Machine Learning

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AI For Image Recognition: What It Is, How It Works & Examples

Artificial Intelligence, Computer vision, Machine Learning, Shaip Blogs

Human beings have the innate ability to distinguish and precisely identify objects, people, animals, and places from photographs. Artificial intelligence is the underlying technology that powers image recognition, enabling computers to analyze and interpret visual data. However, computers don’t come with the capability to classify images. Yet, they can be trained to interpret visual information […]

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AI Models & Ethical Data: Building Trust in Machine Learning

ai training data, Artificial Intelligence, Ethical AI, Machine Learning, Shaip Blogs

In the rapidly evolving landscape of artificial intelligence, one fundamental truth remains constant: the quality and ethics of your training data directly determine the trustworthiness of your AI models. As organizations race to deploy machine learning solutions, the conversation around ethical data collection and responsible AI development has moved from the periphery to the center

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What is Named Entity Recognition (NER) – Example, Use Cases, Benefits & Challenges

Artificial Intelligence, Machine Learning, NER, NLP(Natural Language Processing), Shaip Blogs

Every time we hear a word or read a text, we have the natural ability to identify and categorize the word into people, place, location, values, and more. Humans can quickly recognize a word, categorize it and understand the context. For example, when you hear the word ‘Steve Jobs,’ you can immediately think of at

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22 Best Open-source OCR & Handwriting Datasets to Train your ML models

Artificial Intelligence, Machine Learning, OCR, Shaip Blogs

The rise in optical character recognition usage can primarily be attributed to the increase in the production of automatic recognition systems. As a result, the global market value of OCR technology, pegged at $8.93 billion in 2021, is predicted to grow at a CAGR of 15.4% between 2022 and 2030. But what exactly is OCR

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MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Drug development, Electrical engineering and computer science (EECS), Health sciences and technology, Jameel Clinic, Machine Learning, Medicine, MIT Schwarzman College of Computing, Pharmaceuticals, Proteins, Research, School of Engineering, Special events and guest speakers

More than 300 people across academia and industry spilled into an auditorium to attend a BoltzGen seminar on Thursday, Oct. 30, hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health (MIT Jameel Clinic). Headlining the event was MIT PhD student and BoltzGen’s first author Hannes Stärk, who had announced BoltzGen just a few days

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Researchers discover a shortcoming that makes LLMs less reliable

Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Technology and society

Large language models (LLMs) sometimes learn the wrong lessons, according to an MIT study.Rather than answering a query based on domain knowledge, an LLM could respond by leveraging grammatical patterns it learned during training. This can cause a model to fail unexpectedly when deployed on new tasks.The researchers found that models can mistakenly link certain

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MIT Sea Grant students explore the intersection of technology and offshore aquaculture in Norway

Artificial Intelligence, Center for International Studies, Classes and programs, Collaboration, data, Environment, Europe, Food, Global, Industry, International initiatives, Internships, Machine Learning, Mechanical engineering, MISTI, MIT Schwarzman College of Computing, MIT Sea Grant, Oceanography and ocean engineering, Robotics, School of Engineering, School of Humanities Arts and Social Sciences, Sensors, Software, Students, UAV, Undergraduate, Undergraduate Research Opportunities Program (UROP), Water

Norway is the world’s largest producer of farmed Atlantic salmon and a top exporter of seafood, while the United States remains the largest importer of these products, according to the Food and Agriculture Organization. Two MIT students recently traveled to Trondheim, Norway to explore the cutting-edge technologies being developed and deployed in offshore aquaculture. Beckett Devoe,

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Understanding the nuances of human-like intelligence

Artificial Intelligence, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Faculty, Machine Learning, MIT Schwarzman College of Computing, Profile, School of Engineering

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?These questions may be deeply philosophical, but for Phillip Isola, finding the answers is as much about computation as it

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New AI agent learns to use CAD to create 3D objects from sketches

3-D, Artificial Intelligence, automation, Computer modeling, Design, Machine Learning, Mechanical engineering, Research, School of Engineering, Software

Computer-Aided Design (CAD) is the go-to method for designing most of today’s physical products. Engineers use CAD to turn 2D sketches into 3D models that they can then test and refine before sending a final version to a production line. But the software is notoriously complicated to learn, with thousands of commands to choose from.

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The cost of thinking

Artificial Intelligence, Brain and cognitive sciences, Computer science and technology, Machine Learning, McGovern Institute, Neuroscience, Research, School of Science

Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a

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