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See how you can extract quantitative data from your image using the AI pixel classifier.

More about Mica: https://fcld.ly/mica-yt-tut.

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Mica radically simplifies your workflows, but your workflow is not completed unless you have extracted quantitative information from that image.
Let me show you how easy it is to extract quantitative information with Mica.
First record your multicolor image.
In this case, we want to count the nuclei that we see in that image.
Go to Learn and load the image of interest.
Now you have two classes, the background and the nuclei that we want to quantify.
First of all, draw a background region.
Secondly, draw the object of interest.
Once you are done with that let Mica first create a preview of the annotation that you have created.
If you are happy with that, then do the full training.
Now you have trained an AI model that uses pixel classification in order to segment your nuclei.
Save that model and you can use that model also for all the experiments that you are doing in the future.
Simply go to Results, select the image to quantify, switch to Analysis and you will have access to all the different models that you have trained.
Select the one that you are interested in and Start.
As an output can display the data as histograms, boxplots or even scatterplots.

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Continue reading “How to extract quantitative data from your image using the AI pixel classifier” | >

Gravitational wave astronomy is still in its early stages. So far it has focused on the most energetic and distinct sources of gravitational waves, such as the cataclysmic mergers of black holes and neutron stars. But that will change as our gravitational telescopes improve, and it will allow astronomers to explore the Universe in ways previously impossible.

Although gravitational waves have many similarities to light waves, one distinct difference is that most objects are transparent to gravitational waves. Light can be absorbed, scattered, and blocked by matter, but gravitational waves mostly just pass through matter. They can be lensed by the mass of an object, but not fully blocked.

This means that gravitational waves could be used as a tool to peer inside astronomical bodies, similar to the way X-rays or MRIs allow us to see inside a human’s body.

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Is mind reading possible? An age-old question with multiple unproven answers. Those who study psychology often claim that they can understand what the other person is saying as they study mental processes, brain functions, and behaviour, but even they can be 100 per cent accurate.

A study, published in the journal Nature Neuroscience, attempts to address it as scientists have said that they have come up with a way to decode a stream of words in the brain using MRI scans and artificial intelligence.

The study titled — “Semantic reconstruction of continuous language from non-invasive brain recordings” — noted that the system won’t replicate each word but it reconstructs the brief of what a person hears or imagines. The study was published in the journal Nature Neuroscience.

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ChatGPT may represent one of the biggest disruptions in modern history with it’s powerful A.I based chatbot. But within weeks of ChatGPT’s release, security researchers discovered several cases of people using ChatGPT for everything from malware development to exploit coding. In this video, take a look at the five ways attackers are utilizing ChatGPT for wrong doing.

0:14 Intro to ChatGPT / Natural Language Processing (NLP) & GPT
1:28 Using ChatGPT for Vulnerability Discovery.
1:56 Vulnerability Prompts to Utilize.
3:10 Writing Exploits.
3:35 Exploit Prompts to Utilize.
4:33 Malware Development.
5:00 Malware Examples (Stealers, Command & Control)
5:42 Polymorphic Malware Development Using ChatGPT
6:21 A.I. Based Phishing using NLP (Natural Language Processing)
7:20 ChatGPT Advantages over Traditional Phishing Messages.
7:41 Custom Messages Using GPT-3
8:04 Using Macros and LOLBINs.
9:33 GPT-3 vs GPT-4 (Coming Soon)
9:56 Cybersecurity Considerations and Predictions.

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UNSW Sydney researchers have developed a chip-scale method using OLEDs to image magnetic fields, potentially transforming smartphones into portable quantum sensors. The technique is more scalable and doesn’t require laser input, making the device smaller and mass-producible. The technology could be used in remote medical diagnostics and material defect identification.

Smartphones could one day become portable quantum sensors thanks to a new chip-scale approach that uses organic light-emitting diodes (OLEDs) to image magnetic fields.

Researchers from the ARC Centre of Excellence in Exciton Science at UNSW Sydney have demonstrated that OLEDs, a type of semiconductor material commonly found in flat-screen televisions, smartphone screens, and other digital displays, can be used to map magnetic fields using magnetic resonance.

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Researchers at Caltech have discovered a new phenomenon, “collectively induced transparency” (CIT), where light passes unimpeded through groups of atoms at certain frequencies. This finding could potentially improve quantum memory systems.

A newly discovered phenomenon dubbed “collectively induced transparency” (CIT) causes groups of atoms to abruptly stop reflecting light at specific frequencies.

CIT was discovered by confining ytterbium atoms inside an optical cavity—essentially, a tiny box for light—and blasting them with a laser. Although the laser’s light will bounce off the atoms up to a point, as the frequency of the light is adjusted, a transparency window appears in which the light simply passes through the cavity unimpeded.

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Geoffrey Hinton who won the ‘Nobel Prize of computing’ for his trailblazing work on neural networks is now free to speak about the risks of AI.

Man often dubbed the ‘Godfather of AI’ says part of him now regrets his life’s work.

One of the pioneers in the development of deep learning models that have become the basis for tools like ChatGPT and Bard, has quit Google to warn against the dangers of scaling AI technology too fast.

Alina Grubnyak / Unsplash.

Continue reading “Deep learning pioneer Geoffrey Hinton warns against rapid AI development as he quits Google” | >

Major record labels are going after AI-generated songs, arguing copyright infringement. Legal experts say the approach is far from straightforward.

A certain type of music has been inescapable on TikTok in recent weeks: clips of famous musicians covering other artists’ songs, with combinations that read like someone hit the randomizer button. There’s Drake covering singer-songwriter Colbie Caillat, Michael Jackson covering The Weeknd, and Pop Smoke covering Ice Spice’s “In Ha Mood.” The artists don’t actually perform the songs — they’re all generated using artificial intelligence tools. And the resulting videos have racked up tens of millions of views.

Can human Drake stop his AI voice clone?

Continue reading “Drake’s AI clone is here — and Drake might not be able to stop him” | >