive analytics algorithms to reimagine pig, chicken, cow, goose, and cockroach farming.
At last month’s RE•WORK Deep Learning in Finance Summit in London, leading AI industry practitioners and academics from prestigious universities discussed their research, provided insights on business trends and real-life AI applications, and addressed current challenges facing the AI industry as a whole.
When the NBA’s Golden State Warriors decided to favour three-pointers over two-point shots in 2016, the winning strategy sent a shockwave through professional basketball. This was a “data-driven decision” based on higher score probability, explains Alex Martynov.
As Facebook struggles with fallout from the Cambridge Analytica scandal, its research arm today delivered a welcome bit of good news in deep learning. Research Engineer Dr. Yuxin Wu and Research Scientist Dr. Kaiming He proposed a new Group Normalization (GN) technique they say can accelerate deep neural network training with small batch sizes.
I purchased a Tmall Genie X1 — Alibaba’s flagship smart speaker — at the discounted price of US$15 during China’s November 11 “Singles Day” shopping festival. I was given order number 560,000-ish, and received the product a month later. The speaker is regularly priced at US$79, about the same as its American counterpart Google Mini.
Paige.AI is a New York-based startup that fights cancer with AI. Launched last month as a spinoff from the Memorial Sloan Kettering Cancer Center (MSK) — the largest cancer research institute in the US — Paige.AI has exclusive access to MSK’s IP in the field of computational pathology as well as its dataset of 25 million pathology cancer images (“slides”).
From May 14 to 18, the 30th International Joint Conference on Neural Networks (IJCNN 2017) was held in Anchorage, AK, USA. Continuing the long tradition, the conference is organized by the International Neural Network Society (INNS), in cooperation with the IEEE Computational Intelligence Society (IEEE-CIS).