Toggle light / dark theme

For drone racing enthusiasts. 😃


If you follow autonomous drone racing, you likely remember the crashes as much as the wins. In drone racing, teams compete to see which vehicle is better trained to fly fastest through an obstacle course. But the faster drones fly, the more unstable they become, and at high speeds their aerodynamics can be too complicated to predict. Crashes, therefore, are a common and often spectacular occurrence.

But if they can be pushed to be faster and more nimble, drones could be put to use in time-critical operations beyond the race course, for instance to search for survivors in a natural disaster.

Now, aerospace engineers at MIT have devised an algorithm that helps drones find the fastest route around obstacles without crashing. The new algorithm combines simulations of a drone flying through a virtual obstacle course with data from experiments of a real drone flying through the same course in a .

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.

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.

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.

When the difference enters, it becomes normal and accepted by society to make room for more innovation, change, and difference.

Cyberspace 2021.

The term “cyberspace” first appeared in fiction in the 1980s, incorporating the Internet invented earlier (1969). It’s as if time doesn’t matter, and cyberspace always exists. There might not be a name for it yet, but it sure did, like certain Universal Laws that we are discovering and coining, but that has always existed.

It is the ether of digital existence
!

In 1995, I was also called crazy — albeit nicely, by the way — when, from door to door, I announced the presence of something called the Internet. Entrepreneurs who esteemed me until they warmly welcomed me into their companies, perhaps because of my passion for explaining what was unknown to them, only to decline later what I proposed to them: placing companies in the network of networks.

I was affectionately dubbed crazy for a few more years until the part where “I stopped being crazy” to be another entrepreneur exploring something still strange called the Internet. We were about to reach the so-called “dot-com bubble.” The competition had arrived, and I clapped my hands; I no longer felt alone!

(Obviously, I wasn’t the only one to see the future forming in front of our eyes. I saw color on black and white screens.)

The heights of wisdom, the masters of the universe, began to emerge because they heard that the Internet was a business that made much money, and the gold rush became frantic and ridiculous. A few years later — some weren’t for years — there was a mushroom explosion.

After persuasion resulting from the obvious and not the explanations of insane people (me included), this new industry has matured and revolutionized the world. However, history tends to repeat itself, and several revolutions, large and small, have taken place since then. Some are so natural that change happens overt and viral. But more attention needs to be paid to some revolutionary changes that could jeopardize human existence as we know it.

I’m referring to Artificial Intelligence (AI) which is now everywhere, albeit invisible and tenuous. The exponential acceleration of technology is taking us there to the point of no return.

When Moore’s Law itself becomes outdated, it only means that technological acceleration has gone into “warp” speed. At the risk of us human beings becoming outdated, we must change our reluctance and skepticism.

There is no time for skepticism. Adaptation to what is coming, or what is already here among us, like extraterrestrials, is crucial for the evolution and survival of the human species. I believe we are at another great peak of technological development.

I always pursued the future, not to live outside the reality of the present but to help build it. After all these years of dealing with the “Eye of Agamotto,” I feel the duty and obligation to contribute to a better future and not sit idly by watching what I fear will happen.

Angels and demons lurk between the zeros and ones!

So far, with current conventional computers, including supercomputers, the acceleration is already vertiginous. With quantum computers, the thing becomes much more serious, and if we aren’t up to merging our true knowledge, our human essence, with machines, danger lurks.

Quantum computing powers AI, maximizing it. An exponentiated AI quickly arrives at the AGI. That is the Artificial General Intelligence or Superintelligence that equals or surpasses the average human intelligence. That’s the intelligence of a machine that can successfully perform any intellectual task of any human being.

When we no longer have the artificiality of “our own” intelligence and Superintelligence has emerged, it’s good that the bond between human and machine has already had a real “handshake” to understand each other, just like two “modems,” understood each other in the BBS (Bulletin Board System) time.

We human beings are still — and I believe we always will be — the central computer, albeit with inferior computational resources (for now), and replaced by mighty machines that accelerate our evolution.

There is no way out. It’s inevitable. It’s evolution. So, a challenge and not a problem. Perhaps the greatest human challenge. So far, it’s been warming up. Henceforth, everything done will have to be free of human toxicity so that New AI is, in fact, our best version, the cream of the very best in human beings; its essence in the form of a whole!

A digital transformation is a transition to a different world. The power of adaptation to this different world defines our existence (survival, like Darwin).

As you’ve already noticed, the title of this article (Innovation is a risk!) has a double meaning. Let me complement it with:

Life is a risk!

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.

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.

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.

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!