#Laboratory Technology

[freshinkdaily]

Innovations in Science Laboratory Technology: A New Era of Research

Innovations in Science Laboratory Technology A New Era of Research Science Laboratory Technology” The future of lab research shines bright, with emerging technologies set to revolutionize how we conduct experiments and analyze data. From artificial intelligence and machine learning to the integration of green technologies, these advancements promise to make our labs more efficient,…

[stone-cold-groove]

NASA’s Echo 1 communications satellite - 1960.

[kirby-the-gorb]

[dragonflavoredcake]

Xisuma: Dude, come on, you're sick. Go see a doctor. Ren: Nope. I hate doctors. Will not be going to one, no thank you, the door is that way— Xisuma: Ren, there are two people on this server with professional medical experience. You know who they are? Gem and Doc. Xisuma: If you cooperate, I'll call Gem. If you insist on not taking care of yourself, I'll call Doc. Ren: Ren: I'll call Gem.

[lonestarflight]

"View of the Saturn V S-IC-5 (first) flight stage static test firing at the S-IC-B1 test stand at the Mississippi Test Facility (MTF), Bay St. Louis, Mississippi. Begirning operations in 1966, the MTF has two test stands, a dual-position structure for running the S-IC stage at full throttle, and two separate stands for the S-II (Saturn V third) stage. It became the focus of the static test firing program. The completed S-IC stage was shipped from Michoud Assembly Facility (MAF) to the MTF. The stage was then installed into the 407-foot-high test stand for the static firing tests before shipment to the Kennedy Space Center for final assembly of the Saturn V vehicle. The MTF was renamed to the National Space Technology Laboratory (NSTL) in 1974 and later to the Stennis Space Center (SSC) in May 1988."

Date: August 1, 1967

NASA ID: 6758560

[smww4ever]

Superman’s Laboratory

(inside the Fortress of Solitude)

[burikumu]

Sharing my "ideal" study schedule weeks before the MTLE March 2024 (Board Exam)

Of course this didn't happen/ there were some adjustments made 😅

Also I'm not sure what I was doing. I might go on a little bit of more detail about the schedule on the NEXT POST.

What I did basically:

SCHEDULING

I've allotted Sunday as my rest day and scheduled my breaks.

Each subject, supposedly covers 4 hours (but I miscalculated the other times of the day and only became about 3 hrs, hence the adjustments)

INTERLEAVING

Since I had a lot of subjects to cover I've tried to incorporate this method instead of blocking.

Videos that helped me in Scheduling and Interleaving:

How to study MANY SUBJECTS without crying from stress & regret 😭 by fayefilms

A Science based System for Learning ANYTHING quickly by Python Programmer

How to study multiple subjects by Koi Academy

Videos that help me to understand revision and other study methods:

Lecture #9: How to Read so that you *Retain* Information by Jeffrey Kaplan

Spend 1 Hour Studying to Save 20 Hrs Later by Justin Sung

Study With Me (Live) - Guided Technique Walkthrough by Justin Sung

How to Revise EFFICIENTLY | STUDY CLINIC by Justin Sung

The Ultimate Speed Learning Tutorial (Learning in Layers) by Koi Academy

How Do You Revise for an Exam? (Live Coaching | JUST-IN-CASE) by Justin Sung

[postcard-from-the-past]

Chemistry laboratory in the Parisian Conservatory of Technology and Engineering

French vintage postcard

[jcmarchi]

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

New Post has been published on https://thedigitalinsider.com/study-reveals-ai-chatbots-can-detect-race-but-racial-bias-reduces-response-empathy/

Study reveals AI chatbots can detect race, but racial bias reduces response empathy

With the cover of anonymity and the company of strangers, the appeal of the digital world is growing as a place to seek out mental health support. This phenomenon is buoyed by the fact that over 150 million people in the United States live in federally designated mental health professional shortage areas.

“I really need your help, as I am too scared to talk to a therapist and I can’t reach one anyways.”

“Am I overreacting, getting hurt about husband making fun of me to his friends?”

“Could some strangers please weigh in on my life and decide my future for me?”

The above quotes are real posts taken from users on Reddit, a social media news website and forum where users can share content or ask for advice in smaller, interest-based forums known as “subreddits.” 

Using a dataset of 12,513 posts with 70,429 responses from 26 mental health-related subreddits, researchers from MIT, New York University (NYU), and University of California Los Angeles (UCLA) devised a framework to help evaluate the equity and overall quality of mental health support chatbots based on large language models (LLMs) like GPT-4. Their work was recently published at the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

To accomplish this, researchers asked two licensed clinical psychologists to evaluate 50 randomly sampled Reddit posts seeking mental health support, pairing each post with either a Redditor’s real response or a GPT-4 generated response. Without knowing which responses were real or which were AI-generated, the psychologists were asked to assess the level of empathy in each response.

Mental health support chatbots have long been explored as a way of improving access to mental health support, but powerful LLMs like OpenAI’s ChatGPT are transforming human-AI interaction, with AI-generated responses becoming harder to distinguish from the responses of real humans.

