Lifeboat Foundation

Choosing interesting dissertation topics in ML is the first choice of Master’s and Doctorate scholars nowadays. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Selecting and working on a dissertation topic in machine learning is not an easy task as machine learning uses statistical algorithms to make computers work in a certain way without being explicitly programmed. The main aim of machine learning is to create intelligent machines which can think and work like human beings. This article features the top 10 ML dissertations for Ph.D. students to try in 2022.

Text Mining and Text Classification: Text mining is an AI technology that uses NLP to transform the free text in documents and databases into normalized, structured data suitable for analysis or to drive ML algorithms. This is one of the best research and thesis topics for ML projects.

Recognition of Everyday Activities through Wearable Sensors and Machine Learning: The goal of the research detailed in this dissertation is to explore and develop accurate and quantifiable sensing and machine learning techniques for eventual real-time health monitoring by wearable device systems.

A Royal Astronomical Society press release revealed that during the National Astronomy Meeting (NAM) 2022, currently being hosted at the University of Warwick, scientists will announce the discovery of 40,000 ring galaxies discovered using a “cyborg” approach — a combination of human and machine intelligence.

The work will be presented by Dr. Mike Walmsley of the University of Manchester and the Galaxy Zoo collaboration — a decade-long citizen science project on the Zooniverse platform.

Volunteers for the Galaxy Zoo project look through pictures of galaxies and classify them by shape and features. Studying the morphology of galaxies is an important step in understanding how they interact with their surroundings. In the words of Galaxy Zoo’s “About” section:

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Deepfakes aren’t new, but this AI-powered technology has emerged as a pervasive threat in spreading misinformation and increasing identity fraud. The pandemic made matters worse by creating the ideal conditions for bad actors to take advantage of organizations’ and consumers’ blindspots, further exacerbating fraud and identity theft. Fraud stemming from deepfakes spiked during the pandemic, and poses significant challenges for financial institutions and fintechs that need to accurately authenticate and verify identities.

As cybercriminals continue to use tools like deepfakes to fool identity verification solutions and gain unauthorized access to digital assets and online accounts, it’s essential for organizations to automate the identity verification process to better detect and combat fraud.

“If we get a similar hit rate in detecting texture in tumors, the potential for early diagnosis is huge,” says scientist.

Researchers at University College London.

The potentially early-stage fatal tumors in humans could be noticed by the new x-ray method that collaborates with a deep-learning Artificial Intelligence (AI) algorithm to detect explosives in luggages, according to a report published by MIT Technology Review on Friday.

Researchers develop a new method that uses multiple models to create more complex images with better understanding.

The internet had a collective feel-good moment with the introduction of DALL-E, an artificial intelligence-based image generator inspired by artist Salvador Dali and the lovable robot WALL-E that uses natural language to produce whatever mysterious and beautiful image your heart desires. Seeing typed-out inputs like “smiling gopher holding an ice cream cone” instantly spring to life clearly resonated with the world.

Getting said smiling gopher and attributes to pop up on your screen is not a small task.

Continue reading “AI system makes models like DALL-E 2 more creative” »

“Neuromorphic computing could offer a compelling alternative to traditional AI accelerators by significantly improving power and data efficiency for more complex AI use cases, spanning data centers to extreme edge applications.”

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Can computer systems develop to the point where they can think creatively, identify people or items they have never seen before, and adjust accordingly — all while working more efficiently, with less power? Intel Labs is betting on it, with a new hardware and software approach using neuromorphic computing, which, according to a recent blog post, “uses new algorithmic approaches that emulate how the human brain interacts with the world to deliver capabilities closer to human cognition.”

Continue reading “Is Intel Labs’ brain-inspired AI approach the future of robot learning?” »

Genius.

March 2019 Workshop

Marketing is one of the areas of business operations where it is widely predicted that artificial intelligence (AI) will drive enormous change. In fact, a McKinsey study found that, along with sales, it is the single business function where it will have the most financial impact. This means that if you’re a marketer and you’re not using AI, you’re missing out on the benefits of what is possibly the most transformational technology.

Actually, though, the chances that there are people out there doing marketing today and not using AI in any shape or form is somewhat unlikely.

Continue reading “Artificial Intelligence And The Future Of Marketing” »

The research is still a ways away from helping people who can’t communicate through speech.

An artificial intelligence can decode words and sentences from brain activity with surprising — but still limited — accuracy. Using only a few seconds of brain activity data, the AI guesses what a person has heard. It lists the correct answer in its top 10 possibilities up to 73 percent of the time, researchers found in a preliminary study.

The AI’s “performance was above what many people thought was possible at this stage,” says Giovanni Di Liberto, a computer scientist at Trinity College Dublin who was not involved in the research.

Continue reading “An AI can decode speech from brain activity with surprising accuracy” »