The new stack includes support for the CrossLink-NX family of FPGAs for low power smart vision applications and features customized convolutional neural network (CNN) IP, a flexible accelerator IP that simplifies implementation of common CNN networks and is optimized to further leverage the parallel processing capabilities of FPGAs. For applications like smart vision that require higher Edge AI performance, CrossLink-NX FPGAs running sensAI software deliver twice the performance at half the power when compared to prior releases of the solutions stack. Updates to the NN compiler software tool let developers easily compile a trained NN model and download it to a CrossLink-NX FPGA. When running on a CrossLink-NX FPGA, the sensAI solutions stack offers up to 2.5 Mb of distributed memory and block RAM and additional DSP resources for efficient on-chip implementation of AI workloads to reduce the need for cloud-based analytics. The devices are manufactured in a 28 nm FD-SOI process that delivers a 75 percent reduction in power in comparison to similar competing FPGAs.

Leonel Garcia-Marques CICPSI Faculdade de Psicologia Universidade de Lisboa Portugal garcia_marques@sapo.pt Abstract --An agent, artificial or human, must be continuously adjusting its behaviour in order to thrive in a more or less demanding environment. An artificial agent with the ability to predict human emotional valence in a geospatial and temporal context requires proper adaptation to its mobile device environment with resource consumption strict restrictions (e.g., power from battery). The developed distributed system includes a mobile device embodied agent ( SensAI) plus Cloud-expanded ( Expanse) cognition and memory resources. The system is designed with several adaptive mechanisms in a best effort for the agent to cope with its interacting humans and to be resilient on collecting data for machine learning towards prediction. These mechanisms encompass homeostatic-like adjustments such as auto recovering from an unexpected failure in the mobile device, forgetting repeated data to save local memory, adjusting actions to a proper moment (e.g., notify only when human is interacting), and the Expanse complementary learning algorithms' parameters with auto adjustments. Regarding emotional valence prediction performance, results from a comparison study between state-of-the-art algorithms revealed Extreme Gradient Boosting on average the best model for prediction with efficient energy use, and explainable using feature importance inspection. Therefore, this work contributes with a smartphone sensing-based system, distributed in the Cloud, robust to unexpected behaviours from humans and the environment, able to predict emotional valence states with very good performance. I NTRODUCTION The scientific evidence of epigenetics reveal on/off mechanisms inside chromosomes of human agents and reinforces the importance of any entity continuous adaptation to its environment.

Ideally, we want to get to the point where Sensai AI can help people maximize the reach and effectiveness of every single social media post. I see enormous potential for AI. We're way past the "hype" stage for AI and well into the "becoming meaningful" stage, where all sorts of industries and organizations are implementing exciting new applications for AI. I first saw the opportunity for Sensai emerge from a confluence of factors, and I think a lot of people can relate to this in their personal life: there's this sense that certain aspects of social media are broken, that Facebook and these other big social media platforms have become a cluttered mess of spam and trolls and dehumanization. This became really clear to me around 2010 when I was working out of The Alchemy, a building in a historic part of Detroit that I'd recently purchased and leased to small businesses, creative professionals, and non-profits.

Artificial intelligence hype was pervasive last year and the reality of A.I. is white-hot right now. In fact, 61 percent of enterprises say they implemented A.I. in their organization in 2017 -- up 31 percent from 2016. And, 791 public companies mentioned A.I. in their earnings calls in Q3 2017. It means that A.I. is nearing "big data" or "blockchain" levels of notoriety. Soon, even the most unlikely companies will claim to use the technology (imagine Long Island Iced Tea becoming a blockchain company) to ride the zeitgeist and get some good press.

AI in edge devices is expected to experience mind-blowing growth over the next half decade, with predictions exceeding 100% CAGR. That means our IoT devices are going to be getting smart, and doing it quickly. Our devices are equipped with increasing numbers of increasingly complicated sensors, drowning our devices in data. In most applications, however, it isn't practical to simply push all that raw sensor data up to the cloud for further processing. We need to make some sense of it now, at the edge, embedded inside our device.

Artificial intelligence is being employed by many chatbots, but Helpshift is now out with what it says is the first AI specifically built and optimized for digital customer service. The San Francisco-based firm's new SensAI is being used to power three new offerings for its conversational platform, which is centered around a knowledge base of brand info plus text-based messaging communications with customers. Founded in 2011, Helpshift provides B2C customer support for mobile web, apps and website chat. Although the platform has previously used AI on a limited basis, this is its first widespread use. A new feature called Predict enables the auto-routing of textually-communicated issues to the right agent, which might be guided by such additional info as whether the customer is high-value.

Economist Andrew McAfee concludes in his TedTalk "What will future jobs look like?" (already 3yrs old) that "The new'algorithm enabling' technology is here today, and banks could use it to fundamentally change the value proposition for their customers." "The future belongs to those that can recognize opportunities before they become obvious". Artificial intelligence, which encompasses these days all sorts of'algorithm enabling' technology, is creeping into our lives. Asset management and wealth management is no exception. Mentors at Fintech accelerators are advising entrepreneurs to drop the idea of creating the next Bloomberg and are suggesting a focus on AI finance.