Artificial intelligence has the potential to greatly simplify our lives – but not everyone is a data scientist and not all data scientists are experts in machine learning. Enter AutoAI – a novel approach of designing, training and optimizing machine learning models automatically. With AutoAI, anyone could soon build machine learning pipelines from raw data directly, without writing complex code and performing tedious tuning and optimization, to then automate complicated, labor-intensive tasks. Several IBM papers selected for the AAAI-20 conference in New York demonstrate the value of AutoAI and different approaches to it in great detail. Most AutoAI research currently focuses on three areas: automatically determining the best models for each step of the desired machine learning and data science pipeline (model selection), automatically finding the best architecture of a deep learning-based AI model, and automatically finding the best hyperparameters (parameters for the model training process) for AI models and algorithms.

Assistant Principal Miles Carey oversees a Rocket League practice at Washington-Liberty High School in Arlington, Va. Assistant Principal Miles Carey oversees a Rocket League practice at Washington-Liberty High School in Arlington, Va. Nowadays, if you're a teenager who's good at video games there's a lot more to be had than just a pot of virtual gold. Today, more than 170 colleges and universities participate. Naturally, high schools have followed suit.

Delane Parnell is the cofounder and CEO of PlayVS. If there's ever a constant in the flourishing world of esports, it's that enthusiasm often outpaces the necessary infrastructure to match it. In particular, high school students and teachers who hope to participate in competitive gaming must self-organize without the structure of an official body. Delane Parnell's high school science teacher was someone who took it upon themselves to organize a gaming club for students. He provided the equipment, he kept track of stats and even awarded trophies for the myriad of games they played.

IBM Zurich researchers have developed a generic artificial-intelligence preprocessing building block for accelerating Big Data machine learning algorithms by at least 10 times over existing methods. The approach, which IBM presented Monday (Dec. "Our motivation was how to use hardware accelerators, such as GPUs [graphic processing units] and FPGAs [field-programmable gate arrays], when they do not have enough memory to hold all the data points" for Big Data machine learning, IBM Zurich collaborator Celestine Dünner, co-inventor of the algorithm, told EE Times in advance of the announcement. "To the best of our knowledge, we are first to have generic solution with a 10x speedup," said co-inventor Thomas Parnell, an IBM Zurich mathematician. "Specifically, for traditional, linear machine learning models -- which are widely used for data sets that are too big for neural networks to train on -- we have implemented the techniques on the best reference schemes and demonstrated a minimum of a 10x speedup."

Together with EPFL scientists, our IBM Research team has developed a scheme for training big data sets quickly. It can process a 30 Gigabyte training dataset in less than one minute using a single graphics processing unit (GPU) -- a 10 speedup over existing methods for limited memory training. The results, which efficiently utilize the full potential of the GPU, are being presented at the 2017 NIPS Conference in Long Beach, California. Training a machine learning model on a terabyte-scale dataset is a common, difficult problem. If you're lucky, you may have a server with enough memory to fit all the data, but the training will still take a very long time.

Under the watchful eyes of the public and lawmakers, several traditional automakers and tech companies have been racing to the finish line of "fully autonomous driving." Companies are already testing their cars in real-world conditions on public streets, and to date, some have driven millions of miles. Though opinions vary, it is hypothesized that we will be safer in autonomous vehicles (AKA self-driving cars). And while we would really need significantly more data to determine this as a statistic, I can imagine the experience of riding in full autopilot will be somewhat like flying: though much scarier than driving (for most), you are still safer, statistically-speaking. Regardless, it will be some time before the majority of average consumers have replaced their conventional vehicles.