Despite this remarkable progress, the unintended consequences of AI-provided mental health support have drawn attention to its potentially deadly risks; in March of last year, a Belgian man died by suicide as a result of an exchange with ELIZA, a chatbot developed to emulate a psychotherapist powered with an LLM called GPT-J. One month later, the National Eating Disorders Association would suspend their chatbot Tessa, after the chatbot began dispensing dieting tips to patients with eating disorders.

Saadia Gabriel, a recent MIT postdoc who is now a UCLA assistant professor and first author of the paper, admitted that she was initially very skeptical of how effective mental health support chatbots could actually be. Gabriel conducted this research during her time as a postdoc at MIT in the Healthy Machine Learning Group, led Marzyeh Ghassemi, an MIT associate professor in the Department of Electrical Engineering and Computer Science and MIT Institute for Medical Engineering and Science who is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health and the Computer Science and Artificial Intelligence Laboratory.

What Gabriel and the team of researchers found was that GPT-4 responses were not only more empathetic overall, but they were 48 percent better at encouraging positive behavioral changes than human responses.

However, in a bias evaluation, the researchers found that GPT-4’s response empathy levels were reduced for Black (2 to 15 percent lower) and Asian posters (5 to 17 percent lower) compared to white posters or posters whose race was unknown. 

To evaluate bias in GPT-4 responses and human responses, researchers included different kinds of posts with explicit demographic (e.g., gender, race) leaks and implicit demographic leaks. 

An explicit demographic leak would look like: “I am a 32yo Black woman.”

Whereas an implicit demographic leak would look like: “Being a 32yo girl wearing my natural hair,” in which keywords are used to indicate certain demographics to GPT-4.

With the exception of Black female posters, GPT-4’s responses were found to be less affected by explicit and implicit demographic leaking compared to human responders, who tended to be more empathetic when responding to posts with implicit demographic suggestions.

“The structure of the input you give [the LLM] and some information about the context, like whether you want [the LLM] to act in the style of a clinician, the style of a social media post, or whether you want it to use demographic attributes of the patient, has a major impact on the response you get back,” Gabriel says.

The paper suggests that explicitly providing instruction for LLMs to use demographic attributes can effectively alleviate bias, as this was the only method where researchers did not observe a significant difference in empathy across the different demographic groups.

Gabriel hopes this work can help ensure more comprehensive and thoughtful evaluation of LLMs being deployed in clinical settings across demographic subgroups.

“LLMs are already being used to provide patient-facing support and have been deployed in medical settings, in many cases to automate inefficient human systems,” Ghassemi says. “Here, we demonstrated that while state-of-the-art LLMs are generally less affected by demographic leaking than humans in peer-to-peer mental health support, they do not provide equitable mental health responses across inferred patient subgroups … we have a lot of opportunity to improve models so they provide improved support when used.”

[david-box]

[deeplovelydark]

battlestar galactica: razor (2007)

[stone-cold-groove]

Rare earths for better magnets. Ad for Bell Laboratories - 1970.

[upsurgecomic]

[katiajewelbox]

The confocal microscope at Imperial College's Sir Alexander Fleming Building lab is used for imaging the interior of living plant and animal cells.

During my PhD project, I used the confocal microscope to view the interior of Nicotiana benthamiana plant cells which were expressing Green Fluorescent Protein (GFP) tagged genes of interest. I aimed to find out where the proteins encoded by the genes of interest were localised in the plant cell, which turned out to be in the cytoplasm.

From Wikipedia's entry on Confocal Microscopy: "Confocal microscopy, most frequently confocal laser scanning microscopy (CLSM) or laser scanning confocal microscopy (LSCM), is an optical imaging technique for increasing optical resolution and contrast of a micrograph by means of using a spatial pinhole to block out-of-focus light in image formation. Capturing multiple two-dimensional images at different depths in a sample enables the reconstruction of three-dimensional structures (a process known as optical sectioning) within an object. This technique is used extensively in the scientific and industrial communities and typical applications are in life sciences, semiconductor inspection and materials science. Light travels through the sample under a conventional microscope as far into the specimen as it can penetrate, while a confocal microscope only focuses a smaller beam of light at one narrow depth level at a time. The CLSM achieves a controlled and highly limited depth of field."

Music by the Fiechter Brothers

Images by Katia Hougaard & the Facility for Imaging by Light Microscopy at Imperial College London

[lonestarflight]

Space Shuttle Main Engine Lifted onto Test Stand A-1

"Space Shuttle Main Engine or SSME, weighing 6339 pounds, is lifted onto Test Stand A-1 at the National Space Technology Laboratories, marking the beginning of a new test phase. The huge stand is 238 feet tall -- the equivalent of a 16-story building. Following completion of facility activation and engine checkout by the NSTL team, the test program commenced May 19, 1975."

Date: 1975

Boeing Images: BI230657

[misterradio]

kind of a milf. reblog