Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 238

Machine learning plus insights from genetic research shows the workings of cells – and may help develop new drugs for COVID-19 and other diseases

We combined a machine learning algorithm with knowledge gleaned from hundreds of biological experiments to develop a technique that allows biomedical researchers to figure out the functions of the proteins that turn genes on and off in cells, called transcription factors. This knowledge could make it easier to develop drugs for a wide range of diseases.

Early on during the COVID-19 pandemic, scientists who worked out the genetic code of the RNA molecules of cells in the lungs and intestines found that only a small group of cells in these organs were most vulnerable to being infected by the SARS-CoV-2 virus. That allowed researchers to focus on blocking the virus’s ability to enter these cells. Our technique could make it easier for researchers to find this kind of information.

The biological knowledge we work with comes from this kind of RNA sequencing, which gives researchers a snapshot of the hundreds of thousands of RNA molecules in a cell as they are being translated into proteins. A widely praised machine learning tool, the Seurat analysis platform, has helped researchers all across the world discover new cell populations in healthy and diseased organs. This machine learning tool processes data from single-cell RNA sequencing without any information ahead of time about how these genes function and relate to each other.

AI Wrote Better Phishing Emails Than Humans in a Recent Test

Natural language processing continues to find its way into unexpected corners. This time, it’s phishing emails. In a small study, researchers found that they could use the deep learning language model GPT-3, along with other AI-as-a-service platforms, to significantly lower the barrier to entry for crafting spearphishing campaigns at a massive scale.

Researchers have long debated whether it would be worth the effort for scammers to train machine learning algorithms that could then generate compelling phishing messages. Mass phishing messages are simple and formulaic, after all, and are already highly effective. Highly targeted and tailored “spearphishing” messages are more labor intensive to compose, though. That’s where NLP may come in surprisingly handy.

At the Black Hat and Defcon security conferences in Las Vegas this week, a team from Singapore’s Government Technology Agency presented a recent experiment in which they sent targeted phishing emails they crafted themselves and others generated by an AI-as-a-service platform to 200 of their colleagues. Both messages contained links that were not actually malicious but simply reported back clickthrough rates to the researchers. They were surprised to find that more people clicked the links in the AI-generated messages than the human-written ones—by a significant margin.

Innovation is a risk!

No, it’s not forbidden to innovate, quite the opposite, but it’s always risky to do something different from what people are used to. Risk is the middle name of the bold, the builders of the future. Those who constantly face resistance from skeptics. Those who fail eight times and get up nine.

(Credit: Adobe Stock)

Fernando Pessoa’s “First you find it strange. Then you can’t get enough of it.” contained intolerable toxicity levels for Salazar’s Estado Novo (Portugal). When the level of difference increases, censorship follows. You can’t censor censorship (or can you?) when, deep down, it’s a matter of fear of difference. Yes, it’s fear! Fear of accepting/facing the unknown. Fear of change.

What do I mean by this? Well, I may seem weird or strange with the ideas and actions I take in life, but within my weirdness, there is a kind of “Eye of Agamotto” (sometimes being a curse for me)… What I see is authentic and vivid. Sooner or later, that future I glimpse passes into this reality.

Continue reading “Innovation is a risk!” | >

Microsoft AI Researchers Introduce A Neural Network With 135 Billion Parameters And Deployed It On Bing To Improve Search Results

Transformer-based deep learning models like GPT-3 have been getting much attention in the machine learning world. These models excel at understanding semantic relationships, and they have contributed to large improvements in Microsoft Bing’s search experience. However, these models can fail to capture more nuanced relationships between query and document terms beyond pure semantics.

The Microsoft team of researchers developed a neural network with 135 billion parameters, which is the largest “universal” artificial intelligence that they have running in production. The large number of parameters makes this one of the most sophisticated AI models ever detailed publicly to date. OpenAI’s GPT-3 natural language processing model has 175 billion parameters and remains as the world’s largest neural network built to date.

Microsoft researchers are calling their latest AI project MEB (Make Every Feature Binary). The 135-billion parameter machine is built to analyze queries that Bing users enter. It then helps identify the most relevant pages from around the web with a set of other machine learning algorithms included in its functionality, and without performing tasks entirely on its own.

