Lifeboat Foundation

Article originally published on LINKtoLEADERS under the Portuguese title “Sem saber ler nem escrever!”

In the 80s, “with no knowledge, only intuition”, I discovered the world of computing. I believed computers could do everything, as if it were an electronic God. But when I asked the TIMEX Sinclair 1000 to draw the planet Saturn — I am fascinated by this planet, maybe because it has rings —, I only glimpse a strange message on the black and white TV:

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https://youtu.be/pDSEjaDCtOU?t=2526

Ian Hutchinson’s concerns for existential risk after minute 42.

Ian Hutchinson is a nuclear engineer and plasma physicist at MIT. He has made a number of important contributions in plasma physics including the magnetic confinement of plasmas seeking to enable fusion reactions, which is the energy source of the stars, to be used for practical energy production. Current nuclear reactors are based on fission as we discuss. Ian has also written on the philosophy of science and the relationship between science and religion.

Continue reading “Ian Hutchinson: Nuclear Fusion, Plasma Physics, and Religion” »

Artificial Intelligence (AI) can perform preventive healthcare activities such as health screening, routine check-up and vaccination with expert-level accuracy that can turn out to be cost-effective in the long run. Yet, a new research found that individuals show less trust in preventive care interventions suggested by AI than when the same interventions are prompted by human health experts.

The researchers at Nanyang Technological University (NTU) Singapore studied 15,000 users of a health mobile application and found that emphasising the involvement of a human health expert in an AI-suggested intervention could improve its acceptance and effectiveness.

These findings suggest that the human element remains important even as the healthcare sector increasingly adopts AI to screen, diagnose and treat patients more efficiently. The findings could also contribute to the design of more effective AI-prompted preventive care interventions, said the researchers.

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?” »