Showing posts from June, 2021

Deep Learning for Twitter Personality Inference

Original Source Here While this sample would ideally be larger, the dataset cannot be increased without unbalancing the classes than they already are in the dataset. This is because some (self-declared) personality types only number about 100 profiles in total. ISTPs were the least represented amongst all personalities — a finding consistent with theory, as ISTPs are described to be outdoor-oriented people who enjoy physical activity and working with tools. It wouldn’t be surprising if they don’t prefer the endlessly roundabout conversations on politics and abstract tech that Twitter is famous for. In general, I found that Intuitives (N) are significantly more prominent than Sensing(S) preference profiles. While I could not get an accurate estimate of this disparity in the Twitter population, the ratio is at least ~ 2:1 based on my sample. Many Intuitive types reached the 300 profile limit I specified; we can be sure that the 2:1 ratio is definitely underestimated. The national

Full Scale development of Recommendation system as ML Engineer from scratch.

Original Source Here Full Scale development of Recommendation system as ML Engineer from scratch. Recommendation system is one of the most important and complex software engineering system from many aspects.It is one of the reason of success of giants like netflix,youtube etc. Making a full scale recommendation system as a ready deployable product for a working company requires great knowledge of deep learning,logic programming,database and domain ,apart from these,you should have required software engineering and development knowledge for deployment and maintenance.[“yeah I know all these, huhhhh”] Hii Everyone,I am Aditya Raj,currently in 2nd year at IIIT Allahabad and also working remotely as Machine Learning Engineer at . I have b een working for 5 months now and feel immensely lucky to work at a growing startup,I got to learn ocean of things and got opportunity to design,make and deploy some interesting AI based systems. Well,here I will be talking of

The rise of robotaxis in China

Original Source Here AutoX, Momenta and WeRide took the stage at TC Sessions: Mobility 2021 to discuss the state of robotaxi startups in China and their relationships with local governments in the country. They also talked about overseas expansion — a common trajectory for China’s top autonomous vehicle startups — and shed light on the challenges and opportunities for foreign AV companies eyeing the massive Chinese market. Enterprising governments Worldwide, regulations play a great role in the development of autonomous vehicles. In China, policymaking for autonomous driving is driven from the bottom up rather than a top-down effort by the central government, observed executives from the three Chinese robotaxi startups. Huan Sun, Europe general manager at Momenta, which is backed by the government of Suzhou, a city near Shanghai, said her company had a “very good experience” working with the municipal governments across multiple cities. In China, each local government is incen

This AI system learned to understand videos by watching YouTube

Image Original Source Here Elevate your enterprise data technology and strategy at Transform 2021 . Humans understand events in the world contextually, performing what’s called multimodal reasoning across time to make inferences about the past, present, and future. Given text and an image that seem innocuous when considered apart — e.g., “Look how many people love you” and a picture of a barren desert — people recognize that these elements take on potentially hurtful connotations when they’re paired or juxtaposed, for example. Even the best AI systems struggle in this area. But there’s been progress, most recently from a team at the Allen Institute for Artificial Intelligence and the University of Washington’s Paul G. Allen School of Computer Science & Engineering. In a preprint paper published this month, the researchers detail Multimodal Neural Script Knowledge Models (Merlot) , a system that

Automate Machine Learning using Databricks AutoML — A Glass Box Approach and MLFLow

Original Source Here Next, we need to select the evaluation metric — F1 score (Because the data is imbalanced). We can even configure the stopping criteria — time out and a number of trail runs in the advanced configuration settings. Advanced configuration (Author Created) After setting all the configurations, click on “Start AutoML” to train different iterations of the classification algorithms. Exploring the notebooks generated by AutoML Now that an hour has been passed, AutoML has completed executing different combinations of model iterations. If you take a close look at the metrics, they are automatically sorted by the validation f1_score in descending order such that the best model is at the top of the table. AutoML Notebooks (Author Created) AutoML is integrated with MLflow to tracking all the model parameters and evaluation metrics associated with each run. MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproduc

AI ‘dominated scientific output’ in recent years, UNESCO report shows Original Source Here Elevate your enterprise data technology and strategy at Transform 2021 . The United Nations Educational, Scientific, and Cultural Organization ( UNESCO ) today unveiled its latest Science Report . The massive undertaking — this year’s report totals 762 pages, compiled by 70 authors from 52 countries over 18 months — is published every five years to examine current trends in science governance. This latest edition includes discussion of the rapid progress toward Industry 4.0 and, for the first time, a deep analysis of AI and robotics research around the globe. Going beyond just the global leaders, it offers an overview of almost two dozen countries and global regions, examining AI research, funding, strategies, and more. Overall, the report determines “it is the field of AI and robotics that dominated scientific output” in recent years. “We take a look at t

Transform 2021 puts the spotlight on women in AI Original Source Here Elevate your enterprise data technology and strategy at Transform 2021 . VentureBeat is proud to bring back the Women in AI Breakfast and Awards online for Transform 2021. In the male-dominated tech industry, women are constantly faced with the gender equity gap. There is so much work in the tech industry to become more inclusive of bridging the gender gap while at the same time creating a diverse community. VentureBeat is committed year after year to emphasize the importance of women leaders by giving them the platform to share their stories and obstacles they face in their male-dominated industries. As part of Transform 2021 , we are excited to host our annual Women in AI Breakfast , presented by Capital One, and recognize women leaders’ accomplishments with our Women in AI Awards . Women in AI Breakfast: VentureBeat’s third annual Women in AI Breakfast

Facebook’s AI can copy the style of text in photos from a single word

Image Original Source Here Elevate your enterprise data technology and strategy at Transform 2021 . Facebook today introduced TextStyleBrush , an AI research project that can copy the style of text in a photo from just a single word. The company claims that TextStyleBrush, which can edit and replace arbitrary text in images, is the first “unsupervised” system of its kind that can recognize both typefaces and handwriting. AI-generated images have been advancing at a breakneck pace, and they have obvious business applications, like photorealistic translation of languages in augmented reality (AR) . (The AR market was anticipated to be worth $18.8 billion by the end of 2020, according to Statista.) But building a system that’s flexible enough to understand the nuances of text and handwriting is a difficult challenge, because it means comprehending styles for not just typography and calligraphy but for tran

Plant Disease Detection using Advanced Deep Learning and ReactJS

Original Source Here First, we will design the Flask server to accommodate the pre-trained ResTS architecture. The server will incorporate a route to handle the input image coming from the application and will return a new image that comprises only the salient features along with the disease name and probability. Secondly, we will develop an uncomplicated React application where images can be uploaded and manifested. We will not go into the particulars regarding the ResTS architecture. Please follow the BELOW link to learn how this architecture operates to diagnose plant disease. 1. Creating the server comprising our ResTS (Residual Teacher/Student) model The architecture code is required to be put in the same file as the server. However, this can be changed but due to some errors, I decided to put the whole code of architecture in the same file and load the weights. Also, it is neat to have only one file to run instead of managing mu