Machine Learning Approach for Predicting Risk of Schizophrenia Using a Blood Test

Summary: Blood tests revealed specific epigenetic biomarkers for schizophrenia. Researchers applied machine learning to analyze the CoRSIVs region of the human genome to identify the schizophrenia biomarkers. Testing the model with an independent data set revealed the AI technology can detect schizophrenia with 80% accuracy.

Source: Baylor College of Medicine.

An innovative strategy that analyzes a region of the genome offers the possibility of early diagnosis of schizophrenia, reports a team led by researchers at Baylor College of Medicine. The strategy applied a machine learning algorithm called SPLS-DA to analyze specific regions of the human genome called CoRSIVs, hoping to reveal epigenetic markers for the condition.

DeepMind’s Vibrant New Virtual World Trains Flexible AI With Endless Play

The paper’s authors said they’ve created an endlessly challenging virtual playground for AI. The world, called XLand, is a vibrant video game managed by an AI overlord and populated by algorithms that must learn the skills to navigate it.

The game-managing AI keeps an eye on what the game-playing algorithms are learning and automatically generates new worlds, games, and tasks to continuously confront them with new experiences.

The team said some veteran algorithms faced 3.4 million unique tasks while playing around 700000 games in 4000 XLand worlds. But most notably, they developed a general skillset not related to any one game, but useful in all of them.

Facebook AI Open-Sources ‘Droidlet’, A Platform For Building Robots With Natural Language Processing And Computer Vision To Understand The World Around Them

Robots today have been programmed to vacuum the floor or perform a preset dance, but there is still much work to be done before they can achieve their full potential. This mainly has something to do with how robots are unable to recognize what is in their environment at a deep level and therefore cannot function properly without being told all of these details by humans. For instance, while it may seem like backup programming for when bumping into an object that would help prevent unwanted collisions from happening again, this idea isn’t actually based on understanding anything about chairs because the robot doesn’t know exactly what one is!

Facebook AI team just released Droidlet, a new platform that makes it easier for anyone to build their smart robot. It’s an open-source project explicitly designed with hobbyists and researchers in mind so you can quickly prototype your AI algorithms without having to spend countless hours coding everything from scratch.

Droidlet is a platform for building embodied agents capable of recognizing, reacting to, and navigating the world. It simplifies integrating all kinds of state-of-the-art machine learning algorithms in these systems so that users can prototype new ideas faster than ever before!

DeepMind AI predicts 350,000 protein structures

DeepMind CEO and co-founder. “We believe this work represents the most significant contribution AI has made to advancing the state of scientific knowledge to date. And I think it’s a great illustration and example of the kind of benefits AI can bring to society. We’re just so excited to see what the community is going to do with this.” https://www.futuretimeline.net/images/socialmedia/

AlphaFold is an artificial intelligence (AI) program that uses deep learning to predict the 3D structure of proteins. Developed by DeepMind, a London-based subsidiary of Google, it made headlines in November 2020 when competing in the Critical Assessment of Structure Prediction (CASP). This worldwide challenge is held every two years by the scientific community and is the most well-known protein modelling benchmark. Participants must “blindly” predict the 3D structures of different proteins, and their computational methods are subsequently compared with real-world laboratory results.

The CASP challenge has been held since 1994 and uses a metric known as the Global Distance Test (GDT), ranging from 0 to 100. Winners in previous years had tended to hover around the 30 to 40 mark, with a score of 90 considered to be equivalent to an experimentally determined result. In 2018, however, the team at DeepMind achieved a median of 58.9 for the GDT and an overall score of 68.5 across all targets, by far the highest of any algorithm.

Then in 2020, version 2.0 of their AlphaFold program competed in the CASP, winning once again – this time with even greater accuracy. The AlphaFold 2.0 achieved a median of 92.4 across all targets, with its average margin of error comparable to the width of an atom (0.16 nanometres). Andrei Lupas, biologist at the Max Planck Institute in Germany who assessed the performances of each team in CASP, said of AlphaFold: “This will change medicine. It will change research. It will change bioengineering. It will change everything.”

/* */