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How AI and machine learning are reshaping the way transit systems move traffic patterns – REJournals

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Of the many ways artificial intelligence and machine learning are poised to improve modern life, the promise of impacting mass transit is significant. The world is much different compared with the early days of the pandemic, and people around the world are again leveraging mobility and transit systems for work, leisure and more. Across the U.S., traditional mass transit systems including buses, subways and personal vehicles have returned to struggling through gridlock, rider levels and congestion. However, advanced AI and machine learning solutions built on cloud-based platforms are being deployed to reduce these frustrations. Transportation is one of the most important areas in which modern AI provides a significant advantage over conventional algorithms used in traditional transit system technology.


3 Innovative Ways You Can Use Robots

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Since its inception, the robotics industry has seen a tremendous hike in its revenue growth. The global robotics market is expected to see a CAGR of 24.52% within 2023. Realizing the incredible capabilities of robots, organizations worldwide are investing a lot of their capital to enjoy the benefits of this AI application. Grabbing the opportunity to enhance organizational productivity, automate business services, and stay unique in the crowd of competitors, organizations are finding potential areas where they could replace human tasks with robots. Robots have become one of the key contributors to driving the market revenue significantly.


Using AI to anticipate others' behavior on the road

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Humans may be one of the biggest roadblocks keeping fully autonomous vehicles off city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.) MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time. Their behavior-prediction framework first guesses the relationships between two road users--which car, cyclist, or pedestrian has the right of way, and which agent will yield--and uses those relationships to predict future trajectories for multiple agents.


Amazon flexes retail muscle with physical clothing store – TechCrunch

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Signaling its ambitions to make a dent in the apparel market, Amazon today opened its first physical clothing store, Amazon Style, in the Greater Los Angeles Area. Offering a twist on the traditional experience, visitors to the Glendale, California shop at The Americana At Brand use an app to scan codes on displayed items from Steve Madden, Levi's, Lacoste and other brands to send them directly to a fitting room or pickup counter. As TechCrunch previously reported, Amazon Style features hundreds of brands chosen by "fashion creators" and "feedback provided by millions of customers shopping on Amazon.com." Scanning the QR code next to an item pops up a selector for sizes and colors, as well as details such as customer ratings and adds the item to a list for later perusing. Amazon Style doesn't use the cashierless "Just Walk Out" tech found in Amazon Fresh and Whole Foods locations, instead opting for Amazon's controversial Amazon One palm recognition service.


Global Big Data Conference

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Artificial Intelligence (AI) Patent Application filings continue their explosive growth trend at the U.S. Patent Office (USPTO). At the end of 2020, the USPTO published a report finding an exponential increase in the number of patent application filings from 2002 to 2018. In addition, current data shows that AI-related application filings pertaining to graphics and imaging are taking the lead over AI modeling and simulation applications. In the last quarter of 2020, the United States Patent and Trademark Office (USPTO) reported that patent filings for Artificial Intelligence (AI) related inventions more than doubled from 2002 to 2018. See Office of the Chief Economist, Inventing AI: Tracking The Diffusion Of Artificial Intelligence With Patents, IP DATA HIGHLIGHTS No. 5 (Oct.


Amazon Researchers Develop A New Way To Rewrite Database Queries To Eliminate Redundancies

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As databases grow in size, queries become slower. Database queries frequently contain multiple repetitive procedures that can be eliminated. To discover a full name record in a database for a specific individual, database queries to fetch all entries for the first name, then all entries for the second name, and compute their intersection. This requires accessing the database twice, which is a duplication that can be eliminated to save data retrieval time. Amazon researchers present a method for rewriting complicated SQL queries to eliminate redundancy.


Artificial Intelligence sleep app may mean an end to sleeping pills for insomniacs

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A new artificial intelligence sleep app has been developed that might be able to replace sleeping pills for insomnia sufferers. Sleepio uses an AI algorithm to provide individuals with tailored cognitive behavioural therapy for insomnia (CBT-I). The National Institute for Health and Care Excellence (Nice) said it would save the NHS money as well as reduce prescriptions of medicines such as zolpidem and zopiclone, which can be dependency forming. Its economic analysis found healthcare costs were lower after one year of using Sleepio, mostly because of fewer GP appointments and sleeping pills prescribed. The app provides a digital six-week self-help programme involving a sleep test, weekly interactive CBT-I sessions and keeping a diary about sleeping patterns.


Roboticists go off road to compile data that could train self-driving ATVs: TartanDrive dataset likely largest for off-road environments

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They drove the heavily instrumented ATV aggressively at speeds up to 30 miles an hour. They slid through turns, took it up and down hills, and even got it stuck in the mud -- all while gathering data such as video, the speed of each wheel and the amount of suspension shock travel from seven types of sensors. The resulting dataset, called TartanDrive, includes about 200,000 of these real-world interactions. The researchers believe the data is the largest real-world, multimodal, off-road driving dataset, both in terms of the number of interactions and types of sensors. The five hours of data could be useful for training a self-driving vehicle to navigate off road.


Microsoft's Code-Writing AI Points to the Future of Computers

WIRED

Microsoft just showed how artificial intelligence could find its way into many software applications--by writing code on the fly. At the Microsoft Build developer conference today, the company's chief technology officer, Kevin Scott, demonstrated an AI helper for the game Minecraft. The non-player character within the game is powered by the same machine learning technology Microsoft has been testing for auto-generating software code. The feat hints at how recent advances in AI could change personal computing in years to come by replacing interfaces that you tap, type, and click to navigate into interfaces that you simply have a conversation with. The Minecraft agent responds appropriately to typed commands by converting them into working code behind the scenes using the software API for the game.


Self-Supervised Learning and Its Applications - neptune.ai

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In the past decade, the research and development in AI have skyrocketed, especially after the results of the ImageNet competition in 2012. The focus was largely on supervised learning methods that require huge amounts of labeled data to train systems for specific use cases. In this article, we will explore Self Supervised Learning (SSL) – a hot research topic in a machine learning community. Self-supervised learning (SSL) is an evolving machine learning technique poised to solve the challenges posed by the over-dependence of labeled data. For many years, building intelligent systems using machine learning methods has been largely dependent on good quality labeled data. Consequently, the cost of high-quality annotated data is a major bottleneck in the overall training process.