Truck driving is one of the most popular professions in the United States. However, a staggering 40 percent of the miles logged each year are done with an empty truck. Seattle-based Convoy is revolutionizing the industry by providing better matches for shippers and truckers, allowing them to move freight more efficiently. Using AWS Machine Learning, Convoy recommends the best matches by analyzing millions of shipping jobs along with trucker availability.

Senseye was created to shape the future of computing. Here at Senseye, it is our core mission to bring the most intelligent and creative minds together in order to shape our aspirations for the future. Our goal is to create an emotional resonance between humans and computers that mirrors the intimate resonance that exists between people.

Watchdogs ask EC to delay repo haircut floors. It should come as no surprise that credit card companies supplement their revenues by selling real-time access to consumer transaction data – albeit aggregated and anonymised – and even less of a surprise that enterprising hedge funds have found a way to monetise it. This week, Risk.net reported how scrutinising data from millions of credit card transactions allowed a quant team to infer whether a company's sales are on the up or trending lower – without the need to wait for quarterly sales reports to be published. The analysis was delivered through a machine learning implementation of the random forest technique in which multitudes of decision trees combine to produce predictions. In this case, the algorithm enabled the quant shop to get an early warning on the health of companies whose options it held.

Generative AI models have a propensity for learning complex data distributions, which is why they're great at producing human-like speech and convincing images of burgers and faces. But training these models requires lots of labeled data, and depending on the task at hand, the necessary corpora are sometimes in short supply. The solution might lie in an approach proposed by researchers at Google and ETH Zurich. In a paper published on the preprint server Arxiv.org These self- and semi-supervised techniques together, they say, can outperform state-of-the-art methods on popular benchmarks like ImageNet.

Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.

SkinVision app claims to detect the most common forms of skin cancer. It is an Android and iOS app that allows the user to assess and track changes in the skin spots over time. The user has to submit a photo of their skin using their smartphone camera. After analyzing the image with Artificial Intelligence algorithm, the app delivers the risk assessment. There are three levels of risk described by the app: low, low with symptoms or high.

Foundation introduced BeagleBone-AI SBC at Embedded World 2019 last February. The board is specifically designed for artificial intelligence workloads at the edge thanks to Texas Instruments AM5729 dual-core Cortex-A15 processor that embeds a dual-core C66x DSP, and 4 EVE (Embedded Vision Engine) cores. The BeagleBone Black compatible board was not available at the time, but the Foundation has now formally launched the board, and you can buy BeagleBone-AI for $118 and up with heatsink and antenna on sites such as Mouser, OKdo, or Newark. The solutions is said to offer a "zero-download out-of-box software experience" with TI C66x digital signal processor (DSP) cores and embedded-vision-engine (EVE) cores supported through an optimized TIDL (Texas Instruments Deep Learning) machine learning OpenCL API with pre-installed tools. The Linux powered board targets automation in industrial, commercial and home applications.

Asked what is the biggest misconception about AI, Yoshua Bengio answered without hesitation "AI is not magic." Winner of the 2018 Turing Award (with the other "fathers of the deep learning revolution," Geoffrey Hinton and Yann LeCun), Bengio spoke at the EmTech MIT event about the "amazing progress in AI" while stressing the importance of understanding its current limitations and recognizing that "we are still very far from human-level AI in many ways." Deep learning has moved us a step closer to human-level AI by allowing machines to acquire intuitive knowledge, according to Bengio. Classical AI was missing this "learning component," and deep learning develops intuitive knowledge "by acquiring that knowledge from data, from interacting with the environment, from learning. That's why current AI is working so much better than the old AI."

Most artificial intelligence is generated from machine learning. This is a process that allows a computer to use software and a database of prior instances to learn what to do next. Most of the large cap tech companies are continuing to develop new products that are based on AI. The newest to the crew is Overton which is an Apple AI product. Apple's Overton framework is designed to automate AI system training.

People who are accustomed to listening to Western music, which is based on a system of notes organized in octaves, can usually perceive the similarity between notes that are same but played in different registers -- say, high C and middle C. However, a longstanding question is whether this a universal phenomenon or one that has been ingrained by musical exposure. This question has been hard to answer, in part because of the difficulty in finding people who have not been exposed to Western music. Now, a new study led by researchers from MIT and the Max Planck Institute for Empirical Aesthetics has found that unlike residents of the United States, people living in a remote area of the Bolivian rainforest usually do not perceive the similarities between two versions of the same note played at different registers (high or low). The findings suggest that although there is a natural mathematical relationship between the frequencies of every "C," no matter what octave it's played in, the brain only becomes attuned to those similarities after hearing music based on octaves, says Josh McDermott, an associate professor in MIT's Department of Brain and Cognitive Sciences.