Showing posts from April, 2021

Talking to the Animals — Is AI the New Doctor Dolittle?

Original Source Here Talking to the Animals — Is AI the New Doctor Dolittle? Researchers are venturing into uncharted waters, exploring whether artificial intelligence can help us better understand our animal cousins If you’ve ever seen Finding Dory , the sweet and funny sequel to box-office hit Finding Nemo , you’ll know what it means to ‘speak whale.’ Without the help of a befuddled beluga whale called Bailey, the film’s forgetful fishy heroine would never escape the fictional Marine Life Institute and “just keep swimming” to find her long-lost parents. Thanks to robotics and artificial intelligen c e (specifically natural language processing powered by machine learning), it might also be possible to understand whale lingo outside Disney-Pixar’s imaginary kaleidoscopic multiverse. In probably the most significant interspecies communication project of all time, researchers recently set about the titanic task of translating the Morse code-like series of clicks (or codas) u

How to Improve Marketing Personalization With AI-driven Customer Touchpoints

Original Source Here How to Improve Marketing Personalization With AI-driven Customer Touchpoints Photo by andy kelly on unsplash Customer value increases when you make them feel known and heard. Still, with multiple customer touchpoints and different journeys taken, it is easy to get overwhelmed with all the data and miss personalization completely. What are your touchpoints? Touchpoints are the individual contacts through which customers engage with the business and its products/services. Businesses try to guarantee that clients will be pleased with the interaction whe n they connect with their product, customer service, sales staff, or marketing materials. But this single focus on individual touchpoints misses the bigger — and more significant — picture: the customer’s end-to-end experience. Marketers know that customer experience (CX) is essential to get right. Only by scrutinizing the customer’s experience through their own eyes — along the entire journey taken

3 Beginner Mistakes I’ve Made in My Data Science Career

Original Source Here 1. Believing Complex Algorithms Always Result in Better Solutions “So what are the characteristics of these clustered residents?” my manager asked. We had used the most advanced, recently released model to segment the residents of a smart city. The whole model was a black box, so we have no idea how it does the segmentation but gave the highest accurate clusters. I thought for a minute; I couldn’t come up with an answer. Our model had no interpretability. I hadn’t learned the lesson, though. At a later time, the client turned down our proof of concept for a potential project. “This solution looks promising but let us get back to you. The investment of deploying this solution might be a bit too high.” We had proposed a computer vision system to estimate the mass of fishes, using state-of-the-art object detection and depth estimation models. Still, we hadn’t accounted for the expensive GPU-based computation that came along with that. Whenever I have been

Building a lane detection system*b5ptHu0y7wUeMddy Original Source Here Setting up Environment Make sure that you have opencv installed. Install numpy & matplotlib libraries as we needs them on processing. pip install opencv-python import libraries import cv2 import matplotlib.pyplot as plt import numpy as np Preprocessing of image Greyscale image: complexity of gray level images is lower than that of color image. We can talk about lot of features of images brightness, contrast, edges, shape, contours, texture, perspective, shadows, and so on, without addressing color. After presenting a gray-level image model , it can be extended to color images. Gaussian Filter : The purpose of the gaussian filter is to reduce noise in the image. We do this because the gradients in Canny are really sensitive to noise, so we want to eliminate the most noise possible. cv2.GaussianBlur parameters: img,ksize,sigma img:Image which we are going to take ksize:dimension of the ker

Introducing hierarchical deletion to easily clean up unused resources in Amazon Forecast

Original Source Here Amazon Forecast just launched the ability to hierarchically delete resources at a parent level without having to locate the child resources. You can stay focused on building value-adding forecasting systems and not worry about trying to manage individual resources that are created in your workflow. Forecast uses machine learning (ML) to generate more accurate demand forecasts, without requiring any prior ML experience. Forecast brings the same technology used at to developers as a fully managed service, removing the need to manage resources or rebuild your systems. When importing data, training a predictor, and creating forecasts, Forecast generates resources related to the dataset group. For example, when a predictor is generated using a dataset group, the predictor is the child resource and the dataset group is the parent resource. Previously, it was difficult to delete resources while building your forecasting system because you had to delete the

Research: Enterprises and consumers want to increase AI adoption

Image Original Source Here New research from Juniper Networks has found a growing appetite from both enterprises and consumers to use AI technologies. Juniper surveyed 700 global IT decision-makers for its research and found that most (67%) executives have AI as a top strategic priority for 2021. 95 percent of the respondents believe their organisation would benefit from increasing the use of AI in their daily operations. 82 percent claim it makes employees more productive and 74 percent say it improves staff happiness. However, integrating AI remains a challenge. 73 percent of respondents claim their organisation is struggling with adoption due to issues preparing and expanding their workforce to integrate AI systems. Sharon Mandell, SVP and CIO at Juniper Networks, said: “As a CIO, when I see so much interest in

