Retailers are now applying AI, ML, and robotics in significant parts of the value chain. Above all, AI technologies could eliminate many manual activities in assortments, promotions, and supply chains. The three most remarkable opportunities in the short to medium term are promotions, arrangement, and replenishment. Significant retailers are trying different things with AI around these areas. "Digital native" e-commerce organizations are driving the way, using AI to anticipate trends, optimize advanced warehousing and logistics, set costs, and customize advancements and promotions.
It's highly unlikely that business owners are going to read this and begin to change their perspectives on how we define Data Science. Not because I doubt my influence or anything, but since I'm aware that the majority of my readers are at the beginning of their Data Science journey -- I really dislike the term "aspiring" -- but here is what I wish to tell you all… Stop trying to be good at everything in Data Science, and pick 1 (max 2) area's you want to specialize in and get really good at it! Let's face it... Breaking into Data Science is difficult for a number of reasons. However, I've come to a realization recently that much of the difficulty lies in the fact that the term "Data Scientist" encompasses so many different technical qualities that make it virtually impossible for one individual to meet all these criteria and stay up to date in each area -- and that's okay! I've been listening and speaking to Vin Vashishta, Chief Data Scientist and LinkedIn Top Voice 2019, and he believes that for roles to be defined better then more specialization amongst practitioners must occur.
As artificial intelligence becomes more advanced, previously cutting-edge -- but generic -- AI models are becoming commonplace, such as Google Cloud's Vision AI or Amazon Rekognition. While effective in some use cases, these solutions do not suit industry-specific needs right out of the box. Organizations that seek the most accurate results from their AI projects will simply have to turn to industry-specific models. There are a few ways that companies can generate industry-specific results. One would be to adopt a hybrid approach -- taking an open-source generic AI model and training it further to align with the business' specific needs.
We've all heard about Artificial Intelligence (AI) but only few of us know exactly what it means and how does it impact our everyday life. When thinking about AI, many Baby Boomers and X Gens think of the old sci-fi films and scenes where machines come alive and take over the world. But that's just a funny representation how humans used to perceive the unknown. If you remember the old TV show'Beyond 2000', you may recall that their ideas and inventions were outstanding at the time, which only shows the potential of the technology. What is in fact AI and what are the examples we can see on mobile?
Can you fool Artificial Intelligence? Three years ago, Apple launched IphoneX with cutting-edge facial recognition technology. This advanced AI technique (Face ID) replaced the old fingerprint recognition technology (Touch ID). The latest technology claimed to be more secure and robust. However, shortly after the launch of Face ID, researchers from Vietnam breached it by designing a 3D face mask.
IOU is defined as the ratio of intersection of ground truth and predicted segmentation outputs over their union. If we are calculating for multiple classes, IOU of each class is calculated and their mean is taken. It is a better metric compared to pixel accuracy as if every pixel is given as background in a 2 class input the IOU value is (90/100 0/100)/2 i.e 45% IOU which gives a better representation as compared to 90% accuracy.
Self-supervised learning of depth map prediction and motion estimation from monocular video sequences is of vital importance -- since it realizes a broad range of tasks in robotics and autonomous vehicles. A large number of research efforts have enhanced the performance by tackling illumination variation, occlusions, and dynamic objects, to name a few. However, each of those efforts targets individual goals and endures as separate works. Moreover, most of previous works have adopted the same CNN architecture, not reaping architectural benefits. Therefore, the need to investigate the inter-dependency of the previous methods and the effect of architectural factors remains. To achieve these objectives, we revisit numerous previously proposed self-supervised methods for joint learning of depth and motion, perform a comprehensive empirical study, and unveil multiple crucial insights. Furthermore, we remarkably enhance the performance as a result of our study -- outperforming previous state-of-the-art performance.
The world of artificial intelligence processing is creating numerous opportunities for young companies, especially in the hotly contested area of inference, where a trained neural network is used on a device to make actual predictions. That's the realm of Mountain View, California-based Flex Logix, the seven-year-old startup that has for several years been going after Nvidia's position in the market for "inference at the edge." Flex Monday announced it has gotten $55 million in Series D funding, lead by Mithril Capital Management, and joined by existing investors Lux Capital and Eclipse Ventures, bringing its total to date to $82 million. Geoff Tate, chief executive, told ZDNet in an interview that the company can be successful both licensing its intellectual property to other chip designers, as it already does, or via sales of its forthcoming inference chip, the InferX X1. "We have two paths to being worth a lot of money," said Tate in an interview by phone last week. "Both can be worth tens of billions of dollars" as a business, he said, referring to the licensing and the outright chip sales approaches.
While the software industry has been successful in deploying AI in production, the hardware industry -- including automotive, industrial, and smart retail -- is still in its infancy in terms of AI productization. Major gaps still exist that hinder AI algorithm proofs-of-concept (PoC) from becoming real hardware deployments. These drawbacks are largely due to small data problems, "non-perfect" inputs, and ever-changing "state-of-the-art" models. How can software developers and AI scientists overcome these challenges? The answer lies in adaptable hardware.
Understanding the information contained in the increasing repository of data is of vital importance to behavior sciences , which aim to predict human decision making and enable wide applications, such as mental health evaluation , business recommendation , opinion mining , and entertainment assistance . Analyzing media data on an affective (emotional) level belongs to affective computing, which is defined as "the computing that relates to, arises from, or influences emotions" . The importance of emotions has been emphasized for decades since Minsky introduced the relationship between intelligence and emotion . One famous claim is "The question is not whether intelligent machines can have any emotions, but whether machines can be intelligent without emotions." Based on the types of media data, the research on affective computing can be classified into different categories, such as text [13, 72], image , speech , music , facial expression , video [56, 79], physiological signals , and multi-modal data [52, 41, 80]. The adage "a picture is worth a thousand words" indicates that images can convey rich semantics.