Goto

Collaborating Authors

Services


Large Scale Machine Learning Via SQL On Google BigQuery w/ BQML

#artificialintelligence

The health & safety of our attendees & speakers is our primary concern. While this currently proves to be a tricky time for public gatherings, Dataiku is still committed to providing great tech content & facilitating discussions in the data science space. As such, weve decided to pivot towards online webinars via our partner platform, BrightTalk. Google 2:45pm: Q&A Talk Abstract: In this talk, Sanjay will discuss how to perform machine learning using SQL for a variety of model types & the flexibility of using BQML to import & export models. Speaker bio: Sanjay Agravat is a Machine Learning Specialist for Google Cloud based out of Atlanta, GA.


The Rise of Chatbots

#artificialintelligence

Good customer service is an integral element to the success of any business. With the rise in mobile devices over the last decade, chatbots are increasingly becoming a popular option to interact with users. The popularity of chatbots and their adoption is rapidly increasing as they enable businesses to provide real-time customer service in many e-commerce settings. Let us explore the rise of Artificial Intelligence (AI), Natural Language Processing (NLP), and chatbots for customer service. A chatbot is an AI-based software that interacts with a user through either text or audio.


CHIRPS: Explaining random forest classification

#artificialintelligence

Modern machine learning methods typically produce "black box" models that are opaque to interpretation. Yet, their demand has been increasing in the Human-in-the-Loop processes, that is, those processes that require a human agent to verify, approve or reason about the automated decisions before they can be applied. To facilitate this interpretation, we propose Collection of High Importance Random Path Snippets (CHIRPS); a novel algorithm for explaining random forest classification per data instance. CHIRPS extracts a decision path from each tree in the forest that contributes to the majority classification, and then uses frequent pattern mining to identify the most commonly occurring split conditions. Then a simple, conjunctive form rule is constructed where the antecedent terms are derived from the attributes that had the most influence on the classification.


LinkedIn's 50 Best Startups To Work For In 2020

#artificialintelligence

These and other insights are from LinkedIn's Top Startups 2020: The 50 U.S. companies on the rise published today. This is the 4th annual LinkedIn list of the hottest startups to work for. The list is determined by the billions of actions taken by LinkedIn's 706 million members. The annual list is a reflection of how business and work is evolving through the pandemic, what industries are emerging and growing and where people want to work now, reflecting the current state of the economy and the world. Even in the face of Covid-19, the startups on this year's list are all still innovating and experiencing growth and the majority of the companies on the list are currently hiring, with 3,000 jobs now open on LinkedIn. To be eligible for the list, a company must be independent and privately held, have at least 50 employees, be seven years old or younger, be headquartered in the country on the list which they appear and have a minimum of 15% employee growth over the time period. The top 50 U.S. startups include the following: Full-time headcount: 4,000 Headquarters: New York City Year founded: 2016 What you should know: While the U.S. economy quickly sank into a recession at the start of the pandemic, one of its engines has been roaring: housing.


daviddao/awful-ai

#artificialintelligence

Infer Genetic Disease From Your Face - DeepGestalt can accurately identify some rare genetic disorders using a photograph of a patient's face. This could lead to payers and employers potentially analyzing facial images and discriminating against individuals who have pre-existing conditions or developing medical complications.


Artificial Intelligence Is Ready For Prime Time, But Needs Full Executive Support – IAM Network

#artificialintelligence

Finally, AI is ready for the mainstream. When your enterprise is handling transactions between 25 million sellers and 182 million buyers, supporting 1.5 billion listings, manual decision-making processes just won't cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, "in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more." As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI.


An Introduction to AI

#artificialintelligence

I am Imtiaz Adam, and this article is an introduction to AI key terminologies and methodologies on behalf of myself and DLS (www.dls.ltd). This article has been updated in September 2020 to take into account advances in the field of AI with techniques such as NeuroSymbolic AI, Neuroevolution and Federated Learning. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalise to unseen domains. This is the form of AI that we have today, for example Google Translate.


How can Artificial Intelligence innovate the way we socialise?

#artificialintelligence

Innovation in everything that we do is being driven by technology, including what we do on the internet. From social networking to our online searches, Artificial Intelligence assumes an undeniably significant role in studying our behaviour on digital media platforms and beyond. The greater part of the decisions we make in our day-to-day lives is mostly guided by AI-driven recommendations on our cell phones, personal assistants, chatbots, social network, or other AI technologies. Over 3.8 billion people are actively scrolling through one or the other social media platform such as Snapchat, LinkedIn, or YouTube at any given point of time. All these people and their conversations, searches, likes, dislikes, and more, are being thoroughly read to enable the machine to comprehend their preferences.


Pinaki Laskar posted on LinkedIn

#artificialintelligence

The #AI value chain, 1) AI chip and hardware makers who are looking to power all the AI applications that will be woven into the fabric of organisations big and small globally 2) The #cloud platform and infrastructure providers who will host the AI applications 3) The AI #algorithms and cognitive services building block makers who provide the vision recognition, speech and #deeplearning predictive models to power AI applications 4) Enterprise solution providers whose software is used in customer, HR, and asset management and planning applications 5) Industry vertical solution providers who are looking to use AI to power companies across sectors such as healthcare to finance 6) Corporate takers of AI who are looking to increase revenues, drive efficiencies and deepen their insights The today's AI is presented by what the BigTech and global social media platforms are pushing, it's Narrow /Weak AI /ML /DL, as "Cloud DL/AI Platforms". But this #Machinelearning algorithms are designed to optimize for a cost/loss function, having no intelligence, understanding or reasoning. So it is, Most curve-fitting AI tools available today sold as focused on predicting, identifying, or classifying things, a rote "learning from data/experience".


Top Technologies To Achieve Security And Privacy Of Sensitive Data

#artificialintelligence

Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy. Building data models using sensitive personal data can undermine the privacy of users and can also cause damage to a person if the data gets leaked or misused. A simple solution that companies have employed for years is data anonymisation by removing personally identifiable information in datasets.