AI Weekly: How the power grid can benefit from intelligent software Original Source Here Join Transform 2021 this July 12-16. Register fo r the AI event of the year . Google parent Alphabet’s “moonshot” X lab announced last week at the White House Leaders Summit on Climate that it’s working on a project for the electric grid. Over the past three years, the lab says it has been investigating “new computational tools” designed to bring the grid “out of the industrial age and into the age of the intelligence.” Among other areas, X says it’s experimenting with (1) a real-time virtualization that shows power moving onto and off the grid, (2) tools that simulate what might actually happen on the grid, and (3) a platform to make information about the grid useful to stakeholders. The work is being led by Audrey Zibelman, former managing director at Australian electricity and gas systems operations firm Australia Energy Market O

The Robot Surgeon Will See You Now

Original Source Here Real scalpels, artificial intelligence — what could go wrong? AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

Nothing is Random, Not Even Rolling a Die*eESa90eSNFRZ2guG Original Source Here Photo by Robert Stump on Unsplash Nothing is Random, Not Even Rolling a Die In ancient history, the concepts of chance and randomness were intertwined with that of fate. Many ancient people threw dice to determine fate, and this later evolved into games of chance. Today, we are still using randomness in our daily life explicitly or implicitly. It is however very crucial to understand the underlying concept of randomness and its importance. Firstly, we should understand what defines random value as random. Randomness has multiple applications in finance, game theory, cryptography, artificial intelligence, and many more. So, one of the challenging questions is how likely we can predict it? The answer can bring us to the next level of possibilities in the world. For the moment, we should learn how these random phenomena can affect our life, and how we can make a benefit from that. Randomness in our lif

225 Machine Learning Projects with Python

Original Source Here Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore If you have a story to tell, knowledge to share, or a perspective to offer — welcome home. It’s easy and free to post your thinking on any topic. Write on Medium AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

Slowly, Robo-Surgeons Are Moving Toward the Operating Room

Original Source Here Real scalpels, artificial intelligence — what could go wrong? AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress

Build Your Own Movie Recommender System Using BERT4Rec

Original Source Here Recommendation algorithms are a core part of a lot of services that we use every day, from video recommendations on YouTube to shopping items on Amazon, without forgetting Netflix. In this post, we will implement a simple but powerful recommendation system called BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer . We will apply this model to movie recommendations on a database of around 60,000 movies. The Task Our objective is to recommend movi e s to users given the history of the movies they already watched in the past. This recommendation is learned directly from the data and is personalized for each user. Image By Author The Data We will use the MovieLens-25m dataset ( ). It is a dataset that logs interaction between 162541 users and 62423 movies. We can construct the time-sorted sequence of movies that they interacted with for each user. We will use the

IBM is acquiring Turbonomic to advance AIOps agenda Original Source Here Join Transform 2021 this July 12-16. Register fo r the AI event of the year . IBM announced this week that it is acquiring Turbonomic , provider of application resource management (ARM) and network performance management (NPM) software infused with machine learning algorithms. Terms of the acquisition, which is expected to close this quarter, were not disclosed. The two companies have a long-standing relationship under which IBM has been reselling Turbonomic’s ARM platform. Cisco also resells tools developed by the company. Turbonomic, which is privately held, claims revenues were up 41% for fiscal 2021 and counts Avon, HauteLook, and Litehouse Foods among its customers. Applications and systems management The decision to acquire Turbonomic comes after IBM began revamping its application and systems management portfolio last fall. This push began

Facebook details self-supervised AI that can segment images and videos

Image Original Source Here Join Transform 2021 this July 12-16. Register fo r the AI event of the year . Facebook today announced that it developed an algorithm in collaboration with Inria called DINO that enables the training of transformers , a type of machine learning model, without labeled training data. The company claims it sets a new state-of-the-art among unlabeled data training methods and leads to a model that can discover and segment objects in an image or video without a specific objective. Segmenting objects is used in tasks ranging from swapping out the background of a video chat to teaching robots that navigate through a factory. But it’s considered among the hardest challenges in computer vision because it requires an AI to understand what’s in an image. Segmentation is traditionally performed with supervised learning and requires a volume of annotated examples.