Giving Robots Better Moves: Combining Unique Gripper Designs With AI and Machine Vision

 

MIT alumnus-founded RightHand Robotics has developed picking robots that are more reliable and adaptable in warehouse environments.

For most people, the task of identifying an object, picking it up, and placing it somewhere else is trivial. For robots, it requires the latest in machine intelligence and robotic manipulation.

That’s what MIT spinoff RightHand Robotics has incorporated into its robotic piece-picking systems, which combine unique gripper designs with artificial intelligence and machine vision to help companies sort products and get orders out the door.

“If you buy something at the store, you push the cart down the aisle and pick it yourself. When you order online, there is an equivalent operation inside a fulfillment center,” says RightHand Robotics co-founder Lael Odhner ’04, SM ’06, PhD ’09. “The retailer typically needs to pick up single items, run them through a scanner, and put them into a sorter or conveyor belt to complete the order. It sounds easy until you imagine tens of thousands of orders a day and more than 100,000 unique products stored in a facility the size of 10 or 20 football fields, with the delivery expectation clock ticking.”

RightHand Robotics is helping companies respond to two broad trends that have transformed retail operations. One is the explosion of e-commerce, which only accelerated during the Covid-19 pandemic. The other is a shift to just-in-time inventory fulfillment, in which pharmacies, grocery stores, and apparel companies restock items based on what’s been purchased that day or week to improve efficiency.

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Researchers Enable AI To Use Its “Imagination” – Closer to Humans’ Understanding of the World

 

Researchers Enable AI To Use Its “Imagination” – Closer to Humans’ Understanding of the World

“Humans can separate their learned knowledge by attributes—for instance, shape, pose, position, color—and then recombine them to imagine a new object. Our paper attempts to simulate this process using neural networks.”

The science of imagination

In this new study, the researchers attempt to overcome this limitation using a concept called disentanglement. Disentanglement can be used to generate deep fakes, for instance, by disentangling human face movements and identity. By doing this, said Ge, “people can synthesize new images and videos that substitute the original person’s identity with another person, but keep the original movement.”

Similarly, the new approach takes a group of sample images—rather than one sample at a time as traditional algorithms have done—and mines the similarity between them to achieve something called “controllable disentangled representation learning.”

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Defining AI Engineering

 

An artificial intelligence engineer is an individual who works with traditional machine learning techniques like natural language processing and neural networks to build models that power AI–based applications.

AI engineering is an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts.

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Big Data Security Analytics: A Weapon Against Rising Cyber Security Attacks?

 

1. Security

Data analytics applications or, more specifically, predictive analysis has also helped in dropping crime rates in certain areas. In a few major cities like Los Angeles and Chicago, historical and geographical data has been used to isolate specific areas where crime rates could surge. On that basis, while arrests could not be made on a whim, police patrols could be increased. Thus, using applications of data analytics, crime rates dropped in these areas.

What are the applications of data analytics?

Some of the different data analytics applications that are currently being used in several organizations across the globe are:

1.Security
2.Transportation
3.Risk detection
4.Risk Management
5.Delivery
6.Fast internet allocation
7.Reasonable Expenditure
8.Interaction with customers
Now let's see Cybersecurity in Data analytics

Big Data Security Analytics: A Weapon Against Rising Cyber Security Attacks?
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Meet Flippy, our new kitchen assistant

 

Flipping Burger by Flippy

Flippy is the robot that is an expert at flipping burgers in hamburger restaurant CaliBurger and put them on the bun. It can grill 150-300 patties with excellence in one hour. It's a robot that doesn't need to wear a hairnet while working in the kitchen. Flippy can also change the sides of patties and can select spatulas that are needed at a specific time. David Zito, the CEO of Miso and manufacturer of Flippy shared his thoughts:

''Flippy is the world’s first autonomous robotic kitchen assistant that can learn from its surrounding and acquire new skills over time.''

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How Machine Learning Algorithms Make Self-Driving Cars a Reality

 

Machine learning algorithms make it possible for self-driving cars to exist. They allow a car to collect data on its surroundings from cameras and other sensors, interpret it, and decide what actions to take. Machine learning even allows cars to learn how to perform these tasks as well as (or even better than) humans.

List of Common Machine Learning Algorithms

• Linear Regression.
• Logistic Regression.
• Decision Tree.
• SVM.
• Naive Bayes.
• kNN.
• K-Means.
• Random Forest.

Machine learning in autonomous driving can be supervised or unsupervised. The main difference between the two options lies in the amount of human input required for learning. In supervised learning, a computer interprets data and makes predictions based on input data, then compares those predictions to correct output data in order to improve future predictions. In unsupervised learning, data isn’t labeled. So the computer learns to recognize the inherent structure based on input data only.

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11 Facts about Data Science that you must know

 

 Statistics, Machine Learning, Data Science, or Analytics – whatever you call it, this discipline is on rising in last quarter of century primarily owing to increasing data collection abilities and an exponential increase in computational power. The field is drawing from the pool of engineers, mathematicians, computer scientists, and statisticians, and increasingly, is demanding a multi-faceted approach for successful execution. 

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Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.

 Cancer is an aggressive disease with a low median survival rate.

We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis and continue improving human health in the future.

Highlights

•

Artificial intelligence (AI) has reached new heights in clinical cancer research in recent years.

•

AI is applied to assist cancer diagnosis and prognosis, given its unprecedented accuracy level, which is even higher than that of a general statistical expert.

•

An overview of how AI applied in clinical cancer could be leveraged in this area and thereby contribute to improved human health.

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And also include How AI cure Cancer 

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Data Science vs. Data Analytics vs. Machine Learning: Expert Talk

Data science is a concept used to tackle big data and includes preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets.

A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data.

Machine learning can be defined as the practice of using algorithms to extract data, learn from it, and then forecast future trends for that topic. Traditional machine learning software is statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data. 

Do you know more about this?

1. Data Science vs. Data Analytics

2. Data Science vs. Machine Learning

3. Enroll in Our PGP in Data Analytics, Data Science, AI and Machine Learning Today

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